yambda-discovery-ranking-two-tower-base / run-008-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:18.659Z 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 · 2026-08-31T18:25:20.815Z ## 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 · 2026-08-31T18:25:20.815Z 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 · 2026-08-31T18:25:20.815Z 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 · 2026-08-31T18:25:20.815Z 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": "01a05911-913d-7072-bf9c-edee554c43a2", "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 · 2026-08-31T18:25:20.825Z # 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 · 2026-08-31T18:25:24.714Z ### Thinking unavailable — no text recorded I’ll inspect the data and evaluator first, then establish a valid two-tower baseline, score it, and iterate on sampling/history features while keeping the best valid ranking saved. I’ll write only under `/app/solution/` and keep validation strictly cutoff-correct. ### exec · 2026-08-31T18:25:30.976Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data schemas, sizes, cutoffs, eligibility, and evaluator\",status:\"in_progress\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"pending\"},\n {step:\"Train validation variants and evaluate public NDCG@10\",status:\"pending\"},\n {step:\"Refit selected model on train+validation and create valid test submission\",status:\"pending\"},\n {step:\"Verify artifacts and document reproducible commands\",status:\"pending\"}\n]}); text(p)\n" } ### Tool output · 2026-08-31T18:25:31.027Z Script completed Wall time 0.0 seconds Output: {} ## Step 8 · agent · 2026-08-31T18:25:33.115Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:37.104Z { "code": "const r = await tools.exec_command({cmd:\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:37.172Z 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 #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 9 · agent · 2026-08-31T18:25:40.919Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:55.162Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np, os\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema.names)\\n if p.name.startswith(('train_','val_')):\\n t=pq.read_table(p,columns=['timestamp']).column(0).to_numpy()\\n print(' time',t.min(),t.max())\\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n with np.load(D/name) as z:\\n print(name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=bits.sum(1)\\n print(' eligible min/mean/max',counts.min(),counts.mean(),counts.max(),'uids first',z['uids'][:5],'items first',z['item_ids'][:10])\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),len(pd.read_parquet(D/'target_users.parquet')))\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(20),pd.read_parquet(D/'candidate_catalog.parquet').columns)\\nprint('torch threads env',os.cpu_count())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:55.309Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pyarrow' ## Step 10 · agent · 2026-08-31T18:25:57.847Z ### Thinking unavailable — no text recorded The shell’s default `python` is not the benchmark environment (it lacks the pinned packages), so I’m locating the task runtime before touching the implementation. Once found, I’ll use Parquet metadata and compact aggregates rather than loading the full 319 MB listen log repeatedly. ### exec · 2026-08-31T18:26:05.488Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\npython --version || true\\npython3 --version || true\\nfind / -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -50\\nfind / -maxdepth 4 -type d \\\\( -name site-packages -o -name dist-packages \\\\) 2>/dev/null | head -50\\nenv | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:05.606Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 Python 3.12.13 CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05911-9128-75b1-a0c4-9ebeddfa171e COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=b6b3938d4ce2 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 · 2026-08-31T18:26:07.997Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:12.078Z { "code": "const r = await tools.exec_command({cmd:\"find /task -maxdepth 3 -type f -o -type l | sed -n '1,200p'\\nls -la /usr/local/lib/python3.12/site-packages 2>&1 | sed -n '1,100p'\\npython -m pip list 2>&1 | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:12.828Z Script completed Wall time 0.7 seconds Output: /task/data/target_users.parquet /task/data/test_preference_state.parquet /task/data/candidate_catalog.parquet /task/data/train_unlikes.parquet /task/data/val_likes.parquet /task/data/val_listens.parquet /task/data/val_undislikes.parquet /task/data/train_undislikes.parquet /task/data/train_likes.parquet /task/data/train_listens.parquet /task/data/validation_eligible_candidates.npz /task/data/train_dislikes.parquet /task/data/val_unlikes.parquet /task/data/val_dislikes.parquet /task/data/test_eligible_candidates.npz /task/data/validation_preference_state.parquet /task/.keep /task/requirements.txt /task/ranking_evaluation.py /task/evaluate_public.py 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 Package Version ------- ------- pip 25.0.1 ## Step 12 · agent · 2026-08-31T18:26:15.901Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:20.852Z { "code": "const r = await tools.exec_command({cmd:\"find / -type f -path '*/bin/python*' -o -type l -path '*/bin/python*' 2>/dev/null | head -100\\nfind / -type d -name 'torch' 2>/dev/null | head -30\\nfind / -type d -name 'pyarrow' 2>/dev/null | head -30\\nls -la /opt /opt/* 2>/dev/null | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:21.501Z Script completed Wall time 0.6 seconds Output: /opt/task-venv/bin/python /opt/task-venv/bin/python3 /opt/task-venv/bin/python3.12 /usr/local/bin/python-config /usr/local/bin/python /usr/local/bin/python3.12-config /usr/local/bin/python3 /usr/local/bin/python3-config /usr/local/bin/python3.12 /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch /opt/task-venv/lib/python3.12/site-packages/pyarrow /opt: total 16 drwxr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. drwxr-xr-x 4 root root 4096 Aug 31 18:22 codex-home dr-xr-xr-x 1 root root 4096 Aug 31 18:22 task-venv /opt/codex-home: total 16 drwxr-xr-x 4 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:22 .. drwxr-xr-x 3 root root 4096 Aug 31 18:21 packages drwxr-xr-x 3 root root 4096 Aug 31 18:22 tmp /opt/task-venv: total 32 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:22 .. -r--r--r-- 1 root root 0 Aug 31 18:22 .lock dr-xr-xr-x 1 root root 4096 Aug 31 18:22 bin dr-xr-xr-x 1 root root 4096 Aug 31 18:22 etc dr-xr-xr-x 1 root root 4096 Aug 31 18:22 include dr-xr-xr-x 1 root root 4096 Aug 31 18:22 lib lrwxrwxrwx 1 root root 3 Aug 31 18:22 lib64 -> lib -r--r--r-- 1 root root 171 Aug 31 18:22 pyvenv.cfg dr-xr-xr-x 1 root root 4096 Aug 31 18:22 share ## Step 13 · agent · 2026-08-31T18:26:22.950Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:39.140Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq\\nimport pandas as pd, numpy as np, os, torch\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows',pf.metadata.num_rows,'groups',pf.metadata.num_row_groups,'cols',pf.schema.names)\\n if p.name.startswith(('train_','val_')):\\n t=pq.read_table(p,columns=['timestamp']).column(0).to_numpy()\\n print(' time',t.min(),t.max())\\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n with np.load(D/name) as z:\\n print(name,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n counts=bits.sum(1)\\n print(' eligible min/mean/max',counts.min(),counts.mean(),counts.max(),'uids first',z['uids'][:5],'items first',z['item_ids'][:10])\\nT=pd.read_parquet(D/'target_users.parquet'); C=pd.read_parquet(D/'candidate_catalog.parquet')\\nprint('targets',T.head(),len(T),T.dtypes)\\nprint('catalog',C.head(20),len(C),C.columns,C.dtypes)\\nfor name in ['validation_preference_state.parquet','test_preference_state.parquet']:\\n x=pd.read_parquet(D/name); print(name,x.head(),x.state.value_counts(),x.dtypes)\\nprint('torch',torch.__version__,'threads',torch.get_num_threads(),'cpus',os.cpu_count())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:44.477Z Script completed Wall time 5.3 seconds Output: candidate_catalog.parquet rows 30000 groups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet rows 8456 groups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet rows 484776 groups 1 cols ['uid', 'item_id', 'state'] train_dislikes.parquet rows 101075 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 75 24786580 train_likes.parquet rows 814831 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 80 24786790 train_listens.parquet rows 43484159 groups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] time 0 24786795 train_undislikes.parquet rows 19960 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 15950 24785395 train_unlikes.parquet rows 292665 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 250 24786570 val_dislikes.parquet rows 3563 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 24789920 25393280 val_likes.parquet rows 37795 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 24788815 25393390 val_listens.parquet rows 1501697 groups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] time 24788600 25393395 val_undislikes.parquet rows 606 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 24810715 25393035 val_unlikes.parquet rows 9346 groups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] time 24789005 25393385 validation_preference_state.parquet rows 469124 groups 1 cols ['uid', 'item_id', 'state'] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible min/mean/max 24657 29067.306291390727 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible min/mean/max 24552 29041.3178807947 30000 uids first [100 200 300 600 700] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] targets 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 8456 uid uint32 recent_positive_listens float64 history_positive_listens int64 dtype: object catalog 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 5 5463340 22746 17398 6 6 8213481 22505 17071 7 7 906358 22238 17183 8 8 2859641 18380 13704 9 9 3033749 16682 12267 10 10 2651314 16286 11801 11 11 6853370 15024 11692 12 12 5252571 14897 11331 13 13 5436843 14897 12127 14 14 3418493 14815 9039 15 15 5927996 14751 10424 16 16 6155741 14354 9251 17 17 8902934 14210 9160 18 18 8351511 14061 9739 19 19 5734320 14008 9467 20 30000 Index(['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'], dtype='object') item_id uint32 positive_listens int64 organic_positive_listens uint64 popularity_rank int32 dtype: object validation_preference_state.parquet uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked state liked 418435 disliked 50689 Name: count, dtype: int64 uid uint32 item_id uint32 state object dtype: object test_preference_state.parquet uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked state liked 432659 disliked 52117 Name: count, dtype: int64 uid uint32 item_id uint32 state object dtype: object torch 2.8.0+cpu threads 4 cpus 8 ## Step 14 · agent · 2026-08-31T18:27:03.958Z ### Thinking unavailable — no text recorded ## Step 15 · agent · 2026-08-31T18:27:15.336Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:29.576Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_popularity.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build a structurally valid popularity ranking for either cutoff.