yambda-discovery-ranking-two-tower-base / run-005-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:03.528Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-08-31T18:25:05.279Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-08-31T18:25:05.280Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-08-31T18:25:05.280Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-08-31T18:25:05.280Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a05911-5623-79c0-b6fc-6b425971d257", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-08-31", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-08-31T18:25:05.289Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-08-31T18:25:08.034Z ### Thinking unavailable — no text recorded I’ll build and benchmark the full two-tower pipeline in `/app/solution/`, first auditing the data and evaluator, then training a validation model, preserving the best valid ranking, and finally refitting with validation history for the test submission. I’ll keep the scoring path exactly decomposable into independently computed user/item vectors and avoid any global interaction matrix. ### exec · 2026-08-31T18:25:14.462Z { "code": "const r = await tools.exec_command({cmd:\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:14.529Z Script completed Wall time 0.1 seconds Output: /app candidate_catalog.parquet 349352 bytes target_users.parquet 84127 bytes test_eligible_candidates.npz 7736341 bytes test_preference_state.parquet 1092329 bytes train_dislikes.parquet 857213 bytes train_likes.parquet 5374652 bytes train_listens.parquet 319183255 bytes train_undislikes.parquet 155988 bytes train_unlikes.parquet 1855885 bytes val_dislikes.parquet 36571 bytes val_likes.parquet 298484 bytes val_listens.parquet 9947609 bytes val_undislikes.parquet 7845 bytes val_unlikes.parquet 72536 bytes validation_eligible_candidates.npz 7573821 bytes validation_preference_state.parquet 1061761 bytes """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-08-31T18:25:18.750Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:29.637Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py && python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\\nfrom pathlib import Path\\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, 'rgs', pf.metadata.num_row_groups, 'schema', pf.schema_arrow)\\nprint('npz')\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n print(n,{k:(z[k].shape,z[k].dtype) for k in z.files},'avg eligible',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).mean())\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:29.748Z Script completed Wall time 0.1 seconds Output: #!/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() Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pyarrow' ## Step 9 · agent · 2026-08-31T18:25:32.679Z ### Thinking unavailable — no text recorded The interaction log is large enough that I’m checking its physical layout and available parquet reader before choosing the sampling strategy. The first metadata probe found that the declared `pyarrow` package is not importable in this runtime, so I’m adapting the loader rather than risking a memory-heavy full read. ### exec · 2026-08-31T18:25:42.157Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, numpy as np, torch, sys\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\nfor x in ['pyarrow','fastparquet']:\\n try:\\n m=__import__(x); print(x,m.__version__)\\n except Exception as e: print(x,'ERR',repr(e))\\ntry:\\n print(pd.read_parquet('/task/data/target_users.parquet').head())\\nexcept Exception as e: print('read ERR',type(e),e)\\nPY\\ncommand -v parquet-tools || true\\nfind /usr -iname '*pyarrow*' -maxdepth 6 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:42.243Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ## Step 10 · agent · 2026-08-31T18:25:45.185Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:49.701Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 uv 2>/dev/null || true\\npython --version\\npython -m pip list 2>/dev/null | head -40 || true\\nfind /opt /usr/local /root -type f \\\\( -name 'python' -o -name 'python3' \\\\) 2>/dev/null | head -30\\nenv | sort | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:50.544Z 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 CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05911-560e-72e0-8d10-ff5687a42d43 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=40c965ab66e1 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ## Step 11 · agent · 2026-08-31T18:25:53.081Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:57.602Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /task | sed -n '1,160p'\\nfind / -maxdepth 4 -type f -name 'activate' 2>/dev/null | head -50\\nfind / -maxdepth 5 -type d -name 'site-packages' 2>/dev/null | head -50\\nls -la /opt /usr/local/lib/python3.12/site-packages 2>/dev/null | head -100\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:25:57.744Z Script completed Wall time 0.1 seconds Output: total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:09 data -r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt /opt/task-venv/bin/activate /opt/task-venv/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages /opt: total 16 drwxr-xr-x 1 root root 4096 Aug 31 18:21 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. drwxr-xr-x 4 root root 4096 Aug 31 18:21 codex-home dr-xr-xr-x 1 root root 4096 Aug 31 18:22 task-venv /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 12 · agent · 2026-08-31T18:25:59.636Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:11.115Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, torch\\nfrom pathlib import Path\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\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, 'rgs', pf.metadata.num_row_groups, 'cols',pf.schema_arrow.names)\\nfor n in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(D/n)\\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\\n s=bits.sum(1)\\n print(n,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'eligible mean/min/max',s.mean(),s.min(),s.max())\\nfor n in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\\n x=pd.read_parquet(D/n)\\n print('\\\\n',n,x.shape,x.dtypes.to_dict())\\n print(x.head())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);" } ### Tool output · 2026-08-31T18:26:15.931Z Script completed Wall time 4.8 seconds Output: versions 2.3.3 2.2.6 2.8.0+cpu candidate_catalog.parquet rows 30000 rgs 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet rows 8456 rgs 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet rows 484776 rgs 1 cols ['uid', 'item_id', 'state'] train_dislikes.parquet rows 101075 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet rows 814831 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet rows 43484159 rgs 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_undislikes.parquet rows 19960 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet rows 292665 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet rows 3563 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet rows 37795 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet rows 1501697 rgs 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_undislikes.parquet rows 606 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet rows 9346 rgs 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] validation_preference_state.parquet rows 469124 rgs 1 cols ['uid', 'item_id', 'state'] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible mean/min/max 29067.306291390727 24657 30000 test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible mean/min/max 29041.3178807947 24552 30000 target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked ## Step 13 · agent · 2026-08-31T18:26:35.686Z ### Thinking unavailable — no text recorded ## Step 14 · agent · 2026-08-31T18:26:37.988Z ### Thinking unavailable — no text recorded The catalog has 30,000 items and 8,456 target users, with roughly 29,000 eligible items per user. That makes exhaustive CPU scoring feasible here, while the learned dot-product representation remains production-compatible with MIPS. I’m now measuring how much target-user and catalog-positive history exists so the training examples emphasize organic 80% completions and explicit likes without scanning into a global matrix. ### exec · 2026-08-31T18:26:54.942Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nD='/task/data/'\\ntarget=np.sort(pd.read_parquet(D+'target_users.parquet').uid.to_numpy())\\ncat=np.sort(pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy())\\npf=pq.ParquetFile(D+'train_listens.parquet')\\nagg=np.zeros(12,np.int64); t=time.time()\\nfor i in range(pf.num_row_groups):\\n x=pf.read_row_group(i,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\\n ut=np.isin(x.uid.to_numpy(),target)\\n ic=np.isin(x.item_id.to_numpy(),cat)\\n pos=x.played_ratio_pct.to_numpy()>=50\\n strong=x.played_ratio_pct.to_numpy()>=80\\n org=x.is_organic.to_numpy()==1\\n agg += [len(x),ut.sum(),ic.sum(),(ut&ic).sum(),(ut&ic&pos).sum(),(ut&ic&strong&org).sum(),(ic&pos).sum(),(ic&strong&org).sum(), x.timestamp.min(),x.timestamp.max(),(ut&pos).sum(),(ut&strong&org).sum()]\\n if i%10==9: print(i+1,agg[:8].tolist(),'sec',round(time.time()-t,1))\\nprint('FINAL',agg.tolist(),'sec',time.time()-t)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:26:59.518Z Script completed Wall time 4.6 seconds Output: 10 [10485760, 10426041, 7502693, 7459622, 4752124, 2075856, 4773469, 2085564] sec 0.7 20 [20971520, 20832481, 15010786, 14915893, 9608448, 4148867, 9660026, 4166905] sec 1.5 30 [31457280, 31246755, 22532804, 22383259, 14387816, 6209932, 14467283, 6241205] sec 2.2 40 [41943040, 41683273, 30127131, 29941404, 19245295, 8353545, 19340920, 8391078] sec 2.9 FINAL [43484159, 43222297, 31249613, 31062540, 19959853, 8661020, 20056329, 8699160, 58505, 1041043790, 27367343, 12123010] sec 3.008568048477173 ## Step 15 · agent · 2026-08-31T18:27:09.201Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:23.583Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\\nD='/task/data/'\\npf=pq.ParquetFile(D+'train_listens.parquet')\\ncounts=np.zeros(8,np.int64); vals=[]\\nfor i in range(pf.num_row_groups):\\n x=pf.read_row_group(i,columns=['timestamp']).column(0).to_numpy()\\n counts += [(x<0).sum(),(x<=24786800).sum(),(x>24786800).sum(),(x>1e8).sum(),(x>1e9).sum(),len(x),x.min(),x.max()]\\n vals.append(x[::100])\\na=np.concatenate(vals)\\nprint('counts sums (mins max sums irrelevant)',counts)\\nprint('sample quantiles',np.quantile(a,[0,.001,.01,.1,.25,.5,.75,.9,.99,.999,1]))\\nfor family in ['likes','dislikes','unlikes','undislikes']:\\n x=pd.read_parquet(D+'train_'+family+'.parquet',columns=['timestamp'])\\n print(family,x.timestamp.min(),x.timestamp.max(),x.timestamp.quantile([0,.01,.5,.99,1]).to_dict(),(x.timestamp>24786800).sum())\\nfor family in ['listens','likes']:\\n x=pd.read_parquet(D+'val_'+family+'.parquet',columns=['timestamp'])\\n print('val',family,x.timestamp.min(),x.timestamp.max(),x.timestamp.quantile([0,.01,.5,.99,1]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:27:25.674Z Script completed Wall time 2.1 seconds Output: counts sums (mins max sums irrelevant) [ 0 43484159 0 0 0 43484159 58505 1041043790] sample quantiles [1.40000000e+02 3.84060600e+04 3.63190350e+05 3.32272400e+06 7.74013375e+06 1.42409975e+07 2.00100438e+07 2.29713080e+07 2.46187542e+07 2.47713380e+07 2.47867950e+07] likes 80 24786790 {0.0: 80.0, 0.01: 304381.5, 0.5: 14330500.0, 0.99: 24647098.5, 1.0: 24786790.0} 0 dislikes 75 24786580 {0.0: 75.0, 0.01: 416615.0, 0.5: 13586810.0, 0.99: 24641102.099999998, 1.0: 24786580.0} 0 unlikes 250 24786570 {0.0: 250.0, 0.01: 402560.0, 0.5: 13931030.0, 0.99: 24663950.0, 1.0: 24786570.0} 0 undislikes 15950 24785395 {0.0: 15950.0, 0.01: 487448.65, 0.5: 13358502.5, 0.99: 24593935.85, 1.0: 24785395.0} 0 val listens 24788600 25393395 {0.0: 24788600.0, 0.01: 24803755.0, 0.5: 25097890.0, 0.99: 25386015.2, 1.0: 25393395.0} val likes 24788815 25393390 {0.0: 24788815.0, 0.01: 24809869.4, 0.5: 25051965.0, 0.99: 25386795.6, 1.0: 25393390.0} ## Step 16 · agent · 2026-08-31T18:27:45.419Z ### Thinking unavailable — no text recorded ## Step 17 · agent · 2026-08-31T18:28:04.001Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-08-31T18:28:16.296Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:30.100Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+\\\"\\\"\\\"Create an eligibility-safe popularity baseline for quick diagnostics.\\\"\\\"\\\"\\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 write(stage: str) -> Path:\\n+ archive = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[r], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[eligible][:100]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}\\n+ )\\n+ )\\n+ path = OUT / f\\\"{stage}_popularity.parquet\\\"\\n+ pd.concat(rows, ignore_index=True).astype(\\\"int64\\\").to_parquet(path, index=False)\\n+ return path\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ print(write(\\\"validation\\\"))\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:28:30.173Z Script completed Wall time 0.0 seconds Output: {} ## Step 19 · agent · 2026-08-31T18:28:32.246Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:35.329Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/baseline.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:28:41.604Z Script completed Wall time 6.3 seconds Output: total 16 drwxr-xr-x 1 agent agent 4096 Aug 31 18:28 . drwxr-xr-x 1 agent agent 4096 Aug 31 18:22 .. -rw-r--r-- 1 agent agent 986 Aug 31 18:28 baseline.py /app/solution/validation_popularity.parquet { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 20 · agent · 2026-08-31T18:29:01.096Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T18:29:19.767Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-08-31T18:29:38.448Z ### Thinking unavailable — no text recorded ## Step 23 · agent · 2026-08-31T18:29:57.027Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-08-31T18:30:07.959Z ### Thinking unavailable — no text recorded The popularity baseline scores 0.010710 NDCG@10, which gives a concrete floor. The learned model will use rolling, pre-cutoff weekly backtests as training examples: each user vector pools recent completed listens plus active like/dislike state, while the independent item tower combines a trainable catalog embedding with popularity metadata. Training uses sampled pairwise negatives—linear in observed examples, with no user-ID parameters and no user–item matrix. ### exec · 2026-08-31T18:34:25.206Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based PyTorch two-tower ranker.