yambda-discovery-ranking-two-tower-base / run-001-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:24:59.715Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-08-31T18:25:01.491Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-08-31T18:25:01.491Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-08-31T18:25:01.491Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-08-31T18:25:01.491Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a05911-473b-7cd1-8bc7-0df243e207c9", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-08-31", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-08-31T18:25:01.500Z # 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 · 2026-08-31T18:25:03.845Z ### Thinking unavailable — no text recorded I’ll build and validate the full CPU two-tower pipeline, keep a valid submission saved early, then iterate on ranking quality within the time budget. I’ll first inspect the data and evaluator, establish a scalable baseline, and only use validation outcomes for training-objective tuning—not as validation features. ### exec · 2026-08-31T18:25:10.783Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect data schemas, sizes, eligibility, and public evaluator\",status:\"in_progress\"},\n {step:\"Implement scalable history-derived PyTorch two-tower training and ranking\",status:\"pending\"},\n {step:\"Train baseline, create valid validation/test rankings, and score validation\",status:\"pending\"},\n {step:\"Tune features/objective with public validation and refit on train+validation\",status:\"pending\"},\n {step:\"Verify final artifact and document reproducible submission command\",status:\"pending\"}\n]}); text(p)\n" } ### Tool output · 2026-08-31T18:25:10.818Z Script completed Wall time 0.0 seconds Output: {} ## Step 8 · agent · 2026-08-31T18:25:13.132Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:17.075Z { "code": "const r = await tools.exec_command({cmd:\"find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:17.140Z Script completed Wall time 0.1 seconds Output: candidate_catalog.parquet 349352 bytes target_users.parquet 84127 bytes test_eligible_candidates.npz 7736341 bytes test_preference_state.parquet 1092329 bytes train_dislikes.parquet 857213 bytes train_likes.parquet 5374652 bytes train_listens.parquet 319183255 bytes train_undislikes.parquet 155988 bytes train_unlikes.parquet 1855885 bytes val_dislikes.parquet 36571 bytes val_likes.parquet 298484 bytes val_listens.parquet 9947609 bytes val_undislikes.parquet 7845 bytes val_unlikes.parquet 72536 bytes validation_eligible_candidates.npz 7573821 bytes validation_preference_state.parquet 1061761 bytes #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 9 · agent · 2026-08-31T18:25:21.087Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:36.241Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows', pf.metadata.num_rows, 'cols', pf.schema.names)\\n if p.name.startswith(('train_','val_')):\\n x=pd.read_parquet(p, columns=['uid','timestamp','item_id'])\\n print(' uid',x.uid.nunique(),'item',x.item_id.nunique(),'time',x.timestamp.min(),x.timestamp.max())\\nprint('\\\\ntarget',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n u=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\n cnt=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').sum(1)\\n print(s,u.shape,items.shape,packed.shape,'eligible min/mean/max',cnt.min(),cnt.mean(),cnt.max(),'uids align target',np.array_equal(np.sort(u),np.sort(pd.read_parquet(D/'target_users.parquet').uid.to_numpy())))\\n pref=pd.read_parquet(D/f'{s}_preference_state.parquet')\\n print(' pref',pref.shape,pref.columns.tolist(),pref.state.value_counts().to_dict(),pref.head())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:36.377Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 10 · agent · 2026-08-31T18:25:38.180Z ### Thinking unavailable — no text recorded The default shell Python is not the task’s pinned environment, so I’m locating the provided runtime before touching implementation. The dataset itself is intact; this is just an interpreter-path issue. ### exec · 2026-08-31T18:25:44.595Z { "code": "const r = await tools.exec_command({cmd:\"command -v python; command -v python3; which -a python python3 2>/dev/null; find /opt /usr/local /task -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -30; ls -la /app /task | sed -n '1,160p'; env | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:44.654Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/local/bin/python /usr/local/bin/python3 /app: total 12 drwxr-xr-x 3 agent agent 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:24 .. drwxr-xr-x 2 agent agent 4096 Aug 31 18:22 solution /task: total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:24 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:09 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 CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a05911-4725-7392-a8ba-8e889ed0e99d COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=69e73b1c62d0 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ## Step 11 · agent · 2026-08-31T18:25:47.105Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:51.130Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list 2>&1 | sed -n '1,120p'; find / -type d \\\\( -name 'site-packages' -o -name 'dist-packages' \\\\) 2>/dev/null | head -50; find / -type f -name 'torch*.whl' 2>/dev/null | head\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:25:52.284Z Script completed Wall time 1.1 seconds Output: Package Version ------- ------- pip 25.0.1 /opt/task-venv/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages ## Step 12 · agent · 2026-08-31T18:25:53.787Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:57.541Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,120p'; /opt/task-venv/bin/python - <<'PY'\\nimport pandas,torch,pyarrow,numpy\\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:01.474Z Script completed Wall time 3.9 seconds Output: total 124 dr-xr-xr-x 1 root root 4096 Aug 31 18:21 . dr-xr-xr-x 1 root root 4096 Aug 31 18:21 .. -r--r--r-- 1 root root 9033 Aug 31 18:21 Activate.ps1 -r--r--r-- 1 root root 2158 Aug 31 18:21 activate -r--r--r-- 1 root root 920 Aug 31 18:21 activate.csh -r--r--r-- 1 root root 2195 Aug 31 18:21 activate.fish -r-xr-xr-x 1 root root 302 Aug 31 18:21 chardetect -r-xr-xr-x 1 root root 304 Aug 31 18:21 dotenv -r-xr-xr-x 1 root root 308 Aug 31 18:21 f2py -r-xr-xr-x 1 root root 299 Aug 31 18:21 idna -r-xr-xr-x 1 root root 297 Aug 31 18:21 isympy -r-xr-xr-x 1 root root 305 Aug 31 18:21 jsonschema -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupyter -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupyter-migrate -r-xr-xr-x 1 root root 316 Aug 31 18:21 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Aug 31 18:21 jupyter-trust -r-xr-xr-x 1 root root 311 Aug 31 18:21 jupytext -r-xr-xr-x 1 root root 315 Aug 31 18:21 jupytext-config -r-xr-xr-x 1 root root 301 Aug 31 18:21 kaggle -r-xr-xr-x 1 root root 312 Aug 31 18:21 markdown-it -r-xr-xr-x 1 root root 325 Aug 31 18:21 normalizer -r-xr-xr-x 1 root root 308 Aug 31 18:21 numpy-config -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip3 -r-xr-xr-x 1 root root 313 Aug 31 18:21 pip3.12 lrwxrwxrwx 1 root root 21 Aug 31 18:21 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 18:21 python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 18:21 python3.12 -> python -r-xr-xr-x 1 root root 307 Aug 31 18:21 slugify -r-xr-xr-x 1 root root 321 Aug 31 18:21 torchfrtrace -r-xr-xr-x 1 root root 312 Aug 31 18:21 torchrun -r-xr-xr-x 1 root root 299 Aug 31 18:21 tqdm 2.3.3 2.8.0+cpu 18.1.0 2.2.6 ## Step 13 · agent · 2026-08-31T18:26:03.945Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:19.941Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, 'rows', pf.metadata.num_rows, 'cols', pf.schema.names)\\n if p.name.startswith(('train_','val_')):\\n x=pd.read_parquet(p, columns=['uid','timestamp','item_id'])\\n print(' uid',x.uid.nunique(),'item',x.item_id.nunique(),'time',x.timestamp.min(),x.timestamp.max())\\nprint('\\\\ntarget',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').shape)\\nprint('catalog head/tail\\\\n',pd.concat([pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail()]))\\ntarget=pd.read_parquet(D/'target_users.parquet').uid.to_numpy()\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n u=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\n cnt=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').sum(1)\\n print(s,u.shape,items.shape,packed.shape,'eligible min/mean/max',cnt.min(),cnt.mean(),cnt.max(),'uids align target',np.array_equal(np.sort(u),np.sort(target)))\\n pref=pd.read_parquet(D/f'{s}_preference_state.parquet')\\n print(' pref',pref.shape,pref.columns.tolist(),pref.state.value_counts().to_dict())\\n print(pref.head())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:26:22.934Z Script completed Wall time 3.0 seconds Output: candidate_catalog.parquet rows 30000 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet rows 8456 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet rows 484776 cols ['uid', 'item_id', 'state'] train_dislikes.parquet rows 101075 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 5732 item 50876 time 75 24786580 train_likes.parquet rows 814831 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 8144 item 172303 time 80 24786790 train_listens.parquet rows 43484159 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] uid 9238 item 845395 time 0 24786795 train_undislikes.parquet rows 19960 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 2769 item 14725 time 15950 24785395 train_unlikes.parquet rows 292665 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 6197 item 113072 time 250 24786570 val_dislikes.parquet rows 3563 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 955 item 3135 time 24789920 25393280 val_likes.parquet rows 37795 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 4114 item 22902 time 24788815 25393390 val_listens.parquet rows 1501697 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] uid 8196 item 191747 time 24788600 25393395 val_undislikes.parquet rows 606 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 205 item 579 time 24810715 25393035 val_unlikes.parquet rows 9346 cols ['uid', 'timestamp', 'item_id', 'is_organic'] uid 1313 item 8080 time 24789005 25393385 validation_preference_state.parquet rows 469124 cols ['uid', 'item_id', 'state'] target uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 (8456, 3) catalog head/tail item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 29995 1275692 163 81 29996 29996 1280011 163 76 29997 29997 1309246 163 138 29998 29998 1634958 163 46 29999 29999 1670524 163 73 30000 validation (8456,) (30000,) (8456, 3750) eligible min/mean/max 24657 29067.306291390727 30000 uids align target True pref (469124, 3) ['uid', 'item_id', 'state'] {'liked': 418435, 'disliked': 50689} uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked test (8456,) (30000,) (8456, 3750) eligible min/mean/max 24552 29041.3178807947 30000 uids align target True pref (484776, 3) ['uid', 'item_id', 'state'] {'liked': 432659, 'disliked': 52117} uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked ## Step 14 · agent · 2026-08-31T18:26:42.399Z ### Thinking unavailable — no text recorded ## Step 15 · agent · 2026-08-31T18:26:50.424Z ### Thinking unavailable — no text recorded The benchmark has 8,456 target users, a 30,000-item catalog, and roughly 29,000 eligible items per user. I’m saving an eligibility-safe popularity fallback now, then I’ll measure how much personalization the temporal two-tower adds over it. The training code will stream interactions and use sampled/in-batch negatives, so it never builds a user–item matrix. ### exec · 2026-08-31T18:27:12.151Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/make_popularity.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create a structurally valid popularity fallback for either cutoff.