yambda-discovery-ranking-two-tower-base / run-010-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:51.785Z 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 · 2026-08-31T18:25:53.387Z ## 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 · 2026-08-31T18:25:53.387Z 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 · 2026-08-31T18:25:53.387Z 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 · 2026-08-31T18:25:53.387Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a05912-129f-7880-9bc4-8e125673bb5e", "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 · 2026-08-31T18:25:53.397Z # 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 · 2026-08-31T18:25:55.676Z ### Thinking unavailable — no text recorded I’ll build and validate the full pipeline in `/app/solution/`, keeping the model strictly decomposable into independent history-based user and item towers. I’ll first inspect the data sizes and environment, then establish a valid baseline, train/tune against the public window, and refit on train+validation for the final ranking. ### exec · 2026-08-31T18:26:02.226Z { "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/requirements.txt && find /app -maxdepth 2 -type f -printf '%p\\\\n' | sort | head -100\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:02.299Z 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 # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-08-31T18:26:05.442Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:12.506Z { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\\nD='/task/data'\\nfor fn in sorted(os.listdir(D)):\\n if fn.endswith('.parquet'):\\n p=os.path.join(D,fn); pf=pq.ParquetFile(p)\\n print(fn, pf.metadata.num_rows, pf.schema_arrow)\\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(os.path.join(D,fn)); print(fn,{k:(z[k].shape,z[k].dtype) for k in z.files},'item sample',z['item_ids'][:5], 'uid sample',z['uids'][:3])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:12.571Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pyarrow' ## Step 9 · agent · 2026-08-31T18:26:15.021Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:18.156Z { "code": "const r = await tools.exec_command({\"cmd\":\"which python; python --version; python -m pip list --format=freeze | sed -n '1,120p'; command -v parquet-tools || true; ls -la /task | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:18.964Z Script completed Wall time 0.8 seconds Output: /usr/local/bin/python Python 3.12.13 pip==25.0.1 total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:09 data -r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt ## Step 10 · agent · 2026-08-31T18:26:21.009Z ### Thinking unavailable — no text recorded The default shell is not exposing the task’s pinned Python environment (it currently contains only `pip`), so I’m locating the prebuilt environment rather than installing anything. The input files and task code remain untouched. ### exec · 2026-08-31T18:26:27.484Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -path '*/site-packages/torch' -type d -o -path '*/site-packages/pyarrow' -type d 2>/dev/null | head -30; find / -type f \\\\( -name 'activate' -o -name 'python3.11' -o -name 'python3.12' \\\\) 2>/dev/null | head -80; env | sort\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:27.986Z Script completed Wall time 0.5 seconds Output: /opt/task-venv/lib/python3.12/site-packages/pyarrow /opt/task-venv/lib/python3.12/site-packages/torch /opt/task-venv/bin/activate /usr/local/lib/python3.12/venv/scripts/common/activate /usr/local/bin/python3.12 CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05912-1289-7f81-b626-344c9944f89e COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=bc71cbab2355 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 · 2026-08-31T18:26:29.875Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:37.296Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, os\\nD='/task/data'\\nfor fn in sorted(os.listdir(D)):\\n if fn.endswith('.parquet'):\\n p=os.path.join(D,fn); pf=pq.ParquetFile(p)\\n print('\\\\n',fn, pf.metadata.num_rows, pf.schema_arrow)\\nfor fn in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(os.path.join(D,fn)); print('\\\\n',fn,{k:(z[k].shape,z[k].dtype) for k in z.files},'item sample',z['item_ids'][:5], 'uid sample',z['uids'][:3])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:38.246Z Script completed Wall time 0.9 seconds Output: candidate_catalog.parquet 30000 item_id: uint32 positive_listens: int64 organic_positive_listens: uint64 popularity_rank: int32 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 591 target_users.parquet 8456 uid: uint32 recent_positive_listens: double history_positive_listens: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 472 test_preference_state.parquet 484776 uid: uint32 item_id: uint32 state: string -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 403 train_dislikes.parquet 101075 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 train_likes.parquet 814831 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 train_listens.parquet 43484159 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 played_ratio_pct: uint16 track_length_seconds: uint32 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797 train_undislikes.parquet 19960 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 train_unlikes.parquet 292665 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 val_dislikes.parquet 3563 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 val_likes.parquet 37795 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 val_listens.parquet 1501697 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 played_ratio_pct: uint16 track_length_seconds: uint32 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797 val_undislikes.parquet 606 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 val_unlikes.parquet 9346 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 527 validation_preference_state.parquet 469124 uid: uint32 item_id: uint32 state: string -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 403 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} item sample [5862961 6901374 5635052 3542184 9378983] uid sample [100 200 300] test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} item sample [5862961 6901374 5635052 3542184 9378983] uid sample [100 200 300] ## Step 12 · agent · 2026-08-31T18:26:41.937Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:44.430Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,320p' /task/ranking_evaluation.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":40000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:44.485Z Script completed Wall time 0.0 seconds Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """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), } ## Step 13 · agent · 2026-08-31T18:26:55.219Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:08.378Z { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\nt=pd.read_parquet(D+'target_users.parquet'); cat=pd.read_parquet(D+'candidate_catalog.parquet')\\nprint('target',t.describe(percentiles=[.1,.5,.9,.99]).to_string())\\nfor stage in ['validation','test']:\\n z=np.load(D+stage+'_eligible_candidates.npz'); counts=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\\n print(stage,'eligible',np.quantile(counts,[0,.1,.5,.9,1]),counts.mean())\\nlikes=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nts=set(t.uid.astype(int)); cis=set(cat.item_id.astype(int))\\nfor name,x in [('likes',likes[(likes.uid.isin(ts))&(likes.is_organic==1)]),('strong',ls[(ls.uid.isin(ts))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)])]:\\n print(name,'rows',len(x),'pairs',len(x[['uid','item_id']].drop_duplicates()),'users',x.uid.nunique(),'in cat',x.item_id.isin(cis).mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:10.713Z Script completed Wall time 2.3 seconds Output: /bin/sh: 1: free: not found target uid recent_positive_listens history_positive_listens count 8456.000000 8456.000000 8456.000000 mean 502289.108325 1251.076750 3236.440752 std 287953.395912 1414.340904 3697.575286 min 100.000000 20.000000 20.000000 10% 102050.000000 101.000000 204.000000 50% 503850.000000 784.500000 1920.500000 90% 899950.000000 3004.000000 8096.500000 99% 989745.000000 6541.450000 16804.050000 max 1000000.000000 19592.000000 26597.000000 validation eligible [24657. 28038.5 29242. 29852. 30000. ] 29067.306291390727 test eligible [24552. 28001. 29220. 29835. 30000.] 29041.3178807947 likes rows 20702 pairs 17195 users 2714 in cat 0.5026567481402763 strong rows 421615 pairs 255608 users 6325 in cat 0.6850515280528444 ## Step 14 · agent · 2026-08-31T18:27:28.138Z ### Thinking unavailable — no text recorded The benchmark has 8,456 target users and about 29,000 eligible tracks per user, so exact per-user scoring over the 30k catalog is practical while preserving the production retrieval form. I’m now measuring temporal repetition and catalog coverage; those determine whether the training examples should emphasize long-term taste, recent sessions, or both. ### exec · 2026-08-31T18:28:02.611Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pyarrow.compute as pc, pyarrow as pa\\nimport pandas as pd, numpy as np, time\\nD='/task/data/'\\ntarget=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(np.uint32)\\ncat=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy(np.uint32)\\nT=pa.array(target); C=pa.array(cat)\\ndef target_read(fn,cols):\\n tab=pq.read_table(D+fn,columns=cols)\\n return tab.filter(pc.is_in(tab['uid'],value_set=T)).to_pandas()\\nt=time.time(); tr=target_read('train_listens.parquet',['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('read secs',time.time()-t,'target train',len(tr),'users',tr.uid.nunique(),'items',tr.item_id.nunique(),'memMB',tr.memory_usage(deep=True).sum()/1e6)\\nva=target_read('val_listens.parquet',['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\nvl=target_read('val_likes.parquet',['uid','timestamp','item_id','is_organic'])\\nstrong=va[(va.is_organic==1)&(va.played_ratio_pct>=80)&va.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\nlike=vl[(vl.is_organic==1)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\ngold=pd.concat([strong.assign(grade=1),like.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\n# eligibility exact\\nz=np.load(D+'validation_eligible_candidates.npz'); ui={int(x):i for i,x in enumerate(z['uids'])}; ci={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(row):\\n r=ui[int(row.uid)]; c=ci[int(row.item_id)]; return ((z['packed_eligible'][r,c//8]>>(c%8))&1)!=0\\ngold=gold[gold.apply(elig,axis=1)]\\nprint('gold',len(gold),'users',gold.uid.nunique(),'grade',gold.grade.value_counts().to_dict(),'peruser',gold.groupby('uid').size().quantile([0,.5,.9,.99,1]).to_dict())\\npos=tr[tr.played_ratio_pct>=50]\\nsets={u:set(g.item_id.astype(int)) for u,g in pos.groupby('uid')}\\ngold['seen50']=[int(i) in sets.get(int(u),set()) for u,i in gold[['uid','item_id']].itertuples(index=False)]\\n# recent cutoffs days prior\\nfor days in [1,7,30,90,365,9999]:\\n cutoff=24786800-days*86400\\n ss={u:set(g.item_id.astype(int)) for u,g in pos[pos.timestamp>=cutoff].groupby('uid')}\\n hit=np.array([int(i) in ss.get(int(u),set()) for u,i in gold[['uid','item_id']].itertuples(index=False)])\\n print('seen days',days,'all',hit.mean(),'grade3',hit[gold.grade.to_numpy()==3].mean() if (gold.grade==3).any() else 0)\\nprint('organic frac train target',tr.is_organic.mean(),'positive',len(pos),'pos cat frac',pos.item_id.isin(cat).mean())\\n# global training stats Arrow dictionary count\\nalluid=pq.read_table(D+'train_listens.parquet',columns=['uid'])['uid']\\nprint('all users approx exact',len(pc.unique(alluid)),'rows',len(alluid))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:28:13.633Z Script running with cell ID 8 Wall time 11.0 seconds Output: ## Step 15 · agent · 2026-08-31T18:28:15.188Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:15.954Z { "cell_id": "8", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:28:32.796Z Script completed Wall time 16.8 seconds Output: ## Step 16 · agent · 2026-08-31T18:28:35.676Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:02.799Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pyarrow.parquet as pq, pyarrow.compute as pc, pyarrow as pa\\nimport pandas as pd, numpy as np, time\\nD='/task/data/'; target=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(np.uint32); cat=pd.read_parquet(D+'candidate_catalog.parquet').item_id.to_numpy(np.uint32)\\nT=pa.array(target)\\ndef target_read(fn,cols):\\n tab=pq.read_table(D+fn,columns=cols)\\n return tab.filter(pc.is_in(tab['uid'],value_set=T)).to_pandas()\\nt=time.time(); tr=target_read('train_listens.parquet',['uid','timestamp','item_id','is_organic','played_ratio_pct']); print('read secs',time.time()-t,'target train',len(tr),'users',tr.uid.nunique(),'items',tr.item_id.nunique(),'memMB',tr.memory_usage(deep=True).sum()/1e6,flush=True)\\nva=target_read('val_listens.parquet',['uid','timestamp','item_id','is_organic','played_ratio_pct']); vl=target_read('val_likes.parquet',['uid','timestamp','item_id','is_organic'])\\nstrong=va[(va.is_organic==1)&(va.played_ratio_pct>=80)&va.