\\n+\\n+The script deliberately represents interactions as event streams and sampled\\n+training triples. It never creates a user-by-item matrix. At inference the\\n+two towers are called independently and the only scoring operation is a dot\\n+product between their equal-dimensional outputs.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import os\\n+import random\\n+import shutil\\n+import time\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+import torch.nn as nn\\n+import torch.nn.functional as F\\n+from torch.utils.data import DataLoader, Dataset\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+GAP = 1_800\\n+WEEK = 604_800\\n+STEP = GAP + WEEK\\n+HISTORY_DAYS = 120\\n+MAX_LISTENS = 120\\n+MAX_LIKES = 60\\n+MAX_DISLIKES = 40\\n+EMBED_DIM = 64\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+class IdMap:\\n+ \\\"\\\"\\\"Vectorized mapping from raw integer IDs to compact indices.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: np.ndarray):\\n+ self.raw = np.asarray(raw, dtype=np.int64)\\n+ self.order = np.argsort(self.raw)\\n+ self.sorted = self.raw[self.order]\\n+\\n+ def map(self, values: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ values = np.asarray(values, dtype=np.int64)\\n+ pos = np.searchsorted(self.sorted, values)\\n+ valid = pos < len(self.sorted)\\n+ safe = np.minimum(pos, len(self.sorted) - 1)\\n+ valid &= self.sorted[safe] == values\\n+ mapped = np.full(len(values), -1, dtype=np.int32)\\n+ mapped[valid] = self.order[safe[valid]].astype(np.int32)\\n+ return mapped, valid\\n+\\n+\\n+@dataclass\\n+class EventData:\\n+ user: np.ndarray\\n+ item: np.ndarray\\n+ timestamp: np.ndarray\\n+ ratio: np.ndarray\\n+ organic: np.ndarray\\n+\\n+\\n+@dataclass\\n+class ActionData:\\n+ user: np.ndarray\\n+ item: np.ndarray\\n+ timestamp: np.ndarray\\n+ channel: np.ndarray # 1 liked, -1 disliked\\n+ active: np.ndarray\\n+ organic: np.ndarray\\n+\\n+\\n+def load_listens(\\n+ names: list[str], user_map: IdMap, item_map: IdMap, earliest: int\\n+) -> EventData:\\n+ chunks: list[tuple[np.ndarray, ...]] = []\\n+ for name in names:\\n+ pf = pq.ParquetFile(DATA / name)\\n+ for rg in range(pf.num_row_groups):\\n+ x = pf.read_row_group(\\n+ rg,\\n+ columns=[\\n+ \\\"uid\\\",\\n+ \\\"timestamp\\\",\\n+ \\\"item_id\\\",\\n+ \\\"is_organic\\\",\\n+ \\\"played_ratio_pct\\\",\\n+ ],\\n+ ).to_pandas()\\n+ ts = x.timestamp.to_numpy(dtype=np.int64)\\n+ ratio = x.played_ratio_pct.to_numpy(dtype=np.float32)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv & (ts >= earliest) & (ratio >= 50.0)\\n+ if keep.any():\\n+ chunks.append(\\n+ (\\n+ u[keep],\\n+ i[keep],\\n+ ts[keep].astype(np.int32),\\n+ ratio[keep].astype(np.float16),\\n+ x.is_organic.to_numpy(dtype=np.uint8)[keep],\\n+ )\\n+ )\\n+ arrays = [np.concatenate([c[j] for c in chunks]) for j in range(5)]\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ arrays = [a[order] for a in arrays]\\n+ return EventData(*arrays)\\n+\\n+\\n+def load_actions(\\n+ include_validation: bool, user_map: IdMap, item_map: IdMap, earliest: int\\n+) -> ActionData:\\n+ specs = [\\n+ (\\\"likes\\\", 1, 1),\\n+ (\\\"unlikes\\\", 1, 0),\\n+ (\\\"dislikes\\\", -1, 1),\\n+ (\\\"undislikes\\\", -1, 0),\\n+ ]\\n+ chunks: list[tuple[np.ndarray, ...]] = []\\n+ prefixes = [\\\"train\\\", \\\"val\\\"] if include_validation else [\\\"train\\\"]\\n+ for prefix in prefixes:\\n+ for family, channel, active in specs:\\n+ x = pd.read_parquet(\\n+ DATA / f\\\"{prefix}_{family}.parquet\\\",\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"],\\n+ )\\n+ ts = x.timestamp.to_numpy(dtype=np.int64)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv & (ts >= earliest)\\n+ n = int(keep.sum())\\n+ if n:\\n+ chunks.append(\\n+ (\\n+ u[keep],\\n+ i[keep],\\n+ ts[keep].astype(np.int32),\\n+ np.full(n, channel, dtype=np.int8),\\n+ np.full(n, active, dtype=np.uint8),\\n+ x.is_organic.to_numpy(dtype=np.uint8)[keep],\\n+ )\\n+ )\\n+ arrays = [np.concatenate([c[j] for c in chunks]) for j in range(6)]\\n+ order = np.lexsort((arrays[2], arrays[0]))\\n+ arrays = [a[order] for a in arrays]\\n+ return ActionData(*arrays)\\n+\\n+\\n+def offsets_for_users(user: np.ndarray, n_users: int) -> np.ndarray:\\n+ return np.searchsorted(user, np.arange(n_users + 1)).astype(np.int64)\\n+\\n+\\n+class FeatureBuilder:\\n+ \\\"\\\"\\\"Construct bounded history sequences directly from chronological logs.\\\"\\\"\\\"\\n+\\n+ def __init__(self, events: EventData, actions: ActionData, n_users: int):\\n+ self.e = events\\n+ self.a = actions\\n+ self.eoff = offsets_for_users(events.user, n_users)\\n+ self.aoff = offsets_for_users(actions.user, n_users)\\n+ self.n_users = n_users\\n+\\n+ def one(self, user: int, cutoff: int) -> tuple[np.ndarray, ...]:\\n+ # Most recent occurrence of each listened item, bounded by count and age.\\n+ lo, hi = self.eoff[user], self.eoff[user + 1]\\n+ end = lo + np.searchsorted(self.e.timestamp[lo:hi], cutoff, side=\\\"right\\\")\\n+ min_ts = cutoff - HISTORY_DAYS * 86_400\\n+ listen_i = np.zeros(MAX_LISTENS, dtype=np.int32)\\n+ listen_w = np.zeros(MAX_LISTENS, dtype=np.float32)\\n+ seen: set[int] = set()\\n+ n = 0\\n+ for p in range(end - 1, lo - 1, -1):\\n+ ts = int(self.e.timestamp[p])\\n+ if ts < min_ts:\\n+ break\\n+ item = int(self.e.item[p])\\n+ if item in seen:\\n+ continue\\n+ seen.add(item)\\n+ age_days = (cutoff - ts) / 86_400.0\\n+ completion = min(float(self.e.ratio[p]), 100.0) / 100.0\\n+ organic = 1.35 if self.e.organic[p] else 1.0\\n+ listen_i[n] = item + 1\\n+ listen_w[n] = completion * organic * (0.35 + 0.65 * math.exp(-age_days / 35.0))\\n+ n += 1\\n+ if n == MAX_LISTENS:\\n+ break\\n+\\n+ # Walk explicit actions backward: the first action seen for each\\n+ # (channel,item) is the cutoff-correct active state.\\n+ lo, hi = self.aoff[user], self.aoff[user + 1]\\n+ end = lo + np.searchsorted(self.a.timestamp[lo:hi], cutoff, side=\\\"right\\\")\\n+ likes = np.zeros(MAX_LIKES, dtype=np.int32)\\n+ dislikes = np.zeros(MAX_DISLIKES, dtype=np.int32)\\n+ seen_actions: set[tuple[int, int]] = set()\\n+ nl = nd = 0\\n+ for p in range(end - 1, lo - 1, -1):\\n+ channel, item = int(self.a.channel[p]), int(self.a.item[p])\\n+ key = (channel, item)\\n+ if key in seen_actions:\\n+ continue\\n+ seen_actions.add(key)\\n+ if not self.a.active[p]:\\n+ continue\\n+ if channel == 1 and nl < MAX_LIKES:\\n+ likes[nl] = item + 1\\n+ nl += 1\\n+ elif channel == -1 and nd < MAX_DISLIKES:\\n+ dislikes[nd] = item + 1\\n+ nd += 1\\n+ if nl == MAX_LIKES and nd == MAX_DISLIKES:\\n+ break\\n+\\n+ numeric = np.asarray(\\n+ [\\n+ math.log1p(n) / math.log1p(MAX_LISTENS),\\n+ math.log1p(nl) / math.log1p(MAX_LIKES),\\n+ math.log1p(nd) / math.log1p(MAX_DISLIKES),\\n+ float(self.e.organic[max(lo, end - 500) : end].mean())\\n+ if end > lo\\n+ else 0.0,\\n+ ],\\n+ dtype=np.float32,\\n+ )\\n+ return listen_i, listen_w, likes, dislikes, numeric\\n+\\n+ def many(self, users: np.ndarray, cutoff: int) -> tuple[np.ndarray, ...]:\\n+ rows = [self.one(int(u), cutoff) for u in users]\\n+ return tuple(np.stack([r[j] for r in rows]) for j in range(5))\\n+\\n+\\n+def provided_preference_features(\\n+ path: Path, user_map: IdMap, item_map: IdMap\\n+) -> tuple[np.ndarray, np.ndarray]:\\n+ x = pd.read_parquet(path)\\n+ u, uv = user_map.map(x.uid.to_numpy())\\n+ i, iv = item_map.map(x.item_id.to_numpy())\\n+ keep = uv & iv\\n+ x = pd.DataFrame(\\n+ {\\\"u\\\": u[keep], \\\"i\\\": i[keep] + 1, \\\"state\\\": x.state.to_numpy()[keep]}\\n+ )\\n+ likes = np.zeros((len(user_map.raw), MAX_LIKES), dtype=np.int32)\\n+ dislikes = np.zeros((len(user_map.raw), MAX_DISLIKES), dtype=np.int32)\\n+ for state, target, width in [\\n+ (\\\"liked\\\", likes, MAX_LIKES),\\n+ (\\\"disliked\\\", dislikes, MAX_DISLIKES),\\n+ ]:\\n+ y = x[x.state == state]\\n+ # File order is stable and active state contains no timestamp; retaining\\n+ # the final bounded set keeps request-time work constant.\\n+ y = y.groupby(\\\"u\\\", sort=False).tail(width)\\n+ for uid, vals in y.groupby(\\\"u\\\", sort=False).i:\\n+ arr = vals.to_numpy(dtype=np.int32)[-width:]\\n+ target[int(uid), : len(arr)] = arr\\n+ return likes, dislikes\\n+\\n+\\n+def labels_for_window(\\n+ events: EventData, actions: ActionData, cutoff: int\\n+) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n+ start, end = cutoff + GAP, cutoff + GAP + WEEK\\n+ strong = (\\n+ (events.timestamp >= start)\\n+ & (events.timestamp <= end)\\n+ & (events.organic == 1)\\n+ & (events.ratio.astype(np.float32) >= 80.0)\\n+ )\\n+ likes = (\\n+ (actions.timestamp >= start)\\n+ & (actions.timestamp <= end)\\n+ & (actions.organic == 1)\\n+ & (actions.channel == 1)\\n+ & (actions.active == 1)\\n+ )\\n+ pairs: dict[int, dict[int, int]] = {}\\n+ for u, i in zip(events.user[strong], events.item[strong]):\\n+ pairs.setdefault(int(u), {})[int(i)] = 1\\n+ for u, i in zip(actions.user[likes], actions.item[likes]):\\n+ pairs.setdefault(int(u), {})[int(i)] = 3\\n+ return {\\n+ u: (\\n+ np.fromiter(d.keys(), dtype=np.int32),\\n+ np.fromiter(d.values(), dtype=np.int8),\\n+ )\\n+ for u, d in pairs.items()\\n+ }\\n+\\n+\\n+class SnapshotDataset(Dataset):\\n+ def __init__(self, features: tuple[np.ndarray, ...], labels: list[tuple[np.ndarray, np.ndarray]]):\\n+ self.features = features\\n+ self.labels = labels\\n+\\n+ def __len__(self) -> int:\\n+ return len(self.labels)\\n+\\n+ def __getitem__(self, index: int):\\n+ items, grades = self.labels[index]\\n+ # A new positive is sampled on every epoch. Grade-3 actions are sampled\\n+ # and weighted more heavily to match their larger NDCG gain.\\n+ probs = np.where(grades == 3, 4.0, 1.0)\\n+ p = int(np.random.choice(len(items), p=probs / probs.sum()))\\n+ return tuple(torch.from_numpy(x[index]) for x in self.features) + (\\n+ torch.tensor(int(items[p]), dtype=torch.long),\\n+ torch.tensor(3.0 if grades[p] == 3 else 1.0, dtype=torch.float32),\\n+ )\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user tower; there is intentionally no user-ID table.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, dim: int = EMBED_DIM):\\n+ super().__init__()\\n+ self.listen = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.like = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.dislike = nn.Embedding(n_items + 1, dim, padding_idx=0)\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim * 3 + 4, dim * 2),\\n+ nn.LayerNorm(dim * 2),\\n+ nn.GELU(),\\n+ nn.Dropout(0.08),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+ self.log_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+ @staticmethod\\n+ def mean_pool(embedding: nn.Embedding, ids: torch.Tensor, weights=None):\\n+ mask = ids.ne(0)\\n+ if weights is None:\\n+ weights = mask.float()\\n+ else:\\n+ weights = weights * mask\\n+ values = embedding(ids) * weights.unsqueeze(-1)\\n+ return values.sum(1) / weights.sum(1, keepdim=True).clamp_min(1e-5)\\n+\\n+ def forward(self, listens, listen_weights, likes, dislikes, numeric):\\n+ pooled = torch.cat(\\n+ [\\n+ self.mean_pool(self.listen, listens, listen_weights),\\n+ self.mean_pool(self.like, likes),\\n+ self.mean_pool(self.dislike, dislikes),\\n+ numeric,\\n+ ],\\n+ dim=1,\\n+ )\\n+ vector = F.normalize(self.net(pooled), dim=1)\\n+ return vector * self.log_scale.exp().clamp(2.0, 30.0)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently callable candidate tower.\\\"\\\"\\\"\\n+\\n+ def __init__(self, metadata: np.ndarray, dim: int = EMBED_DIM):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(len(metadata), dim)\\n+ self.register_buffer(\\\"metadata\\\", torch.tensor(metadata, dtype=torch.float32))\\n+ self.meta_net = nn.Sequential(nn.Linear(metadata.shape[1], 24), nn.GELU())\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim + 24, dim * 2),\\n+ nn.LayerNorm(dim * 2),\\n+ nn.GELU(),\\n+ nn.Linear(dim * 2, dim),\\n+ )\\n+\\n+ def forward(self, item_index: torch.Tensor):\\n+ x = torch.cat(\\n+ [self.embedding(item_index), self.meta_net(self.metadata[item_index])], dim=-1\\n+ )\\n+ return F.normalize(self.net(x), dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, metadata: np.ndarray):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items)\\n+ self.item_tower = ItemTower(metadata)\\n+\\n+\\n+def item_metadata(catalog: pd.DataFrame) -> np.ndarray:\\n+ positive = catalog.positive_listens.to_numpy(dtype=np.float32)\\n+ organic = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\\n+ rank = catalog.popularity_rank.to_numpy(dtype=np.float32)\\n+ raw = np.column_stack(\\n+ [\\n+ np.log1p(positive),\\n+ np.log1p(organic),\\n+ organic / np.maximum(positive, 1),\\n+ -np.log1p(rank),\\n+ ]\\n+ ).astype(np.float32)\\n+ return (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\\n+\\n+\\n+def build_training_data(\\n+ builder: FeatureBuilder,\\n+ events: EventData,\\n+ actions: ActionData,\\n+ cutoffs: list[int],\\n+ supplied_train_preferences: tuple[np.ndarray, np.ndarray] | None,\\n+) -> SnapshotDataset:\\n+ feature_parts: list[tuple[np.ndarray, ...]] = []\\n+ all_labels: list[tuple[np.ndarray, np.ndarray]] = []\\n+ for cutoff in cutoffs:\\n+ labels = labels_for_window(events, actions, cutoff)\\n+ users = np.asarray(sorted(labels), dtype=np.int32)\\n+ features = list(builder.many(users, cutoff))\\n+ if supplied_train_preferences is not None and cutoff == TRAIN_END:\\n+ likes, dislikes = supplied_train_preferences\\n+ features[2] = likes[users]\\n+ features[3] = dislikes[users]\\n+ features[4][:, 1] = np.log1p((features[2] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((features[3] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ feature_parts.append(tuple(features))\\n+ all_labels.extend(labels[int(u)] for u in users)\\n+ print(f\\\"snapshot cutoff={cutoff}: {len(users)} labeled users, \\\"\\n+ f\\\"{sum(len(labels[int(u)][0]) for u in users)} positives\\\", flush=True)\\n+ merged = tuple(np.concatenate([p[j] for p in feature_parts]) for j in range(5))\\n+ return SnapshotDataset(merged, all_labels)\\n+\\n+\\n+def train_model(\\n+ model: TwoTower,\\n+ dataset: SnapshotDataset,\\n+ popularity: np.ndarray,\\n+ epochs: int,\\n+ seed: int,\\n+) -> None:\\n+ generator = torch.Generator().manual_seed(seed)\\n+ loader = DataLoader(dataset, batch_size=384, shuffle=True, generator=generator,\\n+ num_workers=0, drop_last=False)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ pop = torch.tensor(popularity, dtype=torch.float32)\\n+ pop = pop / pop.sum()\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ total = 0.0\\n+ seen = 0\\n+ for batch in loader:\\n+ *features, positive, grade_weight = batch\\n+ user_vec = model.user_tower(*features)\\n+ pos_vec = model.item_tower(positive)\\n+ # Half popularity-aware and half uniform negatives. This focuses\\n+ # learning on the candidates likely to compete near rank 10.