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"test\\\"), required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = parser.parse_args()\\n+\\n+ data = Path(\\\"/task/data\\\")\\n+ catalog = pd.read_parquet(data / \\\"candidate_catalog.parquet\\\")\\n+ popularity = dict(zip(catalog.item_id.astype(int), catalog.popularity_rank.astype(int)))\\n+ with np.load(data / f\\\"{args.stage}_eligible_candidates.npz\\\", allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+ order = np.argsort(np.asarray([popularity[int(i)] for i in items]))\\n+ rows: list[tuple[int, int, int]] = []\\n+ for r, uid in enumerate(uids):\\n+ mask = np.unpackbits(packed[r], count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ chosen = order[mask[order]][:100]\\n+ rows.extend((int(uid), int(items[c]), rank) for rank, c in enumerate(chosen, 1))\\n+ args.output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(args.output, index=False)\\n+ print(f\\\"wrote {len(rows)} rows to {args.output}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:27:12.254Z Script completed Wall time 0.0 seconds Output: {} ## Step 16 · agent · 2026-08-31T18:27:14.170Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:18.997Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /app/solution && /opt/task-venv/bin/python /app/solution/make_popularity.py --stage validation --output /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /app/solution/make_popularity.py --stage test --output /app/solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:27:28.270Z Script completed Wall time 9.3 seconds Output: total 4 -rw-r--r-- 1 agent agent 1525 Aug 31 18:27 make_popularity.py wrote 845600 rows to /app/solution/validation_popularity.parquet wrote 845600 rows to /app/solution/submission.parquet { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 17 · agent · 2026-08-31T18:27:44.728Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:12.415Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; pack=z['packed_eligible']; ur={int(u):r for r,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(items)}\\ndef elig(u,i):\\n c=ic.get(int(i)); r=ur.get(int(u)); return c is not None and r is not None and bool((int(pack[r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(D/'val_listens.parquet'); vk=pd.read_parquet(D/'val_likes.parquet')\\nstrong=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=vk[(vk.uid.isin(targets))&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\\ntruth=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\\nprint('truth',len(truth),'users',truth.uid.nunique(),'grades',truth.g.value_counts().to_dict(),'peruser',truth.groupby('uid').size().describe().to_dict())\\n# active pref overlap\\npref=pd.read_parquet(D/'validation_preference_state.parquet'); pset=set(map(tuple,pref[['uid','item_id']].to_numpy()))\\nprint('truth active pref overlap',sum((int(u),int(i)) in pset for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)))\\n# historical action overlap stream recent or all\\npairs=set(map(tuple,truth[['uid','item_id']].to_numpy())); seen=set(); posseen=set(); recent=set()\\nimport pyarrow.parquet as pq\\npf=pq.ParquetFile(D/'train_listens.parquet')\\nfor b in pf.iter_batches(columns=['uid','timestamp','item_id','played_ratio_pct'],batch_size=1000000):\\n x=b.to_pandas(); x=x[x.uid.isin(targets)];\\n for u,t,i,r in x.itertuples(index=False,name=None):\\n key=(int(u),int(i))\\n if key in pairs:\\n seen.add(key)\\n if r>=50: posseen.add(key)\\n if t>=17010800 and r>=50: recent.add(key)\\nprint('truth overlap any listen',len(seen),'positive',len(posseen),'recent positive',len(recent))\\nprint('catalog position relevant ranks',truth.item_id.map(dict(zip(pd.read_parquet(D/'candidate_catalog.parquet').item_id,pd.read_parquet(D/'candidate_catalog.parquet').popularity_rank))).describe(percentiles=[.1,.25,.5,.75,.9]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:28:23.435Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 18 · agent · 2026-08-31T18:28:25.655Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:26.445Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:28:42.595Z Script completed Wall time 16.1 seconds Output: ## Step 19 · agent · 2026-08-31T18:29:02.103Z ### Thinking unavailable — no text recorded ## Step 20 · agent · 2026-08-31T18:29:20.753Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T18:29:37.138Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:29:50.071Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor f in ['train_listens.parquet','train_likes.parquet','validation_preference_state.parquet']:\\n pf=pq.ParquetFile(D/f); b=next(pf.iter_batches(batch_size=100000))\\n x=b.to_pandas(); print(f,x.dtypes.to_dict(),x.memory_usage(deep=True).sum()/len(x),flush=True)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy(); tgt=pd.read_parquet(D/'target_users.parquet').uid.to_numpy()\\n# first row group ratios\\nn=pos=incat=intgt=both=0\\nfor b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(columns=['uid','timestamp','item_id','played_ratio_pct'],batch_size=1000000):\\n x=b.to_pandas(); n+=len(x); m=x.played_ratio_pct>=50; pos+=m.sum(); c=x.item_id.isin(cat); u=x.uid.isin(tgt); incat+=c.sum(); intgt+=u.sum(); both+=(m&c&u).sum()\\n print('progress',n,pos,incat,intgt,both,flush=True)\\nprint('final',n,pos,incat,intgt,both,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:29:52.771Z Script completed Wall time 2.7 seconds Output: train_listens.parquet {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} 19.00132 train_likes.parquet {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} 13.00132 validation_preference_state.parquet {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} 62.33876 progress 1000000 624013 705306 995437 444136 progress 2000000 1241491 1438561 1978664 900355 progress 3000000 1843261 2141829 2973322 1329225 progress 4000000 2487186 2835763 3968056 1773315 progress 5000000 3100531 3552057 4965581 2220892 progress 6000000 3750966 4268527 5961774 2694851 progress 7000000 4388847 5008269 6954314 3169708 progress 8000000 5005169 5752450 7950017 3634986 progress 9000000 5650312 6427389 8947357 4072361 progress 10000000 6246289 7145955 9944145 4507902 progress 11000000 6906925 7856999 10939451 4982336 progress 12000000 7538326 8581510 11925849 5442836 progress 13000000 8206230 9289491 12921372 5914841 progress 14000000 8843415 10019959 13911353 6384722 progress 15000000 9498056 10752544 14904743 6872786 progress 16000000 10158757 11476962 15901690 7356913 progress 17000000 10771301 12182124 16895281 7793592 progress 18000000 11378889 12860909 17892416 8209202 progress 19000000 12027158 13579561 18891315 8683025 progress 20000000 12666120 14308940 19882900 9154647 progress 21000000 13315265 15032871 20860961 9622752 progress 22000000 13965591 15768984 21856787 10102567 progress 23000000 14598270 16507126 22846168 10575035 progress 24000000 15210522 17217331 23829992 10999239 progress 25000000 15856155 17928718 24822948 11453464 progress 26000000 16469329 18648481 25817824 11899851 progress 27000000 17128109 19348920 26812927 12371930 progress 28000000 17773012 20067050 27807595 12836030 progress 29000000 18379579 20757383 28805472 13256228 progress 30000000 19020725 21480869 29800582 13723681 progress 31000000 19680986 22196837 30790341 14195205 progress 32000000 20284344 22938773 31789122 14650619 progress 33000000 20948095 23644974 32783354 15122860 progress 34000000 21597965 24349415 33781837 15585117 progress 35000000 22245369 25060172 34775422 16050245 progress 36000000 22862928 25770937 35772545 16502221 progress 37000000 23462769 26498510 36771874 16955844 progress 38000000 24078262 27235810 37769015 17417609 progress 39000000 24745068 27938531 38766546 17895943 progress 40000000 25354280 28680595 39759571 18348811 progress 41000000 25969686 29422799 40743120 18808825 progress 42000000 26582909 30168368 41740233 19271922 progress 43000000 27211180 30916801 42739563 19754849 progress 43484159 27500755 31249613 43222297 19959853 final 43484159 27500755 31249613 43222297 19959853 ## Step 22 · agent · 2026-08-31T18:30:00.017Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:05.798Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nfor i in [0,1,10,20,30,40,43]:\\n rg=p.metadata.row_group(i)\\n vals=[]\\n for n in ['uid','timestamp','item_id']:\\n j=p.schema.names.index(n); s=rg.column(j).statistics; vals.append((n,s.min,s.max))\\n print(i,rg.num_rows,vals)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:30:06.299Z Script completed Wall time 0.5 seconds Output: Traceback (most recent call last): File "", line 4, in File "pyarrow/_parquet.pyx", line 1030, in pyarrow._parquet.FileMetaData.row_group 0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541)] 1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541)] 10 1048576 [('uid', 241800, 264900), ('timestamp', 15, 24786720), ('item_id', 71, 9390541)] 20 1048576 [('uid', 475900, 502800), ('timestamp', 40, 24786795), ('item_id', 206, 9390541)] 30 1048576 [('uid', 720000, 741900), ('timestamp', 30, 24786775), ('item_id', 26, 9390601)] 40 1048576 [('uid', 965500, 989500), ('timestamp', 230, 24786710), ('item_id', 50, 9390541)] File "pyarrow/_parquet.pyx", line 744, in pyarrow._parquet.RowGroupMetaData.__cinit__ IndexError: 43 out of bounds ## Step 23 · agent · 2026-08-31T18:30:11.087Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:15.879Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq\\nx=pd.read_parquet('/task/data/train_listens.parquet',filters=[('uid','==',100)],columns=['uid','timestamp','item_id','played_ratio_pct'])\\nprint(len(x),x.timestamp.head(20).tolist(),x.timestamp.tail(20).tolist(),x.timestamp.is_monotonic_increasing)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:30:17.462Z Script completed Wall time 1.6 seconds Output: 3266 [39420, 39420, 39625, 40110, 40360, 40380, 40640, 40785, 40885, 41130, 41545, 41580, 41580, 42115, 42115, 42115, 42115, 42115, 42115, 42210] [24748595, 24748600, 24749145, 24749145, 24749145, 24749310, 24749680, 24749885, 24750055, 24750415, 24750420, 24750585, 24751320, 24751320, 24753480, 24753730, 24753790, 24753920, 24754040, 24767450] True ## Step 24 · agent · 2026-08-31T18:30:37.701Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T18:30:56.267Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-08-31T18:31:14.922Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-08-31T18:31:33.552Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:31:52.216Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:32:10.879Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:32:29.509Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:32:33.802Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:24.335Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_two_tower.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a temporal, history-derived PyTorch two-tower ranker.\\n+\\n+The data path is event based: this program never materializes a global\\n+user-item matrix. Each training example consists of a bounded interaction\\n+history at a cutoff and one positive item from the following seven days.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+from dataclasses import dataclass\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+GAP = 1_800\\n+WINDOW = 604_800\\n+STEP = GAP + WINDOW\\n+HISTORY_SPAN = 90 * 86_400\\n+MAX_HISTORY = 192\\n+MAX_STATE = 256\\n+\\n+\\n+@dataclass\\n+class Profile:\\n+ hist: np.ndarray\\n+ hist_weight: np.ndarray\\n+ liked: np.ndarray\\n+ disliked: np.ndarray\\n+ numeric: np.ndarray\\n+ positives: np.ndarray | None = None\\n+ grades: np.ndarray | None = None\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"Encode bounded playback and explicit-preference histories; no uid table.\\\"\\\"\\\"\\n+\\n+ def __init__(self, n_items: int, context_dim: int = 48, latent_dim: int = 63):\\n+ super().__init__()\\n+ self.history_items = nn.EmbeddingBag(\\n+ n_items, context_dim, mode=\\\"sum\\\", include_last_offset=True\\n+ )\\n+ self.liked_items = nn.EmbeddingBag(\\n+ n_items, context_dim, mode=\\\"mean\\\", include_last_offset=True\\n+ )\\n+ self.disliked_items = nn.EmbeddingBag(\\n+ n_items, context_dim, mode=\\\"mean\\\", include_last_offset=True\\n+ )\\n+ self.network = nn.Sequential(\\n+ nn.Linear(context_dim * 3 + 4, 128),\\n+ nn.LayerNorm(128),\\n+ nn.GELU(),\\n+ nn.Linear(128, latent_dim),\\n+ )\\n+ for emb in (self.history_items, self.liked_items, self.disliked_items):\\n+ nn.init.normal_(emb.weight, std=0.04)\\n+\\n+ def forward(\\n+ self,\\n+ hist: torch.Tensor,\\n+ hist_offsets: torch.Tensor,\\n+ hist_weight: torch.Tensor,\\n+ liked: torch.Tensor,\\n+ liked_offsets: torch.Tensor,\\n+ disliked: torch.Tensor,\\n+ disliked_offsets: torch.Tensor,\\n+ numeric: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ h = self.history_items(hist, hist_offsets, per_sample_weights=hist_weight)\\n+ liked_h = self.liked_items(liked, liked_offsets)\\n+ disliked_h = self.disliked_items(disliked, disliked_offsets)\\n+ z = self.network(torch.cat((h, liked_h, disliked_h, numeric), dim=1))\\n+ # The last coordinate supports an item-only prior while preserving an\\n+ # exact same-dimensional dot-product decomposition.\\n+ return torch.cat((F.normalize(z, dim=1), torch.ones_like(z[:, :1])), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Independently encode catalog item identity and offline popularity features.\\\"\\\"\\\"\\n+\\n+ def __init__(self, static_features: torch.Tensor, context_dim: int = 48, latent_dim: int = 63):\\n+ super().__init__()\\n+ n_items = len(static_features)\\n+ self.item_id = nn.Embedding(n_items, context_dim)\\n+ self.register_buffer(\\\"static_features\\\", static_features)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(context_dim + static_features.shape[1], 128),\\n+ nn.LayerNorm(128),\\n+ nn.GELU(),\\n+ nn.Linear(128, latent_dim),\\n+ )\\n+ self.bias = nn.Embedding(n_items, 1)\\n+ nn.init.normal_(self.item_id.weight, std=0.04)\\n+ nn.init.zeros_(self.bias.weight)\\n+\\n+ def forward(self, item: torch.Tensor, popularity_weight: float = 0.0) -> torch.Tensor:\\n+ static = self.static_features[item]\\n+ z = self.network(torch.cat((self.item_id(item), static), dim=1))\\n+ # static[:, 0] is standardized log popularity. This is part of the\\n+ # item vector, not a post-hoc reranker feature.\\n+ prior = self.bias(item) + popularity_weight * static[:, :1]\\n+ return torch.cat((F.normalize(z, dim=1), prior), dim=1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, static_features: torch.Tensor):\\n+ super().__init__()\\n+ self.user_tower = UserTower(len(static_features))\\n+ self.item_tower = ItemTower(static_features)\\n+\\n+\\n+def positions(values: np.ndarray, sorted_ids: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ pos = np.searchsorted(sorted_ids, values)\\n+ valid = pos < len(sorted_ids)\\n+ valid[valid] &= sorted_ids[pos[valid]] == values[valid]\\n+ return pos, valid\\n+\\n+\\n+def load_listens(\\n+ files: list[Path], target_ids: np.ndarray, catalog_ids: np.ndarray, min_time: int\\n+) -> dict[str, np.ndarray]:\\n+ \\\"\\\"\\\"Stream and retain only useful target-user/catalog positive events.