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); like=vl[(vl.is_organic==1)&vl.item_id.isin(cat)][['uid','item_id']].drop_duplicates(); gold=pd.concat([strong.assign(grade=1),like.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nz=np.load(D+'validation_eligible_candidates.npz'); ui={int(x):i for i,x in enumerate(z['uids'])}; ci={int(x):i for i,x in enumerate(z['item_ids'])}\\nmask=[]\\nfor u,i in gold[['uid','item_id']].itertuples(index=False):\\n r=ui[int(u)]; c=ci[int(i)]; mask.append(((z['packed_eligible'][r,c//8]>>(c%8))&1)!=0)\\ngold=gold[np.array(mask)]\\nprint('gold',len(gold),'users',gold.uid.nunique(),'grade',gold.grade.value_counts().to_dict(),flush=True)\\npos=tr[tr.played_ratio_pct>=50]; sets={u:set(g.item_id.astype(int)) for u,g in pos.groupby('uid')}\\nfor days in [1,7,30,90,365,9999]:\\n cutoff=24786800-days*86400; ss={u:set(g.item_id.astype(int)) for u,g in pos[pos.timestamp>=cutoff].groupby('uid')}; hit=np.array([int(i) in ss.get(int(u),set()) for u,i in gold[['uid','item_id']].itertuples(index=False)]); print('seen',days,hit.mean(),'like',hit[gold.grade.to_numpy()==3].mean(),flush=True)\\nprint('org frac',tr.is_organic.mean(),'pos',len(pos),'pos cat',pos.item_id.isin(cat).mean(),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:29:13.821Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 17 · agent · 2026-08-31T18:29:15.314Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:29:16.138Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:29:32.987Z Script completed Wall time 16.8 seconds Output: read secs 1.172682523727417 target train 43222297 users 8456 items 839582 memMB 648.334587 SESSION_ID=83906 ## Step 18 · agent · 2026-08-31T18:29:52.495Z ### Thinking unavailable — no text recorded ## Step 19 · agent · 2026-08-31T18:30:11.144Z ### Thinking unavailable — no text recorded ## Step 20 · agent · 2026-08-31T18:30:15.600Z ### Thinking unavailable — no text recorded A first pass showed that the target cohort accounts for nearly the entire 43.5M-listen table, so loading it as a pandas frame is unnecessarily memory-heavy. I’m switching to row-group streaming and compact integer arrays; this is also the production-scalable pattern the final training code will use. ### exec · 2026-08-31T18:30:56.669Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/explore_stream.py\\n+\\\"\\\"\\\"Memory-bounded diagnostics and compact event extraction for development.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+targets = pd.read_parquet(D / \\\"target_users.parquet\\\")\\n+items = catalog.item_id.to_numpy(np.uint32)\\n+uids = targets.uid.to_numpy(np.uint32)\\n+item_order = np.argsort(items)\\n+uid_order = np.argsort(uids)\\n+sorted_items = items[item_order]\\n+sorted_uids = uids[uid_order]\\n+\\n+\\n+def map_values(values, sorted_values, original_order):\\n+ pos = np.searchsorted(sorted_values, values)\\n+ valid = pos < len(sorted_values)\\n+ valid &= sorted_values[np.minimum(pos, len(sorted_values) - 1)] == values\\n+ mapped = np.zeros(len(values), np.int32)\\n+ mapped[valid] = original_order[pos[valid]]\\n+ return mapped, valid\\n+\\n+\\n+def extract_listens(path):\\n+ out = [[] for _ in range(5)]\\n+ pf = pq.ParquetFile(path)\\n+ for batch in pf.iter_batches(\\n+ batch_size=1_000_000,\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ x = batch.to_pandas()\\n+ raw_i = x.item_id.to_numpy(np.uint32, copy=False)\\n+ raw_u = x.uid.to_numpy(np.uint32, copy=False)\\n+ ii, vi = map_values(raw_i, sorted_items, item_order)\\n+ ui, vu = map_values(raw_u, sorted_uids, uid_order)\\n+ keep = vi & vu & (x.played_ratio_pct.to_numpy() >= 50)\\n+ out[0].append(ui[keep].astype(np.uint16))\\n+ out[1].append(ii[keep].astype(np.uint16))\\n+ out[2].append(x.timestamp.to_numpy(np.uint32, copy=False)[keep])\\n+ out[3].append(x.is_organic.to_numpy(np.uint8, copy=False)[keep])\\n+ out[4].append(np.minimum(x.played_ratio_pct.to_numpy(copy=False)[keep], 255).astype(np.uint8))\\n+ return [np.concatenate(v) for v in out]\\n+\\n+\\n+def extract_actions(path):\\n+ out = [[] for _ in range(4)]\\n+ pf = pq.ParquetFile(path)\\n+ for batch in pf.iter_batches(batch_size=500_000):\\n+ x = batch.to_pandas()\\n+ ii, vi = map_values(x.item_id.to_numpy(np.uint32, copy=False), sorted_items, item_order)\\n+ ui, vu = map_values(x.uid.to_numpy(np.uint32, copy=False), sorted_uids, uid_order)\\n+ keep = vi & vu\\n+ out[0].append(ui[keep].astype(np.uint16))\\n+ out[1].append(ii[keep].astype(np.uint16))\\n+ out[2].append(x.timestamp.to_numpy(np.uint32, copy=False)[keep])\\n+ out[3].append(x.is_organic.to_numpy(np.uint8, copy=False)[keep])\\n+ return [np.concatenate(v) for v in out]\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ for stage in (\\\"train\\\", \\\"val\\\"):\\n+ a = extract_listens(D / f\\\"{stage}_listens.parquet\\\")\\n+ np.savez_compressed(O / f\\\"{stage}_candidate_listens.npz\\\", ui=a[0], ii=a[1], ts=a[2], organic=a[3], ratio=a[4])\\n+ print(stage, \\\"positive candidate listens\\\", len(a[0]))\\n+ for family in (\\\"likes\\\", \\\"dislikes\\\", \\\"unlikes\\\", \\\"undislikes\\\"):\\n+ b = extract_actions(D / f\\\"{stage}_{family}.parquet\\\")\\n+ np.savez_compressed(O / f\\\"{stage}_candidate_{family}.npz\\\", ui=b[0], ii=b[1], ts=b[2], organic=b[3])\\n+ print(stage, family, len(b[0]))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:30:56.702Z Script completed Wall time 0.0 seconds Output: {} ## Step 21 · agent · 2026-08-31T18:30:58.538Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:01.408Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -l /app/solution && /opt/task-venv/bin/python -u /app/solution/explore_stream.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:31:12.428Z Script running with cell ID 11 Wall time 11.0 seconds Output: ## Step 22 · agent · 2026-08-31T18:31:14.244Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:31:15.012Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:31:15.220Z Script completed Wall time 0.2 seconds Output: total 4 -rw-r--r-- 1 agent agent 3116 Aug 31 18:30 explore_stream.py train positive candidate listens 19959853 train likes 472889 train dislikes 54901 train unlikes 77990 train undislikes 5792 val positive candidate listens 650295 val likes 18307 val dislikes 1532 val unlikes 2062 val undislikes 145 ## Step 23 · agent · 2026-08-31T18:31:26.259Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:31.973Z { "code": "let{output,...rest}=await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\nfor st in ['train','val']:\\n z=np.load('/app/solution/'+st+'_candidate_listens.npz')\\n print(st,'sizesMB',[(k,z[k].nbytes/1e6) for k in z.files])\\n for k in ['ui','ts']:\\n a=z[k]; print(k,a[:20],a[-20:], 'nondecreasing',np.mean(a[1:]>=a[:-1]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});text(rest);text(output);\n" } ### Tool output · 2026-08-31T18:31:33.623Z Script completed Wall time 1.6 seconds Output: {"chunk_id":"b3a85e","wall_time_seconds":1.464364244,"exit_code":0,"original_token_count":327} train sizesMB [('ui', 39.919706), ('ii', 39.919706), ('ts', 79.839412), ('organic', 19.959853), ('ratio', 19.959853)] ui [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455] nondecreasing 1.0 ts [39420 39625 40110 40380 40640 40785 41130 42115 42115 42210 42380 42520 43425 43425 43425 43720 43720 44335 44585 44700] [22775555 22775955 22776185 22776355 24595985 24596305 24596645 24596950 24596950 24597095 24597240 24597440 24597565 24597900 24598100 24598295 24598470 24598845 24599500 24599500] nondecreasing 0.9995803576098661 val sizesMB [('ui', 1.30059), ('ii', 1.30059), ('ts', 2.60118), ('organic', 0.650295), ('ratio', 0.650295)] ui [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] [8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455 8455] nondecreasing 1.0 ts [24943605 25247030 25247195 25247565 25249915 25250130 25250440 25251045 25251045 25251045 25251495 25251665 25251785 25252145 25252350 25252490 25252650 25252780 25252950 25253200] [25263025 25263190 25263395 25263610 25263855 25264090 25264390 25264555 25264760 25264920 25265185 25265470 25265705 25265890 25266070 25266340 25266530 25266700 25276965 25277150] nondecreasing 0.9894155566559125 ## Step 24 · agent · 2026-08-31T18:31:42.173Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:32:34.633Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+\\\"\\\"\\\"Generate history/popularity baselines used to initialize model development.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+\\n+\\n+def boundaries(ui, n_users):\\n+ return np.searchsorted(ui, np.arange(n_users + 1), side=\\\"left\\\")\\n+\\n+\\n+def main():\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--variant\\\", type=int, default=0)\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], default=\\\"validation\\\")\\n+ ap.add_argument(\\\"--output\\\", required=True)\\n+ args = ap.parse_args()\\n+ stage_short = \\\"train\\\" if args.stage == \\\"validation\\\" else \\\"final\\\"\\n+ target = pd.read_parquet(D / \\\"target_users.parquet\\\")\\n+ catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+ uids = target.uid.to_numpy(np.int64)\\n+ item_ids = catalog.item_id.to_numpy(np.int64)\\n+ n, m = len(uids), len(item_ids)\\n+ z = np.load(O / (\\\"train_candidate_listens.npz\\\" if args.stage == \\\"validation\\\" else \\\"final_candidate_listens.npz\\\"))\\n+ ui, ii, ts, org = z[\\\"ui\\\"], z[\\\"ii\\\"], z[\\\"ts\\\"], z[\\\"organic\\\"]\\n+ b = boundaries(ui, n)\\n+ eligible = np.load(D / f\\\"{args.stage}_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n+ pref = pd.read_parquet(D / f\\\"{args.stage}_preference_state.parquet\\\")\\n+ uid_map = {int(v): i for i, v in enumerate(uids)}\\n+ item_map = {int(v): i for i, v in enumerate(item_ids)}\\n+ p_ui = pref.uid.map(uid_map).fillna(-1).to_numpy(np.int32)\\n+ p_ii = pref.item_id.map(item_map).fillna(-1).to_numpy(np.int32)\\n+ p_like = (pref.state.to_numpy() == \\\"liked\\\")\\n+ keep = (p_ui >= 0) & (p_ii >= 0)\\n+ p_ui, p_ii, p_like = p_ui[keep], p_ii[keep], p_like[keep]\\n+ po = np.argsort(p_ui, kind=\\\"stable\\\")\\n+ p_ui, p_ii, p_like = p_ui[po], p_ii[po], p_like[po]\\n+ pb = boundaries(p_ui, n)\\n+ cutoff = 24_786_800 if args.stage == \\\"validation\\\" else int(ts.max())\\n+\\n+ # Six lightweight mixtures allow fast selection without retraining.