\\n+ b = len(positive)\\n+ hard = torch.multinomial(pop, b * 8, replacement=True).view(b, 8)\\n+ uniform = torch.randint(0, len(pop), (b, 8))\\n+ negatives = torch.cat([hard, uniform], dim=1)\\n+ neg_vec = model.item_tower(negatives)\\n+ pos_score = (user_vec * pos_vec).sum(1)\\n+ neg_score = (user_vec[:, None, :] * neg_vec).sum(2)\\n+ pair_loss = F.softplus(neg_score - pos_score[:, None]).mean(1)\\n+ loss = (pair_loss * grade_weight).sum() / grade_weight.sum()\\n+ # Small embedding penalty prevents the history lookup from merely\\n+ # memorizing the short collection of rolling snapshots.\\n+ loss = loss + 2e-5 * (\\n+ model.item_tower.embedding.weight.square().mean()\\n+ + model.user_tower.listen.weight.square().mean()\\n+ )\\n+ optimizer.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ total += float(loss) * b\\n+ seen += b\\n+ print(f\\\"epoch {epoch + 1:02d}/{epochs}: loss={total / seen:.5f}\\\", flush=True)\\n+\\n+\\n+@torch.inference_mode()\\n+def write_ranking(\\n+ model: TwoTower,\\n+ features: tuple[np.ndarray, ...],\\n+ eligibility_path: Path,\\n+ destination: Path,\\n+) -> None:\\n+ model.eval()\\n+ archive = np.load(eligibility_path)\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ raw_items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+ item_vectors = model.item_tower(torch.arange(len(raw_items))).cpu()\\n+ rows: list[pd.DataFrame] = []\\n+ batch_size = 128\\n+ for start in range(0, len(uids), batch_size):\\n+ stop = min(start + batch_size, len(uids))\\n+ tensors = [torch.from_numpy(x[start:stop]) for x in features]\\n+ user_vectors = model.user_tower(*tensors).cpu()\\n+ scores = user_vectors @ item_vectors.T\\n+ eligible = np.unpackbits(\\n+ packed[start:stop], axis=1, count=len(raw_items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ scores[~torch.from_numpy(eligible)] = -torch.inf\\n+ top = torch.topk(scores, k=100, dim=1, sorted=True).indices.numpy()\\n+ for r in range(stop - start):\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": uids[start + r],\\n+ \\\"item_id\\\": raw_items[top[r]],\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }\\n+ )\\n+ )\\n+ result = pd.concat(rows, ignore_index=True).astype(\\\"int64\\\")\\n+ temp = destination.with_suffix(\\\".tmp.parquet\\\")\\n+ result.to_parquet(temp, index=False)\\n+ temp.replace(destination)\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=12)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ parser.add_argument(\\\"--output\\\", type=Path)\\n+ args = parser.parse_args()\\n+ seed_everything(args.seed)\\n+ torch.set_num_threads(min(12, os.cpu_count() or 1))\\n+ started = time.time()\\n+\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ archive_name = f\\\"{args.stage}_eligible_candidates.npz\\\"\\n+ archive = np.load(DATA / archive_name)\\n+ raw_users = archive[\\\"uids\\\"].astype(np.int64)\\n+ raw_items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ assert np.array_equal(raw_items, catalog.item_id.to_numpy(dtype=np.int64))\\n+ user_map, item_map = IdMap(raw_users), IdMap(raw_items)\\n+\\n+ include_val = args.stage == \\\"test\\\"\\n+ # Eight clean rolling windows ending before public validation. In final\\n+ # mode, the public validation window is appended as one more supervised\\n+ # snapshot, exactly matching the requested refit protocol.\\n+ cutoffs = [TRAIN_END - n * STEP for n in range(8, 0, -1)]\\n+ if include_val:\\n+ cutoffs.append(TRAIN_END)\\n+ earliest = min(cutoffs) - HISTORY_DAYS * 86_400\\n+ listen_files = [\\\"train_listens.parquet\\\"]\\n+ if include_val:\\n+ listen_files.append(\\\"val_listens.parquet\\\")\\n+ print(f\\\"loading event streams since {earliest}\\\", flush=True)\\n+ events = load_listens(listen_files, user_map, item_map, earliest)\\n+ actions = load_actions(include_val, user_map, item_map, earliest)\\n+ print(f\\\"loaded {len(events.user):,} positive listens and {len(actions.user):,} actions\\\", flush=True)\\n+ builder = FeatureBuilder(events, actions, len(raw_users))\\n+\\n+ validation_preferences = provided_preference_features(\\n+ DATA / \\\"validation_preference_state.parquet\\\", user_map, item_map\\n+ )\\n+ dataset = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ )\\n+ model = TwoTower(len(raw_items), item_metadata(catalog))\\n+ popularity = np.sqrt(catalog.organic_positive_listens.to_numpy(dtype=np.float64) + 5.0)\\n+ train_model(model, dataset, popularity, args.epochs, args.seed)\\n+\\n+ if args.stage == \\\"validation\\\":\\n+ scoring_cutoff = TRAIN_END\\n+ preferences = validation_preferences\\n+ default_output = OUT / \\\"validation.parquet\\\"\\n+ else:\\n+ scoring_cutoff = TRAIN_END + STEP\\n+ preferences = provided_preference_features(\\n+ DATA / \\\"test_preference_state.parquet\\\", user_map, item_map\\n+ )\\n+ default_output = OUT / \\\"submission.parquet\\\"\\n+ features = list(builder.many(np.arange(len(raw_users), dtype=np.int32), scoring_cutoff))\\n+ features[2], features[3] = preferences\\n+ features[4][:, 1] = np.log1p((preferences[0] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((preferences[1] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ destination = args.output or default_output\\n+ write_ranking(model, tuple(features), DATA / archive_name, destination)\\n+ checkpoint = OUT / f\\\"model_{args.stage}.pt\\\"\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"stage\\\": args.stage,\\n+ \\\"seed\\\": args.seed,\\n+ \\\"epochs\\\": args.epochs,\\n+ \\\"architecture\\\": \\\"history pooling user tower / ID+metadata item tower / 64D dot product\\\",\\n+ },\\n+ checkpoint,\\n+ )\\n+ print(json.dumps({\\\"ranking\\\": str(destination), \\\"checkpoint\\\": str(checkpoint),\\n+ \\\"training_examples\\\": len(dataset),\\n+ \\\"elapsed_seconds\\\": round(time.time() - started, 1)}), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:34:25.252Z Script completed Wall time 0.0 seconds Output: {} ## Step 25 · agent · 2026-08-31T18:34:27.697Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:34:30.896Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:34:32.075Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fda1b0","wall_time_seconds":1.001470468,"session_id":96139,"original_token_count":0,"output":""} ## Step 26 · agent · 2026-08-31T18:34:33.899Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:34:35.226Z { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:34:46.244Z Script running with cell ID 13 Wall time 11.0 seconds Output: ## Step 27 · agent · 2026-08-31T18:34:49.900Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:34:50.667Z { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:35:05.248Z Script completed Wall time 14.6 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 5318 labeled users, 164063 positives snapshot cutoff=20540600: 5392 labeled users, 165953 positives snapshot cutoff=21147200: 5411 labeled users, 158923 positives snapshot cutoff=21753800: 5563 labeled users, 165819 positives snapshot cutoff=22360400: 5650 labeled users, 168735 positives snapshot cutoff=22967000: 5754 labeled users, 170431 positives snapshot cutoff=23573600: 5830 labeled users, 171275 positives snapshot cutoff=24180200: 5989 labeled users, 176193 positives /app/solution/build_ranker.py:478: 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 += float(loss) * b epoch 01/10: loss=0.59321 ## Step 28 · agent · 2026-08-31T18:35:09.022Z ### Thinking unavailable — no text recorded The rolling dataset is healthy: each weekly snapshot has about 5,300–6,000 labeled users and roughly 160,000–176,000 relevant candidate events, for over 1.3 million positive labels across eight leakage-free backtests. Training has started; the first epoch’s sampled ranking loss is 0.593. ### exec · 2026-08-31T18:35:13.247Z { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:35:24.299Z Script running with cell ID 14 Wall time 11.0 seconds Output: ## Step 29 · agent · 2026-08-31T18:35:26.245Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:35:27.184Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:35:43.310Z Script completed Wall time 16.1 seconds Output: epoch 02/10: loss=0.54179 epoch 03/10: loss=0.51136 epoch 04/10: loss=0.48345 epoch 05/10: loss=0.46498 epoch 06/10: loss=0.44869 ## Step 30 · agent · 2026-08-31T18:35:45.142Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:35:46.506Z { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:35:57.557Z Script running with cell ID 15 Wall time 11.0 seconds Output: ## Step 31 · agent · 2026-08-31T18:35:59.131Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:35:59.950Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:36:16.551Z Script completed Wall time 16.6 seconds Output: epoch 07/10: loss=0.43527 epoch 08/10: loss=0.41981 epoch 09/10: loss=0.40792 epoch 10/10: loss=0.39613 ## Step 32 · agent · 2026-08-31T18:36:18.529Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:19.904Z { "code": "const r = await tools.write_stdin({session_id:96139,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:36:20.000Z Script completed Wall time 0.0 seconds Output: {"ranking": "/app/solution/validation.parquet", "checkpoint": "/app/solution/model_validation.pt", "training_examples": 44907, "elapsed_seconds": 102.9} ## Step 33 · agent · 2026-08-31T18:36:21.619Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:23.762Z { "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\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:36:27.111Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.016758, "quality_users": 4263 } } ## Step 34 · agent · 2026-08-31T18:36:41.253Z ### Thinking unavailable — no text recorded The first learned model reaches 0.016758 NDCG@10, a 56% lift over the 0.010710 popularity floor. I’m preserving it now and testing a small set of validation-only rank fusions with the catalog prior; this often stabilizes a sampled two-tower’s top ten without changing the model’s production decomposition. Any fusion retained for the final model will be representable as fixed dot-product dimensions, not a request-time cross feature. ### exec · 2026-08-31T18:37:09.925Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_fusion.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Fuse two-tower order with a catalog prior for validation diagnostics.\\n+\\n+Reciprocal-rank fusion is only a tuning helper. The selected catalog prior is\\n+already available to the item tower and can be absorbed into fixed extra dot-\\n+product dimensions in a deployed model.\\n+\\\"\\\"\\\"\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+\\n+\\n+def main():\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--input\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--model-weight\\\", type=float, required=True)\\n+ p.add_argument(\\\"--constant\\\", type=float, default=30.0)\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], default=\\\"validation\\\")\\n+ args = p.parse_args()\\n+ frame = pd.read_parquet(args.input).sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ by_user = {int(u): g.item_id.to_numpy(dtype=np.int64) for u, g in frame.groupby(\\\"uid\\\")}\\n+ z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ out = []\\n+ for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n+ eligible = np.unpackbits(z[\\\"packed_eligible\\\"][row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ pop = items[eligible][:400]\\n+ model = by_user[int(uid)]\\n+ candidates = np.unique(np.concatenate([model, pop]))\\n+ mr = {int(x): r for r, x in enumerate(model, 1)}\\n+ pr = {int(x): r for r, x in enumerate(pop, 1)}\\n+ c = args.constant\\n+ score = np.asarray([\\n+ args.model_weight / (c + mr.get(int(x), 1000))\\n+ + (1.0 - args.model_weight) / (c + pr.get(int(x), 1000))\\n+ for x in candidates\\n+ ])\\n+ chosen = candidates[np.argsort(-score, kind=\\\"stable\\\")[:100]]\\n+ out.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(out, ignore_index=True).astype(\\\"int64\\\").to_parquet(args.output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:37:09.989Z Script completed Wall time 0.0 seconds Output: {} ## Step 35 · agent · 2026-08-31T18:37:15.339Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:21.621Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:37:32.642Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 36 · agent · 2026-08-31T18:37:34.498Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:35.307Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:37:51.805Z Script completed Wall time 16.5 seconds Output: ## Step 37 · agent · 2026-08-31T18:37:55.471Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:58.172Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_fusion_*.parquet 2>/dev/null || true\\nps -ef | rg 'rank_fusion|evaluate_public' || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:37:58.259Z Script completed Wall time 0.0 seconds Output: /bin/sh: 2: ps: not found ## Step 38 · agent · 2026-08-31T18:38:01.166Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:04.950Z { "code": "const r = await tools.exec_command({cmd:\"ls -lah /app/solution | tail -30\\n/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:38:15.971Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 39 · agent · 2026-08-31T18:38:18.837Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:19.617Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:38:35.134Z Script completed Wall time 15.5 seconds Output: total 33M drwxr-xr-x 1 agent agent 4.0K Aug 31 18:37 . drwxr-xr-x 1 agent agent 4.0K Aug 31 18:22 .. drwxr-xr-x 2 agent agent 4.0K Aug 31 18:34 __pycache__ -rw-r--r-- 1 agent agent 986 Aug 31 18:28 baseline.py -rw-r--r-- 1 agent agent 23K Aug 31 18:34 build_ranker.py -rw-r--r-- 1 agent agent 30M Aug 31 18:36 model_validation.pt -rw-r--r-- 1 agent agent 2.0K Aug 31 18:37 rank_fusion.py -rw-r--r-- 1 agent agent 1.5M Aug 31 18:36 validation.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:28 validation_popularity.parquet ## Step 40 · agent · 2026-08-31T18:38:37.762Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:40.783Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_fusion_0.75.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_0.75.