\\\"\\\"\\\"\\n+ cat_sort_order = np.argsort(catalog_ids)\\n+ cat_sorted = catalog_ids[cat_sort_order]\\n+ pieces: dict[str, list[np.ndarray]] = {k: [] for k in (\\\"u\\\", \\\"t\\\", \\\"i\\\", \\\"ratio\\\", \\\"organic\\\")}\\n+ for path in files:\\n+ parquet = pq.ParquetFile(path)\\n+ for batch in parquet.iter_batches(\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ batch_size=1_000_000,\\n+ ):\\n+ x = batch.to_pandas()\\n+ raw_u = x.uid.to_numpy()\\n+ raw_i = x.item_id.to_numpy()\\n+ up, uv = positions(raw_u, target_ids)\\n+ cp, cv = positions(raw_i, cat_sorted)\\n+ keep = uv & cv & (x.timestamp.to_numpy() >= min_time) & (x.played_ratio_pct.to_numpy() >= 50)\\n+ if not keep.any():\\n+ continue\\n+ pieces[\\\"u\\\"].append(up[keep].astype(np.int32))\\n+ pieces[\\\"t\\\"].append(x.timestamp.to_numpy()[keep].astype(np.int64))\\n+ pieces[\\\"i\\\"].append(cat_sort_order[cp[keep]].astype(np.int32))\\n+ pieces[\\\"ratio\\\"].append(x.played_ratio_pct.to_numpy()[keep].astype(np.float32))\\n+ pieces[\\\"organic\\\"].append(x.is_organic.to_numpy()[keep].astype(np.uint8))\\n+ out = {k: np.concatenate(v) for k, v in pieces.items()}\\n+ order = np.lexsort((out[\\\"t\\\"], out[\\\"u\\\"]))\\n+ return {k: v[order] for k, v in out.items()}\\n+\\n+\\n+def load_actions(\\n+ include_validation: bool, target_ids: np.ndarray, catalog_ids: np.ndarray\\n+) -> pd.DataFrame:\\n+ cat_order = np.argsort(catalog_ids)\\n+ cat_sorted = catalog_ids[cat_order]\\n+ frames: list[pd.DataFrame] = []\\n+ specs = [\\n+ (\\\"likes\\\", 0, 1), (\\\"unlikes\\\", 0, 0),\\n+ (\\\"dislikes\\\", 1, 1), (\\\"undislikes\\\", 1, 0),\\n+ ]\\n+ prefixes = [\\\"train\\\", \\\"val\\\"] if include_validation else [\\\"train\\\"]\\n+ for prefix in prefixes:\\n+ for family, channel, active in specs:\\n+ x = pd.read_parquet(DATA / f\\\"{prefix}_{family}.parquet\\\", columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ up, uv = positions(x.uid.to_numpy(), target_ids)\\n+ cp, cv = positions(x.item_id.to_numpy(), cat_sorted)\\n+ keep = uv & cv\\n+ if not keep.any():\\n+ continue\\n+ frames.append(pd.DataFrame({\\n+ \\\"u\\\": up[keep].astype(np.int32),\\n+ \\\"t\\\": x.timestamp.to_numpy()[keep].astype(np.int64),\\n+ \\\"i\\\": cat_order[cp[keep]].astype(np.int32),\\n+ \\\"channel\\\": np.full(keep.sum(), channel, np.int8),\\n+ \\\"active\\\": np.full(keep.sum(), active, np.int8),\\n+ \\\"organic\\\": x.is_organic.to_numpy()[keep].astype(np.int8),\\n+ \\\"positive_action\\\": np.full(keep.sum(), family == \\\"likes\\\", bool),\\n+ }))\\n+ return pd.concat(frames, ignore_index=True).sort_values([\\\"u\\\", \\\"t\\\"])\\n+\\n+\\n+def active_state(actions: pd.DataFrame, cutoff: int) -> tuple[dict[int, np.ndarray], dict[int, np.ndarray]]:\\n+ before = actions[actions.t < cutoff]\\n+ result: list[dict[int, np.ndarray]] = []\\n+ for channel in (0, 1):\\n+ x = before[before.channel == channel].drop_duplicates([\\\"u\\\", \\\"i\\\"], keep=\\\"last\\\")\\n+ x = x[x.active == 1].sort_values([\\\"u\\\", \\\"t\\\"], ascending=[True, False])\\n+ result.append({int(u): g.i.to_numpy(np.int64)[:MAX_STATE] for u, g in x.groupby(\\\"u\\\", sort=False)})\\n+ return result[0], result[1]\\n+\\n+\\n+def supplied_state(path: Path, target_ids: np.ndarray, catalog_ids: np.ndarray) -> tuple[dict[int, np.ndarray], dict[int, np.ndarray]]:\\n+ x = pd.read_parquet(path)\\n+ cat_order = np.argsort(catalog_ids)\\n+ cat_sorted = catalog_ids[cat_order]\\n+ up, uv = positions(x.uid.to_numpy(), target_ids)\\n+ cp, cv = positions(x.item_id.to_numpy(), cat_sorted)\\n+ keep = uv & cv\\n+ x = pd.DataFrame({\\\"u\\\": up[keep], \\\"i\\\": cat_order[cp[keep]], \\\"state\\\": x.state.to_numpy()[keep]})\\n+ out = []\\n+ for state in (\\\"liked\\\", \\\"disliked\\\"):\\n+ y = x[x.state == state]\\n+ out.append({int(u): g.i.to_numpy(np.int64)[:MAX_STATE] for u, g in y.groupby(\\\"u\\\", sort=False)})\\n+ return out[0], out[1]\\n+\\n+\\n+def labels_for_cutoff(\\n+ listens: dict[str, np.ndarray], actions: pd.DataFrame, cutoff: int\\n+) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n+ start, end = cutoff + GAP, cutoff + GAP + WINDOW\\n+ m = ((listens[\\\"t\\\"] >= start) & (listens[\\\"t\\\"] < end) &\\n+ (listens[\\\"organic\\\"] == 1) & (listens[\\\"ratio\\\"] >= 80))\\n+ label: dict[int, dict[int, int]] = {}\\n+ for u, i in zip(listens[\\\"u\\\"][m], listens[\\\"i\\\"][m]):\\n+ label.setdefault(int(u), {})[int(i)] = 1\\n+ likes = actions[(actions.positive_action) & (actions.organic == 1) &\\n+ (actions.t >= start) & (actions.t < end)]\\n+ for u, i in likes[[\\\"u\\\", \\\"i\\\"]].itertuples(index=False, name=None):\\n+ label.setdefault(int(u), {})[int(i)] = 3\\n+ return {\\n+ u: (np.fromiter(items.keys(), dtype=np.int64), np.fromiter(items.values(), dtype=np.float32))\\n+ for u, items in label.items()\\n+ }\\n+\\n+\\n+def user_slices(user_index: np.ndarray, n_users: int) -> tuple[np.ndarray, np.ndarray]:\\n+ return (\\n+ np.searchsorted(user_index, np.arange(n_users), side=\\\"left\\\"),\\n+ np.searchsorted(user_index, np.arange(n_users), side=\\\"right\\\"),\\n+ )\\n+\\n+\\n+def make_profiles(\\n+ cutoff: int,\\n+ users: list[int],\\n+ listens: dict[str, np.ndarray],\\n+ bounds: tuple[np.ndarray, np.ndarray],\\n+ liked: dict[int, np.ndarray],\\n+ disliked: dict[int, np.ndarray],\\n+ labels: dict[int, tuple[np.ndarray, np.ndarray]] | None = None,\\n+) -> list[Profile]:\\n+ left, right = bounds\\n+ profiles: list[Profile] = []\\n+ for u in users:\\n+ sl = slice(left[u], right[u])\\n+ tt, ii = listens[\\\"t\\\"][sl], listens[\\\"i\\\"][sl]\\n+ rr, oo = listens[\\\"ratio\\\"][sl], listens[\\\"organic\\\"][sl]\\n+ lo = np.searchsorted(tt, cutoff - HISTORY_SPAN, side=\\\"left\\\")\\n+ hi = np.searchsorted(tt, cutoff, side=\\\"left\\\")\\n+ # Work backward and retain a request-efficient bounded unique history.\\n+ weights: dict[int, float] = {}\\n+ for j in range(hi - 1, lo - 1, -1):\\n+ item = int(ii[j])\\n+ if item not in weights and len(weights) >= MAX_HISTORY:\\n+ continue\\n+ age_days = (cutoff - int(tt[j])) / 86_400.0\\n+ event_weight = (0.65 + min(float(rr[j]), 100.0) / 100.0) * (1.25 if oo[j] else 1.0)\\n+ event_weight *= math.exp(-age_days / 35.0)\\n+ weights[item] = weights.get(item, 0.0) + event_weight\\n+ hist = np.fromiter(weights.keys(), dtype=np.int64)\\n+ hw = np.fromiter(weights.values(), dtype=np.float32)\\n+ if len(hw):\\n+ hw /= hw.sum()\\n+ lk = liked.get(u, np.empty(0, dtype=np.int64))\\n+ dk = disliked.get(u, np.empty(0, dtype=np.int64))\\n+ numeric = np.asarray([\\n+ math.log1p(max(0, hi - lo)) / 8.0,\\n+ math.log1p(len(hist)) / 6.0,\\n+ math.log1p(len(lk)) / 6.0,\\n+ math.log1p(len(dk)) / 4.0,\\n+ ], dtype=np.float32)\\n+ pos = grade = None\\n+ if labels is not None:\\n+ pos, grade = labels[u]\\n+ profiles.append(Profile(hist, hw, lk, dk, numeric, pos, grade))\\n+ return profiles\\n+\\n+\\n+def flatten(values: list[np.ndarray], weights: list[np.ndarray] | None = None) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:\\n+ lengths = np.asarray([len(x) for x in values], dtype=np.int64)\\n+ offsets = np.concatenate(([0], np.cumsum(lengths)))\\n+ flat = np.concatenate(values) if lengths.sum() else np.empty(0, dtype=np.int64)\\n+ flat_weight = None\\n+ if weights is not None:\\n+ flat_weight = np.concatenate(weights) if lengths.sum() else np.empty(0, dtype=np.float32)\\n+ return (torch.from_numpy(flat).long(), torch.from_numpy(offsets).long(),\\n+ None if flat_weight is None else torch.from_numpy(flat_weight).float())\\n+\\n+\\n+def user_batch(tower: UserTower, profiles: list[Profile]) -> torch.Tensor:\\n+ hist, ho, hw = flatten([p.hist for p in profiles], [p.hist_weight for p in profiles])\\n+ liked, lo, _ = flatten([p.liked for p in profiles])\\n+ disliked, do, _ = flatten([p.disliked for p in profiles])\\n+ numeric = torch.from_numpy(np.stack([p.numeric for p in profiles])).float()\\n+ return tower(hist, ho, hw, liked, lo, disliked, do, numeric)\\n+\\n+\\n+def train_model(\\n+ model: TwoTower,\\n+ profiles: list[Profile],\\n+ popularity: np.ndarray,\\n+ epochs: int,\\n+ seed: int,\\n+ batch_size: int,\\n+) -> None:\\n+ rng = np.random.default_rng(seed)\\n+ torch.manual_seed(seed)\\n+ model.train()\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ # Half uniform and half smoothed-popularity negatives.\\n+ neg_prob = np.sqrt(popularity.astype(np.float64) + 1.0)\\n+ neg_prob /= neg_prob.sum()\\n+ indices = np.arange(len(profiles))\\n+ k = 24\\n+ for epoch in range(epochs):\\n+ rng.shuffle(indices)\\n+ losses = []\\n+ for begin in range(0, len(indices), batch_size):\\n+ ix = indices[begin:begin + batch_size]\\n+ batch = [profiles[int(j)] for j in ix]\\n+ pos = np.asarray([\\n+ rng.choice(p.positives, p=(np.power(2.0, p.grades) - 1.0) /\\n+ (np.power(2.0, p.grades) - 1.0).sum())\\n+ for p in batch\\n+ ], dtype=np.int64)\\n+ n_uniform = rng.integers(0, len(popularity), size=(len(batch), k // 2))\\n+ n_pop = rng.choice(len(popularity), size=(len(batch), k - k // 2), p=neg_prob)\\n+ neg = np.concatenate((n_uniform, n_pop), axis=1)\\n+ collision = neg == pos[:, None]\\n+ neg[collision] = (neg[collision] + 1) % len(popularity)\\n+\\n+ optimizer.zero_grad(set_to_none=True)\\n+ uv = user_batch(model.user_tower, batch)\\n+ pv = model.item_tower(torch.from_numpy(pos), popularity_weight=0.04)\\n+ nv = model.item_tower(torch.from_numpy(neg.reshape(-1)), popularity_weight=0.04).view(len(batch), k, -1)\\n+ ps = (uv * pv).sum(1, keepdim=True)\\n+ ns = (uv[:, None, :] * nv).sum(2)\\n+ # Pairwise softplus directly optimizes sampled ordering.\\n+ loss = F.softplus(ns - ps).mean()\\n+ loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ optimizer.step()\\n+ losses.append(float(loss.detach()))\\n+ print(f\\\"epoch {epoch + 1:02d}/{epochs}: loss={np.mean(losses):.5f}\\\", flush=True)\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(\\n+ model: TwoTower,\\n+ profiles: list[Profile],\\n+ eligibility_path: Path,\\n+ output: Path,\\n+ popularity_weight: float,\\n+) -> None:\\n+ model.eval()\\n+ with np.load(eligibility_path, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(np.int64)\\n+ assert np.array_equal(uids, np.sort(uids)), \\\"eligibility uids must align sorted target ids\\\"\\n+ lookup = {int(item): j for j, item in enumerate(catalog_ids)}\\n+ eligible_to_catalog = np.asarray([lookup[int(item)] for item in eligible_items], dtype=np.int64)\\n+ item_parts = []\\n+ for begin in range(0, len(catalog_ids), 2048):\\n+ ids = torch.arange(begin, min(begin + 2048, len(catalog_ids)))\\n+ item_parts.append(model.item_tower(ids, popularity_weight=popularity_weight).cpu())\\n+ item_vec = torch.cat(item_parts)[torch.from_numpy(eligible_to_catalog)]\\n+\\n+ rows: list[tuple[int, int, int]] = []\\n+ for begin in range(0, len(profiles), 64):\\n+ uv = user_batch(model.user_tower, profiles[begin:begin + 64])\\n+ scores = (uv @ item_vec.T).cpu().numpy()\\n+ for local, r in enumerate(range(begin, min(begin + 64, len(profiles)))):\\n+ mask = np.unpackbits(packed[r], count=len(eligible_items), bitorder=\\\"little\\\").astype(bool)\\n+ s = scores[local]\\n+ s[~mask] = -np.inf\\n+ top = np.argpartition(s, -100)[-100:]\\n+ top = top[np.argsort(s[top])[::-1]]\\n+ rows.extend((int(uids[r]), int(eligible_items[c]), rank_no)\\n+ for rank_no, c in enumerate(top, 1))\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(output, index=False)\\n+ print(f\\\"wrote {len(rows)} ranked rows to {output}\\\", flush=True)\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"final\\\"), required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=12)\\n+ parser.add_argument(\\\"--batch-size\\\", type=int, default=256)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ parser.add_argument(\\\"--popularity-weight\\\", type=float, default=0.04)\\n+ parser.add_argument(\\\"--model-output\\\", type=Path)\\n+ args = parser.parse_args()\\n+ random.seed(args.seed)\\n+ np.random.seed(args.seed)\\n+ torch.set_num_threads(min(8, max(1, torch.get_num_threads())))\\n+\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(np.int64)\\n+ target_ids = np.sort(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64))\\n+ include_validation = args.stage == \\\"final\\\"\\n+ inference_cutoff = TRAIN_END + STEP if include_validation else TRAIN_END\\n+ train_cutoffs = [TRAIN_END - k * STEP for k in range(6, 0, -1)]\\n+ if include_validation:\\n+ train_cutoffs.append(TRAIN_END)\\n+ min_time = min(train_cutoffs) - HISTORY_SPAN\\n+ listen_files = [DATA / \\\"train_listens.parquet\\\"]\\n+ if include_validation:\\n+ listen_files.append(DATA / \\\"val_listens.parquet\\\")\\n+ print(\\\"streaming positive catalog interactions\\\", flush=True)\\n+ listens = load_listens(listen_files, target_ids, catalog_ids, min_time)\\n+ actions = load_actions(include_validation, target_ids, catalog_ids)\\n+ bounds = user_slices(listens[\\\"u\\\"], len(target_ids))\\n+ print(f\\\"retained {len(listens['u']):,} positive catalog events\\\", flush=True)\\n+\\n+ training_profiles: list[Profile] = []\\n+ cutoff_summary = {}\\n+ for cutoff in train_cutoffs:\\n+ labels = labels_for_cutoff(listens, actions, cutoff)\\n+ liked, disliked = active_state(actions, cutoff)\\n+ users = sorted(labels)\\n+ profiles = make_profiles(cutoff, users, listens, bounds, liked, disliked, labels)\\n+ training_profiles.extend(profiles)\\n+ cutoff_summary[str(cutoff)] = {\\\"users\\\": len(users), \\\"positives\\\": int(sum(len(v[0]) for v in labels.values()))}\\n+ print(\\\"training windows:\\\", json.dumps(cutoff_summary, sort_keys=True), flush=True)\\n+\\n+ log_pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ organic_share = (catalog.organic_positive_listens.to_numpy(np.float32) /\\n+ np.maximum(catalog.positive_listens.to_numpy(np.float32), 1))\\n+ static = np.stack(((log_pop - log_pop.mean()) / log_pop.std(),\\n+ (organic_share - organic_share.mean()) / organic_share.std()), axis=1)\\n+ model = TwoTower(torch.from_numpy(static).float())\\n+ train_model(model, training_profiles, catalog.positive_listens.to_numpy(),\\n+ args.epochs, args.seed, args.batch_size)\\n+\\n+ pref_file = DATA / (\\\"test_preference_state.parquet\\\" if include_validation else \\\"validation_preference_state.parquet\\\")\\n+ liked, disliked = supplied_state(pref_file, target_ids, catalog_ids)\\n+ inference_profiles = make_profiles(inference_cutoff, list(range(len(target_ids))), listens,\\n+ bounds, liked, disliked)\\n+ eligibility = DATA / (\\\"test_eligible_candidates.npz\\\" if include_validation else \\\"validation_eligible_candidates.npz\\\")\\n+ rank(model, inference_profiles, eligibility, args.output, args.popularity_weight)\\n+ model_output = args.model_output or OUT / f\\\"two_tower_{args.stage}.pt\\\"\\n+ torch.save({\\\"state_dict\\\": model.state_dict(), \\\"stage\\\": args.stage,\\n+ \\\"seed\\\": args.seed, \\\"epochs\\\": args.epochs,\\n+ \\\"popularity_weight\\\": args.popularity_weight,\\n+ \\\"architecture\\\": \\\"history EmbeddingBag MLP + item embedding MLP, 64-d dot product\\\"}, model_output)\\n+ print(f\\\"saved fitted towers to {model_output}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:36:24.388Z Script completed Wall time 0.0 seconds Output: {} ## Step 32 · agent · 2026-08-31T18:36:26.310Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:29.199Z { "code": "const r = await tools.exec_command({cmd:\"python -m py_compile /app/solution/train_two_tower.py 2>&1 || true; /opt/task-venv/bin/python -m py_compile /app/solution/train_two_tower.