\\n+ params = [\\n+ # popularity, count, last-event, recent-count, organic-count, like\\n+ (1.00, 0.00, 0.00, 0.00, 0.00, 0.00),\\n+ (0.20, 0.55, 3.00, 0.20, 0.20, 3.00),\\n+ (0.10, 0.35, 4.00, 0.30, 0.35, 4.00),\\n+ (0.30, 0.65, 2.00, 0.15, 0.15, 2.50),\\n+ (0.08, 0.20, 5.00, 0.35, 0.50, 5.00),\\n+ (0.45, 0.40, 1.25, 0.10, 0.10, 2.00),\\n+ ]\\n+ pop_w, count_w, rec_w, recent_w, org_w, like_w = params[args.variant]\\n+ base = -pop_w * np.log1p(catalog.popularity_rank.to_numpy(np.float32))\\n+ rows = []\\n+ all_idx = np.arange(m)\\n+ for u in range(n):\\n+ score = base.copy()\\n+ a, c = b[u], b[u + 1]\\n+ xi, xt, xo = ii[a:c].astype(np.int32), ts[a:c], org[a:c]\\n+ if len(xi):\\n+ cnt = np.bincount(xi, minlength=m)\\n+ ocnt = np.bincount(xi, weights=xo, minlength=m)\\n+ last = np.full(m, -1, np.int64)\\n+ np.maximum.at(last, xi, xt)\\n+ recent = np.bincount(xi[xt >= cutoff - 90 * 86400], minlength=m)\\n+ age_days = np.maximum(0, cutoff - last) / 86400.0\\n+ score += count_w * np.log1p(cnt)\\n+ score += org_w * np.log1p(ocnt)\\n+ score += recent_w * np.log1p(recent)\\n+ score += rec_w * np.exp(-age_days / 30.0) * (last >= 0)\\n+ pa, pc = pb[u], pb[u + 1]\\n+ if pc > pa:\\n+ score[p_ii[pa:pc]] += np.where(p_like[pa:pc], like_w, -20.0)\\n+ mask = np.unpackbits(eligible[u], count=m, bitorder=\\\"little\\\").astype(bool)\\n+ score[~mask] = -np.inf\\n+ top = np.argpartition(score, -100)[-100:]\\n+ top = top[np.argsort(score[top])[::-1]]\\n+ rows.append(pd.DataFrame({\\\"uid\\\": uids[u], \\\"item_id\\\": item_ids[top], \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(rows, ignore_index=True).to_parquet(args.output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:32:34.670Z Script completed Wall time 0.0 seconds Output: {} ## Step 25 · agent · 2026-08-31T18:32:40.194Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:32:44.280Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/baseline.py --variant 0 --stage validation --output /app/solution/validation_pop.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:32:55.299Z Script running with cell ID 14 Wall time 11.0 seconds Output: ## Step 26 · agent · 2026-08-31T18:32:56.790Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:32:57.579Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:33:01.060Z Script completed Wall time 3.5 seconds Output: { "model": "/app/solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 27 · agent · 2026-08-31T18:33:04.159Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:08.840Z { "code": "const r = await tools.exec_command({\"cmd\":\"for v in 1 2 3 4 5; do /opt/task-venv/bin/python /app/solution/baseline.py --variant $v --stage validation --output /app/solution/validation_b$v.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_b$v.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:33:19.866Z Script running with cell ID 15 Wall time 11.0 seconds Output: ## Step 28 · agent · 2026-08-31T18:33:21.310Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:33:22.108Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:33:39.036Z Script completed Wall time 16.9 seconds Output: { "model": "/app/solution/validation_b1.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } SESSION_ID=46831 ## Step 29 · agent · 2026-08-31T18:33:42.049Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:43.421Z { "code": "const r = await tools.write_stdin({\"session_id\":46831,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:33:54.441Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 30 · agent · 2026-08-31T18:33:56.000Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:33:56.802Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:34:13.445Z Script completed Wall time 16.6 seconds Output: { "model": "/app/solution/validation_b2.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } { "model": "/app/solution/validation_b3.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 31 · agent · 2026-08-31T18:34:15.532Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:34:16.878Z { "code": "const r = await tools.write_stdin({\"session_id\":46831,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:34:27.905Z Script running with cell ID 17 Wall time 11.0 seconds Output: ## Step 32 · agent · 2026-08-31T18:34:30.314Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:34:31.516Z { "cell_id": "17", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:34:33.276Z Script completed Wall time 1.8 seconds Output: { "model": "/app/solution/validation_b4.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } { "model": "/app/solution/validation_b5.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 33 · agent · 2026-08-31T18:34:52.928Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-08-31T18:35:11.557Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-08-31T18:35:30.184Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-08-31T18:35:48.857Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-08-31T18:36:07.474Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-08-31T18:36:21.609Z ### Thinking unavailable — no text recorded Eligibility appears to exclude the tracks already consumed: every direct-history mixture produced the same top-10 score as popularity alone (0.010710). That makes this a genuine discovery problem. I’m now training the required neural towers on rolling seven-day next-item tasks: the user tower pools recent and long-term history, while the independent item tower learns catalog embeddings and popularity features through sampled pairwise ranking loss. ### exec · 2026-08-31T18:38:51.823Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/two_tower.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-based PyTorch two-tower ranker.\\n+\\n+The training set is a stream of (past history, future positive item) examples;\\n+no user-ID parameters or global user-item matrix are used. Candidate vectors\\n+are produced once by ItemTower. Each request produces one UserTower vector and\\n+all relevance scores are exactly user_vector @ item_vector.T.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import random\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+WINDOW = 7 * 86_400\\n+GAP = 1_800\\n+FINAL_CUTOFF = TRAIN_CUTOFF + GAP + WINDOW\\n+MAX_HISTORY = 256\\n+\\n+\\n+def bounds(ui: np.ndarray, n: int) -> np.ndarray:\\n+ return np.searchsorted(ui, np.arange(n + 1), side=\\\"left\\\")\\n+\\n+\\n+def merge_user_sorted(parts: list[dict[str, np.ndarray]], n_users: int, keys: list[str]):\\n+ \\\"\\\"\\\"Concatenate per-user, preserving timestamp order and uid grouping.\\\"\\\"\\\"\\n+ all_out = {k: [] for k in keys}\\n+ all_ui = []\\n+ part_bounds = [bounds(p[\\\"ui\\\"], n_users) for p in parts]\\n+ for u in range(n_users):\\n+ chunks = []\\n+ for p, b in zip(parts, part_bounds):\\n+ a, c = b[u], b[u + 1]\\n+ if c > a:\\n+ chunks.append({k: p[k][a:c] for k in keys})\\n+ if not chunks:\\n+ continue\\n+ vals = {k: np.concatenate([x[k] for x in chunks]) for k in keys}\\n+ order = np.argsort(vals[\\\"ts\\\"], kind=\\\"stable\\\")\\n+ all_ui.append(np.full(len(order), u, np.uint16))\\n+ for k in keys:\\n+ all_out[k].append(vals[k][order])\\n+ result = {\\\"ui\\\": np.concatenate(all_ui) if all_ui else np.empty(0, np.uint16)}\\n+ for k in keys:\\n+ result[k] = np.concatenate(all_out[k]) if all_out[k] else np.empty(0)\\n+ return result\\n+\\n+\\n+def load_npz(name: str) -> dict[str, np.ndarray]:\\n+ with np.load(O / name) as z:\\n+ return {k: z[k] for k in z.files}\\n+\\n+\\n+def load_events(stage: str, n_users: int):\\n+ listen_parts = [load_npz(\\\"train_candidate_listens.npz\\\")]\\n+ like_parts = [load_npz(\\\"train_candidate_likes.npz\\\")]\\n+ if stage == \\\"final\\\":\\n+ listen_parts.append(load_npz(\\\"val_candidate_listens.npz\\\"))\\n+ like_parts.append(load_npz(\\\"val_candidate_likes.npz\\\"))\\n+ listens = merge_user_sorted(listen_parts, n_users, [\\\"ii\\\", \\\"ts\\\", \\\"organic\\\", \\\"ratio\\\"])\\n+ likes = merge_user_sorted(like_parts, n_users, [\\\"ii\\\", \\\"ts\\\", \\\"organic\\\"])\\n+ return listens, likes\\n+\\n+\\n+def recent_unique(x: np.ndarray, limit: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Last occurrence of up to limit distinct tracks, in time order.\\\"\\\"\\\"\\n+ if len(x) == 0:\\n+ return x\\n+ rev = x[::-1]\\n+ first = np.unique(rev, return_index=True)[1]\\n+ first = np.sort(first)[:limit]\\n+ return rev[first][::-1]\\n+\\n+\\n+def make_examples(listens, likes, n_users: int, inference_cutoff: int, weeks: int):\\n+ \\\"\\\"\\\"Build rolling-cutoff examples from compact event streams.\\\"\\\"\\\"\\n+ lb, kb = bounds(listens[\\\"ui\\\"], n_users), bounds(likes[\\\"ui\\\"], n_users)\\n+ latest_training_context = inference_cutoff - GAP - WINDOW\\n+ cutoffs = latest_training_context - np.arange(weeks - 1, -1, -1) * WINDOW\\n+ contexts, pair_ctx, pair_item, pair_weight = [], [], [], []\\n+ snapshot_meta = []\\n+ for u in range(n_users):\\n+ la, lc = lb[u], lb[u + 1]\\n+ ka, kc = kb[u], kb[u + 1]\\n+ li, lt = listens[\\\"ii\\\"][la:lc], listens[\\\"ts\\\"][la:lc]\\n+ lo, lr = listens[\\\"organic\\\"][la:lc], listens[\\\"ratio\\\"][la:lc]\\n+ ki, kt, ko = likes[\\\"ii\\\"][ka:kc], likes[\\\"ts\\\"][ka:kc], likes[\\\"organic\\\"][ka:kc]\\n+ for cutoff in cutoffs:\\n+ hp = np.searchsorted(lt, cutoff, side=\\\"right\\\")\\n+ hist = recent_unique(li[:hp], MAX_HISTORY)\\n+ if len(hist) < 5:\\n+ continue\\n+ start, end = cutoff + GAP, cutoff + GAP + WINDOW\\n+ p0, p1 = np.searchsorted(lt, [start, end], side=\\\"left\\\")\\n+ good = (lo[p0:p1] == 1) & (lr[p0:p1] >= 80)\\n+ listen_pos = np.unique(li[p0:p1][good])\\n+ q0, q1 = np.searchsorted(kt, [start, end], side=\\\"left\\\")\\n+ like_pos = np.unique(ki[q0:q1][ko[q0:q1] == 1])\\n+ if not len(listen_pos) and not len(like_pos):\\n+ continue\\n+ row = np.full(MAX_HISTORY, 30_000, np.uint16)\\n+ row[-len(hist):] = hist\\n+ cid = len(contexts)\\n+ contexts.append(row)\\n+ snapshot_meta.append((u, int(cutoff)))\\n+ # A like supersedes a listen; its larger weight tracks graded gain.\\n+ liked = set(map(int, like_pos))\\n+ for item in listen_pos:\\n+ if int(item) not in liked:\\n+ pair_ctx.append(cid); pair_item.append(item); pair_weight.append(1.0)\\n+ for item in like_pos:\\n+ pair_ctx.append(cid); pair_item.append(item); pair_weight.append(3.0)\\n+ return (\\n+ np.asarray(contexts, dtype=np.uint16),\\n+ np.asarray(pair_ctx, dtype=np.int32),\\n+ np.asarray(pair_item, dtype=np.uint16),\\n+ np.asarray(pair_weight, dtype=np.float32),\\n+ np.asarray(snapshot_meta, dtype=np.int32),\\n+ )\\n+\\n+\\n+def inference_contexts(listens, n_users: int, cutoff: int) -> np.ndarray:\\n+ b = bounds(listens[\\\"ui\\\"], n_users)\\n+ result = np.full((n_users, MAX_HISTORY), 30_000, np.uint16)\\n+ for u in range(n_users):\\n+ a, c = b[u], b[u + 1]\\n+ ts, ii = listens[\\\"ts\\\"][a:c], listens[\\\"ii\\\"][a:c]\\n+ p = np.searchsorted(ts, cutoff, side=\\\"right\\\")\\n+ hist = recent_unique(ii[:p], MAX_HISTORY)\\n+ if len(hist):\\n+ result[u, -len(hist):] = hist\\n+ return result\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user network: no user-ID lookup exists.\\\"\\\"\\\"\\n+ def __init__(self, n_items: int = 30_000, width: int = 64, out_dim: int = 48):\\n+ super().__init__()\\n+ self.n_items = n_items\\n+ self.history_embedding = nn.Embedding(n_items + 1, width, padding_idx=n_items)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(2 * width, 96), nn.ReLU(), nn.Linear(96, out_dim)\\n+ )\\n+\\n+ def forward(self, history: torch.Tensor) -> torch.Tensor:\\n+ x = self.history_embedding(history)\\n+ mask = (history != self.n_items).unsqueeze(-1)\\n+ long = (x * mask).sum(1) / mask.sum(1).clamp_min(1)\\n+ rx, rm = x[:, -32:], mask[:, -32:]\\n+ recent = (rx * rm).sum(1) / rm.sum(1).clamp_min(1)\\n+ return self.network(torch.cat([long, recent], dim=1))\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently callable item network for offline precomputation.\\\"\\\"\\\"\\n+ def __init__(self, log_popularity: torch.Tensor, n_items: int = 30_000, width: int = 64, out_dim: int = 48):\\n+ super().__init__()\\n+ self.item_embedding = nn.Embedding(n_items, width)\\n+ self.register_buffer(\\\"log_popularity\\\", log_popularity.float())\\n+ self.network = nn.Sequential(\\n+ nn.Linear(width + 1, 96), nn.ReLU(), nn.Linear(96, out_dim)\\n+ )\\n+\\n+ def forward(self, item: torch.Tensor) -> torch.Tensor:\\n+ raw = self.item_embedding(item)\\n+ pop = self.log_popularity[item].unsqueeze(-1)\\n+ return self.network(torch.cat([raw, pop], dim=-1))\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, log_popularity: torch.Tensor):\\n+ super().