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:38:40.847Z Script completed Wall time 0.0 seconds Output: ls: cannot access '/app/solution/validation_fusion_0.75.parquet': No such file or directory ## Step 41 · agent · 2026-08-31T18:38:44.831Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:48.150Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:38:49.337Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"682eb1","wall_time_seconds":1.001457047,"session_id":99485,"original_token_count":0,"output":""} ## Step 42 · agent · 2026-08-31T18:38:51.377Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:52.724Z { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" } ### Tool output · 2026-08-31T18:39:03.745Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 43 · agent · 2026-08-31T18:39:05.286Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:06.038Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:39:22.750Z Script completed Wall time 16.7 seconds Output: ## Step 44 · agent · 2026-08-31T18:39:24.626Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:25.964Z { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" } ### Tool output · 2026-08-31T18:39:36.988Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 45 · agent · 2026-08-31T18:39:39.857Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:40.634Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:39:55.993Z Script completed Wall time 15.4 seconds Output: ## Step 46 · agent · 2026-08-31T18:39:58.022Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:59.359Z { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" } ### Tool output · 2026-08-31T18:40:10.380Z Script running with cell ID 26 Wall time 11.0 seconds Output: ## Step 47 · agent · 2026-08-31T18:40:11.929Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:12.690Z { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:40:29.386Z Script completed Wall time 16.7 seconds Output: ## Step 48 · agent · 2026-08-31T18:40:31.769Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:33.386Z { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" } ### Tool output · 2026-08-31T18:40:44.404Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 49 · agent · 2026-08-31T18:40:46.273Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:47.095Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:41:03.409Z Script completed Wall time 16.3 seconds Output: ## Step 50 · agent · 2026-08-31T18:41:04.890Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:06.257Z { "code": "const r = await tools.write_stdin({session_id:99485,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(r.output);" } ### Tool output · 2026-08-31T18:41:17.279Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:41:21.911Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:23.145Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:41:36.284Z Script completed Wall time 13.1 seconds Output: ## Step 52 · agent · 2026-08-31T18:41:37.972Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:41.670Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:41:41.752Z Script completed Wall time 0.1 seconds Output: /bin/sh -c for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done PID=487 /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.55.parquet --model-weight 0.55 --constant 30 PID=488 /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30 PID=522 /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_0.75.parquet --model-weight 0.75 --constant 30 PID=525 /bin/sh -c /opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 PID=555 /opt/task-venv/bin/python -u /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 PID=556 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=60 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=619 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=66 ## Step 53 · agent · 2026-08-31T18:41:45.111Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:49.495Z { "code": "const r = await tools.exec_command({cmd:\"kill 487 488 522 525 555 556 2>/dev/null || true\\nfor d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T18:41:49.574Z Script completed Wall time 0.1 seconds Output: for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'rank_fusion' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=649 ## Step 54 · agent · 2026-08-31T18:41:57.915Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:12.752Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n- by_user = {int(u): g.item_id.to_numpy(dtype=np.int64) for u, g in frame.groupby(\\\"uid\\\")}\\n z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ frame = frame.set_index([\\\"uid\\\", \\\"rank\\\"])\\n+ model_matrix = np.stack([\\n+ frame.loc[(int(uid), slice(None)), \\\"item_id\\\"].to_numpy(dtype=np.int64)\\n+ for uid in z[\\\"uids\\\"]\\n+ ])\\n items = z[\\\"item_ids\\\"].astype(np.int64)\\n- out = []\\n+ out_items = np.empty((len(z[\\\"uids\\\"]), 100), dtype=np.int64)\\n for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n@@\\n- model = by_user[int(uid)]\\n+ model = model_matrix[row]\\n@@\\n- out.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}))\\n- pd.concat(out, ignore_index=True).astype(\\\"int64\\\").to_parquet(args.output, index=False)\\n+ out_items[row] = chosen\\n+ result = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(z[\\\"uids\\\"].astype(np.int64), 100),\\n+ \\\"item_id\\\": out_items.ravel(),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(out_items)),\\n+ })\\n+ result.to_parquet(args.output, index=False)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:42:12.812Z Script completed Wall time 0.0 seconds Output: {} ## Step 55 · agent · 2026-08-31T18:42:16.255Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:20.465Z { "code": "const r = await tools.exec_command({cmd:\"time /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:42:22.538Z Script completed Wall time 2.1 seconds Output: /bin/sh: 1: time: not found Traceback (most recent call last): File "/task/evaluate_public.py", line 157, in main() File "/task/evaluate_public.py", line 152, in main ranked = load_submission(path, eligibility, targets) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/task/evaluate_public.py", line 113, in load_submission frame = validate_ranking(read_ranking(path), targets) ^^^^^^^^^^^^^^^^^^ File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet return impl.read( ^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read path_or_handle, handles, filesystem = _get_path_or_handle( ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle handles = get_handle( ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle handle = open(handle, ioargs.mode) ^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_fusion_075.parquet' ## Step 56 · agent · 2026-08-31T18:42:25.021Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:29.293Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:42:40.314Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 57 · agent · 2026-08-31T18:42:41.801Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:42.577Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:42:59.474Z Script completed Wall time 16.9 seconds Output: ## Step 58 · agent · 2026-08-31T18:43:01.759Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:06.310Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:43:06.388Z Script completed Wall time 0.1 seconds Output: `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=60 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=66 /bin/sh -c /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet PID=700 /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 PID=701 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=728 ## Step 59 · agent · 2026-08-31T18:43:08.141Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:11.871Z { "code": "const r = await tools.exec_command({cmd:\"for p in 700 701; do if [ -e /proc/$p/status ]; then printf 'PID %s ' $p; rg '^(State|VmRSS|VmSize|Threads):' /proc/$p/status; fi; done\\ncat /proc/701/io 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T18:43:11.932Z Script completed Wall time 0.0 seconds Output: PID 700 State: S (sleeping) VmSize: 2592 kB VmRSS: 1644 kB Threads: 1 PID 701 State: R (running) VmSize: 1557936 kB VmRSS: 269092 kB Threads: 16 rchar: 3132077045 wchar: 0 syscr: 101389 syscw: 0 read_bytes: 0 write_bytes: 0 cancelled_write_bytes: 0 ## Step 60 · agent · 2026-08-31T18:43:16.866Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:18.813Z { "code": "const r = await tools.exec_command({cmd:\"kill 700 701 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:43:18.882Z Script completed Wall time 0.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:43:20.729Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:28.530Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n- frame = frame.set_index([\\\"uid\\\", \\\"rank\\\"])\\n- model_matrix = np.stack([\\n- frame.loc[(int(uid), slice(None)), \\\"item_id\\\"].to_numpy(dtype=np.int64)\\n- for uid in z[\\\"uids\\\"]\\n- ])\\n+ ordered = frame.sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ ordered_uids = ordered.uid.to_numpy(dtype=np.int64).reshape(-1, 100)[:, 0]\\n+ if not np.array_equal(ordered_uids, z[\\\"uids\\\"].astype(np.int64)):\\n+ raise ValueError(\\\"ranking users do not align with eligibility archive\\\")\\n+ model_matrix = ordered.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:43:28.602Z Script completed Wall time 0.0 seconds Output: {} ## Step 62 · agent · 2026-08-31T18:43:30.395Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:34.739Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:43:45.760Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 63 · agent · 2026-08-31T18:43:47.743Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:48.571Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:44:04.921Z Script completed Wall time 16.4 seconds Output: ## Step 64 · agent · 2026-08-31T18:44:06.887Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:11.419Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T18:44:11.492Z Script completed Wall time 0.1 seconds Output: `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=60 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=66 /bin/sh -c /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_075.parquet PID=766 /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_075.parquet --model-weight 0.75 --constant 30 PID=767 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'rank_fusion|evaluate_public' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=793 ## Step 65 · agent · 2026-08-31T18:44:14.340Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:17.539Z { "code": "const r = await tools.exec_command({cmd:\"for p in 766 767; do if [ -e /proc/$p/status ]; then printf 'PID %s ' $p; rg '^(State|VmRSS|VmSize|Threads):' /proc/$p/status; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:44:17.606Z Script completed Wall time 0.0 seconds Output: PID 766 State: S (sleeping) VmSize: 2592 kB VmRSS: 1704 kB Threads: 1 PID 767 State: R (running) VmSize: 1614860 kB VmRSS: 252036 kB Threads: 17 ## Step 66 · agent · 2026-08-31T18:44:19.783Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:21.115Z { "code": "const r = await tools.write_stdin({session_id:766,chars:\"\",yield_time_ms:30000,max_output_tokens:1000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:44:21.198Z Script failed Wall time 0.0 seconds Output: Script error: write_stdin failed: Unknown process id 766 ## Step 67 · agent · 2026-08-31T18:44:24.797Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:28.221Z { "code": "const r = await tools.exec_command({cmd:\"i=0; while [ -e /proc/767 ] && [ $i -lt 30 ]; do sleep 1; i=$((i+1)); done\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:44:39.245Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 68 · agent · 2026-08-31T18:44:41.336Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:42.115Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 1000 } ### Tool output · 2026-08-31T18:44:58.298Z Script completed Wall time 16.2 seconds Output: ## Step 69 · agent · 2026-08-31T18:45:01.106Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:05.837Z { "code": "const r = await tools.exec_command({cmd:\"if [ -e /proc/767/status ]; then rg '^(State|VmRSS|voluntary_ctxt_switches|nonvoluntary_ctxt_switches):' /proc/767/status; cat /proc/767/io; fi\\nls -lh /app/solution/validation_fusion_075.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);" } ### Tool output · 2026-08-31T18:45:05.966Z Script completed Wall time 0.0 seconds Output: State: R (running) VmRSS: 252036 kB voluntary_ctxt_switches: 4 nonvoluntary_ctxt_switches: 641 rchar: 7270611151 wchar: 0 syscr: 234454 syscw: 0 read_bytes: 0 write_bytes: 0 cancelled_write_bytes: 0 ## Step 70 · agent · 2026-08-31T18:45:13.844Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:15.675Z { "code": "const r = await tools.exec_command({cmd:\"kill 766 767 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:45:15.754Z Script completed Wall time 0.0 seconds Output: ## Step 71 · agent · 2026-08-31T18:45:17.401Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:28.830Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n@@\\n- if not np.array_equal(ordered_uids, z[\\\"uids\\\"].astype(np.int64)):\\n+ if not np.array_equal(ordered_uids, uids):\\n@@\\n- out_items = np.empty((len(z[\\\"uids\\\"]), 100), dtype=np.int64)\\n- for row, uid in enumerate(z[\\\"uids\\\"].astype(np.int64)):\\n- eligible = np.unpackbits(z[\\\"packed_eligible\\\"][row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ out_items = np.empty((len(uids), 100), dtype=np.int64)\\n+ for row, uid in enumerate(uids):\\n+ eligible = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n@@\\n- \\\"uid\\\": np.repeat(z[\\\"uids\\\"].astype(np.int64), 100),\\n+ \\\"uid\\\": np.repeat(uids, 100),\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:45:28.922Z Script completed Wall time 0.0 seconds Output: {} ## Step 72 · agent · 2026-08-31T18:45:30.699Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:36.806Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:45:47.826Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 73 · agent · 2026-08-31T18:45:49.361Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:50.141Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:46:06.987Z Script completed Wall time 16.8 seconds Output: { "model": "/app/solution/validation_fusion_0.55.parquet", "scores": { "quality_ndcg@10": 0.016251, "quality_users": 4263 } } ## Step 74 · agent · 2026-08-31T18:46:10.484Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:14.226Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'validation_fusion|evaluate_public' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T18:46:14.306Z Script completed Wall time 0.1 seconds Output: /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_0.85.parquet PID=1029 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'validation_fusion|evaluate_public' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=1048 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=60 `/task/evaluate_public.py` scores a validation-stage ranking against the public python /task/evaluate_public.py solution/validation.parquet PID=66 /bin/sh -c for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --output /app/solution/validation_fusion_${w}.parquet --model-weight $w --constant 30; done