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:36:29.481Z Script completed Wall time 0.3 seconds Output: ## Step 33 · agent · 2026-08-31T18:36:33.427Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:36:38.996Z { "code": "const p = await tools.update_plan({explanation:\"Data inspection is complete and a valid popularity fallback is already saved. The event-streamed temporal two-tower implementation is ready for its first validation run.\",plan:[\n {step:\"Inspect data schemas, sizes, eligibility, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-derived PyTorch two-tower training and ranking\",status:\"completed\"},\n {step:\"Train baseline, create valid validation/test rankings, and score validation\",status:\"in_progress\"},\n {step:\"Tune features/objective with public validation and refit on train+validation\",status:\"pending\"},\n {step:\"Verify final artifact and document reproducible submission command\",status:\"pending\"}\n]}); text(p)\n" } ### Tool output · 2026-08-31T18:36:39.085Z Script completed Wall time 0.0 seconds Output: {} ## Step 34 · agent · 2026-08-31T18:36:40.510Z ### Thinking unavailable — no text recorded The first temporal model is now training on six disjoint seven-day pseudo-future windows. Each example’s user vector sees only events before its cutoff; future organic 80%-listens and likes supply positives. I’ll score it, then tune only the item-prior coordinate and training settings if the result beats the saved fallback. ### exec · 2026-08-31T18:36:46.911Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_tower_w004.parquet --epochs 12 --popularity-weight 0.04 --model-output /app/solution/two_tower_validation.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:36:57.932Z Script running with cell ID 17 Wall time 11.0 seconds Output: ## Step 35 · agent · 2026-08-31T18:36:59.376Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:00.137Z { "cell_id": "17", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:37:17.092Z Script completed Wall time 17.0 seconds Output: {"chunk_id":"5c8836","wall_time_seconds":30.001778442,"session_id":35487,"original_token_count":21,"output":"streaming positive catalog interactions\nretained 10,756,677 positive catalog events\n"} ## Step 36 · agent · 2026-08-31T18:37:20.465Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:21.839Z { "code": "const r = await tools.write_stdin({session_id:35487,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:37:32.858Z Script running with cell ID 18 Wall time 11.0 seconds Output: ## Step 37 · agent · 2026-08-31T18:37:34.528Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:35.270Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:37:51.863Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"7dfe9a","wall_time_seconds":30.00145397,"session_id":35487,"original_token_count":106,"output":"training windows: {\"21147200\": {\"positives\": 158922, \"users\": 5411}, \"21753800\": {\"positives\": 165819, \"users\": 5563}, \"22360400\": {\"positives\": 168734, \"users\": 5650}, \"22967000\": {\"positives\": 170431, \"users\": 5754}, \"23573600\": {\"positives\": 171275, \"users\": 5830}, \"24180200\": {\"positives\": 176193, \"users\": 5989}}\nepoch 01/12: loss=0.53234\nepoch 02/12: loss=0.48581\nepoch 03/12: loss=0.46023\nepoch 04/12: loss=0.44212\n"} ## Step 38 · agent · 2026-08-31T18:37:53.390Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:54.770Z { "code": "const r = await tools.write_stdin({session_id:35487,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:38:05.792Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 39 · agent · 2026-08-31T18:38:07.318Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:08.135Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:38:24.797Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"2c4b74","wall_time_seconds":30.001509512,"session_id":35487,"original_token_count":33,"output":"epoch 05/12: loss=0.42954\nepoch 06/12: loss=0.41530\nepoch 07/12: loss=0.40503\nepoch 08/12: loss=0.39792\nepoch 09/12: loss=0.38838\n"} ## Step 40 · agent · 2026-08-31T18:38:26.141Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:27.528Z { "code": "const r = await tools.write_stdin({session_id:35487,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:38:38.554Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 41 · agent · 2026-08-31T18:38:40.086Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:40.862Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:38:45.495Z Script completed Wall time 4.6 seconds Output: {"chunk_id":"b278c2","wall_time_seconds":17.936587269,"exit_code":0,"original_token_count":53,"output":"epoch 10/12: loss=0.38362\nepoch 11/12: loss=0.37819\nepoch 12/12: loss=0.37275\nwrote 845600 ranked rows to /app/solution/validation_tower_w004.parquet\nsaved fitted towers to /app/solution/two_tower_validation.pt\n"} ## Step 42 · agent · 2026-08-31T18:38:46.908Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:49.176Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_tower_w004.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:38:52.503Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_tower_w004.parquet", "scores": { "quality_ndcg@10": 0.023093, "quality_users": 4263 } } ## Step 43 · agent · 2026-08-31T18:39:03.885Z ### Thinking unavailable — no text recorded The neural ranker scores 0.023093 NDCG@10 versus 0.010710 for popularity—more than a 2× lift. I’m now tuning the explicit popularity coordinate from the same fitted towers, which is cheap and preserves the exact dot-product form; no validation event is being added to the user representation. ### exec · 2026-08-31T18:39:33.084Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rerank_saved.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Apply saved fitted towers with one or more dot-product prior weights.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+from train_two_tower import (\\n+ DATA, HISTORY_SPAN, STEP, TRAIN_END, TwoTower, load_listens, make_profiles,\\n+ rank, supplied_state, user_slices,\\n+)\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"final\\\"), required=True)\\n+ parser.add_argument(\\\"--model\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--output-template\\\", required=True, help=\\\"Python format string with {weight}\\\")\\n+ parser.add_argument(\\\"--weights\\\", nargs=\\\"+\\\", type=float, required=True)\\n+ args = parser.parse_args()\\n+ final = args.stage == \\\"final\\\"\\n+ cutoff = TRAIN_END + STEP if final else TRAIN_END\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(np.int64)\\n+ target_ids = np.sort(pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64))\\n+ files = [DATA / \\\"train_listens.parquet\\\"] + ([DATA / \\\"val_listens.parquet\\\"] if final else [])\\n+ listens = load_listens(files, target_ids, catalog_ids, cutoff - HISTORY_SPAN)\\n+ bounds = user_slices(listens[\\\"u\\\"], len(target_ids))\\n+ pref = DATA / (\\\"test_preference_state.parquet\\\" if final else \\\"validation_preference_state.parquet\\\")\\n+ liked, disliked = supplied_state(pref, target_ids, catalog_ids)\\n+ profiles = make_profiles(cutoff, list(range(len(target_ids))), listens, bounds, liked, disliked)\\n+\\n+ log_pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ share = catalog.organic_positive_listens.to_numpy(np.float32) / np.maximum(catalog.positive_listens.to_numpy(np.float32), 1)\\n+ static = np.stack(((log_pop - log_pop.mean()) / log_pop.std(),\\n+ (share - share.mean()) / share.std()), axis=1)\\n+ model = TwoTower(torch.from_numpy(static).float())\\n+ model.load_state_dict(torch.load(args.model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+ for weight in args.weights:\\n+ output = Path(args.output_template.format(weight=str(weight).replace(\\\".\\\", \\\"p\\\")))\\n+ eligibility = DATA / (\\\"test_eligible_candidates.npz\\\" if final else \\\"validation_eligible_candidates.npz\\\")\\n+ rank(model, profiles, eligibility, output, weight)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:39:33.135Z Script completed Wall time 0.0 seconds Output: {} ## Step 44 · agent · 2026-08-31T18:39:35.258Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:39.190Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/rerank_saved.py --stage validation --model /app/solution/two_tower_validation.pt --output-template '/app/solution/validation_tower_w{weight}.parquet' --weights 0 0.015 0.03 0.06 0.1 0.16\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:39:50.211Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 45 · agent · 2026-08-31T18:39:52.314Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:53.094Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:40:07.615Z Script completed Wall time 14.5 seconds Output: {"chunk_id":"eec1b5","wall_time_seconds":28.243447679,"exit_code":0,"original_token_count":110,"output":"wrote 845600 ranked rows to /app/solution/validation_tower_w0p0.parquet\nwrote 845600 ranked rows to /app/solution/validation_tower_w0p015.parquet\nwrote 845600 ranked rows to /app/solution/validation_tower_w0p03.parquet\nwrote 845600 ranked rows to /app/solution/validation_tower_w0p06.parquet\nwrote 845600 ranked rows to /app/solution/validation_tower_w0p1.parquet\nwrote 845600 ranked rows to /app/solution/validation_tower_w0p16.parquet\n"} ## Step 46 · agent · 2026-08-31T18:40:09.236Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:14.960Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_tower_w0p0.parquet /app/solution/validation_tower_w0p015.parquet /app/solution/validation_tower_w0p03.parquet /app/solution/validation_tower_w0p06.parquet /app/solution/validation_tower_w0p1.parquet /app/solution/validation_tower_w0p16.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:40:25.981Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 47 · agent · 2026-08-31T18:40:27.401Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:28.191Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:40:34.815Z Script completed Wall time 6.6 seconds Output: { "model": "/app/solution/validation_tower_w0p0.parquet", "scores": { "quality_ndcg@10": 0.022885, "quality_users": 4263 } } { "model": "/app/solution/validation_tower_w0p015.parquet", "scores": { "quality_ndcg@10": 0.023033, "quality_users": 4263 } } { "model": "/app/solution/validation_tower_w0p03.parquet", "scores": { "quality_ndcg@10": 0.023078, "quality_users": 4263 } } { "model": "/app/solution/validation_tower_w0p06.parquet", "scores": { "quality_ndcg@10": 0.02303, "quality_users": 4263 } } { "model": "/app/solution/validation_tower_w0p1.parquet", "scores": { "quality_ndcg@10": 0.022267, "quality_users": 4263 } } { "model": "/app/solution/validation_tower_w0p16.parquet", "scores": { "quality_ndcg@10": 0.021518, "quality_users": 4263 } } ## Step 48 · agent · 2026-08-31T18:40:54.285Z ### Thinking unavailable — no text recorded ## Step 49 · agent · 2026-08-31T18:40:57.547Z ### Thinking unavailable — no text recorded Prior tuning is flat around 0.03–0.04, with the original 0.04 run still best at 0.023093. I’m testing a second independently initialized tower and a dot-product ensemble; concatenating the two user vectors and the two item vectors remains a single exact MIPS-compatible two-tower score, while usually reducing embedding variance. ### exec · 2026-08-31T18:41:04.437Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_tower_seed17.parquet --epochs 12 --seed 17 --popularity-weight 0.04 --model-output /app/solution/two_tower_validation_seed17.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:41:15.454Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 50 · agent · 2026-08-31T18:41:16.969Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:17.776Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:41:34.617Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"f25379","wall_time_seconds":30.001575777,"session_id":33732,"original_token_count":21,"output":"streaming positive catalog interactions\nretained 10,756,677 positive catalog events\n"} ## Step 51 · agent · 2026-08-31T18:41:36.181Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:37.600Z { "code": "const r = await tools.write_stdin({session_id:33732,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:41:48.620Z Script running with cell ID 26 Wall time 11.0 seconds Output: ## Step 52 · agent · 2026-08-31T18:41:50.171Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:51.085Z { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:07.624Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"20d8fb","wall_time_seconds":30.000851097,"session_id":33732,"original_token_count":106,"output":"training windows: {\"21147200\": {\"positives\": 158922, \"users\": 5411}, \"21753800\": {\"positives\": 165819, \"users\": 5563}, \"22360400\": {\"positives\": 168734, \"users\": 5650}, \"22967000\": {\"positives\": 170431, \"users\": 5754}, \"23573600\": {\"positives\": 171275, \"users\": 5830}, \"24180200\": {\"positives\": 176193, \"users\": 5989}}\nepoch 01/12: loss=0.53450\nepoch 02/12: loss=0.48472\nepoch 03/12: loss=0.45898\nepoch 04/12: loss=0.44110\n"} ## Step 53 · agent · 2026-08-31T18:42:09.476Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:10.883Z { "code": "const r = await tools.write_stdin({session_id:33732,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:42:21.911Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 54 · agent · 2026-08-31T18:42:23.340Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:24.114Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:42:40.916Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"a28869","wall_time_seconds":30.000356407,"session_id":33732,"original_token_count":33,"output":"epoch 05/12: loss=0.42915\nepoch 06/12: loss=0.41776\nepoch 07/12: loss=0.40692\nepoch 08/12: loss=0.39993\nepoch 09/12: loss=0.39228\n"} ## Step 55 · agent · 2026-08-31T18:42:43.868Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:45.278Z { "code": "const r = await tools.write_stdin({session_id:33732,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:42:56.302Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 56 · agent · 2026-08-31T18:42:57.797Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:58.557Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:43:02.124Z Script completed Wall time 3.6 seconds Output: {"chunk_id":"3f46b2","wall_time_seconds":16.817719363,"exit_code":0,"original_token_count":55,"output":"epoch 10/12: loss=0.38661\nepoch 11/12: loss=0.37888\nepoch 12/12: loss=0.37469\nwrote 845600 ranked rows to /app/solution/validation_tower_seed17.parquet\nsaved fitted towers to /app/solution/two_tower_validation_seed17.pt\n"} ## Step 57 · agent · 2026-08-31T18:43:04.799Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:07.110Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_tower_seed17.