__init__()\\n+ self.user_tower = UserTower()\\n+ self.item_tower = ItemTower(log_popularity)\\n+\\n+\\n+def train_model(model, contexts, pc, pi, pw, epochs: int, seed: int):\\n+ device = torch.device(\\\"cpu\\\")\\n+ model.to(device)\\n+ torch.set_num_threads(max(1, min(8, torch.get_num_threads())))\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ rng = np.random.default_rng(seed)\\n+ # A smooth popularity distribution gives useful hard negatives without\\n+ # reducing training to a popularity classifier.\\n+ neg_prob = torch.from_numpy((1.0 / np.sqrt(np.arange(1, 30_001))).astype(np.float32))\\n+ neg_prob /= neg_prob.sum()\\n+ batch_size, n_neg = 512, 16\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(len(pc))\\n+ total = 0.0\\n+ model.train()\\n+ for start in range(0, len(order), batch_size):\\n+ ix = order[start:start + batch_size]\\n+ h = torch.from_numpy(contexts[pc[ix]].astype(np.int64))\\n+ pos = torch.from_numpy(pi[ix].astype(np.int64))\\n+ weight = torch.from_numpy(pw[ix])\\n+ neg = torch.multinomial(neg_prob, len(ix) * n_neg, replacement=True).reshape(len(ix), n_neg)\\n+ uv = model.user_tower(h)\\n+ pv = model.item_tower(pos)\\n+ nv = model.item_tower(neg)\\n+ ps = (uv * pv).sum(-1)\\n+ ns = torch.einsum(\\\"bd,bkd->bk\\\", uv, nv)\\n+ # Pairwise sampled ranking loss; each score remains a pure dot product.\\n+ loss = (F.softplus(ns - ps[:, None]).mean(1) * weight).mean()\\n+ opt.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ opt.step()\\n+ total += float(loss) * len(ix)\\n+ print(f\\\"epoch {epoch + 1}/{epochs} loss={total / len(pc):.5f}\\\", flush=True)\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(model, contexts, stage: str, uids, item_ids, output: Path):\\n+ model.eval()\\n+ all_items = torch.arange(30_000)\\n+ item_vectors = model.item_tower(all_items).cpu()\\n+ packed = np.load(D / f\\\"{stage}_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n+ frames = []\\n+ for start in range(0, len(uids), 128):\\n+ end = min(start + 128, len(uids))\\n+ h = torch.from_numpy(contexts[start:end].astype(np.int64))\\n+ user_vectors = model.user_tower(h).cpu()\\n+ # This is the exact production scoring operation: MIPS dot product.\\n+ scores = (user_vectors @ item_vectors.T).numpy()\\n+ mask = np.unpackbits(packed[start:end], axis=1, count=30_000, bitorder=\\\"little\\\").astype(bool)\\n+ scores[~mask] = -np.inf\\n+ top = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ top_scores = np.take_along_axis(scores, top, axis=1)\\n+ top = np.take_along_axis(top, np.argsort(top_scores, axis=1)[:, ::-1], axis=1)\\n+ for j in range(end - start):\\n+ frames.append(pd.DataFrame({\\n+ \\\"uid\\\": uids[start + j], \\\"item_id\\\": item_ids[top[j]], \\\"rank\\\": np.arange(1, 101, dtype=np.int64)\\n+ }))\\n+ pd.concat(frames, ignore_index=True).to_parquet(output, index=False)\\n+\\n+\\n+def main():\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ ap.add_argument(\\\"--epochs\\\", type=int, default=4)\\n+ ap.add_argument(\\\"--weeks\\\", type=int, default=24)\\n+ ap.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ ap.add_argument(\\\"--output\\\", type=Path)\\n+ ap.add_argument(\\\"--checkpoint\\\", type=Path)\\n+ args = ap.parse_args()\\n+ random.seed(args.seed); np.random.seed(args.seed); torch.manual_seed(args.seed)\\n+ targets = pd.read_parquet(D / \\\"target_users.parquet\\\")\\n+ catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+ uids = targets.uid.to_numpy(np.int64); item_ids = catalog.item_id.to_numpy(np.int64)\\n+ internal_stage = \\\"validation\\\" if args.stage == \\\"validation\\\" else \\\"final\\\"\\n+ cutoff = TRAIN_CUTOFF if args.stage == \\\"validation\\\" else FINAL_CUTOFF\\n+ listens, likes = load_events(\\\"final\\\" if args.stage == \\\"final\\\" else \\\"validation\\\", len(uids))\\n+ cache = O / f\\\"examples_{args.stage}_{args.weeks}w.npz\\\"\\n+ if cache.exists():\\n+ with np.load(cache) as z:\\n+ contexts, pc, pi, pw = z[\\\"contexts\\\"], z[\\\"pc\\\"], z[\\\"pi\\\"], z[\\\"pw\\\"]\\n+ else:\\n+ contexts, pc, pi, pw, meta = make_examples(listens, likes, len(uids), cutoff, args.weeks)\\n+ np.savez_compressed(cache, contexts=contexts, pc=pc, pi=pi, pw=pw, meta=meta)\\n+ print(f\\\"training contexts={len(contexts):,} positive_pairs={len(pc):,} likes_weighted={(pw > 1).sum():,}\\\", flush=True)\\n+ logpop = -torch.log1p(torch.from_numpy(catalog.popularity_rank.to_numpy(np.float32)))\\n+ logpop = (logpop - logpop.mean()) / logpop.std()\\n+ model = TwoTower(logpop)\\n+ model = train_model(model, contexts, pc, pi, pw, args.epochs, args.seed)\\n+ checkpoint = args.checkpoint or O / f\\\"two_tower_{args.stage}.pt\\\"\\n+ torch.save({\\\"model\\\": model.state_dict(), \\\"stage\\\": args.stage, \\\"seed\\\": args.seed}, checkpoint)\\n+ request_history = inference_contexts(listens, len(uids), cutoff)\\n+ output = args.output or O / (\\\"validation.parquet\\\" if args.stage == \\\"validation\\\" else \\\"submission.parquet\\\")\\n+ rank(model, request_history, internal_stage, uids, item_ids, output)\\n+ print(json.dumps({\\\"output\\\": str(output), \\\"checkpoint\\\": str(checkpoint)}), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:38:51.870Z Script completed Wall time 0.0 seconds Output: {} ## Step 39 · agent · 2026-08-31T18:38:53.721Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:57.095Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/two_tower.py --stage validation --epochs 3 --weeks 24 --output /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:39:08.120Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T18:39:10.531Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:11.040Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:39:27.286Z Script completed Wall time 16.2 seconds Output: training contexts=112,946 positive_pairs=3,438,643 likes_weighted=131,437 /app/solution/two_tower.py:219: 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) * len(ix) SESSION_ID=23109 ## Step 41 · agent · 2026-08-31T18:39:29.244Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:30.537Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:39:41.567Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 42 · agent · 2026-08-31T18:39:44.007Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:45.036Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:40:00.575Z Script completed Wall time 15.5 seconds Output: ## Step 43 · agent · 2026-08-31T18:40:04.130Z ### Thinking unavailable — no text recorded The rolling sampler produced 112,946 history snapshots and 3.44M positive pairs across 24 weeks—enough to learn collaborative similarity without materializing a user-item matrix. Training is underway on CPU; after the first validation score I’ll use the remaining time only on changes supported by that score. ### exec · 2026-08-31T18:40:08.186Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:19.221Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T18:40:21.563Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:22.345Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:40:38.226Z Script completed Wall time 15.9 seconds Output: ## Step 45 · agent · 2026-08-31T18:40:40.051Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:41.398Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:40:52.435Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T18:40:55.191Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:55.966Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:41:11.446Z Script completed Wall time 15.5 seconds Output: ## Step 47 · agent · 2026-08-31T18:41:15.458Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:16.802Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:27.837Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 48 · agent · 2026-08-31T18:41:30.262Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:31.056Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:41:46.846Z Script completed Wall time 15.8 seconds Output: ## Step 49 · agent · 2026-08-31T18:41:48.845Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:50.154Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:01.186Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 50 · agent · 2026-08-31T18:42:03.464Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:04.148Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:42:20.191Z Script completed Wall time 16.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:42:21.613Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:22.936Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:33.972Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 52 · agent · 2026-08-31T18:42:35.492Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:36.291Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:42:52.978Z Script completed Wall time 16.7 seconds Output: ## Step 53 · agent · 2026-08-31T18:42:55.339Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:57.616Z { "code": "const r = await tools.exec_command({\"cmd\":\"ps -eo pid,etime,pcpu,pmem,rss,args | grep '[t]wo_tower.py'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:42:57.770Z Script completed Wall time 0.1 seconds Output: /bin/sh: 1: ps: not found ## Step 54 · agent · 2026-08-31T18:42:59.537Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:00.874Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:43:11.909Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 55 · agent · 2026-08-31T18:43:13.484Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:14.258Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:43:30.919Z Script completed Wall time 16.7 seconds Output: ## Step 56 · agent · 2026-08-31T18:43:32.669Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:34.028Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:43:45.059Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 57 · agent · 2026-08-31T18:43:46.774Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:47.582Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:44:04.068Z Script completed Wall time 16.5 seconds Output: ## Step 58 · agent · 2026-08-31T18:44:08.064Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:09.595Z { "code": "const r = await tools.write_stdin({\"session_id\":23109,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:44:10.235Z Script completed Wall time 0.6 seconds Output: Traceback (most recent call last): File "/app/solution/two_tower.py", line 286, in main() File "/app/solution/two_tower.py", line 276, in main model = train_model(model, contexts, pc, pi, pw, args.epochs, args.seed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/two_tower.py", line 216, in train_model loss.backward() File "/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py", line 647, in backward torch.autograd.backward( File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py", line 354, in backward _engine_run_backward( File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ## Step 59 · agent · 2026-08-31T18:44:25.307Z ### Thinking unavailable — no text recorded The first implementation exposed a CPU bottleneck: it recomputed the same user vector once for every positive in a weekly window. I stopped that run and am deduplicating training by history snapshot—sampling positives per snapshot each epoch—so the objective is unchanged in form but user-tower work drops by roughly 30×. ### exec · 2026-08-31T18:44:47.163Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/two_tower.py\\n@@\\n def train_model(model, contexts, pc, pi, pw, epochs: int, seed: int):\\n@@\\n- for epoch in range(epochs):\\n- order = rng.permutation(len(pc))\\n+ # Pair rows are contiguous by context. Sampling two outcomes per context per\\n+ # epoch avoids recomputing an identical history vector dozens of times.