for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done PID=885 ## Step 75 · agent · 2026-08-31T18:46:16.008Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:18.001Z { "code": "const r = await tools.exec_command({cmd:\"while [ -e /proc/885 ]; do sleep 1; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:46:18.071Z Script completed Wall time 0.0 seconds Output: ## Step 76 · agent · 2026-08-31T18:46:20.391Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:23.756Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.55 0.65 0.75 0.85 0.92; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_fusion_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:46:34.778Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T18:46:36.992Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:37.788Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:46:40.441Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_fusion_0.55.parquet", "scores": { "quality_ndcg@10": 0.016251, "quality_users": 4263 } } { "model": "/app/solution/validation_fusion_0.65.parquet", "scores": { "quality_ndcg@10": 0.016701, "quality_users": 4263 } } { "model": "/app/solution/validation_fusion_0.75.parquet", "scores": { "quality_ndcg@10": 0.017016, "quality_users": 4263 } } { "model": "/app/solution/validation_fusion_0.85.parquet", "scores": { "quality_ndcg@10": 0.016989, "quality_users": 4263 } } { "model": "/app/solution/validation_fusion_0.92.parquet", "scores": { "quality_ndcg@10": 0.017035, "quality_users": 4263 } } ## Step 78 · agent · 2026-08-31T18:46:58.713Z ### Thinking unavailable — no text recorded Fusion testing shows only a modest gain, peaking at 0.017035 with a 92% model weight, so personalization is carrying nearly all of the lift. I’m keeping that artifact and inspecting hit rates by relevance grade before the next training run; this will tell me whether to spend capacity on explicit likes or strong organic listens. ### exec · 2026-08-31T18:47:21.307Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom ranking_evaluation import CandidateSet,ranked_items,validate_ranking,ndcg_at_k\\nfrom evaluate_public import validation_truth\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); gold=validation_truth(e,targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nfor name in ['validation_popularity.parquet','validation.parquet','validation_fusion_0.92.parquet']:\\n x=validate_ranking(pd.read_parquet('/app/solution/'+name),targets); rec=ranked_items(x)\\n hit1=hit3=total1=total3=users_hit=0; vals=[]\\n for u,g in gold.items():\\n top=set(rec[u][:10]); h=[g.get(i,0) for i in top]\\n hit1+=sum(v==1 for v in h); hit3+=sum(v==3 for v in h); total1+=sum(v==1 for v in g.values()); total3+=sum(v==3 for v in g.values()); users_hit+=any(v for v in h); vals.append(ndcg_at_k(rec[u],g,10))\\n print(name,'ndcg',np.mean(vals),'users_hit',users_hit,'hits grade1/3',hit1,hit3,'recall',hit1/total1,hit3/max(total3,1))\\nranks1=[];ranks3=[]\\nfor g in gold.values():\\n for i,v in g.items(): (ranks3 if v==3 else ranks1).append(rank[i])\\nfor n,a in [('g1',ranks1),('g3',ranks3)]: print(n,'n',len(a),'pop rank quantiles',np.quantile(a,[0,.1,.25,.5,.75,.9,.99,1]))\\nprint('gold users avg grades',np.mean([len(g) for g in gold.values()]),'grade3 total',len(ranks3),'grade1',len(ranks1))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:47:25.401Z Script completed Wall time 4.1 seconds Output: validation_popularity.parquet ndcg 0.010709951701903768 users_hit 229 hits grade1/3 334 34 recall 0.010014391940513312 0.007697532261716097 validation.parquet ndcg 0.016757940681045823 users_hit 348 hits grade1/3 408 71 recall 0.012233149436315663 0.01607425854652479 validation_fusion_0.92.parquet ndcg 0.01703496952046796 users_hit 355 hits grade1/3 413 69 recall 0.012383065483329335 0.015621462531129726 g1 n 33352 pop rank quantiles [1.00000e+00 4.08000e+02 1.69375e+03 5.54500e+03 1.32740e+04 2.16560e+04 2.91020e+04 3.00000e+04] g3 n 4417 pop rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04 2.88126e+04 2.99890e+04] gold users avg grades 8.859723199624677 grade3 total 4417 grade1 33352 ## Step 79 · agent · 2026-08-31T18:47:35.640Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:38.782Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 18 --seed 20260901 --output /app/solution/validation_seed2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:47:39.958Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"1846ae","wall_time_seconds":1.000883111,"session_id":47446,"original_token_count":0,"output":""} ## Step 80 · agent · 2026-08-31T18:47:59.785Z ### Thinking unavailable — no text recorded ## Step 81 · agent · 2026-08-31T18:48:02.119Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:03.340Z { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:48:14.361Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 82 · agent · 2026-08-31T18:48:16.281Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:17.073Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:48:33.366Z Script completed Wall time 16.3 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 5318 labeled users, 164063 positives snapshot cutoff=20540600: 5392 labeled users, 165953 positives snapshot cutoff=21147200: 5411 labeled users, 158923 positives snapshot cutoff=21753800: 5563 labeled users, 165819 positives snapshot cutoff=22360400: 5650 labeled users, 168735 positives snapshot cutoff=22967000: 5754 labeled users, 170431 positives snapshot cutoff=23573600: 5830 labeled users, 171275 positives snapshot cutoff=24180200: 5989 labeled users, 176193 positives /app/solution/build_ranker.py:478: 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 += float(loss) * b epoch 01/18: loss=0.58654 epoch 02/18: loss=0.53702 epoch 03/18: loss=0.50527 ## Step 83 · agent · 2026-08-31T18:48:35.586Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:36.997Z { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:48:48.047Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 84 · agent · 2026-08-31T18:48:58.322Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:59.126Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:07.048Z Script completed Wall time 7.9 seconds Output: epoch 04/18: loss=0.48211 epoch 05/18: loss=0.46221 epoch 06/18: loss=0.44632 epoch 07/18: loss=0.43340 epoch 08/18: loss=0.41662 ## Step 85 · agent · 2026-08-31T18:49:08.632Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:10.015Z { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:49:21.069Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 86 · agent · 2026-08-31T18:49:24.122Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:24.826Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:40.080Z Script completed Wall time 15.3 seconds Output: epoch 09/18: loss=0.40759 epoch 10/18: loss=0.39683 epoch 11/18: loss=0.38677 epoch 12/18: loss=0.37735 ## Step 87 · agent · 2026-08-31T18:49:41.487Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:42.880Z { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:49:53.922Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 88 · agent · 2026-08-31T18:49:55.547Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:56.337Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:12.919Z Script completed Wall time 16.6 seconds Output: epoch 13/18: loss=0.37004 epoch 14/18: loss=0.35896 epoch 15/18: loss=0.34928 epoch 16/18: loss=0.34116 ## Step 89 · agent · 2026-08-31T18:50:14.975Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:16.321Z { "code": "const r = await tools.write_stdin({session_id:47446,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:50:27.364Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 90 · agent · 2026-08-31T18:50:29.183Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:30.114Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:30.211Z Script completed Wall time 0.0 seconds Output: epoch 17/18: loss=0.33154 epoch 18/18: loss=0.32212 {"ranking": "/app/solution/validation_seed2.parquet", "checkpoint": "/app/solution/model_validation.pt", "training_examples": 44907, "elapsed_seconds": 165.5} ## Step 91 · agent · 2026-08-31T18:50:33.176Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:36.984Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_seed2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:50:40.333Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_seed2.parquet", "scores": { "quality_ndcg@10": 0.011489, "quality_users": 4263 } } ## Step 92 · agent · 2026-08-31T18:50:49.545Z ### Thinking unavailable — no text recorded The longer run overfit: despite continuing loss reduction, its public score fell to 0.011489 at epoch 18. I’m rejecting it and retaining the 10-epoch ranking. This is useful evidence that the rolling objective needs early stopping near 8–10 epochs; next I’m checking how much validation relevance consists of repeat listening, which determines whether a recency-aware history pool or broader latent discovery signal deserves the next iteration. ### exec · 2026-08-31T18:51:26.784Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/repeat_diagnostic.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build a history-repeat diagnostic ranking (not the submitted model).\\\"\\\"\\\"\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+CUTOFF = 24_786_800\\n+\\n+\\n+def main():\\n+ users = pd.read_parquet(D / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\").item_id.to_numpy(dtype=np.int64)\\n+ us = np.sort(users)\\n+ it = np.sort(catalog)\\n+ chunks = []\\n+ pf = pq.ParquetFile(D / \\\"train_listens.parquet\\\")\\n+ for rg in range(pf.num_row_groups):\\n+ x = pf.read_row_group(rg, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]).to_pandas()\\n+ uv = np.isin(x.uid.to_numpy(), us)\\n+ iv = np.isin(x.item_id.to_numpy(), it)\\n+ keep = uv & iv & (x.played_ratio_pct.to_numpy() >= 50) & (x.timestamp.to_numpy() >= CUTOFF - 90 * 86400)\\n+ y = x.loc[keep, [\\\"uid\\\", \\\"item_id\\\", \\\"timestamp\\\", \\\"is_organic\\\"]]\\n+ y[\\\"organic_count\\\"] = y.is_organic.astype(np.int16)\\n+ y = y.groupby([\\\"uid\\\", \\\"item_id\\\"], as_index=False).agg(count=(\\\"timestamp\\\", \\\"size\\\"), last=(\\\"timestamp\\\", \\\"max\\\"), organic_count=(\\\"organic_count\\\", \\\"sum\\\"))\\n+ chunks.append(y)\\n+ hist = pd.concat(chunks, ignore_index=True).groupby([\\\"uid\\\", \\\"item_id\\\"], as_index=False).agg(count=(\\\"count\\\", \\\"sum\\\"), last=(\\\"last\\\", \\\"max\\\"), organic_count=(\\\"organic_count\\\", \\\"sum\\\"))\\n+ # Several simple rankings can be selected by changing this expression.\\n+ age = (CUTOFF - hist[\\\"last\\\"]) / 86400.0\\n+ hist[\\\"score\\\"] = np.log1p(hist[\\\"organic_count\\\"] * 2 + hist[\\\"count\\\"]) - 0.015 * age\\n+ by_user = {int(u): g.sort_values(\\\"score\\\", ascending=False).item_id.to_numpy(dtype=np.int64) for u, g in hist.groupby(\\\"uid\\\")}\\n+ z = np.load(D / \\\"validation_eligible_candidates.npz\\\")\\n+ z_uids = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64); packed = z[\\\"packed_eligible\\\"]\\n+ result = np.empty((len(z_uids), 100), dtype=np.int64)\\n+ for row, uid in enumerate(z_uids):\\n+ eligible = np.unpackbits(packed[row], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ allowed = set(items[eligible].tolist())\\n+ personal = [i for i in by_user.get(int(uid), []) if i in allowed]\\n+ chosen = list(dict.fromkeys(personal + items[eligible].tolist()))[:100]\\n+ result[row] = chosen\\n+ pd.DataFrame({\\\"uid\\\": np.repeat(z_uids,100), \\\"item_id\\\":result.ravel(), \\\"rank\\\":np.tile(np.arange(1,101),len(z_uids))}).astype(\\\"int64\\\").to_parquet(O/\\\"validation_repeat.parquet\\\",index=False)\\n+ hist.to_parquet(O/\\\"repeat_history.parquet\\\",index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:51:26.837Z Script completed Wall time 0.0 seconds Output: {} ## Step 93 · agent · 2026-08-31T18:51:28.776Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:31.615Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/repeat_diagnostic.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:51:42.633Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 94 · agent · 2026-08-31T18:51:44.183Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:45.010Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 5000 } ### Tool output · 2026-08-31T18:52:01.795Z Script completed Wall time 16.8 seconds Output: ## Step 95 · agent · 2026-08-31T18:52:03.741Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:08.583Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do if [ -r \\\"$d/cmdline\\\" ]; then tr '\\\\0' ' ' < \\\"$d/cmdline\\\" | rg 'repeat_diagnostic|evaluate_public.*validation_repeat' && printf ' PID=%s\\\\n' \\\"${d##*/}\\\"; fi; done\\nls -lh /app/solution/validation_repeat.parquet /app/solution/repeat_history.parquet 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T18:52:08.671Z Script completed Wall time 0.1 seconds Output: /bin/sh -c /opt/task-venv/bin/python /app/solution/repeat_diagnostic.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet PID=1330 /opt/task-venv/bin/python /app/solution/repeat_diagnostic.py PID=1331 /bin/sh -c for d in /proc/[0-9]*; do if [ -r "$d/cmdline" ]; then tr '\0' ' ' < "$d/cmdline" | rg 'repeat_diagnostic|evaluate_public.*validation_repeat' && printf ' PID=%s\n' "${d##*/}"; fi; done PID=1357 ## Step 96 · agent · 2026-08-31T18:52:12.595Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:14.675Z { "code": "const r = await tools.exec_command({cmd:\"while [ -e /proc/1330 ]; do sleep 1; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);" } ### Tool output · 2026-08-31T18:52:17.707Z Script completed Wall time 3.0 seconds Output: ## Step 97 · agent · 2026-08-31T18:52:20.251Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:23.085Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_repeat.parquet /app/solution/repeat_history.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_repeat.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:52:26.378Z Script completed Wall time 3.3 seconds Output: -rw-r--r-- 1 agent agent 46M Aug 31 18:52 /app/solution/repeat_history.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:52 /app/solution/validation_repeat.parquet { "model": "/app/solution/validation_repeat.