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = parser.parse_args()\\n+ artifact = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ uids = artifact[\\\"uids\\\"].astype(np.int64)\\n+ items = artifact[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = artifact[\\\"packed_eligible\\\"]\\n+ rows = []\\n+ for row, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[row], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ chosen = items[eligible][:100]\\n+ rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\\n+ args.output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(\\n+ args.output, index=False\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:27:29.626Z Script completed Wall time 0.0 seconds Output: {} ## Step 16 · agent · 2026-08-31T18:27:31.395Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:34.913Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /app/solution && /opt/task-venv/bin/python /app/solution/build_popularity.py --stage validation --output /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:27:41.044Z Script completed Wall time 6.1 seconds Output: total 16 drwxr-xr-x 1 agent agent 4096 Aug 31 18:27 . drwxr-xr-x 1 agent agent 4096 Aug 31 18:22 .. -rw-r--r-- 1 agent agent 1148 Aug 31 18:27 build_popularity.py { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 17 · agent · 2026-08-31T18:27:46.717Z ### Thinking unavailable — no text recorded The valid popularity fallback scores 0.010710 over 4,263 labeled users and is now saved. The main opportunity is personalization: I’m measuring how much target-user history intersects the 30k catalog, how recent histories behave, and whether validation relevance favors repeats, neighbors of prior tracks, or broad popularity. That will determine the most efficient history encoder and negative sampler. ### exec · 2026-08-31T18:28:15.317Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd, pyarrow.parquet as pq, time\\nD=Path('/task/data')\\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(np.int64)\\nT=set(targets.tolist()); C=set(cat.tolist())\\nfor fname in ['train_listens.parquet','val_listens.parquet']:\\n pf=pq.ParquetFile(D/fname); n_t=n_tc=n_pos=n_posc=n_strongorg=0; us=set(); st=time.time()\\n for b in pf.iter_batches(batch_size=1_000_000,columns=['uid','item_id','played_ratio_pct','is_organic']):\\n x=b.to_pandas(); mt=x.uid.isin(T); y=x[mt]; n_t+=len(y); n_tc+=y.item_id.isin(C).sum();\\n p=y.played_ratio_pct>=50; n_pos+=p.sum(); n_posc+=(p & y.item_id.isin(C)).sum(); n_strongorg+=((y.played_ratio_pct>=80)&(y.is_organic==1)&y.item_id.isin(C)).sum(); us.update(y.uid.unique())\\n print(fname,'target',n_t,'target-cat',n_tc,'positive',n_pos,'positive-cat',n_posc,'strongorgcat',n_strongorg,'users',len(us),'sec',time.time()-st)\\nfor fname in ['train_likes.parquet','val_likes.parquet']:\\n x=pd.read_parquet(D/fname); y=x[x.uid.isin(T)]; print(fname,'target',len(y),'cat',y.item_id.isin(C).sum(),'organic-cat',((y.is_organic==1)&y.item_id.isin(C)).sum())\\n# validation truth ranks under popularity and whether train seen\\nvl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\nrel1=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.item_id.isin(C)][['uid','item_id']].drop_duplicates(); rel1['g']=1\\nrel3=vk[(vk.uid.isin(T))&(vk.is_organic==1)&vk.item_id.isin(C)][['uid','item_id']].drop_duplicates(); rel3['g']=3\\ntruth=pd.concat([rel1,rel3]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nrank={v:i+1 for i,v in enumerate(cat)}\\nprint('raw truth',len(truth),'users',truth.uid.nunique(),'grade counts',truth.g.value_counts().to_dict())\\nprint('pop ranks q',truth.item_id.map(rank).dropna().quantile([0,.1,.25,.5,.75,.9,1]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:28:26.338Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 18 · agent · 2026-08-31T18:28:28.106Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:28.873Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:28:32.391Z Script completed Wall time 3.5 seconds Output: train_listens.parquet target 43222297 target-cat 31062540 positive 27367343 positive-cat 19959853 strongorgcat 8661020 users 8456 sec 14.3329176902771 val_listens.parquet target 1482553 target-cat 999062 positive 955678 positive-cat 650295 strongorgcat 288828 users 7729 sec 0.5363175868988037 train_likes.parquet target 690968 cat 472889 organic-cat 223046 val_likes.parquet target 33177 cat 18307 organic-cat 10406 raw truth 175729 users 6186 grade counts {1: 167201, 3: 8528} pop ranks q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 1.0: 30000.0} ## Step 19 · agent · 2026-08-31T18:28:40.834Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:52.996Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data'); z=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; ip={int(x):i for i,x in enumerate(items)}; ur={int(x):i for i,x in enumerate(uids)}\\nsel=set(map(int,uids[:20])); seen={u:set() for u in sel}; cnt={u:0 for u in sel}\\nfor b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct'],batch_size=1_000_000):\\n x=b.to_pandas(); x=x[x.uid.isin(sel)&(x.played_ratio_pct>=50)&x.item_id.isin(ip)]\\n for u,g in x.groupby('uid'): seen[int(u)].update(map(int,g.item_id))\\nfor u in list(sel)[:20]:\\n bits=np.unpackbits(z['packed_eligible'][ur[u]],count=len(items),bitorder='little')\\n ss=seen[u]; elig_seen=sum(bits[ip[i]] for i in ss)\\n print(u,'seen',len(ss),'eligible seen',elig_seen,'excluded total',len(items)-bits.sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:29:03.201Z Script completed Wall time 10.2 seconds Output: 900 seen 26 eligible seen 0 excluded total 334 2200 seen 1043 eligible seen 0 excluded total 1096 800 seen 1910 eligible seen 0 excluded total 2524 1700 seen 1429 eligible seen 0 excluded total 2164 300 seen 45 eligible seen 0 excluded total 69 2100 seen 562 eligible seen 0 excluded total 645 700 seen 1233 eligible seen 0 excluded total 1653 1600 seen 1207 eligible seen 0 excluded total 1524 2500 seen 555 eligible seen 0 excluded total 636 200 seen 163 eligible seen 0 excluded total 168 1100 seen 1337 eligible seen 0 excluded total 1440 2000 seen 265 eligible seen 0 excluded total 374 600 seen 657 eligible seen 0 excluded total 680 1500 seen 503 eligible seen 0 excluded total 524 2400 seen 437 eligible seen 0 excluded total 471 100 seen 600 eligible seen 0 excluded total 682 1000 seen 429 eligible seen 0 excluded total 499 1900 seen 1786 eligible seen 0 excluded total 2041 1400 seen 1139 eligible seen 0 excluded total 1264 2300 seen 1064 eligible seen 0 excluded total 1406 ## Step 20 · agent · 2026-08-31T18:29:24.045Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T18:29:42.707Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:47.175Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nfor i in range(p.metadata.num_row_groups):\\n c=p.metadata.row_group(i).column(p.schema.names.index('timestamp')).statistics\\n print(i,c.min,c.max,p.metadata.row_group(i).num_rows)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:29:47.669Z Script completed Wall time 0.5 seconds Output: 0 15 24786690 1048576 1 185 24786725 1048576 2 15320 24786790 1048576 3 3625 24786795 1048576 4 5735 24786740 1048576 5 120 24786775 1048576 6 140 24786750 1048576 7 220 24786745 1048576 8 1890 24786770 1048576 9 65 24786735 1048576 10 15 24786720 1048576 11 350 24786795 1048576 12 60 24786730 1048576 13 1370 24786790 1048576 14 5 24786770 1048576 15 30 24786785 1048576 16 9145 24786750 1048576 17 95 24786795 1048576 18 20 24786760 1048576 19 0 24786760 1048576 20 40 24786795 1048576 21 300 24786790 1048576 22 45 24786730 1048576 23 530 24786730 1048576 24 8680 24786790 1048576 25 65 24786785 1048576 26 20 24786785 1048576 27 40 24786725 1048576 28 125 24786770 1048576 29 7330 24786785 1048576 30 30 24786775 1048576 31 60 24786670 1048576 32 20 24786795 1048576 33 355 24786795 1048576 34 565 24786735 1048576 35 205 24786785 1048576 36 50 24786740 1048576 37 1190 24786775 1048576 38 190 24786705 1048576 39 10 24786760 1048576 40 230 24786710 1048576 41 20 24786690 492543 ## Step 22 · agent · 2026-08-31T18:29:50.804Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:54.527Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nx=pq.read_table('/task/data/train_listens.parquet',columns=['uid','timestamp','item_id']).slice(0,30).to_pandas(); print(x.to_string(index=False))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:29:56.482Z Script completed Wall time 1.9 seconds Output: uid timestamp item_id 100 39420 8326270 100 39420 1441281 100 39625 286361 100 40110 732449 100 40360 3397170 100 40380 7849270 100 40640 1449307 100 40785 6887232 100 40885 2996197 100 41130 6474571 100 41545 7847600 100 41580 7509517 100 41580 470320 100 42115 6641244 100 42115 2581903 100 42115 5485459 100 42115 5474898 100 42115 3088475 100 42115 7521298 100 42210 8329413 100 42380 1853659 100 42520 1302519 100 43425 7498961 100 43425 3630949 100 43425 2729049 100 43425 732246 100 43425 5910506 100 43720 1907213 100 43720 6451550 100 43910 2170705 ## Step 23 · agent · 2026-08-31T18:30:00.427Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:02.109Z { "code": "const r = await tools.exec_command({cmd:\"free -h && df -h /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:30:02.161Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: free: not found ## Step 24 · agent · 2026-08-31T18:30:03.923Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:06.021Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,12p' /proc/meminfo\\ndf -h /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:30:06.072Z Script completed Wall time 0.0 seconds Output: MemTotal: 15988980 kB MemFree: 1914336 kB MemAvailable: 14934508 kB Buffers: 227092 kB Cached: 12500220 kB SwapCached: 0 kB Active: 501636 kB Inactive: 12498080 kB Active(anon): 298856 kB Inactive(anon): 0 kB Active(file): 202780 kB Inactive(file): 12498080 kB Filesystem Size Used Avail Use% Mounted on overlay 96G 14G 83G 15% / ## Step 25 · agent · 2026-08-31T18:30:25.706Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-08-31T18:30:44.357Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-08-31T18:31:02.996Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:31:21.622Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:31:40.312Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:31:58.933Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:32:04.792Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:16.502Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/model.py\\n+\\\"\\\"\\\"Strictly decomposable PyTorch two-tower retrieval model.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import torch\\n+from torch import nn\\n+import torch.nn.functional as F\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"Encode positive/negative interaction history without a user-ID table.\\\"\\\"\\\"\\n+\\n+ def __init__(self, num_items: int, hidden_dim: int = 96, output_dim: int = 64):\\n+ super().__init__()\\n+ # Index zero is padding. This table represents tracks in histories, not users.\\n+ self.history_embedding = nn.Embedding(num_items + 1, hidden_dim, padding_idx=0)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(hidden_dim * 3 + 4, 192),\\n+ nn.LayerNorm(192),\\n+ nn.SiLU(),\\n+ nn.Linear(192, 128),\\n+ nn.SiLU(),\\n+ nn.Linear(128, output_dim),\\n+ )\\n+\\n+ @staticmethod\\n+ def _pool(emb: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\\n+ denom = weights.sum(dim=1, keepdim=True).clamp_min(1e-6)\\n+ return (emb * weights.unsqueeze(-1)).sum(dim=1) / denom\\n+\\n+ def forward(\\n+ self,\\n+ history_ids: torch.Tensor,\\n+ long_weights: torch.Tensor,\\n+ short_weights: torch.Tensor,\\n+ negative_ids: torch.Tensor,\\n+ negative_weights: torch.Tensor,\\n+ numeric: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ positive = self.history_embedding(history_ids)\\n+ long_pool = self._pool(positive, long_weights)\\n+ short_pool = self._pool(positive, short_weights)\\n+ negative = self._pool(self.history_embedding(negative_ids), negative_weights)\\n+ vector = self.network(torch.cat([long_pool, short_pool, negative, numeric], dim=1))\\n+ return F.normalize(vector, dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently encode a catalog item for offline ANN indexing.\\\"\\\"\\\"\\n+\\n+ def __init__(self, num_items: int, feature_dim: int = 4, hidden_dim: int = 96, output_dim: int = 64):\\n+ super().__init__()\\n+ self.item_embedding = nn.Embedding(num_items + 1, hidden_dim, padding_idx=0)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(hidden_dim + feature_dim, 160),\\n+ nn.LayerNorm(160),\\n+ nn.SiLU(),\\n+ nn.Linear(160, 128),\\n+ nn.SiLU(),\\n+ nn.Linear(128, output_dim),\\n+ )\\n+\\n+ def forward(self, item_ids: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ vector = self.network(torch.cat([self.item_embedding(item_ids), features], dim=-1))\\n+ return F.normalize(vector, dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, num_items: int, output_dim: int = 64):\\n+ super().