\\n+ starts = np.searchsorted(pc, np.arange(len(contexts)), side=\\\"left\\\")\\n+ ends = np.searchsorted(pc, np.arange(len(contexts)), side=\\\"right\\\")\\n+ context_ids = np.arange(len(contexts), dtype=np.int32)\\n+ for epoch in range(epochs):\\n+ sampled = []\\n+ for _ in range(2):\\n+ sampled.append(starts + (rng.random(len(contexts)) * (ends - starts)).astype(np.int32))\\n+ chosen = np.concatenate(sampled)\\n+ chosen_context = np.tile(context_ids, 2)\\n+ order = rng.permutation(len(chosen))\\n total = 0.0\\n model.train()\\n for start in range(0, len(order), batch_size):\\n- ix = order[start:start + batch_size]\\n- h = torch.from_numpy(contexts[pc[ix]].astype(np.int64))\\n- pos = torch.from_numpy(pi[ix].astype(np.int64))\\n- weight = torch.from_numpy(pw[ix])\\n+ ox = order[start:start + batch_size]\\n+ ix = chosen[ox]\\n+ h = torch.from_numpy(contexts[chosen_context[ox]].astype(np.int64))\\n+ pos = torch.from_numpy(pi[ix].astype(np.int64))\\n+ weight = torch.from_numpy(pw[ix])\\n@@\\n- total += float(loss) * len(ix)\\n- print(f\\\"epoch {epoch + 1}/{epochs} loss={total / len(pc):.5f}\\\", flush=True)\\n+ total += float(loss.detach()) * len(ix)\\n+ print(f\\\"epoch {epoch + 1}/{epochs} loss={total / len(chosen):.5f}\\\", flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:44:47.215Z Script completed Wall time 0.0 seconds Output: {} ## Step 60 · agent · 2026-08-31T18:44:48.968Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:52.296Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/two_tower.py --stage validation --epochs 6 --weeks 24 --output /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:45:03.317Z Script running with cell ID 31 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:45:04.848Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:05.668Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:45:22.481Z Script completed Wall time 16.8 seconds Output: training contexts=112,946 positive_pairs=3,438,643 likes_weighted=131,437 SESSION_ID=56108 ## Step 62 · agent · 2026-08-31T18:45:25.173Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:26.601Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:45:37.633Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 63 · agent · 2026-08-31T18:45:40.095Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:40.869Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:45:56.639Z Script completed Wall time 15.8 seconds Output: epoch 1/6 loss=0.72343 ## Step 64 · agent · 2026-08-31T18:45:58.686Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:59.980Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:46:11.013Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T18:46:12.816Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:13.586Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:46:30.018Z Script completed Wall time 16.4 seconds Output: epoch 2/6 loss=0.64798 ## Step 66 · agent · 2026-08-31T18:46:31.561Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:32.903Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:46:43.934Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:46:45.543Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:46.332Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:47:02.943Z Script completed Wall time 16.6 seconds Output: epoch 3/6 loss=0.58702 ## Step 68 · agent · 2026-08-31T18:47:04.761Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:06.099Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:47:17.129Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 69 · agent · 2026-08-31T18:47:18.746Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:19.557Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:47:36.138Z Script completed Wall time 16.6 seconds Output: epoch 4/6 loss=0.54309 ## Step 70 · agent · 2026-08-31T18:47:37.553Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:38.902Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:47:49.933Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 71 · agent · 2026-08-31T18:47:51.466Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:52.247Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:48:08.936Z Script completed Wall time 16.7 seconds Output: epoch 5/6 loss=0.50948 ## Step 72 · agent · 2026-08-31T18:48:10.484Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:11.832Z { "code": "const r = await tools.write_stdin({\"session_id\":56108,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:48:22.862Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 73 · agent · 2026-08-31T18:48:25.126Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:25.904Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:48:27.724Z Script completed Wall time 1.8 seconds Output: epoch 6/6 loss=0.47792 {"output": "/app/solution/validation.parquet", "checkpoint": "/app/solution/two_tower_validation.pt"} ## Step 74 · agent · 2026-08-31T18:48:29.349Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:32.017Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution | sort -k5h\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:48:35.734Z Script completed Wall time 3.7 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.004033, "quality_users": 4263 } } total 138M -rw-r--r-- 1 agent agent 1.8K Aug 31 18:31 val_candidate_undislikes.npz -rw-r--r-- 1 agent agent 3.1K Aug 31 18:30 explore_stream.py -rw-r--r-- 1 agent agent 3.7K Aug 31 18:32 baseline.py -rw-r--r-- 1 agent agent 8.7K Aug 31 18:31 val_candidate_dislikes.npz -rw-r--r-- 1 agent agent 11K Aug 31 18:31 val_candidate_unlikes.npz -rw-r--r-- 1 agent agent 13K Aug 31 18:44 two_tower.py -rw-r--r-- 1 agent agent 31K Aug 31 18:31 train_candidate_undislikes.npz -rw-r--r-- 1 agent agent 78K Aug 31 18:31 val_candidate_likes.npz -rw-r--r-- 1 agent agent 245K Aug 31 18:31 train_candidate_dislikes.npz -rw-r--r-- 1 agent agent 330K Aug 31 18:31 train_candidate_unlikes.npz -rw-r--r-- 1 agent agent 712K Aug 31 18:32 validation_pop.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:33 validation_b1.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:33 validation_b2.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:33 validation_b3.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:34 validation_b4.parquet -rw-r--r-- 1 agent agent 712K Aug 31 18:34 validation_b5.parquet -rw-r--r-- 1 agent agent 1.6M Aug 31 18:48 validation.parquet -rw-r--r-- 1 agent agent 2.1M Aug 31 18:31 train_candidate_likes.npz -rw-r--r-- 1 agent agent 2.8M Aug 31 18:31 val_candidate_listens.npz -rw-r--r-- 1 agent agent 15M Aug 31 18:48 two_tower_validation.pt -rw-r--r-- 1 agent agent 24M Aug 31 18:39 examples_validation_24w.npz -rw-r--r-- 1 agent agent 89M Aug 31 18:31 train_candidate_listens.npz ## Step 75 · agent · 2026-08-31T18:49:10.672Z ### Thinking unavailable — no text recorded ## Step 76 · agent · 2026-08-31T18:49:16.867Z ### Thinking unavailable — no text recorded The uncalibrated collaborative model scores 0.004033, below the 0.010710 popularity anchor. I’m preserving the valid anchor and testing a dot-product-compatible calibration: append a constant coordinate to the user vector and a popularity coordinate to the item vector. This retains exact two-tower/MIPS serving while letting validation determine how much collaborative signal is useful. ### exec · 2026-08-31T18:49:45.736Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/calibrate.py\\n+\\\"\\\"\\\"Validation-only sweep of a dot-product-compatible popularity coordinate.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import sys\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+sys.path.insert(0, \\\"/app/solution\\\")\\n+from two_tower import TwoTower, load_events, inference_contexts, TRAIN_CUTOFF\\n+\\n+D = Path(\\\"/task/data\\\"); O = Path(\\\"/app/solution\\\")\\n+target = pd.read_parquet(D / \\\"target_users.parquet\\\")\\n+catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+uids = target.uid.to_numpy(np.int64); items = catalog.item_id.to_numpy(np.int64)\\n+logpop = -torch.log1p(torch.from_numpy(catalog.popularity_rank.to_numpy(np.float32)))\\n+logpop = (logpop - logpop.mean()) / logpop.std()\\n+model = TwoTower(logpop)\\n+model.load_state_dict(torch.load(O / \\\"two_tower_validation.pt\\\", map_location=\\\"cpu\\\")[\\\"model\\\"])\\n+model.eval()\\n+listens, _ = load_events(\\\"validation\\\", len(uids))\\n+ctx = inference_contexts(listens, len(uids), TRAIN_CUTOFF)\\n+with torch.inference_mode():\\n+ iv = model.item_tower(torch.arange(30_000))\\n+ uv = torch.cat([model.user_tower(torch.from_numpy(ctx[s:s+128].astype(np.int64))) for s in range(0, len(ctx), 128)])\\n+raw = (uv @ iv.T).numpy()\\n+print(\\\"dot quantiles\\\", np.quantile(raw, [0,.01,.5,.99,1]), \\\"pop\\\", np.quantile(logpop.numpy(),[0,.01,.5,.99,1]))\\n+packed = np.load(D / \\\"validation_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n+mask = np.unpackbits(packed, axis=1, count=30_000, bitorder=\\\"little\\\").astype(bool)\\n+for alpha in [0.1,0.3,0.7,1.5,3.0,7.0,15.0,30.0,100.0]:\\n+ scores = raw + alpha * logpop.numpy()[None]\\n+ scores[~mask] = -np.inf\\n+ top=np.argpartition(scores,-100,axis=1)[:,-100:]\\n+ ss=np.take_along_axis(scores,top,axis=1)\\n+ top=np.take_along_axis(top,np.argsort(ss,axis=1)[:,::-1],axis=1)\\n+ frames=[]\\n+ for u in range(len(uids)):\\n+ frames.append(pd.DataFrame({'uid':uids[u],'item_id':items[top[u]],'rank':np.arange(1,101)}))\\n+ pd.concat(frames,ignore_index=True).to_parquet(O/f\\\"validation_alpha_{alpha:g}.parquet\\\",index=False)\\n+ print(alpha, flush=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:49:45.810Z Script completed Wall time 0.0 seconds Output: {} ## Step 77 · agent · 2026-08-31T18:49:49.631Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:10.883Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate.py\\n@@\\n with torch.inference_mode():\\n iv = model.item_tower(torch.arange(30_000))\\n uv = torch.cat([model.user_tower(torch.from_numpy(ctx[s:s+128].astype(np.int64))) for s in range(0, len(ctx), 128)])\\n-raw = (uv @ iv.T).numpy()\\n-print(\\\"dot quantiles\\\", np.quantile(raw, [0,.01,.5,.99,1]), \\\"pop\\\", np.quantile(logpop.numpy(),[0,.01,.5,.99,1]))\\n packed = np.load(D / \\\"validation_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n-mask = np.unpackbits(packed, axis=1, count=30_000, bitorder=\\\"little\\\").astype(bool)\\n for alpha in [0.1,0.3,0.7,1.5,3.0,7.0,15.0,30.0,100.0]:\\n- scores = raw + alpha * logpop.numpy()[None]\\n- scores[~mask] = -np.inf\\n- top=np.argpartition(scores,-100,axis=1)[:,-100:]\\n- ss=np.take_along_axis(scores,top,axis=1)\\n- top=np.take_along_axis(top,np.argsort(ss,axis=1)[:,::-1],axis=1)\\n frames=[]\\n- for u in range(len(uids)):\\n- frames.append(pd.DataFrame({'uid':uids[u],'item_id':items[top[u]],'rank':np.arange(1,101)}))\\n+ for s in range(0,len(uids),128):\\n+ e=min(s+128,len(uids))\\n+ scores=(uv[s:e]@iv.T).numpy()+alpha*logpop.numpy()[None]\\n+ mask=np.unpackbits(packed[s:e],axis=1,count=30_000,bitorder='little').astype(bool)\\n+ scores[~mask]=-np.inf\\n+ top=np.argpartition(scores,-100,axis=1)[:,-100:]\\n+ ss=np.take_along_axis(scores,top,axis=1)\\n+ top=np.take_along_axis(top,np.argsort(ss,axis=1)[:,::-1],axis=1)\\n+ for j in range(e-s):\\n+ frames.append(pd.DataFrame({'uid':uids[s+j],'item_id':items[top[j]],'rank':np.arange(1,101)}))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:50:10.976Z Script completed Wall time 0.0 seconds Output: {} ## Step 78 · agent · 2026-08-31T18:50:13.060Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:15.571Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/calibrate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:50:26.596Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T18:50:28.431Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:29.234Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:50:44.649Z Script completed Wall time 15.4 seconds Output: 0.1 0.3 0.7 1.5 3.0 7.0 15.0 30.0 100.0 ## Step 80 · agent · 2026-08-31T18:50:46.539Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:50.824Z { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.1 0.3 0.7 1.5 3 7 15 30 100; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_alpha_$a.parquet | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:51:01.846Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T18:51:03.938Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:04.744Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:51:21.009Z Script completed Wall time 16.3 seconds Output: { "model": "/app/solution/validation_alpha_0.1.parquet", "scores": { "quality_ndcg@10": 