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:43:10.447Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_tower_seed17.parquet", "scores": { "quality_ndcg@10": 0.023714, "quality_users": 4263 } } ## Step 58 · agent · 2026-08-31T18:43:21.658Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:52.068Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rerank_saved.py\\n@@\\n import torch\\n@@\\n parser.add_argument(\\\"--stage\\\", choices=(\\\"validation\\\", \\\"final\\\"), required=True)\\n- parser.add_argument(\\\"--model\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--model\\\", type=Path, nargs=\\\"+\\\", required=True)\\n@@\\n- model = TwoTower(torch.from_numpy(static).float())\\n- model.load_state_dict(torch.load(args.model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+ models = []\\n+ for model_path in args.model:\\n+ model = TwoTower(torch.from_numpy(static).float())\\n+ model.load_state_dict(torch.load(model_path, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n+ models.append(model)\\n for weight in args.weights:\\n output = Path(args.output_template.format(weight=str(weight).replace(\\\".\\\", \\\"p\\\")))\\n eligibility = DATA / (\\\"test_eligible_candidates.npz\\\" if final else \\\"validation_eligible_candidates.npz\\\")\\n- rank(model, profiles, eligibility, output, weight)\\n+ if len(models) == 1:\\n+ rank(models[0], profiles, eligibility, output, weight)\\n+ continue\\n+ # Concatenation makes the mean ensemble itself an exact dot product.\\n+ with np.load(eligibility, allow_pickle=False) as z:\\n+ uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n+ lookup = {int(item): j for j, item in enumerate(catalog_ids)}\\n+ remap = torch.tensor([lookup[int(item)] for item in eligible_items])\\n+ item_vectors = []\\n+ for model in models:\\n+ model.eval()\\n+ chunks = [model.item_tower(torch.arange(b, min(b + 2048, len(catalog_ids))), weight)\\n+ for b in range(0, len(catalog_ids), 2048)]\\n+ item_vectors.append(torch.cat(chunks)[remap])\\n+ rows = []\\n+ with torch.inference_mode():\\n+ for begin in range(0, len(profiles), 64):\\n+ scores = None\\n+ batch = profiles[begin:begin + 64]\\n+ for model, iv in zip(models, item_vectors):\\n+ part = user_batch(model.user_tower, batch) @ iv.T\\n+ scores = part if scores is None else scores + part\\n+ scores = (scores / len(models)).cpu().numpy()\\n+ for local, r in enumerate(range(begin, min(begin + 64, len(profiles)))):\\n+ mask = np.unpackbits(packed[r], count=len(eligible_items), bitorder=\\\"little\\\").astype(bool)\\n+ s = scores[local]; s[~mask] = -np.inf\\n+ top = np.argpartition(s, -100)[-100:]\\n+ top = top[np.argsort(s[top])[::-1]]\\n+ rows.extend((int(uids[r]), int(eligible_items[c]), rank_no)\\n+ for rank_no, c in enumerate(top, 1))\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(output, index=False)\\n+ print(f\\\"wrote {len(rows)} ensemble rows to {output}\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:43:52.146Z Script completed Wall time 0.0 seconds Output: {} ## Step 59 · agent · 2026-08-31T18:43:54.632Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:57.893Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rerank_saved.py\\n@@\\n DATA, HISTORY_SPAN, STEP, TRAIN_END, TwoTower, load_listens, make_profiles,\\n- rank, supplied_state, user_slices,\\n+ rank, supplied_state, user_batch, user_slices,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:43:57.951Z Script completed Wall time 0.0 seconds Output: {} ## Step 60 · agent · 2026-08-31T18:44:00.020Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:05.021Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/rerank_saved.py && /opt/task-venv/bin/python -u /app/solution/rerank_saved.py --stage validation --model /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_seed17.pt --output-template '/app/solution/validation_ensemble_w{weight}.parquet' --weights 0.02 0.04 0.06\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:44:16.040Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T18:44:17.644Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:18.496Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:44:28.722Z Script completed Wall time 10.2 seconds Output: {"chunk_id":"7b5ea5","wall_time_seconds":23.520448828,"exit_code":0,"original_token_count":59,"output":"wrote 845600 ensemble rows to /app/solution/validation_ensemble_w0p02.parquet\nwrote 845600 ensemble rows to /app/solution/validation_ensemble_w0p04.parquet\nwrote 845600 ensemble rows to /app/solution/validation_ensemble_w0p06.parquet\n"} ## Step 62 · agent · 2026-08-31T18:44:31.262Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:33.634Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:44:43.559Z Script completed Wall time 9.9 seconds Output: { "model": "/app/solution/validation_ensemble_w0p02.parquet", "scores": { "quality_ndcg@10": 0.023947, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_w0p04.parquet", "scores": { "quality_ndcg@10": 0.023785, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_w0p06.parquet", "scores": { "quality_ndcg@10": 0.023231, "quality_users": 4263 } } ## Step 63 · agent · 2026-08-31T18:45:03.259Z ### Thinking unavailable — no text recorded ## Step 64 · agent · 2026-08-31T18:45:06.758Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:24.472Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys;sys.path.insert(0,'/app/solution')\\nfrom train_two_tower import *\\nimport pandas as pd,numpy as np\\ncat=pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy(np.int64); users=np.sort(pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64))\\nls=load_listens([DATA/'train_listens.parquet'],users,cat,0); bd=user_slices(ls['u'],len(users)); act=load_actions(False,users,cat); labels=labels_for_cutoff(ls,act,TRAIN_END)\\nz=np.load(DATA/'validation_eligible_candidates.npz'); eitems=z['item_ids']; packed=z['packed_eligible']; lookup={int(x):i for i,x in enumerate(cat)}; ecol=np.array([lookup[int(x)] for x in eitems]); inv=np.empty(len(cat),int); inv[ecol]=np.arange(len(cat))\\nremoved=[]; overlap=[]; label_total=label_elig=label_seen=0\\nfor u in range(len(users)):\\n mask=np.unpackbits(packed[u],count=len(eitems),bitorder='little').astype(bool); rem=set(ecol[~mask]); removed.append(len(rem))\\n l,r=bd[0][u],bd[1][u]; seen=set(ls['i'][l:r][ls['t'][l:r]\", line 14, in \nZeroDivisionError: division by zero\n"} ## Step 66 · agent · 2026-08-31T18:45:46.003Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:59.169Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys;sys.path.insert(0,'/app/solution')\\nfrom train_two_tower import *\\nimport pandas as pd,numpy as np\\ncat=pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy(np.int64); users=np.sort(pd.read_parquet(DATA/'target_users.parquet').uid.to_numpy(np.int64))\\nls=load_listens([DATA/'train_listens.parquet'],users,cat,0); bd=user_slices(ls['u'],len(users)); act=load_actions(False,users,cat)\\nfor cutoff in [21147200,22967000,24180200]:\\n labels=labels_for_cutoff(ls,act,cutoff); total=seen90=seenall=0\\n for u,(pos,g) in labels.items():\\n l,r=bd[0][u],bd[1][u]; tt=ls['t'][l:r]; ii=ls['i'][l:r]\\n allset=set(ii[tt=cutoff-HISTORY_SPAN)].tolist())\\n total+=len(pos);seenall+=sum(int(i) in allset for i in pos);seen90+=sum(int(i) in recent for i in pos)\\n print(cutoff,total,'seen all',seenall,seenall/total,'seen90',seen90,seen90/total,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:46:10.188Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T18:46:11.867Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:12.918Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:46:15.340Z Script completed Wall time 2.4 seconds Output: {"chunk_id":"329883","wall_time_seconds":15.990692517,"exit_code":0,"original_token_count":63,"output":"21147200 158922 seen all 117744 0.740891758221014 seen90 108318 0.6815796428436591\n22967000 170431 seen all 124083 0.728054168549149 seen90 114341 0.6708932060481955\n24180200 176193 seen all 128259 0.7279460591510446 seen90 117495 0.6668539612810952\n"} ## Step 68 · agent · 2026-08-31T18:46:29.888Z ### Thinking unavailable — no text recorded ## Step 69 · agent · 2026-08-31T18:46:42.220Z ### Thinking unavailable — no text recorded A useful eligibility pattern emerged: about 70% of excluded catalog items are explained by the user’s prior positive plays, and roughly 73% of my pseudo-window labels are repeat plays that would likely have been outside the retrieval boundary. I’m correcting the temporal training labels to learn discovery among unseen candidates, which should align the loss much more closely with the scored objective. ### exec · 2026-08-31T18:47:03.723Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\\nD='/task/data'; users=np.sort(pd.read_parquet(f'{D}/target_users.parquet').uid.to_numpy())\\nrng=np.random.default_rng(1); sample=np.sort(rng.choice(users,300,replace=False)); uset=set(map(int,sample)); cat=set(map(int,pd.read_parquet(f'{D}/candidate_catalog.parquet').item_id));\\nanyseen={int(u):set() for u in sample}; posseen={int(u):set() for u in sample}; recent={int(u):set() for u in sample}\\nfor b in pq.ParquetFile(f'{D}/train_listens.parquet').iter_batches(columns=['uid','timestamp','item_id','played_ratio_pct'],batch_size=1000000):\\n x=b.to_pandas(); x=x[x.uid.isin(uset)&x.item_id.isin(cat)]\\n for u,t,i,r in x.itertuples(index=False,name=None):\\n anyseen[int(u)].add(int(i))\\n if r>=50: posseen[int(u)].add(int(i))\\n if r>=50 and t>=24786800-90*86400: recent[int(u)].add(int(i))\\nz=np.load(f'{D}/validation_eligible_candidates.npz'); zr={int(u):r for r,u in enumerate(z['uids'])}; items=z['item_ids']; packed=z['packed_eligible'];\\nvals=[]\\nfor u in sample:\\n mask=np.unpackbits(packed[zr[int(u)]],count=len(items),bitorder='little').astype(bool); rem=set(map(int,items[~mask]));\\n vals.append((len(rem),len(rem&anyseen[int(u)]),len(rem&posseen[int(u)]),len(rem&recent[int(u)]),len(anyseen[int(u)])))\\na=np.array(vals);print('means removed, any overlap, pos overlap, recent pos overlap, any seen',a.mean(0));print('shares',a[:,1:4].sum(0)/a[:,0].sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:47:13.265Z Script completed Wall time 9.5 seconds Output: {"chunk_id":"8a8cf0","wall_time_seconds":9.365519283,"exit_code":0,"original_token_count":45,"output":"means removed, any overlap, pos overlap, recent pos overlap, any seen [890.07 888.49 620.60333333 331.91333333 888.49 ]\nshares [0.99822486 0.69725228 0.372907 ]\n"} ## Step 70 · agent · 2026-08-31T18:47:32.886Z ### Thinking unavailable — no text recorded ## Step 71 · agent · 2026-08-31T18:47:51.521Z ### Thinking unavailable — no text recorded ## Step 72 · agent · 2026-08-31T18:47:54.405Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:30.959Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n def load_listens(\\n@@\\n return {k: v[order] for k, v in out.items()}\\n+\\n+\\n+def load_first_seen(\\n+ files: list[Path], target_ids: np.ndarray, catalog_ids: np.ndarray\\n+) -> dict[str, np.ndarray]:\\n+ \\\"\\\"\\\"Stream all plays and compact them to first timestamp per user-item pair.\\\"\\\"\\\"\\n+ cat_sort_order = np.argsort(catalog_ids)\\n+ cat_sorted = catalog_ids[cat_sort_order]\\n+ partial_u, partial_i, partial_t = [], [], []\\n+ for path in files:\\n+ for batch in pq.ParquetFile(path).iter_batches(\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"], batch_size=1_000_000\\n+ ):\\n+ x = batch.to_pandas()\\n+ up, uv = positions(x.uid.to_numpy(), target_ids)\\n+ cp, cv = positions(x.item_id.to_numpy(), cat_sorted)\\n+ keep = uv & cv\\n+ if not keep.any():\\n+ continue\\n+ compact = pd.DataFrame({\\n+ \\\"u\\\": up[keep].astype(np.int32),\\n+ \\\"i\\\": cat_sort_order[cp[keep]].astype(np.int32),\\n+ \\\"t\\\": x.timestamp.to_numpy()[keep].astype(np.int64),\\n+ }).groupby([\\\"u\\\", \\\"i\\\"], sort=False, as_index=False).t.min()\\n+ partial_u.append(compact.u.to_numpy(np.int32))\\n+ partial_i.append(compact.i.to_numpy(np.int32))\\n+ partial_t.append(compact.t.to_numpy(np.int64))\\n+ u, i, t = np.concatenate(partial_u), np.concatenate(partial_i), np.concatenate(partial_t)\\n+ order = np.lexsort((t, i, u))\\n+ u, i, t = u[order], i[order], t[order]\\n+ first = np.ones(len(u), dtype=bool)\\n+ first[1:] = (u[1:] != u[:-1]) | (i[1:] != i[:-1])\\n+ # Ordering by (u, i, t) makes the retained duplicate the first timestamp.\\n+ return {\\\"u\\\": u[first], \\\"i\\\": i[first], \\\"t\\\": t[first]}\\n@@\\n def labels_for_cutoff(\\n- listens: dict[str, np.ndarray], actions: pd.DataFrame, cutoff: int\\n+ listens: dict[str, np.ndarray], actions: pd.DataFrame, cutoff: int,\\n+ first_seen: dict[str, np.ndarray] | None = None,\\n ) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n@@\\n for u, i in likes[[\\\"u\\\", \\\"i\\\"]].itertuples(index=False, name=None):\\n label.setdefault(int(u), {})[int(i)] = 3\\n+ # Eligibility excludes tracks the user has already played. Filtering the\\n+ # pseudo-future labels to first discoveries aligns training with retrieval.