__init__()\\n+ self.user_tower = UserTower(num_items, output_dim=output_dim)\\n+ self.item_tower = ItemTower(num_items, output_dim=output_dim)\\n+\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Preprocess events, train the two towers, and emit an eligible ranking.\\n+\\n+The data path is event-stream based: it never constructs a user-item matrix.\\n+Catalog item IDs are mapped to dense embedding indices only.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+from collections import defaultdict\\n+from pathlib import Path\\n+from typing import Iterator\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+import torch.nn.functional as F\\n+\\n+from model import TwoTower\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+SHELF_GAP = 1_800\\n+WINDOW = 7 * 24 * 60 * 60\\n+DAY = 24 * 60 * 60\\n+MAX_HISTORY = 256\\n+MAX_NEGATIVE = 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+def iter_user_groups(path: Path) -> Iterator[tuple[int, dict[str, np.ndarray]]]:\\n+ \\\"\\\"\\\"Stream a uid/time-sorted listen parquet, retaining at most one user carry.\\\"\\\"\\\"\\n+ columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ carry: dict[str, np.ndarray] | None = None\\n+ for batch in pq.ParquetFile(path).iter_batches(batch_size=750_000, columns=columns):\\n+ current = {name: batch.column(i).to_numpy(zero_copy_only=False) for i, name in enumerate(columns)}\\n+ if carry is not None:\\n+ current = {name: np.concatenate([carry[name], current[name]]) for name in columns}\\n+ uids = current[\\\"uid\\\"]\\n+ boundaries = np.flatnonzero(uids[1:] != uids[:-1]) + 1\\n+ starts = np.r_[0, boundaries]\\n+ ends = np.r_[boundaries, len(uids)]\\n+ for start, end in zip(starts[:-1], ends[:-1]):\\n+ yield int(uids[start]), {name: values[start:end] for name, values in current.items()}\\n+ start = int(starts[-1])\\n+ carry = {name: values[start:] for name, values in current.items()}\\n+ if carry is not None and len(carry[\\\"uid\\\"]):\\n+ yield int(carry[\\\"uid\\\"][0]), carry\\n+\\n+\\n+def event_groups(paths: list[Path], columns: list[str]) -> dict[int, pd.DataFrame]:\\n+ frames = [pd.read_parquet(path, columns=columns) for path in paths]\\n+ frame = pd.concat(frames, ignore_index=True) if len(frames) > 1 else frames[0]\\n+ frame = frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"])\\n+ return {int(uid): group.drop(columns=\\\"uid\\\") for uid, group in frame.groupby(\\\"uid\\\", sort=False)}\\n+\\n+\\n+def map_catalog(raw_items: np.ndarray, sorted_ids: np.ndarray, sorted_to_pos: np.ndarray) -> np.ndarray:\\n+ at = np.searchsorted(sorted_ids, raw_items)\\n+ good = at < len(sorted_ids)\\n+ result = np.full(len(raw_items), -1, dtype=np.int32)\\n+ matched = good & (sorted_ids[np.minimum(at, len(sorted_ids) - 1)] == raw_items)\\n+ result[matched] = sorted_to_pos[at[matched]]\\n+ return result\\n+\\n+\\n+def active_items(events: pd.DataFrame | None, cutoff: int, item_map) -> np.ndarray:\\n+ if events is None or events.empty:\\n+ return np.empty(0, dtype=np.int32)\\n+ rows = events[events.timestamp < cutoff]\\n+ if rows.empty:\\n+ return np.empty(0, dtype=np.int32)\\n+ # Last action independently controls each explicit channel.\\n+ last = rows.sort_values(\\\"timestamp\\\").drop_duplicates(\\\"item_id\\\", keep=\\\"last\\\")\\n+ last = last[last.active == 1]\\n+ return np.asarray([item_map.get(int(x), -1) for x in last.item_id if int(x) in item_map], dtype=np.int32)\\n+\\n+\\n+def state_channel(on_paths: list[Path], off_paths: list[Path]) -> dict[int, pd.DataFrame]:\\n+ frames = []\\n+ for path in on_paths:\\n+ x = pd.read_parquet(path, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"])\\n+ x[\\\"active\\\"] = np.int8(1)\\n+ frames.append(x)\\n+ for path in off_paths:\\n+ x = pd.read_parquet(path, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"])\\n+ x[\\\"active\\\"] = np.int8(0)\\n+ frames.append(x)\\n+ frame = pd.concat(frames, ignore_index=True).sort_values([\\\"uid\\\", \\\"timestamp\\\"])\\n+ return {int(uid): group.drop(columns=\\\"uid\\\") for uid, group in frame.groupby(\\\"uid\\\", sort=False)}\\n+\\n+\\n+def select_history(\\n+ positions: np.ndarray,\\n+ timestamps: np.ndarray,\\n+ ratios: np.ndarray,\\n+ organic: np.ndarray,\\n+ cutoff: int,\\n+ liked: np.ndarray,\\n+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n+ mask = (positions >= 0) & (timestamps < cutoff) & (ratios >= 50)\\n+ p, t, r, o = positions[mask], timestamps[mask], ratios[mask], organic[mask]\\n+ if len(p):\\n+ # Aggregate event strength and retain last-event recency per unique track.\\n+ order = np.argsort(p, kind=\\\"stable\\\")\\n+ p, t, r, o = p[order], t[order], r[order], o[order]\\n+ unique, starts = np.unique(p, return_index=True)\\n+ strength = np.add.reduceat(np.clip(r / 100.0, 0.5, 1.25) * (1.0 + 0.45 * o), starts)\\n+ last_time = np.maximum.reduceat(t, starts)\\n+ base = np.log1p(strength)\\n+ long_w = base * np.exp(-(cutoff - last_time) / (120 * DAY))\\n+ short_w = base * np.exp(-(cutoff - last_time) / (18 * DAY))\\n+ else:\\n+ unique = np.empty(0, dtype=np.int32)\\n+ long_w = short_w = np.empty(0, dtype=np.float32)\\n+ if len(liked):\\n+ all_ids = np.unique(np.concatenate([unique, liked]))\\n+ lw = np.zeros(len(all_ids), np.float32)\\n+ sw = np.zeros(len(all_ids), np.float32)\\n+ if len(unique):\\n+ ix = np.searchsorted(all_ids, unique)\\n+ lw[ix], sw[ix] = long_w, short_w\\n+ ix = np.searchsorted(all_ids, liked)\\n+ lw[ix] += 2.5\\n+ sw[ix] += 1.5\\n+ unique, long_w, short_w = all_ids, lw, sw\\n+ if len(unique) > MAX_HISTORY:\\n+ keep = np.argpartition(long_w + short_w, -MAX_HISTORY)[-MAX_HISTORY:]\\n+ unique, long_w, short_w = unique[keep], long_w[keep], short_w[keep]\\n+ return unique.astype(np.int32), long_w.astype(np.float32), short_w.astype(np.float32), mask\\n+\\n+\\n+def pad(values: np.ndarray, size: int, dtype, offset: int = 0) -> np.ndarray:\\n+ result = np.zeros(size, dtype=dtype)\\n+ n = min(size, len(values))\\n+ if n:\\n+ result[:n] = values[:n] + offset\\n+ return result\\n+\\n+\\n+def build_cache(stage: str, output: Path, weeks: int) -> None:\\n+ test = stage == \\\"test\\\"\\n+ listen_paths = [DATA / \\\"train_listens.parquet\\\"]\\n+ suffixes = [\\\"train\\\"]\\n+ if test:\\n+ listen_paths.append(DATA / \\\"val_listens.parquet\\\")\\n+ suffixes.append(\\\"val\\\")\\n+ end = 25_393_400 if test else 24_786_800\\n+ cutoffs = [end - j * (WINDOW + SHELF_GAP) for j in range(weeks, 0, -1)]\\n+\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ raw_catalog = catalog.item_id.to_numpy(np.int64)\\n+ item_map = {int(item): i for i, item in enumerate(raw_catalog)}\\n+ order = np.argsort(raw_catalog)\\n+ sorted_ids, sorted_to_pos = raw_catalog[order], order.astype(np.int32)\\n+\\n+ like_on = [DATA / f\\\"{s}_likes.parquet\\\" for s in suffixes]\\n+ like_off = [DATA / f\\\"{s}_unlikes.parquet\\\" for s in suffixes]\\n+ dislike_on = [DATA / f\\\"{s}_dislikes.parquet\\\" for s in suffixes]\\n+ dislike_off = [DATA / f\\\"{s}_undislikes.parquet\\\" for s in suffixes]\\n+ likes = state_channel(like_on, like_off)\\n+ dislikes = state_channel(dislike_on, dislike_off)\\n+ organic_likes = event_groups(like_on, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+\\n+ # Validation has one stream. Test appends the small validation stream by uid.\\n+ val_listens = {}\\n+ if test:\\n+ vf = pd.read_parquet(listen_paths[1], columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"])\\n+ val_listens = {int(uid): g for uid, g in vf.groupby(\\\"uid\\\", sort=False)}\\n+\\n+ histories, longs, shorts, negatives, neg_weights, numerics = [], [], [], [], [], []\\n+ target_lists, target_gains = [], []\\n+ inference = None\\n+ targets = set(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.astype(int))\\n+ for number, (uid, data) in enumerate(iter_user_groups(listen_paths[0])):\\n+ if uid not in targets:\\n+ continue\\n+ if test and uid in val_listens:\\n+ extra = val_listens[uid]\\n+ for name in [\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]:\\n+ data[name] = np.concatenate([data[name], extra[name].to_numpy()])\\n+ chronology = np.argsort(data[\\\"timestamp\\\"], kind=\\\"stable\\\")\\n+ data = {name: value[chronology] for name, value in data.items()}\\n+ pos = map_catalog(data[\\\"item_id\\\"].astype(np.int64), sorted_ids, sorted_to_pos)\\n+ user_likes = likes.get(uid)\\n+ user_dislikes = dislikes.get(uid)\\n+ user_organic_likes = organic_likes.get(uid)\\n+\\n+ for cutoff in cutoffs + [end]:\\n+ liked = active_items(user_likes, cutoff, item_map)\\n+ disliked = active_items(user_dislikes, cutoff, item_map)\\n+ hist, lw, sw, _ = select_history(\\n+ pos, data[\\\"timestamp\\\"], data[\\\"played_ratio_pct\\\"], data[\\\"is_organic\\\"], cutoff, liked\\n+ )\\n+ if not len(hist):\\n+ continue\\n+ histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\\n+ longs.append(pad(lw, MAX_HISTORY, np.float32))\\n+ shorts.append(pad(sw, MAX_HISTORY, np.float32))\\n+ negatives.append(pad(disliked[:MAX_NEGATIVE], MAX_NEGATIVE, np.int32, 1))\\n+ neg_weights.append(pad(np.ones(min(len(disliked), MAX_NEGATIVE)), MAX_NEGATIVE, np.float32))\\n+ numerics.append(np.asarray([\\n+ math.log1p(len(hist)) / 6.0,\\n+ math.log1p(np.count_nonzero(data[\\\"timestamp\\\"] < cutoff)) / 10.0,\\n+ math.log1p(len(liked)) / 5.0,\\n+ math.log1p(len(disliked)) / 4.0,\\n+ ], np.float32))\\n+ if cutoff == end:\\n+ target_lists.append(np.empty(0, np.int32))\\n+ target_gains.append(np.empty(0, np.float32))\\n+ continue\\n+\\n+ prior_seen = set(pos[(pos >= 0) & (data[\\\"timestamp\\\"] < cutoff)].tolist())\\n+ future = (\\n+ (data[\\\"timestamp\\\"] >= cutoff + SHELF_GAP)\\n+ & (data[\\\"timestamp\\\"] < cutoff + SHELF_GAP + WINDOW)\\n+ & (data[\\\"is_organic\\\"] == 1)\\n+ & (data[\\\"played_ratio_pct\\\"] >= 80)\\n+ & (pos >= 0)\\n+ )\\n+ grades: dict[int, int] = {int(x): 1 for x in np.unique(pos[future]) if int(x) not in prior_seen}\\n+ if user_organic_likes is not None:\\n+ q = user_organic_likes\\n+ q = q[(q.timestamp >= cutoff + SHELF_GAP) & (q.timestamp < cutoff + SHELF_GAP + WINDOW) & (q.is_organic == 1)]\\n+ for item in q.item_id:\\n+ mapped = item_map.get(int(item))\\n+ if mapped is not None and mapped not in prior_seen:\\n+ grades[mapped] = 3\\n+ item_targets = np.fromiter(grades.keys(), dtype=np.int32)\\n+ gains = np.fromiter((7.0 if g == 3 else 1.0 for g in grades.values()), dtype=np.float32)\\n+ target_lists.append(item_targets)\\n+ target_gains.append(gains)\\n+\\n+ if number and number % 1500 == 0:\\n+ print(f\\\"preprocessed {number:,} users\\\", flush=True)\\n+\\n+ # The last context for every processed user is inference; train contexts are nonempty target rows.\\n+ lengths = np.asarray([len(x) for x in target_lists], np.int32)\\n+ is_train = lengths > 0\\n+ is_infer = lengths == 0\\n+ offsets = np.r_[0, np.cumsum(lengths[is_train], dtype=np.int64)]\\n+ flat_targets = np.concatenate([x for x, ok in zip(target_lists, is_train) if ok]).astype(np.int32)\\n+ flat_gains = np.concatenate([x for x, ok in zip(target_gains, is_train) if ok]).astype(np.float32)\\n+ # recover context uids: construction produces weeks+1 contexts/user unless empty history\\n+ # Re-run order cheaply from target uids; all target users have sufficient history here.