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 98 · agent · 2026-08-31T18:52:46.823Z ### Thinking unavailable — no text recorded ## Step 99 · agent · 2026-08-31T18:52:57.968Z ### Thinking unavailable — no text recorded The repeat-listen diagnostic collapses exactly to the popularity score, confirming that the eligibility service removes previously consumed tracks. That exposes the main modeling mismatch: my rolling training labels still included repeat listens even though they can never enter the shelf. I’m correcting the sampled objective to keep only cutoff-novel positives before retraining; the user history remains the signal, but it will now predict unseen items rather than reconstruct consumed ones. ### exec · 2026-08-31T18:53:14.414Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n def labels_for_window(\\n events: EventData, actions: ActionData, cutoff: int\\n ) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n@@\\n for u, i in zip(actions.user[likes], actions.item[likes]):\\n pairs.setdefault(int(u), {})[int(i)] = 3\\n+ # The platform's candidate boundary removes consumed tracks. Reproduce\\n+ # that crucial aspect at every pseudo-cutoff: labels already present in\\n+ # the pre-cutoff positive history are not valid discovery targets. This\\n+ # is a sparse per-user set lookup over observed events, not a matrix.\\n+ if pairs:\\n+ n_users = int(events.user.max()) + 1\\n+ event_offsets = offsets_for_users(events.user, n_users)\\n+ novel: dict[int, dict[int, int]] = {}\\n+ for u, grades in pairs.items():\\n+ lo, hi = event_offsets[u], event_offsets[u + 1]\\n+ before = lo + np.searchsorted(\\n+ events.timestamp[lo:hi], cutoff, side=\\\"right\\\"\\n+ )\\n+ consumed = set(map(int, events.item[lo:before]))\\n+ kept = {item: grade for item, grade in grades.items() if item not in consumed}\\n+ if kept:\\n+ novel[u] = kept\\n+ pairs = novel\\n return {\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:53:14.463Z Script completed Wall time 0.0 seconds Output: {} ## Step 100 · agent · 2026-08-31T18:53:16.555Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:19.450Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --output /app/solution/validation_novel.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:53:20.627Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e38935","wall_time_seconds":1.000418798,"session_id":72485,"original_token_count":0,"output":""} ## Step 101 · agent · 2026-08-31T18:53:22.185Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:25.454Z { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:53:36.474Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T18:53:38.329Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:39.108Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:55.486Z Script completed Wall time 16.4 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 4225 labeled users, 45005 positives snapshot cutoff=20540600: 4315 labeled users, 47973 positives snapshot cutoff=21147200: 4338 labeled users, 45917 positives snapshot cutoff=21753800: 4459 labeled users, 49300 positives snapshot cutoff=22360400: 4540 labeled users, 48291 positives snapshot cutoff=22967000: 4562 labeled users, 50197 positives snapshot cutoff=23573600: 4694 labeled users, 50667 positives snapshot cutoff=24180200: 4778 labeled users, 51636 positives /app/solution/build_ranker.py:496: 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 += float(loss) * b ## Step 103 · agent · 2026-08-31T18:53:58.235Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:58.613Z { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:54:09.659Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T18:54:11.283Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:12.087Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:54:28.666Z Script completed Wall time 16.6 seconds Output: epoch 01/10: loss=0.62228 epoch 02/10: loss=0.55714 epoch 03/10: loss=0.51068 epoch 04/10: loss=0.46700 epoch 05/10: loss=0.42923 epoch 06/10: loss=0.40015 ## Step 105 · agent · 2026-08-31T18:54:30.664Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:32.594Z { "code": "const r = await tools.write_stdin({session_id:72485,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:54:43.645Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T18:54:46.778Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:47.522Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:54:57.380Z Script completed Wall time 9.9 seconds Output: epoch 07/10: loss=0.37907 epoch 08/10: loss=0.36506 epoch 09/10: loss=0.34721 epoch 10/10: loss=0.33319 {"ranking": "/app/solution/validation_novel.parquet", "checkpoint": "/app/solution/model_validation.pt", "training_examples": 35911, "elapsed_seconds": 93.8} ## Step 107 · agent · 2026-08-31T18:54:58.995Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:01.255Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_novel.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:55:04.678Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation_novel.parquet", "scores": { "quality_ndcg@10": 0.01277, "quality_users": 4263 } } ## Step 108 · agent · 2026-08-31T18:55:09.411Z ### Thinking unavailable — no text recorded ## Step 109 · agent · 2026-08-31T18:55:23.012Z ### Thinking unavailable — no text recorded Novel-only training reaches 0.012770—better than popularity but worse than the mixed next-item objective. The repeats are not rankable, yet they still teach strong item–item affinity, so deleting them discards useful representation learning. I’m retaining the original 0.017035 best and testing the more principled combination: broad next-item pretraining followed by a short novel-item fine-tune, rather than training either signal in isolation. ### exec · 2026-08-31T18:55:51.073Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n def labels_for_window(\\n- events: EventData, actions: ActionData, cutoff: int\\n+ events: EventData, actions: ActionData, cutoff: int, novel_only: bool = False\\n ) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n@@\\n- if pairs:\\n+ if novel_only and pairs:\\n@@\\n def build_training_data(\\n@@\\n supplied_train_preferences: tuple[np.ndarray, np.ndarray] | None,\\n+ novel_only: bool = False,\\n ) -> SnapshotDataset:\\n@@\\n- labels = labels_for_window(events, actions, cutoff)\\n+ labels = labels_for_window(events, actions, cutoff, novel_only=novel_only)\\n@@\\n def train_model(\\n@@\\n epochs: int,\\n seed: int,\\n+ learning_rate: float = 2.5e-3,\\n ) -> None:\\n@@\\n- optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n@@\\n parser.add_argument(\\\"--output\\\", type=Path)\\n+ parser.add_argument(\\n+ \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"pretrain\\\",\\n+ help=\\\"pretrain uses broad positives then a short novel-candidate fine-tune\\\",\\n+ )\\n@@\\n dataset = build_training_data(\\n builder, events, actions, cutoffs,\\n validation_preferences if include_val else None,\\n+ novel_only=args.label_mode == \\\"novel\\\",\\n )\\n@@\\n- train_model(model, dataset, popularity, args.epochs, args.seed)\\n+ if args.label_mode == \\\"pretrain\\\":\\n+ broad = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ novel_only=False,\\n+ )\\n+ broad_epochs = max(1, args.epochs - 2)\\n+ train_model(model, broad, popularity, broad_epochs, args.seed)\\n+ # Two gentle epochs align the pretrained affinity space to the actual\\n+ # unseen-candidate boundary without forgetting co-consumption structure.\\n+ novel = build_training_data(\\n+ builder, events, actions, cutoffs,\\n+ validation_preferences if include_val else None,\\n+ novel_only=True,\\n+ )\\n+ train_model(model, novel, popularity, args.epochs - broad_epochs,\\n+ args.seed + 1, learning_rate=6e-4)\\n+ dataset = novel\\n+ else:\\n+ train_model(model, dataset, popularity, args.epochs, args.seed)\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:55:51.142Z Script completed Wall time 0.0 seconds Output: {} ## Step 110 · agent · 2026-08-31T18:55:56.275Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:00.132Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n if args.label_mode == \\\"pretrain\\\":\\n- broad = build_training_data(\\n- builder, events, actions, cutoffs,\\n- validation_preferences if include_val else None,\\n- novel_only=False,\\n- )\\n+ broad = dataset\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:56:00.205Z Script completed Wall time 0.0 seconds Output: {} ## Step 111 · agent · 2026-08-31T18:56:02.187Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:05.972Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode pretrain --output /app/solution/validation_pretrain.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:56:07.150Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d82d57","wall_time_seconds":1.001007921,"session_id":19121,"original_token_count":0,"output":""} ## Step 112 · agent · 2026-08-31T18:56:08.888Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:10.233Z { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:56:21.250Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 113 · agent · 2026-08-31T18:56:22.712Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:23.505Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:56:40.256Z Script completed Wall time 16.8 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 5318 labeled users, 164063 positives snapshot cutoff=20540600: 5392 labeled users, 165953 positives snapshot cutoff=21147200: 5411 labeled users, 158923 positives snapshot cutoff=21753800: 5563 labeled users, 165819 positives snapshot cutoff=22360400: 5650 labeled users, 168735 positives snapshot cutoff=22967000: 5754 labeled users, 170431 positives snapshot cutoff=23573600: 5830 labeled users, 171275 positives snapshot cutoff=24180200: 5989 labeled users, 176193 positives /app/solution/build_ranker.py:498: 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 += float(loss) * b epoch 01/8: loss=0.59321 ## Step 114 · agent · 2026-08-31T18:56:42.225Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:43.557Z { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:56:54.582Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 115 · agent · 2026-08-31T18:56:56.247Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:57.152Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:13.592Z Script completed Wall time 16.4 seconds Output: epoch 02/8: loss=0.54179 epoch 03/8: loss=0.51136 epoch 04/8: loss=0.48345 epoch 05/8: loss=0.46498 ## Step 116 · agent · 2026-08-31T18:57:15.355Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:16.713Z { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:57:27.756Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 117 · agent · 2026-08-31T18:57:30.149Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:31.010Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:46.757Z Script completed Wall time 15.7 seconds Output: epoch 06/8: loss=0.44869 epoch 07/8: loss=0.43527 epoch 08/8: loss=0.41981 snapshot cutoff=19934000: 4225 labeled users, 45005 positives snapshot cutoff=20540600: 4315 labeled users, 47973 positives snapshot cutoff=21147200: 4338 labeled users, 45917 positives snapshot cutoff=21753800: 4459 labeled users, 49300 positives snapshot cutoff=22360400: 4540 labeled users, 48291 positives snapshot cutoff=22967000: 4562 labeled users, 50197 positives ## Step 118 · agent · 2026-08-31T18:57:48.446Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:49.813Z { "code": "const r = await tools.write_stdin({session_id:19121,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T18:58:00.835Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 119 · agent · 2026-08-31T18:58:02.508Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:03.255Z { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:07.340Z Script completed Wall time 4.1 seconds Output: snapshot cutoff=23573600: 4694 labeled users, 50667 positives snapshot cutoff=24180200: 4778 labeled users, 51636 positives epoch 01/2: loss=0.42226 epoch 02/2: loss=0.41066 {"ranking": "/app/solution/validation_pretrain.parquet", "checkpoint": "/app/solution/model_validation.pt", "training_examples": 35911, "elapsed_seconds": 117.0} ## Step 120 · agent · 2026-08-31T18:58:08.764Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:10.981Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pretrain.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T18:58:14.330Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_pretrain.parquet", "scores": { "quality_ndcg@10": 0.015141, "quality_users": 4263 } } ## Step 121 · agent · 2026-08-31T18:58:20.159Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:28.239Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_fusion.py\\n@@\\n p.add_argument(\\\"--constant\\\", type=float, default=30.0)\\n+ p.add_argument(\\\"--prior-ranking\\\", type=Path, help=\\\"optional second ranking instead of popularity\\\")\\n@@\\n model_matrix = ordered.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n+ prior_matrix = None\\n+ if args.prior_ranking:\\n+ prior = pd.read_parquet(args.prior_ranking).sort_values([\\\"uid\\\", \\\"rank\\\"])\\n+ prior_matrix = prior.item_id.to_numpy(dtype=np.int64).reshape(-1, 100)\\n@@\\n- pop = items[eligible][:400]\\n+ pop = prior_matrix[row] if prior_matrix is not None else items[eligible][:400]\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:58:28.345Z Script completed Wall time 0.0 seconds Output: {} ## Step 122 · agent · 2026-08-31T18:58:32.148Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:38.501Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.5 0.65 0.8 0.9; do /opt/task-venv/bin/python /app/solution/rank_fusion.py --input /app/solution/validation.parquet --prior-ranking /app/solution/validation_novel.parquet --output /app/solution/validation_ensemble_${w}.parquet --model-weight $w --constant 30; done\\nfor w in 0.5 0.65 0.8 0.9; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);" } ### Tool output · 2026-08-31T18:58:49.522Z Script running with cell ID 78 Wall time 11.0 seconds Output: ## Step 123 · agent · 2026-08-31T18:58:51.182Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:52.158Z { "cell_id": "78", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:04.008Z Script completed Wall time 11.8 seconds Output: { "model": "/app/solution/validation_ensemble_0.5.parquet", "scores": { "quality_ndcg@10": 0.019391, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_0.65.parquet", "scores": { "quality_ndcg@10": 0.019662, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_0.8.parquet", "scores": { "quality_ndcg@10": 0.018767, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_0.9.parquet", "scores": { "quality_ndcg@10": 0.017606, "quality_users": 4263 } } ## Step 124 · agent · 2026-08-31T18:59:23.699Z ### Thinking unavailable — no text recorded ## Step 125 · agent · 2026-08-31T18:59:29.892Z ### Thinking unavailable — no text recorded The two learned signals are strongly complementary: validation-only reciprocal-rank diagnostics reach 0.019662 when broad affinity gets 65% weight and novel-item training 35%. I won’t use reciprocal ranks in the final system because that would violate the required pure-dot-product scorer. Instead, I’m preserving separate checkpoints and combining them by concatenating scaled tower vectors; the resulting weighted score is exactly one dot product and candidate vectors remain independently precomputable. ### exec · 2026-08-31T18:59:38.450Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_ranker.py\\n@@\\n- \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"pretrain\\\",\\n+ \\\"--label-mode\\\", choices=[\\\"all\\\", \\\"novel\\\", \\\"pretrain\\\"], default=\\\"all\\\",\\n@@\\n- checkpoint = OUT / f\\\"model_{args.stage}.pt\\\"\\n+ checkpoint = OUT / f\\\"model_{args.stage}_{destination.stem}.pt\\\"\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T18:59:38.590Z Script completed Wall time 0.0 seconds Output: {} ## Step 126 · agent · 2026-08-31T18:59:44.703Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:48.149Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode all --output /app/solution/validation_all_checkpoint.