\\n+ if first_seen is not None:\\n+ fu = first_seen[\\\"u\\\"]\\n+ for u in list(label):\\n+ left = np.searchsorted(fu, u, side=\\\"left\\\")\\n+ right = np.searchsorted(fu, u, side=\\\"right\\\")\\n+ already_seen = set(first_seen[\\\"i\\\"][left:right][first_seen[\\\"t\\\"][left:right] < cutoff].tolist())\\n+ if already_seen:\\n+ label[u] = {i: grade for i, grade in label[u].items() if i not in already_seen}\\n+ if not label[u]:\\n+ del label[u]\\n@@\\n print(\\\"streaming positive catalog interactions\\\", flush=True)\\n listens = load_listens(listen_files, target_ids, catalog_ids, min_time)\\n+ print(\\\"compacting all plays to first-seen eligibility history\\\", flush=True)\\n+ first_seen = load_first_seen(listen_files, target_ids, catalog_ids)\\n actions = load_actions(include_validation, target_ids, catalog_ids)\\n@@\\n- labels = labels_for_cutoff(listens, actions, cutoff)\\n+ labels = labels_for_cutoff(listens, actions, cutoff, first_seen)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:48:31.013Z Script completed Wall time 0.0 seconds Output: {} ## Step 73 · agent · 2026-08-31T18:48:34.764Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:39.425Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_two_tower.py && /opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_unseen_seed17.parquet --epochs 14 --seed 17 --popularity-weight 0.03 --model-output /app/solution/two_tower_validation_unseen_seed17.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:48:50.445Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 74 · agent · 2026-08-31T18:48:54.817Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:55.061Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:09.605Z Script completed Wall time 14.5 seconds Output: {"chunk_id":"e5ef2b","wall_time_seconds":30.001224134,"session_id":47342,"original_token_count":35,"output":"streaming positive catalog interactions\ncompacting all plays to first-seen eligibility history\nretained 10,756,677 positive catalog events\n"} ## Step 75 · agent · 2026-08-31T18:49:11.068Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:12.643Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:49:23.663Z Script running with cell ID 39 Wall time 11.0 seconds Output: ## Step 76 · agent · 2026-08-31T18:49:25.687Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:26.476Z { "cell_id": "39", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:49:42.668Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"203631","wall_time_seconds":30.001421588,"session_id":47342,"original_token_count":0,"output":""} ## Step 77 · agent · 2026-08-31T18:49:44.675Z ### Thinking unavailable — no text recorded The corrected dataset is compacted to first-seen timestamps rather than an interaction matrix. Training preparation is a little slower because it scans all prior plays to reconstruct the platform boundary, but the stored representation remains sparse event/pair data and scales linearly with observed interactions. ### exec · 2026-08-31T18:49:48.707Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:49:59.730Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 78 · agent · 2026-08-31T18:50:03.641Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:04.424Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:18.734Z Script completed Wall time 14.3 seconds Output: {"chunk_id":"76590d","wall_time_seconds":30.001164983,"session_id":47342,"original_token_count":0,"output":""} ## Step 79 · agent · 2026-08-31T18:50:20.493Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:21.915Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:50:32.936Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 80 · agent · 2026-08-31T18:50:34.478Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:50:35.255Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:50:51.939Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"1f2e2f","wall_time_seconds":30.001319188,"session_id":47342,"original_token_count":0,"output":""} ## Step 81 · agent · 2026-08-31T18:50:56.624Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:58.006Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:51:09.038Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 82 · agent · 2026-08-31T18:51:10.685Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:11.447Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:51:28.041Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"0aae4f","wall_time_seconds":30.000859794,"session_id":47342,"original_token_count":0,"output":""} ## Step 83 · agent · 2026-08-31T18:51:29.883Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:32.763Z { "code": "const r = await tools.exec_command({cmd:\"ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -p 47342 --ppid 47342 | sed -n '1,20p'; free -m\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:51:32.859Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: ps: not found /bin/sh: 1: free: not found ## Step 84 · agent · 2026-08-31T18:51:34.629Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:36.038Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:51:47.063Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T18:51:51.524Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:52.298Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:06.067Z Script completed Wall time 13.8 seconds Output: {"chunk_id":"21e816","wall_time_seconds":30.001477448,"session_id":47342,"original_token_count":0,"output":""} ## Step 86 · agent · 2026-08-31T18:52:09.791Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:11.198Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:52:22.226Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T18:52:28.570Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:28.577Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:52:41.229Z Script completed Wall time 12.7 seconds Output: {"chunk_id":"00e9c1","wall_time_seconds":30.001377341,"session_id":47342,"original_token_count":0,"output":""} ## Step 88 · agent · 2026-08-31T18:52:43.530Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:44.958Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:52:55.979Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T18:52:58.545Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:59.340Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:14.983Z Script completed Wall time 15.6 seconds Output: {"chunk_id":"9034e4","wall_time_seconds":30.001154206,"session_id":47342,"original_token_count":0,"output":""} ## Step 90 · agent · 2026-08-31T18:53:17.200Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:21.474Z { "code": "const r = await tools.exec_command({cmd:\"for d in /proc/[0-9]*; do test -r \\\"$d/cmdline\\\" || continue; c=$(tr '\\\\0' ' ' < \\\"$d/cmdline\\\"); case \\\"$c\\\" in *train_two_tower*) echo \\\"$d $c\\\"; awk '/VmRSS|VmSize|State|Threads/{print}' \\\"$d/status\\\";; esac; done\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:53:21.570Z Script completed Wall time 0.1 seconds Output: /proc/1005 /bin/sh -c /opt/task-venv/bin/python -m py_compile /app/solution/train_two_tower.py && /opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_unseen_seed17.parquet --epochs 14 --seed 17 --popularity-weight 0.03 --model-output /app/solution/two_tower_validation_unseen_seed17.pt State: S (sleeping) VmSize: 2592 kB VmRSS: 1740 kB Threads: 1 /proc/1009 /opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_unseen_seed17.parquet --epochs 14 --seed 17 --popularity-weight 0.03 --model-output /app/solution/two_tower_validation_unseen_seed17.pt State: R (running) VmSize: 2888228 kB VmRSS: 1149912 kB Threads: 20 /proc/1108 /bin/sh -c for d in /proc/[0-9]*; do test -r "$d/cmdline" || continue; c=$(tr '' ' ' < "$d/cmdline"); case "$c" in *train_two_tower*) echo "$d $c"; awk '/VmRSS|VmSize|State|Threads/{print}' "$d/status";; esac; done State: S (sleeping) VmSize: 2592 kB VmRSS: 1808 kB Threads: 1 ## Step 91 · agent · 2026-08-31T18:53:23.522Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:24.922Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:53:35.943Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 92 · agent · 2026-08-31T18:53:37.595Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:38.401Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:53:54.947Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"4dad66","wall_time_seconds":30.000839971,"session_id":47342,"original_token_count":0,"output":""} ## Step 93 · agent · 2026-08-31T18:53:59.830Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:02.878Z { "code": "const r = await tools.exec_command({cmd:\"awk '{print \\\"utime_ticks=\\\" $14, \\\"stime_ticks=\\\" $15}' /proc/1009/stat; awk '/VmRSS|VmHWM|VmSwap|State/{print}' /proc/1009/status\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:54:03.829Z Script completed Wall time 0.0 seconds Output: utime_ticks=15900 stime_ticks=16621 State: R (running) VmHWM: 1268244 kB VmRSS: 1195004 kB VmSwap: 0 kB ## Step 94 · agent · 2026-08-31T18:54:06.250Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:07.625Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:54:18.646Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 95 · agent · 2026-08-31T18:54:20.276Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:22.361Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:54:37.651Z Script completed Wall time 15.3 seconds Output: {"chunk_id":"450b9e","wall_time_seconds":30.000609478,"session_id":47342,"original_token_count":0,"output":""} ## Step 96 · agent · 2026-08-31T18:54:51.423Z ### Thinking unavailable — no text recorded The first-seen compaction is CPU-bound in a generic group-by path, so I’m replacing it with a faster linear scan that exploits the logs’ documented ordering by user and time. This changes no modeling logic; it makes the corrected approach practical to refit twice for the final ensemble. ### exec · 2026-08-31T18:54:55.516Z { "code": "const r = await tools.write_stdin({session_id:47342,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:54:55.823Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"1fdd89","wall_time_seconds":0.284572089,"exit_code":130,"original_token_count":228,"output":"Traceback (most recent call last):\n File \"/app/solution/train_two_tower.py\", line 515, in \n main()\n File \"/app/solution/train_two_tower.py\", line 483, in main\n labels = labels_for_cutoff(listens, actions, cutoff, first_seen)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_two_tower.py\", line 271, in labels_for_cutoff\n left = np.searchsorted(fu, u, side=\"left\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 1527, in searchsorted\n return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"} ## Step 97 · agent · 2026-08-31T18:55:15.709Z ### Thinking unavailable — no text recorded ## Step 98 · agent · 2026-08-31T18:55:16.535Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:55.739Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n def load_first_seen(\\n- files: list[Path], target_ids: np.ndarray, catalog_ids: np.ndarray\\n+ files: list[Path], target_ids: np.ndarray, catalog_ids: np.ndarray,\\n+ cache_path: Path | None = None,\\n ) -> dict[str, np.ndarray]:\\n \\\"\\\"\\\"Stream all plays and compact them to first timestamp per user-item pair.\\\"\\\"\\\"\\n+ if cache_path is not None and cache_path.exists():\\n+ with np.load(cache_path, allow_pickle=False) as z:\\n+ return {k: z[k].copy() for k in (\\\"u\\\", \\\"i\\\", \\\"t\\\", \\\"left\\\", \\\"right\\\")}\\n cat_sort_order = np.argsort(catalog_ids)\\n cat_sorted = catalog_ids[cat_sort_order]\\n- partial_u, partial_i, partial_t = [], [], []\\n+ file_u, file_i, file_t = [], [], []\\n for path in files:\\n+ chunks_u, chunks_i, chunks_t = [], [], []\\n for batch in pq.ParquetFile(path).iter_batches(\\n columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\"], batch_size=1_000_000\\n ):\\n@@\\n keep = uv & cv\\n if not keep.any():\\n continue\\n- compact = pd.DataFrame({\\n- \\\"u\\\": up[keep].astype(np.int32),\\n- \\\"i\\\": cat_sort_order[cp[keep]].astype(np.int32),\\n- \\\"t\\\": x.timestamp.to_numpy()[keep].astype(np.int64),\\n- }).groupby([\\\"u\\\", \\\"i\\\"], sort=False, as_index=False).t.min()\\n- partial_u.append(compact.u.to_numpy(np.int32))\\n- partial_i.append(compact.i.to_numpy(np.int32))\\n- partial_t.append(compact.t.to_numpy(np.int64))\\n- u, i, t = np.concatenate(partial_u), np.concatenate(partial_i), np.concatenate(partial_t)\\n+ chunks_u.append(up[keep].astype(np.int32))\\n+ chunks_i.append(cat_sort_order[cp[keep]].astype(np.int32))\\n+ chunks_t.append(x.timestamp.to_numpy()[keep].astype(np.int64))\\n+ raw_u, raw_i, raw_t = np.concatenate(chunks_u), np.concatenate(chunks_i), np.concatenate(chunks_t)\\n+ # Source rows are user-major and time-major within user. np.unique's\\n+ # returned index is therefore the first play timestamp for that item.\\n+ left = np.searchsorted(raw_u, np.arange(len(target_ids)), side=\\\"left\\\")\\n+ right = np.searchsorted(raw_u, np.arange(len(target_ids)), side=\\\"right\\\")\\n+ for uidx in range(len(target_ids)):\\n+ l, r = left[uidx], right[uidx]\\n+ if l == r:\\n+ continue\\n+ unique_i, first_index = np.unique(raw_i[l:r], return_index=True)\\n+ file_u.append(np.full(len(unique_i), uidx, dtype=np.int32))\\n+ file_i.append(unique_i.astype(np.int32))\\n+ file_t.append(raw_t[l:r][first_index].astype(np.int64))\\n+ u, i, t = np.concatenate(file_u), np.concatenate(file_i), np.concatenate(file_t)\\n order = np.lexsort((t, i, u))\\n@@\\n- return {\\\"u\\\": u[first], \\\"i\\\": i[first], \\\"t\\\": t[first]}\\n+ result = {\\\"u\\\": u[first], \\\"i\\\": i[first], \\\"t\\\": t[first]}\\n+ result[\\\"left\\\"], result[\\\"right\\\"] = user_slices(result[\\\"u\\\"], len(target_ids))\\n+ if cache_path is not None:\\n+ np.savez(cache_path, **result)\\n+ return result\\n@@\\n if first_seen is not None:\\n- fu = first_seen[\\\"u\\\"]\\n for u in list(label):\\n- left = np.searchsorted(fu, u, side=\\\"left\\\")\\n- right = np.searchsorted(fu, u, side=\\\"right\\\")\\n+ left, right = first_seen[\\\"left\\\"][u], first_seen[\\\"right\\\"][u]\\n@@\\n- first_seen = load_first_seen(listen_files, target_ids, catalog_ids)\\n+ cache = OUT / (\\\"first_seen_final.npz\\\" if include_validation else \\\"first_seen_validation.npz\\\")\\n+ first_seen = load_first_seen(listen_files, target_ids, catalog_ids, cache)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:55:55.804Z Script completed Wall time 0.0 seconds Output: {} ## Step 99 · agent · 2026-08-31T18:55:58.671Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:03.398Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_two_tower.py && /opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_unseen_seed17.parquet --epochs 14 --seed 17 --popularity-weight 0.03 --model-output /app/solution/two_tower_validation_unseen_seed17.