\\n+ user_order = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ infer_count = int(is_infer.sum())\\n+ if infer_count != len(user_order):\\n+ raise RuntimeError(f\\\"expected {len(user_order)} inference contexts, got {infer_count}\\\")\\n+ np.savez_compressed(\\n+ output,\\n+ history=np.stack(histories), long=np.stack(longs), short=np.stack(shorts),\\n+ negative=np.stack(negatives), negative_weight=np.stack(neg_weights), numeric=np.stack(numerics),\\n+ train_rows=np.flatnonzero(is_train).astype(np.int32), infer_rows=np.flatnonzero(is_infer).astype(np.int32),\\n+ target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=user_order,\\n+ )\\n+ print(json.dumps({\\\"cache\\\": str(output), \\\"contexts\\\": len(histories), \\\"train_contexts\\\": int(is_train.sum()), \\\"targets\\\": len(flat_targets)}))\\n+\\n+\\n+def item_features() -> np.ndarray:\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ positive = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ organic = np.log1p(catalog.organic_positive_listens.to_numpy(np.float32))\\n+ share = catalog.organic_positive_listens.to_numpy(np.float32) / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1)\\n+ rank = np.log1p(catalog.popularity_rank.to_numpy(np.float32))\\n+ raw = np.c_[positive, organic, share, rank].astype(np.float32)\\n+ return (raw - raw.mean(0)) / (raw.std(0) + 1e-6)\\n+\\n+\\n+def tensor_rows(cache, name: str, rows: np.ndarray, device=\\\"cpu\\\") -> torch.Tensor:\\n+ return torch.from_numpy(cache[name][rows]).to(device)\\n+\\n+\\n+def train_model(cache_path: Path, model_path: Path, epochs: int, seed: int, dim: int) -> None:\\n+ seed_everything(seed)\\n+ torch.set_num_threads(min(8, torch.get_num_threads()))\\n+ cache = np.load(cache_path)\\n+ rows = cache[\\\"train_rows\\\"]\\n+ offsets, targets, gains = cache[\\\"target_offsets\\\"], cache[\\\"targets\\\"], cache[\\\"target_gains\\\"]\\n+ features = torch.from_numpy(item_features())\\n+ num_items = len(features)\\n+ model = TwoTower(num_items, output_dim=dim)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ generator = np.random.default_rng(seed)\\n+ batch_size, negatives_per = 192, 40\\n+ pop_prob = np.power(np.arange(1, num_items + 1, dtype=np.float64), -0.35)\\n+ pop_prob /= pop_prob.sum()\\n+\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ sequence = generator.permutation(len(rows))\\n+ total = batches = 0.0\\n+ for start in range(0, len(sequence), batch_size):\\n+ cx = sequence[start:start + batch_size]\\n+ data_rows = rows[cx]\\n+ chosen = []\\n+ for index in cx:\\n+ lo, hi = offsets[index], offsets[index + 1]\\n+ probability = gains[lo:hi].astype(np.float64)\\n+ probability /= probability.sum()\\n+ chosen.append(targets[lo + generator.choice(hi - lo, p=probability)])\\n+ positive = np.asarray(chosen, np.int64)\\n+ # Half popularity-shaped and half uniform negatives improves both head and tail separation.\\n+ n = len(cx)\\n+ hard = generator.choice(num_items, size=(n, negatives_per // 2), p=pop_prob)\\n+ uniform = generator.integers(0, num_items, size=(n, negatives_per - negatives_per // 2))\\n+ negative = np.concatenate([hard, uniform], axis=1).astype(np.int64)\\n+ collision = negative == positive[:, None]\\n+ negative[collision] = (negative[collision] + 1) % num_items\\n+\\n+ user = model.user_tower(\\n+ tensor_rows(cache, \\\"history\\\", data_rows), tensor_rows(cache, \\\"long\\\", data_rows),\\n+ tensor_rows(cache, \\\"short\\\", data_rows), tensor_rows(cache, \\\"negative\\\", data_rows),\\n+ tensor_rows(cache, \\\"negative_weight\\\", data_rows), tensor_rows(cache, \\\"numeric\\\", data_rows),\\n+ )\\n+ pos_ids = torch.from_numpy(positive + 1)\\n+ pos_vec = model.item_tower(pos_ids, features[positive])\\n+ neg_ids = torch.from_numpy(negative + 1)\\n+ neg_vec = model.item_tower(neg_ids, features[negative])\\n+ pos_score = (user * pos_vec).sum(1, keepdim=True)\\n+ neg_score = (user[:, None, :] * neg_vec).sum(2)\\n+ loss = F.softplus((neg_score - pos_score) * 9.0).mean()\\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); batches += 1\\n+ print(f\\\"epoch {epoch + 1:02d} loss={total / batches:.5f}\\\", flush=True)\\n+ torch.save({\\\"state_dict\\\": model.state_dict(), \\\"num_items\\\": num_items, \\\"dim\\\": dim, \\\"seed\\\": seed}, model_path)\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(cache_path: Path, model_path: Path, stage: str, output: Path, pop_blend: float) -> None:\\n+ cache = np.load(cache_path)\\n+ checkpoint = torch.load(model_path, map_location=\\\"cpu\\\", weights_only=True)\\n+ model = TwoTower(checkpoint[\\\"num_items\\\"], output_dim=checkpoint[\\\"dim\\\"])\\n+ model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n+ model.eval()\\n+ features = torch.from_numpy(item_features())\\n+ all_ids = torch.arange(1, checkpoint[\\\"num_items\\\"] + 1)\\n+ item_vectors = []\\n+ for start in range(0, len(all_ids), 2048):\\n+ ids = all_ids[start:start + 2048]\\n+ item_vectors.append(model.item_tower(ids, features[start:start + len(ids)]))\\n+ item_vectors = torch.cat(item_vectors)\\n+ rows = cache[\\\"infer_rows\\\"]\\n+ user_vectors = []\\n+ for start in range(0, len(rows), 256):\\n+ ix = rows[start:start + 256]\\n+ user_vectors.append(model.user_tower(\\n+ tensor_rows(cache, \\\"history\\\", ix), tensor_rows(cache, \\\"long\\\", ix), tensor_rows(cache, \\\"short\\\", ix),\\n+ tensor_rows(cache, \\\"negative\\\", ix), tensor_rows(cache, \\\"negative_weight\\\", ix), tensor_rows(cache, \\\"numeric\\\", ix),\\n+ ))\\n+ user_vectors = torch.cat(user_vectors)\\n+ eligibility = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ uids = eligibility[\\\"uids\\\"].astype(np.int64)\\n+ candidate_items = eligibility[\\\"item_ids\\\"].astype(np.int64)\\n+ if not np.array_equal(uids, cache[\\\"infer_uids\\\"]):\\n+ raise RuntimeError(\\\"cache user order does not align with eligibility\\\")\\n+ pop = -np.log1p(np.arange(len(candidate_items), dtype=np.float32))\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ result = []\\n+ for start in range(0, len(uids), 128):\\n+ stop = min(start + 128, len(uids))\\n+ score = (user_vectors[start:stop] @ item_vectors.T).numpy()\\n+ score += pop_blend * pop[None, :]\\n+ mask = np.unpackbits(eligibility[\\\"packed_eligible\\\"][start:stop], axis=1, count=len(candidate_items), bitorder=\\\"little\\\").astype(bool)\\n+ score[~mask] = -np.inf\\n+ top = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ top_scores = np.take_along_axis(score, top, axis=1)\\n+ top = np.take_along_axis(top, np.argsort(-top_scores, axis=1), axis=1)\\n+ for row, uid in enumerate(uids[start:stop]):\\n+ result.extend((int(uid), int(candidate_items[col]), rank_) for rank_, col in enumerate(top[row], 1))\\n+ pd.DataFrame(result, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(output, index=False)\\n+\\n+\\n+def parse_args() -> argparse.Namespace:\\n+ p = argparse.ArgumentParser()\\n+ sub = p.add_subparsers(dest=\\\"command\\\", required=True)\\n+ q = sub.add_parser(\\\"preprocess\\\")\\n+ q.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ q.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--weeks\\\", type=int, default=6)\\n+ q = sub.add_parser(\\\"train\\\")\\n+ q.add_argument(\\\"--cache\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--model\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--epochs\\\", type=int, default=16)\\n+ q.add_argument(\\\"--seed\\\", type=int, default=2026)\\n+ q.add_argument(\\\"--dim\\\", type=int, default=64)\\n+ q = sub.add_parser(\\\"rank\\\")\\n+ q.add_argument(\\\"--cache\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--model\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ q.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ q.add_argument(\\\"--pop-blend\\\", type=float, default=0.0)\\n+ return p.parse_args()\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ args = parse_args()\\n+ if args.command == \\\"preprocess\\\":\\n+ build_cache(args.stage, args.output, args.weeks)\\n+ elif args.command == \\\"train\\\":\\n+ train_model(args.cache, args.model, args.epochs, args.seed, args.dim)\\n+ else:\\n+ rank(args.cache, args.model, args.stage, args.output, args.pop_blend)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:36:16.543Z Script completed Wall time 0.0 seconds Output: {} ## Step 32 · agent · 2026-08-31T18:36:26.825Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:40.143Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- target_lists, target_gains = [], []\\n+ target_lists, target_gains, infer_flags, infer_uids = [], [], [], []\\n@@\\n histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\\n@@\\n numerics.append(np.asarray([\\n@@\\n ], np.float32))\\n+ infer_flags.append(cutoff == end)\\n if cutoff == end:\\n target_lists.append(np.empty(0, np.int32))\\n target_gains.append(np.empty(0, np.float32))\\n+ infer_uids.append(uid)\\n continue\\n@@\\n- is_infer = lengths == 0\\n+ is_infer = np.asarray(infer_flags, dtype=bool)\\n@@\\n- # recover context uids: construction produces weeks+1 contexts/user unless empty history\\n- # Re-run order cheaply from target uids; all target users have sufficient history here.\\n- user_order = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ user_order = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n@@\\n- target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=user_order,\\n+ target_offsets=offsets, targets=flat_targets, target_gains=flat_gains, infer_uids=np.asarray(infer_uids, np.int64),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:36:40.193Z Script completed Wall time 0.0 seconds Output: {} ## Step 33 · agent · 2026-08-31T18:36:47.411Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:52.388Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py /app/solution/build_popularity.py && /usr/bin/time -f 'elapsed=%E maxrss=%MKB' /opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:36:52.564Z Script completed Wall time 0.2 seconds Output: /bin/sh: 1: /usr/bin/time: not found ## Step 34 · agent · 2026-08-31T18:36:54.285Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:57.512Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:37:08.531Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 35 · agent · 2026-08-31T18:37:10.181Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:10.990Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:37:27.693Z Script completed Wall time 16.7 seconds Output: /app/solution/train_ranker.py:120: RuntimeWarning: overflow encountered in exp short_w = base * np.exp(-(cutoff - last_time) / (18 * DAY)) /app/solution/train_ranker.py:130: RuntimeWarning: overflow encountered in cast lw[ix], sw[ix] = long_w, short_w /app/solution/train_ranker.py:138: RuntimeWarning: overflow encountered in cast return unique.astype(np.int32), long_w.astype(np.float32), short_w.astype(np.float32), mask preprocessed 1,500 users SESSION_ID=62573 ## Step 36 · agent · 2026-08-31T18:37:33.017Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-08-31T18:37:35.513Z ### Thinking unavailable — no text recorded The first streaming pass exposed an unsigned-timestamp arithmetic issue in recency decay; I’m correcting that before training so old events cannot overflow their weights. The event scan itself is progressing normally and remains memory-bounded by one user plus compact cached contexts. ### exec · 2026-08-31T18:37:39.387Z { "code": "const r = await tools.write_stdin({session_id:62573,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:37:40.136Z Script completed Wall time 0.7 seconds Output: Traceback (most recent call last): File "/app/solution/train_ranker.py", line 410, in build_cache(args.stage, args.output, args.weeks) File "/app/solution/train_ranker.py", line 199, in build_cache disliked = active_items(user_dislikes, cutoff, item_map) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 78, in active_items rows = events[events.timestamp < cutoff] ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/ops/common.py", line 62, in new_method @wraps(method) KeyboardInterrupt ## Step 38 · agent · 2026-08-31T18:37:49.889Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:52.703Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def select_history(\\n@@\\n ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n+ timestamps = timestamps.astype(np.int64, copy=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:37:52.758Z Script completed Wall time 0.0 seconds Output: {} ## Step 39 · agent · 2026-08-31T18:37:55.241Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:58.373Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:38:09.394Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T18:38:11.357Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:12.215Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:38:28.553Z Script completed Wall time 16.3 seconds Output: preprocessed 1,500 users SESSION_ID=54126 ## Step 41 · agent · 2026-08-31T18:38:30.598Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:32.455Z { "code": "const r = await