0.005531, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.3.parquet", "scores": { "quality_ndcg@10": 0.008872, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.7.parquet", "scores": { "quality_ndcg@10": 0.013451, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_1.5.parquet", "scores": { "quality_ndcg@10": 0.012965, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_3.parquet", "scores": { "quality_ndcg@10": 0.011949, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_7.parquet", "scores": { "quality_ndcg@10": 0.01142, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_15.parquet", "scores": { "quality_ndcg@10": 0.010981, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_30.parquet", "scores": { "quality_ndcg@10": 0.010763, "quality_users": 4263 } } SESSION_ID=71556 ## Step 82 · agent · 2026-08-31T18:51:22.721Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:24.485Z { "code": "const r = await tools.write_stdin({\"session_id\":71556,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:51:24.548Z Script completed Wall time 0.0 seconds Output: { "model": "/app/solution/validation_alpha_100.parquet", "scores": { "quality_ndcg@10": 0.010711, "quality_users": 4263 } } ## Step 83 · agent · 2026-08-31T18:51:33.447Z ### Thinking unavailable — no text recorded The calibration is useful: validation peaks at 0.013451 with a 0.7 popularity coordinate, a 25.6% lift over popularity alone while remaining one exact dot product. I’ll tighten that coefficient locally, then refit the selected configuration on train+validation and validate the final artifact’s eligibility/schema. ### exec · 2026-08-31T18:51:40.155Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate.py\\n@@\\n-for alpha in [0.1,0.3,0.7,1.5,3.0,7.0,15.0,30.0,100.0]:\\n+for alpha in [0.45,0.55,0.65,0.75,0.85,1.0]:\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:51:40.208Z Script completed Wall time 0.0 seconds Output: {} ## Step 84 · agent · 2026-08-31T18:51:42.121Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:44.595Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/calibrate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:51:55.614Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T18:51:57.636Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:58.449Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:52:06.563Z Script completed Wall time 8.1 seconds Output: 0.45 0.55 0.65 0.75 0.85 1.0 ## Step 86 · agent · 2026-08-31T18:52:08.054Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:12.276Z { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.45 0.55 0.65 0.75 0.85 1.0; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_alpha_$a.parquet | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:52:23.299Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T18:52:25.467Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:26.262Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:52:33.111Z Script completed Wall time 6.8 seconds Output: { "model": "/app/solution/validation_alpha_0.45.parquet", "scores": { "quality_ndcg@10": 0.011381, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.55.parquet", "scores": { "quality_ndcg@10": 0.012317, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.65.parquet", "scores": { "quality_ndcg@10": 0.013114, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.75.parquet", "scores": { "quality_ndcg@10": 0.01342, "quality_users": 4263 } } { "model": "/app/solution/validation_alpha_0.85.parquet", "scores": { "quality_ndcg@10": 0.013774, "quality_users": 4263 } } Traceback (most recent call last): File "/task/evaluate_public.py", line 157, in main() File "/task/evaluate_public.py", line 152, in main ranked = load_submission(path, eligibility, targets) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/task/evaluate_public.py", line 113, in load_submission frame = validate_ranking(read_ranking(path), targets) ^^^^^^^^^^^^^^^^^^ File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet return impl.read( ^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read path_or_handle, handles, filesystem = _get_path_or_handle( ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle handles = get_handle( ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle handle = open(handle, ioargs.mode) ^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_alpha_1.0.parquet' ## Step 88 · agent · 2026-08-31T18:52:46.228Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:50.358Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/two_tower.py --stage validation --epochs 6 --weeks 24 --seed 20260901 --output /app/solution/validation_seed2.parquet --checkpoint /app/solution/two_tower_validation_seed2.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:53:01.381Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T18:53:03.228Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:03.993Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:53:20.549Z Script completed Wall time 16.6 seconds Output: training contexts=112,946 positive_pairs=3,438,643 likes_weighted=131,437 SESSION_ID=74167 ## Step 90 · agent · 2026-08-31T18:53:22.025Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:23.728Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:53:34.759Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 91 · agent · 2026-08-31T18:53:36.241Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:37.045Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:53:53.763Z Script completed Wall time 16.7 seconds Output: epoch 1/6 loss=0.72450 ## Step 92 · agent · 2026-08-31T18:53:57.054Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:58.419Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:54:09.453Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 93 · agent · 2026-08-31T18:54:11.395Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:12.209Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:54:28.460Z Script completed Wall time 16.3 seconds Output: epoch 2/6 loss=0.65094 ## Step 94 · agent · 2026-08-31T18:54:29.985Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:31.337Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:54:42.367Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 95 · agent · 2026-08-31T18:54:44.172Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:44.953Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:55:01.373Z Script completed Wall time 16.4 seconds Output: epoch 3/6 loss=0.58940 ## Step 96 · agent · 2026-08-31T18:55:03.303Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:04.650Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:55:15.679Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 97 · agent · 2026-08-31T18:55:17.518Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:18.302Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:55:34.685Z Script completed Wall time 16.4 seconds Output: epoch 4/6 loss=0.54509 ## Step 98 · agent · 2026-08-31T18:55:36.232Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:37.540Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:55:48.572Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 99 · agent · 2026-08-31T18:55:50.376Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:51.107Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:56:07.580Z Script completed Wall time 16.5 seconds Output: epoch 5/6 loss=0.51074 ## Step 100 · agent · 2026-08-31T18:56:09.088Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:10.465Z { "code": "const r = await tools.write_stdin({\"session_id\":74167,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:56:21.495Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 101 · agent · 2026-08-31T18:56:23.695Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:25.432Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:56:32.946Z Script completed Wall time 7.5 seconds Output: epoch 6/6 loss=0.48615 {"output": "/app/solution/validation_seed2.parquet", "checkpoint": "/app/solution/two_tower_validation_seed2.pt"} ## Step 102 · agent · 2026-08-31T18:56:38.049Z ### Thinking unavailable — no text recorded A second independently seeded tower is now trained. I’m checking a serving-safe ensemble by concatenating the two user embeddings and the two item embeddings (scaled equally), plus the calibrated popularity coordinate. Concatenation still yields one precomputable item vector, one request-time user vector, and one dot product. ### exec · 2026-08-31T18:56:57.313Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate.py\\n@@\\n model = TwoTower(logpop)\\n model.load_state_dict(torch.load(O / \\\"two_tower_validation.pt\\\", map_location=\\\"cpu\\\")[\\\"model\\\"])\\n model.eval()\\n+model2 = TwoTower(logpop)\\n+model2.load_state_dict(torch.load(O / \\\"two_tower_validation_seed2.pt\\\", map_location=\\\"cpu\\\")[\\\"model\\\"])\\n+model2.eval()\\n@@\\n with torch.inference_mode():\\n iv = model.item_tower(torch.arange(30_000))\\n uv = torch.cat([model.user_tower(torch.from_numpy(ctx[s:s+128].astype(np.int64))) for s in range(0, len(ctx), 128)])\\n+ iv2 = model2.item_tower(torch.arange(30_000))\\n+ uv2 = torch.cat([model2.user_tower(torch.from_numpy(ctx[s:s+128].astype(np.int64))) for s in range(0, len(ctx), 128)])\\n packed = np.load(D / \\\"validation_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n-for alpha in [0.45,0.55,0.65,0.75,0.85,1.0]:\\n+for alpha in [0.6,0.75,0.85,0.95,1.1,1.3]:\\n@@\\n- scores=(uv[s:e]@iv.T).numpy()+alpha*logpop.numpy()[None]\\n+ scores=(0.5*(uv[s:e]@iv.T)+0.5*(uv2[s:e]@iv2.T)).numpy()+alpha*logpop.numpy()[None]\\n@@\\n- pd.concat(frames,ignore_index=True).to_parquet(O/f\\\"validation_alpha_{alpha:g}.parquet\\\",index=False)\\n+ pd.concat(frames,ignore_index=True).to_parquet(O/f\\\"validation_ensemble_alpha_{alpha:g}.parquet\\\",index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:56:57.378Z Script completed Wall time 0.0 seconds Output: {} ## Step 103 · agent · 2026-08-31T18:56:59.130Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:02.037Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/calibrate.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:57:13.058Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T18:57:14.692Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:15.471Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:57:27.663Z Script completed Wall time 12.2 seconds Output: 0.6 0.75 0.85 0.95 1.1 1.3 ## Step 105 · agent · 2026-08-31T18:57:29.157Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:33.430Z { "code": "const r = await tools.exec_command({\"cmd\":\"for a in 0.6 0.75 0.85 0.95 1.1 1.3; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble_alpha_$a.parquet | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:57:44.455Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T18:57:46.651Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:47.447Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:57:55.445Z Script completed Wall time 8.0 seconds Output: { "model": "/app/solution/validation_ensemble_alpha_0.6.parquet", "scores": { "quality_ndcg@10": 0.014458, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_alpha_0.75.parquet", "scores": { "quality_ndcg@10": 0.014893, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_alpha_0.85.parquet", "scores": { "quality_ndcg@10": 0.015001, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_alpha_0.95.parquet", "scores": { "quality_ndcg@10": 0.014741, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_alpha_1.1.parquet", "scores": { "quality_ndcg@10": 0.014226, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_alpha_1.3.parquet", "scores": { "quality_ndcg@10": 0.013743, "quality_users": 4263 } } ## Step 107 · agent · 2026-08-31T18:58:03.228Z ### Thinking unavailable — no text recorded The concatenated ensemble reaches 0.015001 NDCG@10 at coefficient 0.85, a 40.1% lift over the popularity anchor and 11.4% over the best single tower. I’ve selected it and am now doing the prescribed refit twice on train+validation; the public-window labels are used only now, as historical training outcomes for the hidden cutoff. ### exec · 2026-08-31T18:58:10.751Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/two_tower.py --stage final --epochs 6 --weeks 24 --seed 20260831 --output /app/solution/submission_seed1.parquet --checkpoint /app/solution/two_tower_final_seed1.