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:56:14.420Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T18:56:15.973Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:16.790Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:56:33.581Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"696d54","wall_time_seconds":30.00105259,"session_id":73529,"original_token_count":35,"output":"streaming positive catalog interactions\ncompacting all plays to first-seen eligibility history\nretained 10,756,677 positive catalog events\n"} ## Step 101 · agent · 2026-08-31T18:56:35.279Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:36.677Z { "code": "const r = await tools.write_stdin({session_id:73529,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:56:47.698Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T18:56:50.364Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:51.189Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:06.704Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"90e814","wall_time_seconds":30.001592999,"session_id":73529,"original_token_count":111,"output":"training windows: {\"21147200\": {\"positives\": 34876, \"users\": 3860}, \"21753800\": {\"positives\": 38433, \"users\": 4015}, \"22360400\": {\"positives\": 38119, \"users\": 4060}, \"22967000\": {\"positives\": 40315, \"users\": 4104}, \"23573600\": {\"positives\": 40520, \"users\": 4256}, \"24180200\": {\"positives\": 41754, \"users\": 4299}}\nepoch 01/14: loss=0.55345\nepoch 02/14: loss=0.45818\nepoch 03/14: loss=0.42263\nepoch 04/14: loss=0.40528\nepoch 05/14: loss=0.38540\n"} ## Step 103 · agent · 2026-08-31T18:57:08.369Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:09.832Z { "code": "const r = await tools.write_stdin({session_id:73529,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:57:20.858Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T18:57:22.652Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:23.433Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:57:39.862Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"d3892f","wall_time_seconds":30.001631311,"session_id":73529,"original_token_count":59,"output":"epoch 06/14: loss=0.37748\nepoch 07/14: loss=0.36584\nepoch 08/14: loss=0.35218\nepoch 09/14: loss=0.34804\nepoch 10/14: loss=0.34070\nepoch 11/14: loss=0.33444\nepoch 12/14: loss=0.32909\nepoch 13/14: loss=0.32366\nepoch 14/14: loss=0.32086\n"} ## Step 105 · agent · 2026-08-31T18:57:42.808Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:44.222Z { "code": "const r = await tools.write_stdin({session_id:73529,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:57:44.396Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"d638e3","wall_time_seconds":0.153543539,"exit_code":0,"original_token_count":38,"output":"wrote 845600 ranked rows to /app/solution/validation_unseen_seed17.parquet\nsaved fitted towers to /app/solution/two_tower_validation_unseen_seed17.pt\n"} ## Step 106 · agent · 2026-08-31T18:57:46.600Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:48.951Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_unseen_seed17.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:57:52.279Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_unseen_seed17.parquet", "scores": { "quality_ndcg@10": 0.020342, "quality_users": 4263 } } ## Step 107 · agent · 2026-08-31T18:58:03.194Z ### Thinking unavailable — no text recorded The strict unseen-only loss is directionally correct but too sparse by itself (0.020342), so it is not replacing the 0.023947 ensemble. I’m using it as a short fine-tuning stage on the stronger repeat-inclusive representation; that keeps collaborative signal from abundant events while nudging the last training steps toward discovery eligibility. ### exec · 2026-08-31T18:58:17.217Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n def train_model(\\n@@\\n batch_size: int,\\n+ learning_rate: float = 2.5e-3,\\n ) -> None:\\n@@\\n- optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n@@\\n parser.add_argument(\\\"--popularity-weight\\\", type=float, default=0.04)\\n parser.add_argument(\\\"--model-output\\\", type=Path)\\n+ parser.add_argument(\\\"--init-model\\\", type=Path)\\n+ parser.add_argument(\\\"--learning-rate\\\", type=float, default=2.5e-3)\\n@@\\n model = TwoTower(torch.from_numpy(static).float())\\n+ if args.init_model:\\n+ model.load_state_dict(torch.load(args.init_model, map_location=\\\"cpu\\\", weights_only=True)[\\\"state_dict\\\"])\\n train_model(model, training_profiles, catalog.positive_listens.to_numpy(),\\n- args.epochs, args.seed, args.batch_size)\\n+ args.epochs, args.seed, args.batch_size, args.learning_rate)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:58:17.290Z Script completed Wall time 0.0 seconds Output: {} ## Step 108 · agent · 2026-08-31T18:58:19.983Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:24.617Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage validation --output /app/solution/validation_finetune_seed17.parquet --epochs 3 --seed 1717 --learning-rate 0.0005 --init-model /app/solution/two_tower_validation_seed17.pt --popularity-weight 0.03 --model-output /app/solution/two_tower_validation_finetune_seed17.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:58:35.637Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 109 · agent · 2026-08-31T18:58:37.584Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:38.361Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:58:54.796Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"9ac2f9","wall_time_seconds":30.000487686,"session_id":64305,"original_token_count":35,"output":"streaming positive catalog interactions\ncompacting all plays to first-seen eligibility history\nretained 10,756,677 positive catalog events\n"} ## Step 110 · agent · 2026-08-31T18:58:56.374Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:57.775Z { "code": "const r = await tools.write_stdin({session_id:64305,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:59:08.795Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 111 · agent · 2026-08-31T18:59:10.512Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:11.324Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T18:59:21.998Z Script completed Wall time 10.7 seconds Output: {"chunk_id":"1dad35","wall_time_seconds":24.198127191,"exit_code":0,"original_token_count":136,"output":"training windows: {\"21147200\": {\"positives\": 34876, \"users\": 3860}, \"21753800\": {\"positives\": 38433, \"users\": 4015}, \"22360400\": {\"positives\": 38119, \"users\": 4060}, \"22967000\": {\"positives\": 40315, \"users\": 4104}, \"23573600\": {\"positives\": 40520, \"users\": 4256}, \"24180200\": {\"positives\": 41754, \"users\": 4299}}\nepoch 01/3: loss=0.38691\nepoch 02/3: loss=0.37814\nepoch 03/3: loss=0.36924\nwrote 845600 ranked rows to /app/solution/validation_finetune_seed17.parquet\nsaved fitted towers to /app/solution/two_tower_validation_finetune_seed17.pt\n"} ## Step 112 · agent · 2026-08-31T18:59:23.597Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:27.378Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_finetune_seed17.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output)\n" } ### Tool output · 2026-08-31T18:59:30.683Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation_finetune_seed17.parquet", "scores": { "quality_ndcg@10": 0.023655, "quality_users": 4263 } } ## Step 113 · agent · 2026-08-31T18:59:34.516Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:38.846Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/rerank_saved.py --stage validation --model /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_finetune_seed17.pt --output-template '/app/solution/validation_ensemble_ft_w{weight}.parquet' --weights 0.015 0.025 0.035\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T18:59:49.865Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 114 · agent · 2026-08-31T18:59:56.742Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:57.541Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:00:02.597Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"34c313","wall_time_seconds":23.571288611,"exit_code":0,"original_token_count":62,"output":"wrote 845600 ensemble rows to /app/solution/validation_ensemble_ft_w0p015.parquet\nwrote 845600 ensemble rows to /app/solution/validation_ensemble_ft_w0p025.parquet\nwrote 845600 ensemble rows to /app/solution/validation_ensemble_ft_w0p035.parquet\n"} ## Step 115 · agent · 2026-08-31T19:00:05.610Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:08.798Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_ensemble_ft_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' '; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T19:00:18.720Z Script completed Wall time 9.9 seconds Output: { "model": "/app/solution/validation_ensemble_ft_w0p015.parquet", "scores": { "quality_ndcg@10": 0.024065, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_ft_w0p025.parquet", "scores": { "quality_ndcg@10": 0.023693, "quality_users": 4263 } } { "model": "/app/solution/validation_ensemble_ft_w0p035.parquet", "scores": { "quality_ndcg@10": 0.02344, "quality_users": 4263 } } ## Step 116 · agent · 2026-08-31T19:00:36.528Z ### Thinking unavailable — no text recorded The hybrid ensemble reaches 0.024065, the best public score so far. I’m locking that recipe now: one repeat-inclusive tower plus an independently seeded tower lightly fine-tuned on first-discovery labels, concatenated into a 128-dimensional exact dot product. Next I’ll refit both components with validation events included as observed history/training outcomes, write the final ranking, and run structural eligibility checks. ### exec · 2026-08-31T19:00:49.396Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_two_tower.py\\n@@\\n parser.add_argument(\\\"--init-model\\\", type=Path)\\n parser.add_argument(\\\"--learning-rate\\\", type=float, default=2.5e-3)\\n+ parser.add_argument(\\\"--filter-seen-labels\\\", action=\\\"store_true\\\",\\n+ help=\\\"exclude previously played items from pseudo-window positives\\\")\\n@@\\n- print(\\\"compacting all plays to first-seen eligibility history\\\", flush=True)\\n- cache = OUT / (\\\"first_seen_final.npz\\\" if include_validation else \\\"first_seen_validation.npz\\\")\\n- first_seen = load_first_seen(listen_files, target_ids, catalog_ids, cache)\\n+ first_seen = None\\n+ if args.filter_seen_labels:\\n+ print(\\\"compacting all plays to first-seen eligibility history\\\", flush=True)\\n+ cache = OUT / (\\\"first_seen_final.npz\\\" if include_validation else \\\"first_seen_validation.npz\\\")\\n+ first_seen = load_first_seen(listen_files, target_ids, catalog_ids, cache)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:00:49.441Z Script completed Wall time 0.0 seconds Output: {} ## Step 117 · agent · 2026-08-31T19:00:52.426Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:56.294Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage final --output /app/solution/final_component_seed1.parquet --epochs 12 --seed 20260831 --popularity-weight 0.015 --model-output /app/solution/two_tower_final_seed1.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:01:07.314Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 118 · agent · 2026-08-31T19:01:08.974Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:09.752Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:26.474Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"a4c185","wall_time_seconds":30.001334975,"session_id":7640,"original_token_count":21,"output":"streaming positive catalog interactions\nretained 11,406,972 positive catalog events\n"} ## Step 119 · agent · 2026-08-31T19:01:28.205Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:29.616Z { "code": "const r = await tools.write_stdin({session_id:7640,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:01:40.637Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 120 · agent · 2026-08-31T19:01:42.269Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:43.121Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:01:59.640Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"311288","wall_time_seconds":30.000839635,"session_id":7640,"original_token_count":106,"output":"training windows: {\"21147200\": {\"positives\": 158922, \"users\": 5411}, \"21753800\": {\"positives\": 165819, \"users\": 5563}, \"22360400\": {\"positives\": 168734, \"users\": 5650}, \"22967000\": {\"positives\": 170431, \"users\": 5754}, \"23573600\": {\"positives\": 171275, \"users\": 5830}, \"24180200\": {\"positives\": 176193, \"users\": 5989}, \"24786800\": {\"positives\": 175729, \"users\": 6186}}\nepoch 01/12: loss=0.53158\nepoch 02/12: loss=0.47970\n"} ## Step 121 · agent · 2026-08-31T19:02:01.355Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:02.757Z { "code": "const r = await tools.write_stdin({session_id:7640,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:02:13.782Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 122 · agent · 2026-08-31T19:02:18.156Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:19.118Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:02:32.786Z Script completed Wall time 13.7 seconds Output: {"chunk_id":"ec9f9f","wall_time_seconds":30.00118281,"session_id":7640,"original_token_count":33,"output":"epoch 03/12: loss=0.45291\nepoch 04/12: loss=0.43718\nepoch 05/12: loss=0.42077\nepoch 06/12: loss=0.41037\nepoch 07/12: loss=0.39833\n"} ## Step 123 · agent · 2026-08-31T19:02:37.365Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:38.742Z { "code": "const r = await tools.write_stdin({session_id:7640,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:02:49.782Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 124 · agent · 2026-08-31T19:02:51.614Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:52.399Z { "cell_id": "68", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:08.786Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"3de529","wall_time_seconds":30.000792587,"session_id":7640,"original_token_count":33,"output":"epoch 08/12: loss=0.39075\nepoch 09/12: loss=0.38482\nepoch 10/12: loss=0.37690\nepoch 11/12: loss=0.37307\nepoch 12/12: loss=0.36863\n"} ## Step 125 · agent · 2026-08-31T19:03:10.444Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:11.866Z { "code": "const r = await tools.write_stdin({session_id:7640,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:03:12.408Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"0548f9","wall_time_seconds":0.522550641,"exit_code":0,"original_token_count":34,"output":"wrote 845600 ranked rows to /app/solution/final_component_seed1.parquet\nsaved fitted towers to /app/solution/two_tower_final_seed1.pt\n"} ## Step 126 · agent · 2026-08-31T19:03:14.422Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:18.717Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage final --output /app/solution/final_component_seed17_base.parquet --epochs 12 --seed 17 --popularity-weight 0.015 --model-output /app/solution/two_tower_final_seed17_base.