tools.write_stdin({session_id:54126,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:38:43.473Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 42 · agent · 2026-08-31T18:38:45.549Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:46.429Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:39:02.476Z Script completed Wall time 16.0 seconds Output: preprocessed 3,000 users SESSION_ID=54126 ## Step 43 · agent · 2026-08-31T18:39:04.066Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:05.966Z { "code": "const r = await tools.write_stdin({session_id:54126,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:39:16.984Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T18:39:19.263Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:20.230Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:39:35.988Z Script completed Wall time 15.8 seconds Output: preprocessed 4,500 users preprocessed 6,000 users SESSION_ID=54126 ## Step 45 · agent · 2026-08-31T18:39:38.153Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:39.572Z { "code": "const r = await tools.write_stdin({session_id:54126,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:39:50.591Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T18:39:52.117Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:52.894Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:40:04.676Z Script completed Wall time 11.8 seconds Output: preprocessed 7,500 users preprocessed 9,000 users Traceback (most recent call last): File "/app/solution/train_ranker.py", line 411, in build_cache(args.stage, args.output, args.weeks) File "/app/solution/train_ranker.py", line 258, in build_cache raise RuntimeError(f"expected {len(user_order)} inference contexts, got {infer_count}") RuntimeError: expected 8456 inference contexts, got 8449 ## Step 47 · agent · 2026-08-31T18:40:11.361Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:13.986Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if not len(hist):\\n- continue\\n histories.append(pad(hist, MAX_HISTORY, np.int32, 1))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:40:14.037Z Script completed Wall time 0.0 seconds Output: {} ## Step 48 · agent · 2026-08-31T18:40:15.659Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:18.787Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 6 --output /app/solution/validation_cache.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:40:29.806Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 49 · agent · 2026-08-31T18:40:31.463Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:32.227Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:40:48.967Z Script completed Wall time 16.7 seconds Output: preprocessed 1,500 users SESSION_ID=14477 ## Step 50 · agent · 2026-08-31T18:40:52.189Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:53.781Z { "code": "const r = await tools.write_stdin({session_id:14477,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:41:04.799Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:41:07.733Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:08.534Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:41:23.802Z Script completed Wall time 15.3 seconds Output: preprocessed 3,000 users SESSION_ID=14477 ## Step 52 · agent · 2026-08-31T18:41:25.363Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:27.217Z { "code": "const r = await tools.write_stdin({session_id:14477,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:41:38.236Z Script running with cell ID 29 Wall time 11.0 seconds Output: ## Step 53 · agent · 2026-08-31T18:41:41.118Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:41.851Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:41:57.244Z Script completed Wall time 15.4 seconds Output: preprocessed 4,500 users preprocessed 6,000 users SESSION_ID=14477 ## Step 54 · agent · 2026-08-31T18:42:01.198Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:03.074Z { "code": "const r = await tools.write_stdin({session_id:14477,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:42:14.095Z Script running with cell ID 30 Wall time 11.0 seconds Output: ## Step 55 · agent · 2026-08-31T18:42:15.983Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:16.814Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:42:32.691Z Script completed Wall time 15.9 seconds Output: preprocessed 7,500 users preprocessed 9,000 users {"cache": "/app/solution/validation_cache.npz", "contexts": 59192, "train_contexts": 24594, "targets": 234017} ## Step 56 · agent · 2026-08-31T18:42:35.407Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:45.035Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution/validation_cache.npz && /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\nz=np.load('/app/solution/validation_cache.npz')\\nfor k in z.files:\\n x=z[k]; print(k,x.shape,x.dtype,'finite' if x.dtype.kind=='f' and np.isfinite(x).all() else '')\\nprint('uid aligned',np.array_equal(z['infer_uids'],np.load('/task/data/validation_eligible_candidates.npz')['uids']))\\nprint('target context label quantiles',np.quantile(np.diff(z['target_offsets']),[0,.25,.5,.75,.9,.99,1]))\\nPY\\n/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --epochs 14 --seed 2026 --dim 64\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:42:56.056Z Script running with cell ID 31 Wall time 11.0 seconds Output: ## Step 57 · agent · 2026-08-31T18:42:57.832Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:58.780Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:43:15.219Z Script completed Wall time 16.4 seconds Output: -rw-r--r-- 1 agent agent 98M Aug 31 18:42 /app/solution/validation_cache.npz history (59192, 256) int32 long (59192, 256) float32 finite short (59192, 256) float32 finite negative (59192, 64) int32 negative_weight (59192, 64) float32 finite numeric (59192, 4) float32 finite train_rows (24594,) int32 infer_rows (8456,) int32 target_offsets (24595,) int64 targets (234017,) int32 target_gains (234017,) float32 finite infer_uids (8456,) int64 uid aligned True target context label quantiles [ 1. 2. 4. 11. 22. 76. 349.] /app/solution/train_ranker.py:334: 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); batches += 1 SESSION_ID=81188 ## Step 58 · agent · 2026-08-31T18:43:17.302Z ### Thinking unavailable — no text recorded Preprocessing produced 24,594 nonempty temporal training contexts and 234,017 unseen organic positives across six rolling weeks; all 8,456 inference users align exactly with eligibility. The model is now training with mixed popularity-shaped and uniform sampled negatives, which preserves the production-friendly event/sampling path and avoids any global interaction matrix. ### exec · 2026-08-31T18:43:21.895Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:43:32.917Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 59 · agent · 2026-08-31T18:43:34.477Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:35.298Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:43:51.920Z Script completed Wall time 16.6 seconds Output: SESSION_ID=81188 ## Step 60 · agent · 2026-08-31T18:43:53.738Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:55.603Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:44:06.633Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:44:09.152Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:09.953Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:44:25.636Z Script completed Wall time 15.7 seconds Output: epoch 01 loss=0.58880 SESSION_ID=81188 ## Step 62 · agent · 2026-08-31T18:44:28.586Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:30.567Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:44:41.586Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 63 · agent · 2026-08-31T18:44:43.153Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:43.909Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:45:00.589Z Script completed Wall time 16.7 seconds Output: SESSION_ID=81188 ## Step 64 · agent · 2026-08-31T18:45:04.231Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:06.134Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:45:17.155Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:45:19.128Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:19.925Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:45:36.159Z Script completed Wall time 16.2 seconds Output: SESSION_ID=81188 ## Step 66 · agent · 2026-08-31T18:45:37.690Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:39.590Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:45:50.608Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:45:53.426Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:54.185Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:46:09.611Z Script completed Wall time 15.4 seconds Output: epoch 02 loss=0.51000 SESSION_ID=81188 ## Step 68 · agent · 2026-08-31T18:46:21.604Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:23.124Z { "code": "const r = await tools.write_stdin({session_id:81188,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:5000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:46:23.562Z Script completed Wall time 0.4 seconds Output: Traceback (most recent call last): File "/app/solution/train_ranker.py", line 411, in train_model(args.cache, args.model, args.epochs, args.seed, args.dim) File "/app/solution/train_ranker.py", line 320, in train_model tensor_rows(cache, "short", data_rows), tensor_rows(cache, "negative", data_rows), ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 278, in tensor_rows return torch.from_numpy(cache[name][rows]).to(device) ~~~~~^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1098, in _read1 self._update_crc(data) File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1023, in _update_crc self._running_crc = crc32(newdata, self._running_crc) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ## Step 69 · agent · 2026-08-31T18:46:29.062Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:36.381Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def train_model(cache_path: Path, model_path: Path, epochs: int, seed: int, dim: int) -> None:\\n seed_everything(seed)\\n- torch.set_num_threads(min(8, torch.get_num_threads()))\\n- cache = np.load(cache_path)\\n+ torch.set_num_threads(8)\\n+ with np.load(cache_path) as archive:\\n+ cache = {name: archive[name] for name in archive.files}\\n@@\\n- batch_size, negatives_per = 192, 40\\n+ batch_size, negatives_per = 256, 32\\n@@\\n- total += float(loss); batches += 1\\n+ total += float(loss.detach()); batches += 1\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:46:36.457Z Script completed Wall time 0.0 seconds Output: {} ## Step 70 · agent · 2026-08-31T18:46:40.441Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:44.154Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --epochs 10 --seed 2026 --dim 64\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:46:55.182Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 71 · agent · 2026-08-31T18:46:56.934Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:57.675Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:14.351Z Script completed Wall time 16.7 seconds Output: epoch 01 loss=0.59207 epoch 02 loss=0.52318 epoch 03 loss=0.46337 SESSION_ID=43340 ## Step 72 · agent · 2026-08-31T18:47:16.530Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:18.397Z { "code": "const r = await tools.write_stdin({session_id:43340,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:47:29.433Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 73 · agent · 2026-08-31T18:47:31.214Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:32.033Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:47:48.445Z Script completed Wall time 16.4 seconds Output: epoch 04 loss=0.41948 epoch 05 loss=0.38892 epoch 06 loss=0.36797 epoch 07 loss=0.35050 epoch 08 loss=0.33681 epoch 09 loss=0.32130 SESSION_ID=43340 ## Step 74 · agent · 2026-08-31T18:47:50.145Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:52.206Z { "code": "const r = await tools.write_stdin({session_id:43340,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:47:52.274Z Script completed Wall time 0.0 seconds Output: epoch 10 loss=0.31241 ## Step 75 · agent · 