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T18:59:49.326Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"31ab1d","wall_time_seconds":1.000960007,"session_id":47955,"original_token_count":0,"output":""} ## Step 127 · agent · 2026-08-31T18:59:52.292Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:53.135Z { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:00:04.153Z Script running with cell ID 81 Wall time 11.0 seconds Output: ## Step 128 · agent · 2026-08-31T19:00:07.154Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:08.147Z { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:00:23.171Z Script completed Wall time 15.0 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 5318 labeled users, 164063 positives snapshot cutoff=20540600: 5392 labeled users, 165953 positives snapshot cutoff=21147200: 5411 labeled users, 158923 positives snapshot cutoff=21753800: 5563 labeled users, 165819 positives snapshot cutoff=22360400: 5650 labeled users, 168735 positives snapshot cutoff=22967000: 5754 labeled users, 170431 positives snapshot cutoff=23573600: 5830 labeled users, 171275 positives snapshot cutoff=24180200: 5989 labeled users, 176193 positives /app/solution/build_ranker.py:498: 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 += float(loss) * b epoch 01/10: loss=0.59321 ## Step 129 · agent · 2026-08-31T19:00:24.869Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:26.401Z { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:00:37.442Z Script running with cell ID 82 Wall time 11.0 seconds Output: ## Step 130 · agent · 2026-08-31T19:00:39.166Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:40.021Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:00:56.444Z Script completed Wall time 16.4 seconds Output: epoch 02/10: loss=0.54179 epoch 03/10: loss=0.51136 epoch 04/10: loss=0.48345 epoch 05/10: loss=0.46498 ## Step 131 · agent · 2026-08-31T19:00:58.064Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:59.427Z { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:01:10.463Z Script running with cell ID 83 Wall time 11.0 seconds Output: ## Step 132 · agent · 2026-08-31T19:01:12.565Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:13.390Z { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:29.472Z Script completed Wall time 16.1 seconds Output: epoch 06/10: loss=0.44869 epoch 07/10: loss=0.43527 epoch 08/10: loss=0.41981 epoch 09/10: loss=0.40792 ## Step 133 · agent · 2026-08-31T19:01:30.940Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:32.341Z { "code": "const r = await tools.write_stdin({session_id:47955,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:01:35.518Z Script completed Wall time 3.2 seconds Output: epoch 10/10: loss=0.39613 {"ranking": "/app/solution/validation_all_checkpoint.parquet", "checkpoint": "/app/solution/model_validation_validation_all_checkpoint.pt", "training_examples": 44907, "elapsed_seconds": 103.3} ## Step 134 · agent · 2026-08-31T19:01:38.046Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:41.049Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage validation --epochs 10 --seed 20260831 --label-mode novel --output /app/solution/validation_novel_checkpoint.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:01:42.227Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"909f17","wall_time_seconds":1.000198739,"session_id":14276,"original_token_count":0,"output":""} ## Step 135 · agent · 2026-08-31T19:01:44.374Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:46.908Z { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:01:57.931Z Script running with cell ID 86 Wall time 11.0 seconds Output: ## Step 136 · agent · 2026-08-31T19:01:59.643Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:00.406Z { "cell_id": "86", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:16.930Z Script completed Wall time 16.5 seconds Output: loading event streams since 9566000 loaded 13,748,805 positive listens and 418,226 actions snapshot cutoff=19934000: 4225 labeled users, 45005 positives snapshot cutoff=20540600: 4315 labeled users, 47973 positives snapshot cutoff=21147200: 4338 labeled users, 45917 positives snapshot cutoff=21753800: 4459 labeled users, 49300 positives snapshot cutoff=22360400: 4540 labeled users, 48291 positives snapshot cutoff=22967000: 4562 labeled users, 50197 positives snapshot cutoff=23573600: 4694 labeled users, 50667 positives snapshot cutoff=24180200: 4778 labeled users, 51636 positives /app/solution/build_ranker.py:498: 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 += float(loss) * b ## Step 137 · agent · 2026-08-31T19:02:18.622Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:19.990Z { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:02:31.019Z Script running with cell ID 87 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T19:02:32.942Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:33.752Z { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:50.030Z Script completed Wall time 16.3 seconds Output: epoch 01/10: loss=0.62228 epoch 02/10: loss=0.55714 epoch 03/10: loss=0.51068 epoch 04/10: loss=0.46700 epoch 05/10: loss=0.42923 ## Step 139 · agent · 2026-08-31T19:02:51.906Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:53.407Z { "code": "const r = await tools.write_stdin({session_id:14276,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:03:04.449Z Script running with cell ID 88 Wall time 11.0 seconds Output: ## Step 140 · agent · 2026-08-31T19:03:14.571Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:15.359Z { "cell_id": "88", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:19.772Z Script completed Wall time 4.4 seconds Output: epoch 06/10: loss=0.40015 epoch 07/10: loss=0.37907 epoch 08/10: loss=0.36506 epoch 09/10: loss=0.34721 epoch 10/10: loss=0.33319 {"ranking": "/app/solution/validation_novel_checkpoint.parquet", "checkpoint": "/app/solution/model_validation_validation_novel_checkpoint.pt", "training_examples": 35911, "elapsed_seconds": 94.6} ## Step 141 · agent · 2026-08-31T19:03:29.338Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:25.231Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/score_ensemble.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Apply two trained tower pairs as one exact concatenated-dot-product model.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import math\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+import torch.nn as nn\\n+\\n+from build_ranker import (\\n+ DATA, GAP, HISTORY_DAYS, MAX_DISLIKES, MAX_LIKES, STEP, TRAIN_END,\\n+ ActionData, FeatureBuilder, IdMap, TwoTower, item_metadata, load_actions,\\n+ load_listens, provided_preference_features,\\n+)\\n+\\n+\\n+class ConcatenatedUserTower(nn.Module):\\n+ \\\"\\\"\\\"One user tower whose output concatenates scaled component vectors.\\\"\\\"\\\"\\n+\\n+ def __init__(self, first: nn.Module, second: nn.Module, weight: float):\\n+ super().__init__()\\n+ self.first, self.second = first, second\\n+ self.a, self.b = math.sqrt(weight), math.sqrt(1.0 - weight)\\n+\\n+ def forward(self, *features):\\n+ return torch.cat([self.a * self.first(*features), self.b * self.second(*features)], dim=-1)\\n+\\n+\\n+class ConcatenatedItemTower(nn.Module):\\n+ \\\"\\\"\\\"Offline item counterpart; its dot product equals the weighted score.\\\"\\\"\\\"\\n+\\n+ def __init__(self, first: nn.Module, second: nn.Module, weight: float):\\n+ super().__init__()\\n+ self.first, self.second = first, second\\n+ self.a, self.b = math.sqrt(weight), math.sqrt(1.0 - weight)\\n+\\n+ def forward(self, item_indices):\\n+ return torch.cat([self.a * self.first(item_indices), self.b * self.second(item_indices)], dim=-1)\\n+\\n+\\n+@torch.inference_mode()\\n+def write(user_tower, item_tower, features, archive_path, output):\\n+ user_tower.eval(); item_tower.eval()\\n+ z = np.load(archive_path)\\n+ uids = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = z[\\\"packed_eligible\\\"]\\n+ iv = item_tower(torch.arange(len(items))).cpu()\\n+ chosen = np.empty((len(uids), 100), dtype=np.int32)\\n+ for lo in range(0, len(uids), 128):\\n+ hi = min(lo + 128, len(uids))\\n+ uv = user_tower(*[torch.from_numpy(x[lo:hi]) for x in features]).cpu()\\n+ scores = uv @ iv.T\\n+ eligible = np.unpackbits(packed[lo:hi], axis=1, count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ scores[~torch.from_numpy(eligible)] = -torch.inf\\n+ chosen[lo:hi] = torch.topk(scores, 100, dim=1).indices.numpy()\\n+ frame = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(uids, 100),\\n+ \\\"item_id\\\": items[chosen.ravel()],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(uids)),\\n+ }).astype(\\\"int64\\\")\\n+ temp = output.with_suffix(\\\".tmp.parquet\\\")\\n+ frame.to_parquet(temp, index=False); temp.replace(output)\\n+\\n+\\n+def main():\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ p.add_argument(\\\"--all-model\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--novel-model\\\", type=Path, required=True)\\n+ p.add_argument(\\\"--weight\\\", type=float, required=True)\\n+ p.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = p.parse_args()\\n+ torch.set_num_threads(12)\\n+ stage = args.stage\\n+ archive_path = DATA / f\\\"{stage}_eligible_candidates.npz\\\"\\n+ z = np.load(archive_path)\\n+ users = z[\\\"uids\\\"].astype(np.int64); items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ user_map, item_map = IdMap(users), IdMap(items)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ meta = item_metadata(catalog)\\n+ first, second = TwoTower(len(items), meta), TwoTower(len(items), meta)\\n+ first.load_state_dict(torch.load(args.all_model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+ second.load_state_dict(torch.load(args.novel_model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+\\n+ include_val = stage == \\\"test\\\"\\n+ cutoff = TRAIN_END + STEP if include_val else TRAIN_END\\n+ earliest = cutoff - HISTORY_DAYS * 86_400\\n+ listens = [\\\"train_listens.parquet\\\"] + ([\\\"val_listens.parquet\\\"] if include_val else [])\\n+ events = load_listens(listens, user_map, item_map, earliest)\\n+ actions = load_actions(include_val, user_map, item_map, earliest)\\n+ builder = FeatureBuilder(events, actions, len(users))\\n+ features = list(builder.many(np.arange(len(users), dtype=np.int32), cutoff))\\n+ pref_name = \\\"test_preference_state.parquet\\\" if include_val else \\\"validation_preference_state.parquet\\\"\\n+ prefs = provided_preference_features(DATA / pref_name, user_map, item_map)\\n+ features[2], features[3] = prefs\\n+ features[4][:, 1] = np.log1p((prefs[0] != 0).sum(1)) / math.log1p(MAX_LIKES)\\n+ features[4][:, 2] = np.log1p((prefs[1] != 0).sum(1)) / math.log1p(MAX_DISLIKES)\\n+ user_tower = ConcatenatedUserTower(first.user_tower, second.user_tower, args.weight)\\n+ item_tower = ConcatenatedItemTower(first.item_tower, second.item_tower, args.weight)\\n+ write(user_tower, item_tower, tuple(features), archive_path, args.output)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T19:04:25.282Z Script completed Wall time 0.0 seconds Output: {} ## Step 142 · agent · 2026-08-31T19:04:29.809Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:36.320Z { "code": "const r = await tools.exec_command({cmd:\"for w in 0.4 0.55 0.65 0.75; do /opt/task-venv/bin/python /app/solution/score_ensemble.py --stage validation --all-model /app/solution/model_validation_validation_all_checkpoint.pt --novel-model /app/solution/model_validation_validation_novel_checkpoint.pt --weight $w --output /app/solution/validation_dot_${w}.parquet; done\\nfor w in 0.4 0.55 0.65 0.75; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_dot_${w}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:04:37.497Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"2e974d","wall_time_seconds":1.001195381,"session_id":77163,"original_token_count":0,"output":""} ## Step 143 · agent · 2026-08-31T19:04:39.340Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:40.678Z { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:04:51.700Z Script running with cell ID 91 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T19:04:53.904Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:54.715Z { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:05:10.704Z Script completed Wall time 16.0 seconds Output: ## Step 145 · agent · 2026-08-31T19:05:12.426Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:13.805Z { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:05:24.823Z Script running with cell ID 92 Wall time 11.0 seconds Output: ## Step 146 · agent · 2026-08-31T19:05:27.011Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:27.737Z { "cell_id": "92", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:05:43.827Z Script completed Wall time 16.1 seconds Output: { "model": "/app/solution/validation_dot_0.4.parquet", "scores": { "quality_ndcg@10": 0.018559, "quality_users": 4263 } } { "model": "/app/solution/validation_dot_0.55.parquet", "scores": { "quality_ndcg@10": 0.020363, "quality_users": 4263 } } ## Step 147 · agent · 2026-08-31T19:05:49.623Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:49.627Z { "code": "const r = await tools.write_stdin({session_id:77163,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:05:49.679Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation_dot_0.65.parquet", "scores": { "quality_ndcg@10": 0.020298, "quality_users": 4263 } } { "model": "/app/solution/validation_dot_0.75.parquet", "scores": { "quality_ndcg@10": 0.020288, "quality_users": 4263 } } ## Step 148 · agent · 2026-08-31T19:05:59.635Z ### Thinking unavailable — no text recorded The exact concatenated-dot-product ensemble validates at 0.020363 NDCG@10 with a 55% broad-affinity / 45% novel-item weighting, outperforming both individual towers and the rank-fusion diagnostic. I’m locking that choice, creating a structurally valid fallback submission immediately, then refitting both components on `train + val` and replacing the fallback only after the final eligibility checks pass. ### exec · 2026-08-31T19:06:11.718Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/app/solution /opt/task-venv/bin/python - <<'PY'\\nfrom baseline import write\\nimport shutil\\np=write('test')\\nshutil.copyfile(p,'/app/solution/submission.parquet')\\nprint(p)\\nPY\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); x=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); x=validate_ranking(x,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)); print('fallback valid',x.shape)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);" } ### Tool output · 2026-08-31T19:06:17.371Z Script completed Wall time 5.6 seconds Output: /app/solution/test_popularity.parquet fallback valid (845600, 3) ## Step 149 · agent · 2026-08-31T19:06:19.405Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:22.369Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --epochs 10 --seed 20260831 --label-mode all --output /app/solution/test_all_component.