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T18:58:21.772Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 108 · agent · 2026-08-31T18:58:23.297Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:24.068Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:58:40.938Z Script completed Wall time 16.9 seconds Output: training contexts=115,164 positive_pairs=3,496,127 likes_weighted=135,974 SESSION_ID=71860 ## Step 109 · agent · 2026-08-31T18:58:42.698Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:44.042Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:58:55.075Z Script running with cell ID 58 Wall time 11.0 seconds Output: ## Step 110 · agent · 2026-08-31T18:58:56.985Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:57.754Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:59:14.081Z Script completed Wall time 16.3 seconds Output: epoch 1/6 loss=0.72375 ## Step 111 · agent · 2026-08-31T18:59:16.847Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:18.200Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:59:29.228Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 112 · agent · 2026-08-31T18:59:30.736Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:31.535Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:59:48.233Z Script completed Wall time 16.7 seconds Output: ## Step 113 · agent · 2026-08-31T18:59:50.086Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:51.434Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:00:02.462Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 114 · agent · 2026-08-31T19:00:04.091Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:04.875Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:00:21.468Z Script completed Wall time 16.6 seconds Output: epoch 2/6 loss=0.64552 ## Step 115 · agent · 2026-08-31T19:00:23.128Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:24.479Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:00:35.515Z Script running with cell ID 61 Wall time 11.0 seconds Output: ## Step 116 · agent · 2026-08-31T19:00:37.356Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:38.177Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:00:54.520Z Script completed Wall time 16.3 seconds Output: epoch 3/6 loss=0.58027 ## Step 117 · agent · 2026-08-31T19:00:56.527Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:57.880Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:01:08.910Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 118 · agent · 2026-08-31T19:01:12.154Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:12.927Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:01:27.916Z Script completed Wall time 15.0 seconds Output: epoch 4/6 loss=0.53226 ## Step 119 · agent · 2026-08-31T19:01:29.593Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:30.950Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:01:41.983Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 120 · agent · 2026-08-31T19:01:44.330Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:45.127Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:02:00.991Z Script completed Wall time 15.9 seconds Output: epoch 5/6 loss=0.49120 ## Step 121 · agent · 2026-08-31T19:02:02.544Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:03.885Z { "code": "const r = await tools.write_stdin({\"session_id\":71860,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:02:14.914Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 122 · agent · 2026-08-31T19:02:16.780Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:17.560Z { "cell_id": "64", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:02:17.625Z Script completed Wall time 0.0 seconds Output: epoch 6/6 loss=0.45576 Traceback (most recent call last): File "/app/solution/two_tower.py", line 297, in main() File "/app/solution/two_tower.py", line 292, in main rank(model, request_history, internal_stage, uids, item_ids, output) File "/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/two_tower.py", line 240, in rank packed = np.load(D / f"{stage}_eligible_candidates.npz")["packed_eligible"] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 451, in load fid = stack.enter_context(open(os.fspath(file), "rb")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/task/data/final_eligible_candidates.npz' ## Step 123 · agent · 2026-08-31T19:02:20.689Z ### Thinking unavailable — no text recorded The first final refit completed and its checkpoint was safely written, but ranking hit a stage-name bug (`final` versus the actual `test_eligible_candidates.npz`). No model work was lost. I’m correcting that mapping before the second refit and final ensemble generation. ### exec · 2026-08-31T19:02:25.928Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/two_tower.py\\n@@\\n- internal_stage = \\\"validation\\\" if args.stage == \\\"validation\\\" else \\\"final\\\"\\n+ internal_stage = \\\"validation\\\" if args.stage == \\\"validation\\\" else \\\"test\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:02:25.992Z Script completed Wall time 0.0 seconds Output: {} ## Step 124 · agent · 2026-08-31T19:02:29.709Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:34.805Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/two_tower_final_seed1.pt /app/solution/examples_final_24w.npz && /opt/task-venv/bin/python -u /app/solution/two_tower.py --stage final --epochs 6 --weeks 24 --seed 20260901 --output /app/solution/submission_seed2.parquet --checkpoint /app/solution/two_tower_final_seed2.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:02:45.834Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 125 · agent · 2026-08-31T19:02:47.632Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:48.460Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:03:05.000Z Script completed Wall time 16.5 seconds Output: -rw-r--r-- 1 agent agent 24M Aug 31 18:58 /app/solution/examples_final_24w.npz -rw-r--r-- 1 agent agent 15M Aug 31 19:02 /app/solution/two_tower_final_seed1.pt training contexts=115,164 positive_pairs=3,496,127 likes_weighted=135,974 SESSION_ID=38492 ## Step 126 · agent · 2026-08-31T19:03:06.766Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:08.137Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:03:19.168Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 127 · agent · 2026-08-31T19:03:21.576Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:22.344Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:03:38.174Z Script completed Wall time 15.8 seconds Output: epoch 1/6 loss=0.72354 ## Step 128 · agent · 2026-08-31T19:03:39.847Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:41.202Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:03:52.230Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 129 · agent · 2026-08-31T19:03:54.128Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:54.916Z { "cell_id": "68", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:04:11.234Z Script completed Wall time 16.3 seconds Output: epoch 2/6 loss=0.64750 ## Step 130 · agent · 2026-08-31T19:04:12.732Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:14.119Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:04:25.149Z Script running with cell ID 69 Wall time 11.0 seconds Output: ## Step 131 · agent · 2026-08-31T19:04:26.893Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:27.692Z { "cell_id": "69", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:04:44.153Z Script completed Wall time 16.5 seconds Output: epoch 3/6 loss=0.58391 ## Step 132 · agent · 2026-08-31T19:04:45.888Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:47.232Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:04:58.257Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 133 · agent · 2026-08-31T19:05:00.053Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:00.851Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:05:17.263Z Script completed Wall time 16.4 seconds Output: epoch 4/6 loss=0.53783 ## Step 134 · agent · 2026-08-31T19:05:18.936Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:20.254Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:05:31.283Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 135 · agent · 2026-08-31T19:05:33.618Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:34.428Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:05:50.291Z Script completed Wall time 15.9 seconds Output: epoch 5/6 loss=0.50548 ## Step 136 · agent · 2026-08-31T19:05:51.997Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:53.348Z { "code": "const r = await tools.write_stdin({\"session_id\":38492,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:06:04.381Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 137 · agent · 2026-08-31T19:06:06.150Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:06.920Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:06:19.835Z Script completed Wall time 12.9 seconds Output: epoch 6/6 loss=0.47900 {"output": "/app/solution/submission_seed2.parquet", "checkpoint": "/app/solution/two_tower_final_seed2.pt"} ## Step 138 · agent · 2026-08-31T19:06:33.204Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:20.105Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/serve.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Precompute ensemble item vectors and emit the hidden-cutoff ranking.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import math\\n+import sys\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+\\n+sys.path.insert(0, str(Path(__file__).resolve().parent))\\n+from two_tower import TwoTower, load_events, inference_contexts, FINAL_CUTOFF\\n+\\n+D = Path(\\\"/task/data\\\")\\n+O = Path(\\\"/app/solution\\\")\\n+ALPHA = 0.85\\n+\\n+\\n+class ServingUserTower(nn.Module):\\n+ \\\"\\\"\\\"One request-time tower producing a 97-dimensional user vector.\\\"\\\"\\\"\\n+ def __init__(self, first: TwoTower, second: TwoTower):\\n+ super().__init__()\\n+ self.first = first.user_tower\\n+ self.second = second.user_tower\\n+\\n+ def forward(self, history: torch.Tensor) -> torch.Tensor:\\n+ scale = math.sqrt(0.5)\\n+ a, b = scale * self.first(history), scale * self.second(history)\\n+ return torch.cat([a, b, torch.ones((len(history), 1), device=history.device)], dim=1)\\n+\\n+\\n+class ServingItemTower(nn.Module):\\n+ \\\"\\\"\\\"Offline tower producing vectors of the same dimensionality.\\\"\\\"\\\"\\n+ def __init__(self, first: TwoTower, second: TwoTower, normalized_logpop: torch.Tensor):\\n+ super().__init__()\\n+ self.first = first.item_tower\\n+ self.second = second.item_tower\\n+ self.register_buffer(\\\"normalized_logpop\\\", normalized_logpop)\\n+\\n+ def forward(self, item: torch.Tensor) -> torch.Tensor:\\n+ scale = math.sqrt(0.5)\\n+ a, b = scale * self.first(item), scale * self.second(item)\\n+ p = ALPHA * self.normalized_logpop[item].unsqueeze(-1)\\n+ return torch.cat([a, b, p], dim=-1)\\n+\\n+\\n+def load_model(path: Path, logpop: torch.Tensor) -> TwoTower:\\n+ model = TwoTower(logpop)\\n+ model.load_state_dict(torch.load(path, map_location=\\\"cpu\\\", weights_only=True)[\\\"model\\\"])\\n+ model.eval()\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def main():\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--output\\\", type=Path, default=O / \\\"submission.parquet\\\")\\n+ ap.add_argument(\\\"--checkpoint-1\\\", type=Path, default=O / \\\"two_tower_final_seed1.pt\\\")\\n+ ap.add_argument(\\\"--checkpoint-2\\\", type=Path, default=O / \\\"two_tower_final_seed2.pt\\\")\\n+ args = ap.parse_args()\\n+ target = pd.read_parquet(D / \\\"target_users.parquet\\\")\\n+ catalog = pd.read_parquet(D / \\\"candidate_catalog.parquet\\\")\\n+ uids = target.uid.to_numpy(np.int64); items = catalog.item_id.to_numpy(np.int64)\\n+ logpop = -torch.log1p(torch.from_numpy(catalog.popularity_rank.to_numpy(np.float32)))\\n+ logpop = (logpop - logpop.mean()) / logpop.std()\\n+ first, second = load_model(args.checkpoint_1, logpop), load_model(args.checkpoint_2, logpop)\\n+ user_tower = ServingUserTower(first, second).eval()\\n+ item_tower = ServingItemTower(first, second, logpop).eval()\\n+ listens, _ = load_events(\\\"final\\\", len(uids))\\n+ contexts = inference_contexts(listens, len(uids), FINAL_CUTOFF)\\n+\\n+ # Production equivalent: item vectors are computed once and placed in MIPS.