2026-08-31T18:47:57.141Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:01.317Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def rank(cache_path: Path, model_path: Path, stage: str, output: Path, pop_blend: float) -> None:\\n- cache = np.load(cache_path)\\n+ with np.load(cache_path) as archive:\\n+ cache = {name: archive[name] for name in archive.files}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:48:01.384Z Script completed Wall time 0.0 seconds Output: {} ## Step 76 · agent · 2026-08-31T18:48:07.079Z ### Thinking unavailable — no text recorded Training is now efficient after caching the compressed arrays once in memory; the ten-epoch loss fell from 0.592 to 0.312. I’m generating cutoff-valid rankings over all 30,000 candidates and testing only a small popularity-blend grid—the tower score remains the user/item dot product, while the blend check tells me whether the learned item tower already calibrated head-item prevalence sufficiently. ### exec · 2026-08-31T18:48:16.662Z { "code": "const r = await tools.exec_command({cmd:\"for b in 0 0.02 0.05 0.08 0.12 0.18; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache.npz --model /app/solution/validation_model.pt --stage validation --pop-blend \\\"$b\\\" --output \\\"/app/solution/validation_b${b}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_b${b}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:48:27.691Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T18:48:29.228Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:30.077Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:48:46.850Z Script completed Wall time 16.8 seconds Output: { "model": "/app/solution/validation_b0.parquet", "scores": { "quality_ndcg@10": 0.014898, "quality_users": 4263 } } SESSION_ID=7674 ## Step 78 · agent · 2026-08-31T18:48:51.017Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:52.888Z { "code": "const r = await tools.write_stdin({session_id:7674,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:49:03.916Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:49:05.869Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:06.537Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:49:22.916Z Script completed Wall time 16.4 seconds Output: { "model": "/app/solution/validation_b0.02.parquet", "scores": { "quality_ndcg@10": 0.016418, "quality_users": 4263 } } { "model": "/app/solution/validation_b0.05.parquet", "scores": { "quality_ndcg@10": 0.018334, "quality_users": 4263 } } SESSION_ID=7674 ## Step 80 · agent · 2026-08-31T18:49:25.571Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:27.268Z { "code": "const r = await tools.write_stdin({session_id:7674,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:49:38.286Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T18:49:39.892Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:40.633Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:49:57.265Z Script completed Wall time 16.6 seconds Output: { "model": "/app/solution/validation_b0.08.parquet", "scores": { "quality_ndcg@10": 0.017738, "quality_users": 4263 } } { "model": "/app/solution/validation_b0.12.parquet", "scores": { "quality_ndcg@10": 0.015999, "quality_users": 4263 } } { "model": "/app/solution/validation_b0.18.parquet", "scores": { "quality_ndcg@10": 0.013797, "quality_users": 4263 } } ## Step 82 · agent · 2026-08-31T18:50:16.955Z ### Thinking unavailable — no text recorded ## Step 83 · agent · 2026-08-31T18:50:22.029Z ### Thinking unavailable — no text recorded The personalized tower reaches 0.018334 at a 0.05 popularity blend, versus 0.010710 for popularity alone (about a 71% lift). I’m expanding the rolling temporal supervision from six to fourteen weeks; this should improve collaborative track geometry without changing inference cost or violating the temporal boundary. The current 0.018334 ranking remains saved as the fallback. ### exec · 2026-08-31T18:50:28.459Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage validation --weeks 14 --output /app/solution/validation_cache_w14.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:50:39.480Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 84 · agent · 2026-08-31T18:50:43.245Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:44.043Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:58.639Z Script completed Wall time 14.6 seconds Output: SESSION_ID=40623 ## Step 85 · agent · 2026-08-31T18:51:00.109Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:02.003Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:51:13.022Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 86 · agent · 2026-08-31T18:51:14.930Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:15.678Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:32.025Z Script completed Wall time 16.3 seconds Output: preprocessed 1,500 users SESSION_ID=40623 ## Step 87 · agent · 2026-08-31T18:51:35.088Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:36.874Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:51:47.896Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 88 · agent · 2026-08-31T18:51:49.710Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:50.279Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:06.900Z Script completed Wall time 16.6 seconds Output: preprocessed 3,000 users SESSION_ID=40623 ## Step 89 · agent · 2026-08-31T18:52:08.468Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:10.271Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:52:21.293Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 90 · agent · 2026-08-31T18:52:22.962Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:23.768Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:40.296Z Script completed Wall time 16.5 seconds Output: preprocessed 4,500 users SESSION_ID=40623 ## Step 91 · agent · 2026-08-31T18:52:41.847Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:43.693Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:52:54.712Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 92 · agent · 2026-08-31T18:52:56.636Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:57.257Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:13.715Z Script completed Wall time 16.5 seconds Output: preprocessed 6,000 users SESSION_ID=40623 ## Step 93 · agent · 2026-08-31T18:53:15.333Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:17.154Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:53:28.176Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 94 · agent · 2026-08-31T18:53:29.793Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:30.527Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:47.180Z Script completed Wall time 16.7 seconds Output: preprocessed 7,500 users SESSION_ID=40623 ## Step 95 · agent · 2026-08-31T18:53:48.768Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:50.612Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:54:01.630Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T18:54:03.202Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:03.974Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:54:20.634Z Script completed Wall time 16.7 seconds Output: SESSION_ID=40623 ## Step 97 · agent · 2026-08-31T18:54:22.112Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:23.988Z { "code": "const r = await tools.write_stdin({session_id:40623,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:54:35.010Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T18:54:36.847Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:37.634Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:54:44.458Z Script completed Wall time 6.8 seconds Output: preprocessed 9,000 users {"cache": "/app/solution/validation_cache_w14.npz", "contexts": 126840, "train_contexts": 52461, "targets": 492274} ## Step 99 · agent · 2026-08-31T18:54:47.699Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:51.526Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --epochs 10 --seed 2026 --dim 64\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:55:02.549Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T18:55:04.945Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:05.453Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:21.717Z Script completed Wall time 16.3 seconds Output: epoch 01 loss=0.56491 SESSION_ID=83725 ## Step 101 · agent · 2026-08-31T18:55:23.880Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:26.197Z { "code": "const r = await tools.write_stdin({session_id:83725,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:55:37.238Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T18:55:39.033Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:39.842Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:55:56.252Z Script completed Wall time 16.4 seconds Output: epoch 02 loss=0.49672 epoch 03 loss=0.44811 epoch 04 loss=0.41821 SESSION_ID=83725 ## Step 103 · agent · 2026-08-31T18:55:58.476Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:00.338Z { "code": "const r = await tools.write_stdin({session_id:83725,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:56:11.377Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T18:56:13.403Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:14.167Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:56:30.390Z Script completed Wall time 16.2 seconds Output: epoch 05 loss=0.39901 epoch 06 loss=0.38602 epoch 07 loss=0.36958 SESSION_ID=83725 ## Step 105 · agent · 2026-08-31T18:56:32.665Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:34.532Z { "code": "const r = await tools.write_stdin({session_id:83725,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:56:45.557Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T18:56:47.383Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:48.206Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:04.560Z Script completed Wall time 16.4 seconds Output: epoch 08 loss=0.35282 epoch 09 loss=0.33951 SESSION_ID=83725 ## Step 107 · agent · 2026-08-31T18:57:06.256Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:08.103Z { "code": "const r = await tools.write_stdin({session_id:83725,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:57:08.194Z Script completed Wall time 0.0 seconds Output: epoch 10 loss=0.32587 ## Step 108 · agent · 2026-08-31T18:57:10.121Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:16.447Z { "code": "const r = await tools.exec_command({cmd:\"for b in 0.03 0.045 0.06 0.075; do /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --stage validation --pop-blend \\\"$b\\\" --output \\\"/app/solution/validation_w14_b${b}.parquet\\\"; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_w14_b${b}.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:57:27.473Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 109 · agent · 2026-08-31T18:57:29.134Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:29.967Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T18:57:46.633Z Script completed Wall time 16.7 seconds Output: { "model": "/app/solution/validation_w14_b0.03.parquet", "scores": { "quality_ndcg@10": 0.019052, "quality_users": 4263 } } SESSION_ID=54812 ## Step 110 · agent · 2026-08-31T18:57:48.239Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:50.165Z { "code": "const r = await tools.write_stdin({session_id:54812,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:58:01.183Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 111 · agent · 2026-08-31T18:58:02.788Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:03.606Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T18:58:20.187Z Script completed Wall time 16.6 seconds Output: { "model": "/app/solution/validation_w14_b0.045.parquet", "scores": { "quality_ndcg@10": 0.019178, "quality_users": 4263 } } { "model": "/app/solution/validation_w14_b0.06.parquet", "scores": { "quality_ndcg@10": 0.019164, "quality_users": 4263 } } SESSION_ID=54812 ## Step 112 · agent · 2026-08-31T18:58:21.897Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:23.733Z { "code": "const r = await tools.write_stdin({session_id:54812,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:58:26.390Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_w14_b0.075.parquet", "scores": { "quality_ndcg@10": 0.018046, "quality_users": 4263 } } ## Step 113 · agent · 2026-08-31T18:58:46.052Z ### Thinking unavailable — no text recorded ## Step 114 · agent · 2026-08-31T18:58:54.444Z ### Thinking unavailable — no text recorded Fourteen weeks improves the public score again to 0.019178, with the optimum centered tightly around a 0.045 calibration weight. I’m checking a second initialization for variance and possible ensemble gain; if it is not complementary, I’ll retain the stronger single model and proceed directly to the required train+validation refit. ### exec · 2026-08-31T18:59:04.733Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14_s314.pt --epochs 10 --seed 314 --dim 64 && /opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14_s314.pt --stage validation --pop-blend 0.045 --output /app/solution/validation_w14_s314.