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":14000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:06:23.545Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d39135","wall_time_seconds":1.000772135,"session_id":67089,"original_token_count":0,"output":""} ## Step 150 · agent · 2026-08-31T19:06:25.294Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:26.668Z { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:06:37.689Z Script running with cell ID 96 Wall time 11.0 seconds Output: ## Step 151 · agent · 2026-08-31T19:06:39.413Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:40.215Z { "cell_id": "96", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:06:56.703Z Script completed Wall time 16.5 seconds Output: loading event streams since 9566000 loaded 14,399,100 positive listens and 440,272 actions snapshot cutoff=19934000: 5318 labeled users, 164063 positives snapshot cutoff=20540600: 5392 labeled users, 165953 positives snapshot cutoff=21147200: 5411 labeled users, 158923 positives snapshot cutoff=21753800: 5563 labeled users, 165819 positives snapshot cutoff=22360400: 5650 labeled users, 168735 positives snapshot cutoff=22967000: 5754 labeled users, 170431 positives snapshot cutoff=23573600: 5830 labeled users, 171275 positives snapshot cutoff=24180200: 5989 labeled users, 176193 positives snapshot cutoff=24786800: 6186 labeled users, 175729 positives /app/solution/build_ranker.py:498: 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 += float(loss) * b ## Step 152 · agent · 2026-08-31T19:06:58.376Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:59.723Z { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:07:10.753Z Script running with cell ID 97 Wall time 11.0 seconds Output: ## Step 153 · agent · 2026-08-31T19:07:13.696Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:14.652Z { "cell_id": "97", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:07:29.764Z Script completed Wall time 15.1 seconds Output: epoch 01/10: loss=0.59121 epoch 02/10: loss=0.54127 epoch 03/10: loss=0.50785 epoch 04/10: loss=0.48479 ## Step 154 · agent · 2026-08-31T19:07:36.910Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:36.923Z { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:07:47.965Z Script running with cell ID 98 Wall time 11.0 seconds Output: ## Step 155 · agent · 2026-08-31T19:07:49.858Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:50.663Z { "cell_id": "98", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:08:06.973Z Script completed Wall time 16.3 seconds Output: epoch 05/10: loss=0.46258 epoch 06/10: loss=0.44288 epoch 07/10: loss=0.43119 epoch 08/10: loss=0.41586 ## Step 156 · agent · 2026-08-31T19:08:08.656Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:10.059Z { "code": "const r = await tools.write_stdin({session_id:67089,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:08:21.097Z Script running with cell ID 99 Wall time 11.0 seconds Output: ## Step 157 · agent · 2026-08-31T19:08:22.815Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:23.609Z { "cell_id": "99", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:08:23.728Z Script completed Wall time 0.0 seconds Output: epoch 09/10: loss=0.40578 epoch 10/10: loss=0.39861 {"ranking": "/app/solution/test_all_component.parquet", "checkpoint": "/app/solution/model_test_test_all_component.pt", "training_examples": 51093, "elapsed_seconds": 114.9} ## Step 158 · agent · 2026-08-31T19:08:27.318Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:30.358Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --epochs 10 --seed 20260831 --label-mode novel --output /app/solution/test_novel_component.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":14000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:08:31.535Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"76b75e","wall_time_seconds":1.000543118,"session_id":8264,"original_token_count":0,"output":""} ## Step 159 · agent · 2026-08-31T19:08:34.462Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:35.846Z { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:08:46.864Z Script running with cell ID 101 Wall time 11.0 seconds Output: ## Step 160 · agent · 2026-08-31T19:08:48.703Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:49.526Z { "cell_id": "101", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:09:05.870Z Script completed Wall time 16.3 seconds Output: loading event streams since 9566000 loaded 14,399,100 positive listens and 440,272 actions snapshot cutoff=19934000: 4225 labeled users, 45005 positives snapshot cutoff=20540600: 4315 labeled users, 47973 positives snapshot cutoff=21147200: 4338 labeled users, 45917 positives snapshot cutoff=21753800: 4459 labeled users, 49300 positives snapshot cutoff=22360400: 4540 labeled users, 48291 positives snapshot cutoff=22967000: 4562 labeled users, 50197 positives snapshot cutoff=23573600: 4694 labeled users, 50667 positives snapshot cutoff=24180200: 4778 labeled users, 51636 positives snapshot cutoff=24786800: 4799 labeled users, 48252 positives /app/solution/build_ranker.py:498: 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 += float(loss) * b ## Step 161 · agent · 2026-08-31T19:09:08.151Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:09.551Z { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:09:20.581Z Script running with cell ID 102 Wall time 11.0 seconds Output: ## Step 162 · agent · 2026-08-31T19:09:22.365Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:09:23.215Z { "cell_id": "102", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:09:39.590Z Script completed Wall time 16.4 seconds Output: epoch 01/10: loss=0.61669 epoch 02/10: loss=0.55944 epoch 03/10: loss=0.51130 epoch 04/10: loss=0.46934 ## Step 163 · agent · 2026-08-31T19:09:41.635Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:42.984Z { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:09:54.009Z Script running with cell ID 103 Wall time 11.0 seconds Output: ## Step 164 · agent · 2026-08-31T19:09:56.597Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:09:57.424Z { "cell_id": "103", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:10:13.017Z Script completed Wall time 15.6 seconds Output: epoch 05/10: loss=0.43242 epoch 06/10: loss=0.40577 epoch 07/10: loss=0.38599 epoch 08/10: loss=0.36507 epoch 09/10: loss=0.35812 ## Step 165 · agent · 2026-08-31T19:10:14.522Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:15.898Z { "code": "const r = await tools.write_stdin({session_id:8264,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output);" } ### Tool output · 2026-08-31T19:10:19.761Z Script completed Wall time 3.9 seconds Output: epoch 10/10: loss=0.35291 {"ranking": "/app/solution/test_novel_component.parquet", "checkpoint": "/app/solution/model_test_test_novel_component.pt", "training_examples": 40710, "elapsed_seconds": 105.3} ## Step 166 · agent · 2026-08-31T19:10:22.899Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:26.632Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/score_ensemble.py --stage test --all-model /app/solution/model_test_test_all_component.pt --novel-model /app/solution/model_test_test_novel_component.pt --weight 0.55 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:10:27.808Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e3416a","wall_time_seconds":1.001108228,"session_id":73874,"original_token_count":0,"output":""} ## Step 167 · agent · 2026-08-31T19:10:30.375Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:31.776Z { "code": "const r = await tools.write_stdin({session_id:73874,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);" } ### Tool output · 2026-08-31T19:10:41.547Z Script completed Wall time 9.8 seconds Output: ## Step 168 · agent · 2026-08-31T19:10:52.129Z ### Thinking unavailable — no text recorded The final refit completed on nine rolling snapshots, including the public validation window as observed training history/outcomes. The 128-dimensional concatenated tower has now replaced the fallback submission. I’m performing the independent schema, rank, duplicate, user-set, and test-eligibility audit, then I’ll record the exact reproducible command and validation score in the README. ### exec · 2026-08-31T19:11:22.150Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/run_final.sh\\n+#!/bin/sh\\n+set -eu\\n+\\n+PYTHON=/opt/task-venv/bin/python\\n+SOLUTION=/app/solution\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/build_ranker.py\\\" \\\\\\n+ --stage test --epochs 10 --seed 20260831 --label-mode all \\\\\\n+ --output \\\"$SOLUTION/test_all_component.parquet\\\"\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/build_ranker.py\\\" \\\\\\n+ --stage test --epochs 10 --seed 20260831 --label-mode novel \\\\\\n+ --output \\\"$SOLUTION/test_novel_component.parquet\\\"\\n+\\n+\\\"$PYTHON\\\" \\\"$SOLUTION/score_ensemble.py\\\" \\\\\\n+ --stage test \\\\\\n+ --all-model \\\"$SOLUTION/model_test_test_all_component.pt\\\" \\\\\\n+ --novel-model \\\"$SOLUTION/model_test_test_novel_component.pt\\\" \\\\\\n+ --weight 0.55 \\\\\\n+ --output \\\"$SOLUTION/submission.parquet\\\"\\n*** Add File: /app/solution/README.md\\n+# History-based CPU two-tower ranker\\n+\\n+## Model\\n+\\n+The final scorer is one exact 128-dimensional inner product. It concatenates\\n+two independently trained 64-dimensional tower pairs with square-root scaling,\\n+so its score is `0.55 * broad_score + 0.45 * novel_score`. Candidate vectors\\n+are produced offline by the item tower and the request path computes one user\\n+vector before MIPS retrieval.\\n+\\n+The user tower has **no user-ID embedding**. It pools bounded sequences of the\\n+user's recent positive listens (completion, organic source, and recency\\n+weighted), active likes, and active dislikes, then applies a LayerNorm/GELU\\n+MLP. The independent item tower applies an MLP to a trainable catalog-item\\n+embedding plus standardized popularity/organic metadata. Each 64D component\\n+is normalized; `score_ensemble.py` defines the concatenated user and item\\n+towers explicitly.\\n+\\n+## Training\\n+\\n+Training uses eight leakage-free rolling seven-day windows from `train_*`.\\n+The hidden-test refit adds the public validation window as a ninth supervised\\n+snapshot and uses `train_* + val_*` as request-time history. The broad model\\n+learns co-consumption from all organic 80%-complete listens and organic likes;\\n+the novel model removes items consumed before each pseudo-cutoff to match the\\n+eligibility boundary. Both optimize grade-weighted sampled BPR with a mixture\\n+of popularity-aware and uniform negatives. Training is event-stream based and\\n+never constructs or decomposes a user-item matrix.\\n+\\n+Public validation NDCG@10: **0.020363** (4,263 scored users). The selected\\n+weight and 10-epoch early stop were chosen only on the public window.\\n+\\n+## Reproduce the final submission\\n+\\n+```sh\\n+sh /app/solution/run_final.sh\\n+```\\n+\\n+The result is `/app/solution/submission.parquet` with columns `uid`, `item_id`,\\n+and `rank`.\\n*** End Patch\";\nconst result = await tools.apply_patch(patch);\ntext(result);\n" } ### Tool output · 2026-08-31T19:11:22.266Z Script completed Wall time 0.0 seconds Output: {} ## Step 169 · agent · 2026-08-31T19:11:26.800Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:39.857Z { "code": "const r = await tools.exec_command({cmd:\"chmod +x /app/solution/run_final.sh\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\\nx=pd.read_parquet(P)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ny=validate_ranking(x,targets)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(u,i) for u,i in y[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\\nassert list(x.columns)==['uid','item_id','rank']\\nassert not bad\\nprint({'path':str(P),'rows':len(y),'users':y.uid.nunique(),'items':y.item_id.nunique(),'columns':list(y.columns),'ineligible':len(bad),'bytes':P.stat().st_size})\\nprint(y.sort_values(['uid','rank']).head(12).to_string(index=False))\\nPY\\n/opt/task-venv/bin/python -m py_compile /app/solution/build_ranker.py /app/solution/score_ensemble.py\\nls -lh /app/solution/submission.parquet /app/solution/model_test_test_all_component.pt /app/solution/model_test_test_novel_component.pt /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);" } ### Tool output · 2026-08-31T19:11:42.665Z Script completed Wall time 2.8 seconds Output: {'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'items': 4401, 'columns': ['uid', 'item_id', 'rank'], 'ineligible': 0, 'bytes': 1511937} uid item_id rank 100 8250570 1 100 8931972 2 100 8335640 3 100 5488609 4 100 2722711 5 100 5234367 6 100 6414726 7 100 672002 8 100 2416157 9 100 4948220 10 100 1758776 11 100 1336851 12 -rw-r--r-- 1 agent agent 1.8K Aug 31 19:11 /app/solution/README.md -rw-r--r-- 1 agent agent 30M Aug 31 19:08 /app/solution/model_test_test_all_component.pt -rw-r--r-- 1 agent agent 30M Aug 31 19:10 /app/solution/model_test_test_novel_component.pt -rw-r--r-- 1 agent agent 1.5M Aug 31 19:10 /app/solution/submission.parquet ## Step 170 · agent · 2026-08-31T19:11:47.091Z ### Thinking unavailable — no text recorded Built and validated the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/application code: [build_ranker.py](/app/solution/build_ranker.py), [score_ensemble.py](/app/solution/score_ensemble.py) - Reproduction command: [run_final.sh](/app/solution/run_final.sh) - Documentation: [README.md](/app/solution/README.md) - Public validation NDCG@10: **0.020363** Final audit passed: 845,600 rows, 8,456 users, ranks 1–100, no duplicates, and zero test-ineligible items.