\\n+ item_vectors = item_tower(torch.arange(30_000))\\n+ packed = np.load(D / \\\"test_eligible_candidates.npz\\\")[\\\"packed_eligible\\\"]\\n+ frames = []\\n+ for start in range(0, len(uids), 128):\\n+ end = min(start + 128, len(uids))\\n+ user_vectors = user_tower(torch.from_numpy(contexts[start:end].astype(np.int64)))\\n+ scores = (user_vectors @ item_vectors.T).numpy() # exact final relevance\\n+ eligible = np.unpackbits(packed[start:end], axis=1, count=30_000, bitorder=\\\"little\\\").astype(bool)\\n+ scores[~eligible] = -np.inf\\n+ top = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ values = np.take_along_axis(scores, top, axis=1)\\n+ top = np.take_along_axis(top, np.argsort(values, axis=1)[:, ::-1], axis=1)\\n+ for row in range(end - start):\\n+ frames.append(pd.DataFrame({\\n+ \\\"uid\\\": uids[start + row],\\n+ \\\"item_id\\\": items[top[row]],\\n+ \\\"rank\\\": np.arange(1, 101, dtype=np.int64),\\n+ }))\\n+ pd.concat(frames, ignore_index=True).to_parquet(args.output, index=False)\\n+ print(args.output)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:07:20.888Z Script completed Wall time 0.0 seconds Output: {} ## Step 139 · agent · 2026-08-31T19:07:23.007Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:25.867Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/serve.py --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:07:35.786Z Script completed Wall time 9.9 seconds Output: /app/solution/submission.parquet ## Step 140 · agent · 2026-08-31T19:07:55.558Z ### Thinking unavailable — no text recorded ## Step 141 · agent · 2026-08-31T19:08:01.338Z ### Thinking unavailable — no text recorded Both final checkpoints are fitted and the ensemble submission is generated. Before handoff I’m doing one last read-only validation of the deliverable and a quick cutoff-correct check of whether active likes improve the history vector; I’ll retain the already valid submission unless that public result is better. ### exec · 2026-08-31T19:08:22.778Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/two_tower.py\\n@@\\n def inference_contexts(listens, n_users: int, cutoff: int) -> np.ndarray:\\n@@\\n return result\\n+\\n+\\n+def augment_active_likes(contexts: np.ndarray, likes, stage: str, uids, item_ids) -> np.ndarray:\\n+ \\\"\\\"\\\"Move cutoff-active liked catalog tracks to the recent end of history.\\\"\\\"\\\"\\n+ pref = pd.read_parquet(D / f\\\"{stage}_preference_state.parquet\\\")\\n+ pref = pref[pref.state == \\\"liked\\\"]\\n+ uid_map = {int(v): i for i, v in enumerate(uids)}\\n+ item_map = {int(v): i for i, v in enumerate(item_ids)}\\n+ pu = pref.uid.map(uid_map).fillna(-1).to_numpy(np.int32)\\n+ pii = pref.item_id.map(item_map).fillna(-1).to_numpy(np.int32)\\n+ valid = (pu >= 0) & (pii >= 0)\\n+ pu, pii = pu[valid], pii[valid]\\n+ order = np.argsort(pu, kind=\\\"stable\\\"); pu, pii = pu[order], pii[order]\\n+ pb, lb = bounds(pu, len(uids)), bounds(likes[\\\"ui\\\"], len(uids))\\n+ out = contexts.copy()\\n+ for u in range(len(uids)):\\n+ active = pii[pb[u]:pb[u + 1]]\\n+ if not len(active):\\n+ continue\\n+ event_items = likes[\\\"ii\\\"][lb[u]:lb[u + 1]]\\n+ chosen = recent_unique(event_items[np.isin(event_items, active)], 32)\\n+ if not len(chosen):\\n+ chosen = active[-32:]\\n+ old = out[u][out[u] != 30_000]\\n+ old = old[~np.isin(old, chosen)]\\n+ merged = np.concatenate([old, chosen])[-MAX_HISTORY:]\\n+ out[u].fill(30_000); out[u, -len(merged):] = merged\\n+ return out\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:08:22.827Z Script completed Wall time 0.0 seconds Output: {} ## Step 142 · agent · 2026-08-31T19:08:26.383Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:36.358Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate.py\\n@@\\n-from two_tower import TwoTower, load_events, inference_contexts, TRAIN_CUTOFF\\n+from two_tower import TwoTower, load_events, inference_contexts, augment_active_likes, TRAIN_CUTOFF\\n@@\\n-listens, _ = load_events(\\\"validation\\\", len(uids))\\n+listens, likes = load_events(\\\"validation\\\", len(uids))\\n ctx = inference_contexts(listens, len(uids), TRAIN_CUTOFF)\\n+ctx = augment_active_likes(ctx, likes, \\\"validation\\\", uids, items)\\n@@\\n-for alpha in [0.6,0.75,0.85,0.95,1.1,1.3]:\\n+for alpha in [0.75,0.85,0.95]:\\n@@\\n- pd.concat(frames,ignore_index=True).to_parquet(O/f\\\"validation_ensemble_alpha_{alpha:g}.parquet\\\",index=False)\\n+ pd.concat(frames,ignore_index=True).to_parquet(O/f\\\"validation_pref_ensemble_alpha_{alpha:g}.parquet\\\",index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:08:36.464Z Script completed Wall time 0.0 seconds Output: {} ## Step 143 · agent · 2026-08-31T19:08:38.382Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:42.900Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/calibrate.py && for a in 0.75 0.85 0.95; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pref_ensemble_alpha_$a.parquet | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-08-31T19:08:53.927Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T19:08:56.615Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:57.456Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:09:11.356Z Script completed Wall time 13.9 seconds Output: 0.75 0.85 0.95 { "model": "/app/solution/validation_pref_ensemble_alpha_0.75.parquet", "scores": { "quality_ndcg@10": 0.015181, "quality_users": 4263 } } { "model": "/app/solution/validation_pref_ensemble_alpha_0.85.parquet", "scores": { "quality_ndcg@10": 0.015113, "quality_users": 4263 } } { "model": "/app/solution/validation_pref_ensemble_alpha_0.95.parquet", "scores": { "quality_ndcg@10": 0.014972, "quality_users": 4263 } } ## Step 145 · agent · 2026-08-31T19:09:15.509Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:22.076Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/serve.py\\n@@\\n-from two_tower import TwoTower, load_events, inference_contexts, FINAL_CUTOFF\\n+from two_tower import TwoTower, load_events, inference_contexts, augment_active_likes, FINAL_CUTOFF\\n@@\\n-ALPHA = 0.85\\n+ALPHA = 0.75\\n@@\\n- listens, _ = load_events(\\\"final\\\", len(uids))\\n+ listens, likes = load_events(\\\"final\\\", len(uids))\\n contexts = inference_contexts(listens, len(uids), FINAL_CUTOFF)\\n+ contexts = augment_active_likes(contexts, likes, \\\"test\\\", uids, items)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:09:22.189Z Script completed Wall time 0.0 seconds Output: {} ## Step 146 · agent · 2026-08-31T19:09:23.888Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:26.264Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/serve.py --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:09:36.979Z Script completed Wall time 10.7 seconds Output: /app/solution/submission.parquet ## Step 147 · agent · 2026-08-31T19:09:46.064Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:08.663Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+## Model\\n+\\n+`two_tower.py` defines separate PyTorch networks with no user-ID parameters.\\n+`UserTower` mean-pools the last 256 distinct interacted catalog tracks at two\\n+scales (all 256 and the most recent 32), then applies a 128-to-96-to-48 ReLU\\n+MLP. At serving, cutoff-active liked tracks are moved into the recent portion\\n+of that history. `ItemTower` applies a 65-to-96-to-48 ReLU MLP to a trainable\\n+64-dimensional item embedding and normalized log-popularity feature.\\n+\\n+Two seeded models are concatenated, with `sqrt(0.5)` scaling, and one calibrated\\n+popularity coordinate is appended. The resulting serving user and item vectors\\n+both have 97 dimensions. Candidate vectors are independently precomputable.\\n+The final score in `serve.py` is exactly `user_vector @ item_vector.T`; in\\n+production the item vectors can therefore be stored in a maximum-inner-product\\n+nearest-neighbor index and only one user vector is computed per request.\\n+\\n+## Training and selection\\n+\\n+Training uses event-stream rolling temporal examples, not a global interaction\\n+matrix. Each example pairs a history before a cutoff with organic >=80%-played\\n+or organically liked catalog tracks from the following seven-day window after\\n+the 30-minute gap. Sixteen popularity-smoothed negatives train a sampled BPR\\n+(`softplus(negative_score - positive_score)`) objective; like examples receive\\n+weight 3. The public model uses train events only. The selected model is then\\n+refit from scratch on train+validation for the hidden cutoff.\\n+\\n+Public validation graded NDCG@10: **0.015181** over 4,263 scored users. The\\n+popularity-only development anchor was 0.010710.\\n+\\n+## Reproduce\\n+\\n+The pinned environment's Python is `/opt/task-venv/bin/python`. Compact streamed\\n+event artifacts and fitted checkpoints are retained in this directory. To\\n+regenerate the final submission from the fitted towers:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/serve.py --output /app/solution/submission.parquet\\n+```\\n+\\n+To rebuild from source, first run `explore_stream.py`, then run `two_tower.py\\n+--stage final --epochs 6 --weeks 24` for seeds `20260831` and `20260901` with\\n+checkpoint paths `two_tower_final_seed1.pt` and `two_tower_final_seed2.pt`, and\\n+finally run the serving command above.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:10:08.735Z Script completed Wall time 0.0 seconds Output: {} ## Step 148 · agent · 2026-08-31T19:10:16.261Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:28.882Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/explore_stream.py /app/solution/two_tower.py /app/solution/serve.py && /opt/task-venv/bin/python - <<'PY'\\nimport sys, pandas as pd, numpy as np\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking, CandidateSet\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nx=validate_ranking(pd.read_parquet(P),targets)\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nok=all(c.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(x),'users':x.uid.nunique(),'eligible':ok,'duplicate_pairs':int(x.duplicated(['uid','item_id']).sum()),'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max()),'bytes':P.stat().st_size})\\nprint(x.head(12).to_string(index=False))\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/two_tower_final_seed1.pt /app/solution/two_tower_final_seed2.pt /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:10:32.079Z Script completed Wall time 3.2 seconds Output: {'rows': 845600, 'users': 8456, 'eligible': True, 'duplicate_pairs': 0, 'rank_min': 1, 'rank_max': 100, 'bytes': 1512893} uid item_id rank 100 2621516 1 100 6006390 2 100 1051073 3 100 2185191 4 100 1525699 5 100 4522882 6 100 489014 7 100 5436843 8 100 3335443 9 100 5951957 10 100 5672520 11 100 5252571 12 -rw-r--r-- 1 agent agent 2.3K Aug 31 19:10 /app/solution/README.md -rw-r--r-- 1 agent agent 1.5M Aug 31 19:09 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 15M Aug 31 19:02 /app/solution/two_tower_final_seed1.pt -rw-r--r-- 1 agent agent 15M Aug 31 19:06 /app/solution/two_tower_final_seed2.pt ## Step 149 · agent · 2026-08-31T19:10:34.178Z ### Thinking unavailable — no text recorded Implemented and validated the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Documentation: [README.md](/app/solution/README.md) - Training/model code: [two_tower.py](/app/solution/two_tower.py) - Serving code: [serve.py](/app/solution/serve.py) - Public NDCG@10: **0.015181** - Submission: 845,600 rows, 8,456 users, ranks 1–100, no duplicates, all items test-eligible - Final score is exactly a dot product between independently generated 97-dimensional user and item vectors.