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_w14_s314.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:59:15.763Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 115 · agent · 2026-08-31T18:59:17.779Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:18.567Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T18:59:34.931Z Script completed Wall time 16.4 seconds Output: epoch 01 loss=0.56264 SESSION_ID=87734 ## Step 116 · agent · 2026-08-31T18:59:36.766Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:38.619Z { "code": "const r = await tools.write_stdin({session_id:87734,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T18:59:49.669Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 117 · agent · 2026-08-31T18:59:51.335Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:52.121Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T19:00:08.681Z Script completed Wall time 16.6 seconds Output: epoch 02 loss=0.49339 epoch 03 loss=0.44482 epoch 04 loss=0.41867 SESSION_ID=87734 ## Step 118 · agent · 2026-08-31T19:00:10.203Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:12.118Z { "code": "const r = await tools.write_stdin({session_id:87734,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:00:23.166Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 119 · agent · 2026-08-31T19:00:24.873Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:25.655Z { "cell_id": "64", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T19:00:42.181Z Script completed Wall time 16.5 seconds Output: epoch 05 loss=0.40006 epoch 06 loss=0.38560 epoch 07 loss=0.37323 SESSION_ID=87734 ## Step 120 · agent · 2026-08-31T19:00:44.816Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:46.656Z { "code": "const r = await tools.write_stdin({session_id:87734,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:00:57.701Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 121 · agent · 2026-08-31T19:00:59.412Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:00.153Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 16000 } ### Tool output · 2026-08-31T19:01:16.700Z Script completed Wall time 16.5 seconds Output: epoch 08 loss=0.35850 epoch 09 loss=0.34477 epoch 10 loss=0.33173 SESSION_ID=87734 ## Step 122 · agent · 2026-08-31T19:01:21.562Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:23.462Z { "code": "const r = await tools.write_stdin({session_id:87734,chars:\"\",yield_time_ms:30000,max_output_tokens:16000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:01:32.254Z Script completed Wall time 8.8 seconds Output: { "model": "/app/solution/validation_w14_s314.parquet", "scores": { "quality_ndcg@10": 0.018042, "quality_users": 4263 } } ## Step 123 · agent · 2026-08-31T19:01:52.102Z ### Thinking unavailable — no text recorded ## Step 124 · agent · 2026-08-31T19:02:05.857Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:33.128Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/model.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n self.item_tower = ItemTower(num_items, output_dim=output_dim)\\n+\\n+\\n+class CalibratedUserTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment user tower with a constant coordinate for popularity calibration.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: UserTower):\\n+ super().__init__()\\n+ self.base = base\\n+\\n+ def forward(self, *args: torch.Tensor) -> torch.Tensor:\\n+ vector = self.base(*args)\\n+ return torch.cat([vector, torch.ones_like(vector[:, :1])], dim=1)\\n+\\n+\\n+class CalibratedItemTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment item tower; its output remains independently precomputable.\\\"\\\"\\\"\\n+\\n+ def __init__(self, base: ItemTower, popularity: torch.Tensor, weight: float):\\n+ super().__init__()\\n+ self.base = base\\n+ self.register_buffer(\\\"popularity\\\", popularity)\\n+ self.weight = float(weight)\\n+\\n+ def forward(self, item_ids: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ vector = self.base(item_ids, features)\\n+ coordinate = self.popularity[item_ids - 1].unsqueeze(-1) * self.weight\\n+ return torch.cat([vector, coordinate], dim=-1)\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-from model import TwoTower\\n+from model import CalibratedItemTower, CalibratedUserTower, TwoTower\\n@@\\n model = TwoTower(checkpoint[\\\"num_items\\\"], output_dim=checkpoint[\\\"dim\\\"])\\n model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n model.eval()\\n features = torch.from_numpy(item_features())\\n+ pop = -np.log1p(np.arange(checkpoint[\\\"num_items\\\"], dtype=np.float32))\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ user_tower = CalibratedUserTower(model.user_tower)\\n+ item_tower = CalibratedItemTower(model.item_tower, torch.from_numpy(pop), pop_blend)\\n all_ids = torch.arange(1, checkpoint[\\\"num_items\\\"] + 1)\\n item_vectors = []\\n for start in range(0, len(all_ids), 2048):\\n ids = all_ids[start:start + 2048]\\n- item_vectors.append(model.item_tower(ids, features[start:start + len(ids)]))\\n+ item_vectors.append(item_tower(ids, features[start:start + len(ids)]))\\n@@\\n- user_vectors.append(model.user_tower(\\n+ user_vectors.append(user_tower(\\n@@\\n- pop = -np.log1p(np.arange(len(candidate_items), dtype=np.float32))\\n- pop = (pop - pop.mean()) / pop.std()\\n result = []\\n@@\\n stop = min(start + 128, len(uids))\\n score = (user_vectors[start:stop] @ item_vectors.T).numpy()\\n- score += pop_blend * pop[None, :]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:02:33.296Z Script completed Wall time 0.0 seconds Output: {} ## Step 125 · agent · 2026-08-31T19:02:35.915Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:40.927Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/validation_w14_b0.045.parquet /app/solution/validation.parquet\\n/opt/task-venv/bin/python -m py_compile /app/solution/model.py /app/solution/train_ranker.py\\n/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage test --weeks 14 --output /app/solution/test_cache_w14.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:02:51.495Z Script completed Wall time 10.6 seconds Output: Traceback (most recent call last): File "/app/solution/train_ranker.py", line 412, in build_cache(args.stage, args.output, args.weeks) File "/app/solution/train_ranker.py", line 192, in build_cache data = {name: value[chronology] for name, value in data.items()} ~~~~~^^^^^^^^^^^^ IndexError: index 3266 is out of bounds for axis 0 with size 3266 ## Step 126 · agent · 2026-08-31T19:02:55.770Z ### Thinking unavailable — no text recorded The first refit pass hit a merge-only bug: the validation events were appended to the four event fields, while the unused `uid` array retained its old length. I’ve isolated it to test-stage preprocessing; validation artifacts and the chosen score are unaffected. I’m dropping that redundant per-group field and restarting the refit scan. ### exec · 2026-08-31T19:03:02.745Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n targets = set(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.astype(int))\\n for number, (uid, data) in enumerate(iter_user_groups(listen_paths[0])):\\n if uid not in targets:\\n continue\\n+ data.pop(\\\"uid\\\", None)\\n if test and uid in val_listens:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:03:02.872Z Script completed Wall time 0.0 seconds Output: {} ## Step 127 · agent · 2026-08-31T19:03:04.735Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:07.911Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py preprocess --stage test --weeks 14 --output /app/solution/test_cache_w14.npz\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:03:18.931Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 128 · agent · 2026-08-31T19:03:20.551Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:21.322Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:03:38.091Z Script completed Wall time 16.8 seconds Output: SESSION_ID=90796 ## Step 129 · agent · 2026-08-31T19:03:39.698Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:41.570Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:03:52.588Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 130 · agent · 2026-08-31T19:03:54.256Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:55.031Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:04:11.591Z Script completed Wall time 16.6 seconds Output: preprocessed 1,500 users SESSION_ID=90796 ## Step 131 · agent · 2026-08-31T19:04:15.342Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:17.228Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:04:28.249Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 132 · agent · 2026-08-31T19:04:30.084Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:30.704Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:04:47.252Z Script completed Wall time 16.5 seconds Output: preprocessed 3,000 users SESSION_ID=90796 ## Step 133 · agent · 2026-08-31T19:04:48.842Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:50.711Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:05:01.732Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 134 · agent · 2026-08-31T19:05:03.647Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:04.426Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:05:20.734Z Script completed Wall time 16.3 seconds Output: preprocessed 4,500 users SESSION_ID=90796 ## Step 135 · agent · 2026-08-31T19:05:22.943Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:24.805Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:05:35.824Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 136 · agent · 2026-08-31T19:05:37.481Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:38.335Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:05:54.826Z Script completed Wall time 16.5 seconds Output: preprocessed 6,000 users SESSION_ID=90796 ## Step 137 · agent · 2026-08-31T19:05:57.155Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:59.048Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:06:10.067Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T19:06:11.832Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:12.611Z { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:06:29.071Z Script completed Wall time 16.5 seconds Output: SESSION_ID=90796 ## Step 139 · agent · 2026-08-31T19:06:30.738Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:32.710Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:06:43.732Z Script running with cell ID 76 Wall time 11.0 seconds Output: ## Step 140 · agent · 2026-08-31T19:06:45.434Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:46.249Z { "cell_id": "76", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:07:02.735Z Script completed Wall time 16.5 seconds Output: preprocessed 7,500 users SESSION_ID=90796 ## Step 141 · agent · 2026-08-31T19:07:04.700Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:06.587Z { "code": "const r = await tools.write_stdin({session_id:90796,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:07:17.612Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 142 · agent · 2026-08-31T19:07:23.494Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:24.410Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:07:32.397Z Script completed Wall time 8.0 seconds Output: preprocessed 9,000 users {"cache": "/app/solution/test_cache_w14.npz", "contexts": 126840, "train_contexts": 53540, "targets": 500389} ## Step 143 · agent · 2026-08-31T19:07:34.891Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:38.613Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py train --cache /app/solution/test_cache_w14.npz --model /app/solution/final_model.pt --epochs 10 --seed 2026 --dim 64\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:07:49.637Z Script running with cell ID 78 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T19:07:51.381Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:52.186Z { "cell_id": "78", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:08:08.807Z Script completed Wall time 16.6 seconds Output: epoch 01 loss=0.56720 SESSION_ID=67881 ## Step 145 · agent · 2026-08-31T19:08:10.455Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:12.536Z { "code": "const r = await tools.write_stdin({session_id:67881,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:08:23.581Z Script running with cell ID 79 Wall time 11.0 seconds Output: ## Step 146 · agent · 2026-08-31T19:08:26.326Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:27.077Z { "cell_id": "79", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:08:42.589Z Script completed Wall time 15.5 seconds Output: epoch 02 loss=0.49599 epoch 03 loss=0.44594 epoch 04 loss=0.42056 SESSION_ID=67881 ## Step 147 · agent · 2026-08-31T19:08:44.126Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:45.982Z { "code": "const r = await tools.write_stdin({session_id:67881,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:08:57.018Z Script running with cell ID 80 Wall time 11.0 seconds Output: ## Step 148 · agent · 2026-08-31T19:08:59.137Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:59.906Z { "cell_id": "80", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:09:16.024Z Script completed Wall time 16.1 seconds Output: epoch 05 loss=0.40152 epoch 06 loss=0.38533 SESSION_ID=67881 ## Step 149 · agent · 2026-08-31T19:09:17.597Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:19.518Z { "code": "const r = await tools.write_stdin({session_id:67881,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:09:30.573Z Script running with cell ID 81 Wall time 11.0 seconds Output: ## Step 150 · agent · 2026-08-31T19:09:33.993Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:09:34.786Z { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:09:49.586Z Script completed Wall time 14.8 seconds Output: epoch 07 loss=0.37165 epoch 08 loss=0.35662 epoch 09 loss=0.34501 SESSION_ID=67881 ## Step 151 · agent · 2026-08-31T19:09:53.302Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:55.141Z { "code": "const r = await tools.write_stdin({session_id:67881,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:09:56.230Z Script completed Wall time 1.1 seconds Output: epoch 10 loss=0.32992 ## Step 152 · agent · 2026-08-31T19:09:59.436Z ### Thinking unavailable — no text recorded The required refit is complete: it uses train plus validation history, including the public window as observed temporal supervision, with 53,540 contexts and 500,389 unseen organic positives. I’m now writing the test ranking through the exact 65-dimensional deployed dot product (64 learned coordinates plus one calibrated popularity coordinate), then independently checking every schema, rank, duplicate, user, and eligibility invariant. ### exec · 2026-08-31T19:10:09.923Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/test_cache_w14.npz --model /app/solution/final_model.pt --stage test --pop-blend 0.045 --output /app/solution/submission.parquet\\n/opt/task-venv/bin/python /app/solution/train_ranker.py rank --cache /app/solution/validation_cache_w14.npz --model /app/solution/validation_model_w14.pt --stage validation --pop-blend 0.045 --output /app/solution/validation.parquet\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:10:20.945Z Script running with cell ID 83 Wall time 11.0 seconds Output: ## Step 153 · agent · 2026-08-31T19:10:24.075Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:10:24.922Z { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 14000 } ### Tool output · 2026-08-31T19:10:40.104Z Script completed Wall time 15.2 seconds Output: SESSION_ID=62874 ## Step 154 · agent · 2026-08-31T19:10:41.933Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:43.840Z { "code": "const r = await tools.write_stdin({session_id:62874,chars:\"\",yield_time_ms:30000,max_output_tokens:14000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`)\n" } ### Tool output · 2026-08-31T19:10:43.917Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.019178, "quality_users": 4263 } } ## Step 155 · agent · 2026-08-31T19:11:02.645Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:26.011Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower ranker\\n+\\n+## Model\\n+\\n+`model.py` defines separate trainable `UserTower` and `ItemTower` PyTorch\\n+networks. The user tower contains no user-ID embedding: it pools up to 256\\n+catalog tracks from positive listen/like history into long- and short-recency\\n+views, separately pools active dislikes, appends four history-size features,\\n+and applies a 192/128-unit MLP. The item tower combines a trainable track\\n+embedding with four catalog popularity features and applies a 160/128-unit\\n+MLP. Both return 64-dimensional normalized vectors.\\n+\\n+Deployment wraps these towers with one calibrated popularity coordinate. The\\n+resulting independently computable user and item vectors are both 65-D and the\\n+complete relevance score is exactly their inner product. Item vectors can be\\n+materialized offline in one pass; request-time work is one history-based user\\n+tower evaluation followed by maximum-inner-product retrieval.\\n+\\n+## Training\\n+\\n+`train_ranker.py` streams uid/time-sorted event Parquets one user at a time and\\n+never constructs or factorizes a global user-item matrix. Sixteen-byte catalog\\n+indices are not used as user IDs. Supervision comes from fourteen rolling,\\n+cutoff-correct temporal backtests: a context uses only earlier interactions and\\n+targets previously unseen catalog tracks with an organic 80% listen (gain 1)\\n+or organic like (gain 7). The pairwise softplus/BPR objective uses 32 sampled\\n+negatives per positive, half popularity-shaped and half uniform. Validation\\n+selection used train events only; the final model was refit from scratch with\\n+train plus validation events as history/supervision.\\n+\\n+## Validation\\n+\\n+Public graded NDCG@10: **0.019178** over 4,263 scored users. The popularity-only\\n+fallback scored 0.010710. The retained validation ranking is\\n+`validation.parquet`.\\n+\\n+## Final submission command\\n+\\n+Run from `/app/solution` with the task's read-only virtual environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python train_ranker.py preprocess --stage test --weeks 14 --output test_cache_w14.npz && \\\\\\n+/opt/task-venv/bin/python train_ranker.py train --cache test_cache_w14.npz --model final_model.pt --epochs 10 --seed 2026 --dim 64 && \\\\\\n+/opt/task-venv/bin/python train_ranker.py rank --cache test_cache_w14.npz --model final_model.pt --stage test --pop-blend 0.045 --output submission.parquet\\n+```\\n+\\n+The checked-in `submission.parquet` was produced by that command.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:11:26.146Z Script completed Wall time 0.0 seconds Output: {} ## Step 156 · agent · 2026-08-31T19:11:31.645Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:45.316Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nframe=validate_ranking(pd.read_parquet(S),targets)\\ncandidates=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(int(u),int(i)) for u,i in frame[['uid','item_id']].itertuples(index=False,name=None) if not candidates.is_eligible(int(u),int(i))]\\nprint({'rows':len(frame),'users':frame.uid.nunique(),'items_per_user_min':int(frame.groupby('uid').size().min()),'items_per_user_max':int(frame.groupby('uid').size().max()),'rank_min':int(frame['rank'].min()),'rank_max':int(frame['rank'].max()),'duplicate_pairs':int(frame.duplicated(['uid','item_id']).sum()),'ineligible':len(bad),'dtypes':{k:str(v) for k,v in frame.dtypes.items()}})\\nPY\\nsha256sum /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/final_model.pt\\nls -lh /app/solution | sed -n '1,120p'\\n/opt/task-venv/bin/python -m py_compile /app/solution/*.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T19:11:48.452Z Script completed Wall time 3.1 seconds Output: {'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'duplicate_pairs': 0, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}} 99eca83d4bdfe351db6f8a34e912f223d239624d96c010698b8859ab60dc6be9 /app/solution/submission.parquet 9e2e79936c0e5080e3ab390d0d873eb52a6c157eb1974372a2e0dbeffcc979f2 /app/solution/validation.parquet 4c235367aed81ae34f579f31d2f4f81bdfb20377f892439a96655ca7dc963bf9 /app/solution/final_model.pt total 588M -rw-r--r-- 1 agent agent 2.4K Aug 31 19:11 README.md drwxr-xr-x 2 agent agent 4.0K Aug 31 19:02 __pycache__ -rw-r--r-- 1 agent agent 1.2K Aug 31 18:27 build_popularity.py -rw-r--r-- 1 agent agent 23M Aug 31 19:09 final_model.pt -rw-r--r-- 1 agent agent 3.7K Aug 31 19:02 model.py -rw-r--r-- 1 agent agent 1.4M Aug 31 19:10 submission.parquet -rw-r--r-- 1 agent agent 194M Aug 31 19:07 test_cache_w14.npz -rw-r--r-- 1 agent agent 20K Aug 31 19:03 train_ranker.py -rw-r--r-- 1 agent agent 1.4M Aug 31 19:10 validation.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:48 validation_b0.02.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 18:49 validation_b0.05.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 18:49 validation_b0.08.parquet -rw-r--r-- 1 agent agent 1.3M Aug 31 18:49 validation_b0.12.parquet -rw-r--r-- 1 agent agent 1.2M Aug 31 18:49 validation_b0.18.parquet -rw-r--r-- 1 agent agent 1.5M Aug 31 18:48 validation_b0.parquet -rw-r--r-- 1 agent agent 98M Aug 31 18:42 validation_cache.npz -rw-r--r-- 1 agent agent 190M Aug 31 18:54 validation_cache_w14.npz -rw-r--r-- 1 agent agent 23M Aug 31 18:47 validation_model.pt -rw-r--r-- 1 agent agent 23M Aug 31 18:57 validation_model_w14.pt -rw-r--r-- 1 agent agent 23M Aug 31 19:01 validation_model_w14_s314.pt -rw-r--r-- 1 agent agent 1.4M Aug 31 18:57 validation_w14_b0.03.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 18:57 validation_w14_b0.045.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 18:58 validation_w14_b0.06.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 18:58 validation_w14_b0.075.parquet -rw-r--r-- 1 agent agent 1.4M Aug 31 19:01 validation_w14_s314.parquet ## Step 157 · agent · 2026-08-31T19:11:52.346Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:57.285Z { "code": "const p = await tools.update_plan({explanation:\"Implemented, tuned, refit, ranked, and independently validated the final artifact.\",plan:[\n {step:\"Inspect data schemas, sizes, cutoffs, eligibility, and evaluator\",status:\"completed\"},\n {step:\"Implement scalable PyTorch two-tower training and ranking pipeline\",status:\"completed\"},\n {step:\"Train validation variants and evaluate public NDCG@10\",status:\"completed\"},\n {step:\"Refit selected model on train+validation and create valid test submission\",status:\"completed\"},\n {step:\"Verify artifacts and document reproducible commands\",status:\"completed\"}\n]}); text(p)\n" } ### Tool output · 2026-08-31T19:11:57.413Z Script completed Wall time 0.0 seconds Output: {} ## Step 158 · agent · 2026-08-31T19:12:01.775Z ### Thinking unavailable — no text recorded Implemented and verified the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/ranking pipeline: [train_ranker.py](/app/solution/train_ranker.py) - Tower definitions: [model.py](/app/solution/model.py) - Documentation: [README.md](/app/solution/README.md) - Public validation ranking: [validation.parquet](/app/solution/validation.parquet) Public NDCG@10: **0.019178** over 4,263 users. Final submission validation: - 845,600 rows - 8,456 users - Exactly 100 unique items and ranks 1–100 per user - Zero ineligible items - Final model refitted on train + validation history - Exact 65-dimensional user/item dot-product scoring - No user-ID embeddings or global user-item matrix construction