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:03:29.738Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 127 · agent · 2026-08-31T19:03:31.564Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:32.339Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:03:48.896Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"080848","wall_time_seconds":30.000907313,"session_id":66524,"original_token_count":21,"output":"streaming positive catalog interactions\nretained 11,406,972 positive catalog events\n"} ## Step 128 · agent · 2026-08-31T19:03:52.629Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:53.999Z { "code": "const r = await tools.write_stdin({session_id:66524,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:04:05.019Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 129 · agent · 2026-08-31T19:04:06.973Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:07.759Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:04:24.023Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"f47c69","wall_time_seconds":30.001052216,"session_id":66524,"original_token_count":112,"output":"training windows: {\"21147200\": {\"positives\": 158922, \"users\": 5411}, \"21753800\": {\"positives\": 165819, \"users\": 5563}, \"22360400\": {\"positives\": 168734, \"users\": 5650}, \"22967000\": {\"positives\": 170431, \"users\": 5754}, \"23573600\": {\"positives\": 171275, \"users\": 5830}, \"24180200\": {\"positives\": 176193, \"users\": 5989}, \"24786800\": {\"positives\": 175729, \"users\": 6186}}\nepoch 01/12: loss=0.53215\nepoch 02/12: loss=0.48125\nepoch 03/12: loss=0.45679\n"} ## Step 130 · agent · 2026-08-31T19:04:25.889Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:27.310Z { "code": "const r = await tools.write_stdin({session_id:66524,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:04:38.332Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 131 · agent · 2026-08-31T19:04:43.500Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:43.502Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:04:57.336Z Script completed Wall time 13.8 seconds Output: {"chunk_id":"0eaf88","wall_time_seconds":30.001583593,"session_id":66524,"original_token_count":33,"output":"epoch 04/12: loss=0.43482\nepoch 05/12: loss=0.42287\nepoch 06/12: loss=0.41110\nepoch 07/12: loss=0.40124\nepoch 08/12: loss=0.39351\n"} ## Step 132 · agent · 2026-08-31T19:04:59.728Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:01.146Z { "code": "const r = await tools.write_stdin({session_id:66524,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:05:12.167Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 133 · agent · 2026-08-31T19:05:14.118Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:14.923Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:05:30.996Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"9e9f47","wall_time_seconds":29.825010252,"exit_code":0,"original_token_count":63,"output":"epoch 09/12: loss=0.38578\nepoch 10/12: loss=0.37815\nepoch 11/12: loss=0.37524\nepoch 12/12: loss=0.36923\nwrote 845600 ranked rows to /app/solution/final_component_seed17_base.parquet\nsaved fitted towers to /app/solution/two_tower_final_seed17_base.pt\n"} ## Step 134 · agent · 2026-08-31T19:05:33.975Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:39.418Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/train_two_tower.py --stage final --output /app/solution/final_component_seed17_finetuned.parquet --epochs 3 --seed 1717 --learning-rate 0.0005 --filter-seen-labels --init-model /app/solution/two_tower_final_seed17_base.pt --popularity-weight 0.015 --model-output /app/solution/two_tower_final_seed17_finetuned.pt\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:05:50.438Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 135 · agent · 2026-08-31T19:05:52.078Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:52.882Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:06:09.598Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"fd1755","wall_time_seconds":30.001760497,"session_id":88345,"original_token_count":35,"output":"streaming positive catalog interactions\ncompacting all plays to first-seen eligibility history\nretained 11,406,972 positive catalog events\n"} ## Step 136 · agent · 2026-08-31T19:06:11.504Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:14.178Z { "code": "const r = await tools.write_stdin({session_id:88345,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:06:25.195Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 137 · agent · 2026-08-31T19:06:26.929Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:27.733Z { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:06:44.199Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"617f40","wall_time_seconds":30.001487413,"session_id":88345,"original_token_count":110,"output":"training windows: {\"21147200\": {\"positives\": 34876, \"users\": 3860}, \"21753800\": {\"positives\": 38433, \"users\": 4015}, \"22360400\": {\"positives\": 38119, \"users\": 4060}, \"22967000\": {\"positives\": 40315, \"users\": 4104}, \"23573600\": {\"positives\": 40520, \"users\": 4256}, \"24180200\": {\"positives\": 41754, \"users\": 4299}, \"24786800\": {\"positives\": 37997, \"users\": 4291}}\nepoch 01/3: loss=0.38373\nepoch 02/3: loss=0.37327\nepoch 03/3: loss=0.36722\n"} ## Step 138 · agent · 2026-08-31T19:06:48.042Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:49.718Z { "code": "const r = await tools.write_stdin({session_id:88345,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:06:49.849Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"625cdf","wall_time_seconds":0.00000306,"exit_code":0,"original_token_count":39,"output":"wrote 845600 ranked rows to /app/solution/final_component_seed17_finetuned.parquet\nsaved fitted towers to /app/solution/two_tower_final_seed17_finetuned.pt\n"} ## Step 139 · agent · 2026-08-31T19:06:52.969Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:57.102Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/rerank_saved.py --stage final --model /app/solution/two_tower_final_seed1.pt /app/solution/two_tower_final_seed17_finetuned.pt --output-template '/app/solution/submission.parquet' --weights 0.015\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(JSON.stringify(r))\n" } ### Tool output · 2026-08-31T19:07:08.122Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 140 · agent · 2026-08-31T19:07:09.995Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:11.021Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:07:15.886Z Script completed Wall time 4.9 seconds Output: {"chunk_id":"5bb2e7","wall_time_seconds":18.603475083,"exit_code":0,"original_token_count":16,"output":"wrote 845600 ensemble rows to /app/solution/submission.parquet\n"} ## Step 141 · agent · 2026-08-31T19:07:35.902Z ### Thinking unavailable — no text recorded ## Step 142 · agent · 2026-08-31T19:07:41.222Z ### Thinking unavailable — no text recorded The final 128-dimensional ensemble ranking is written. I’m doing the last invariant pass now—exact user coverage, ranks 1–100, uniqueness, integer types, and every item’s test eligibility bit—then I’ll freeze the best public validation file and add the reproducible build command and README. ### exec · 2026-08-31T19:07:53.089Z { "code": "const r = await tools.exec_command({cmd:\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nx=validate_ranking(pd.read_parquet(S),targets)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=sum(not e.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(),'duplicates':int(x.duplicated(['uid','item_id']).sum()),'ineligible':bad,'dtypes':{c:str(t) for c,t in x.dtypes.items()},'bytes':S.stat().st_size})\\nPY\\ndu -sh /app/solution && find /app/solution -maxdepth 1 -type f -printf '%f %s\\\\n' | sort\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output)\n" } ### Tool output · 2026-08-31T19:07:55.764Z Script completed Wall time 2.7 seconds Output: {'rows': 845600, 'users': 8456, 'duplicates': 0, 'ineligible': 0, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'bytes': 1394953} 429M /app/solution final_component_seed1.parquet 1398714 final_component_seed17_base.parquet 1397570 final_component_seed17_finetuned.parquet 1287856 first_seen_final.npz 129543654 first_seen_validation.npz 126035126 make_popularity.py 1525 rerank_saved.py 4515 submission.parquet 1394953 train_two_tower.py 24877 two_tower_final_seed1.pt 23576957 two_tower_final_seed17_base.pt 23577165 two_tower_final_seed17_finetuned.pt 23577285 two_tower_validation.pt 23576933 two_tower_validation_finetune_seed17.pt 23577381 two_tower_validation_seed17.pt 23577165 two_tower_validation_unseen_seed17.pt 23577333 validation_ensemble_ft_w0p015.parquet 1397079 validation_ensemble_ft_w0p025.parquet 1396696 validation_ensemble_ft_w0p035.parquet 1396293 validation_ensemble_w0p02.parquet 1398587 validation_ensemble_w0p04.parquet 1397866 validation_ensemble_w0p06.parquet 1396856 validation_finetune_seed17.parquet 1395251 validation_popularity.parquet 728599 validation_tower_seed17.parquet 1397676 validation_tower_w004.parquet 1398344 validation_tower_w0p0.parquet 1399878 validation_tower_w0p015.parquet 1399328 validation_tower_w0p03.parquet 1398868 validation_tower_w0p06.parquet 1397637 validation_tower_w0p1.parquet 1395749 validation_tower_w0p16.parquet 1288045 validation_unseen_seed17.parquet 1306713 ## Step 143 · agent · 2026-08-31T19:08:15.905Z ### Thinking unavailable — no text recorded ## Step 144 · agent · 2026-08-31T19:08:21.522Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:00.486Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.sh\\n+#!/bin/sh\\n+set -eu\\n+\\n+PYTHON=/opt/task-venv/bin/python\\n+SOLUTION=/app/solution\\n+\\n+\\\"$PYTHON\\\" -u \\\"$SOLUTION/train_two_tower.py\\\" \\\\\\n+ --stage final --output \\\"$SOLUTION/final_component_seed1.parquet\\\" \\\\\\n+ --epochs 12 --seed 20260831 --popularity-weight 0.015 \\\\\\n+ --model-output \\\"$SOLUTION/two_tower_final_seed1.pt\\\"\\n+\\n+\\\"$PYTHON\\\" -u \\\"$SOLUTION/train_two_tower.py\\\" \\\\\\n+ --stage final --output \\\"$SOLUTION/final_component_seed17_base.parquet\\\" \\\\\\n+ --epochs 12 --seed 17 --popularity-weight 0.015 \\\\\\n+ --model-output \\\"$SOLUTION/two_tower_final_seed17_base.pt\\\"\\n+\\n+\\\"$PYTHON\\\" -u \\\"$SOLUTION/train_two_tower.py\\\" \\\\\\n+ --stage final --output \\\"$SOLUTION/final_component_seed17_finetuned.parquet\\\" \\\\\\n+ --epochs 3 --seed 1717 --learning-rate 0.0005 --filter-seen-labels \\\\\\n+ --init-model \\\"$SOLUTION/two_tower_final_seed17_base.pt\\\" \\\\\\n+ --popularity-weight 0.015 \\\\\\n+ --model-output \\\"$SOLUTION/two_tower_final_seed17_finetuned.pt\\\"\\n+\\n+\\\"$PYTHON\\\" -u \\\"$SOLUTION/rerank_saved.py\\\" \\\\\\n+ --stage final \\\\\\n+ --model \\\"$SOLUTION/two_tower_final_seed1.pt\\\" \\\"$SOLUTION/two_tower_final_seed17_finetuned.pt\\\" \\\\\\n+ --output-template \\\"$SOLUTION/submission.parquet\\\" --weights 0.015\\n+\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower ranker\\n+\\n+## Model\\n+\\n+The final ranker is a variance-reduced ensemble of two independently trained\\n+two-tower networks. Each component emits a 64-dimensional user vector and an\\n+independently precomputable 64-dimensional item vector. The deployed\\n+128-dimensional vectors are the scaled concatenations of the two components,\\n+so the final score is exactly one dot product:\\n+\\n+`score(u, i) = <[u1,u2]/sqrt(2), [v1,v2]/sqrt(2)>`.\\n+\\n+There is no user-ID embedding. The user tower consumes a bounded 90-day history\\n+(at most 192 distinct positive-play items), with completion, organic-source,\\n+recency, and repeat weights, plus cutoff-correct active liked and disliked item\\n+sets. Three 48-dimensional `EmbeddingBag` encoders and four log-count features\\n+feed a 128-unit LayerNorm/GELU MLP. Its output is 63 L2-normalized taste\\n+coordinates plus a constant prior coordinate.\\n+\\n+The item tower uses a 48-dimensional trainable catalog-item embedding and two\\n+offline catalog features (log positive popularity and organic share), followed\\n+by its own 128-unit LayerNorm/GELU MLP. It emits 63 normalized coordinates plus\\n+a learned/popularity prior coordinate. Thus all 30,000 item vectors can be\\n+materialized offline and request-time work is one user-tower evaluation plus\\n+MIPS retrieval. Eligibility is applied only as the platform-owned retrieval\\n+boundary.\\n+\\n+## Training\\n+\\n+Training uses event streams and sparse bounded histories; it never constructs\\n+or decomposes a global user-item interaction matrix. Six non-overlapping\\n+historical cutoff/query windows are used for validation-stage fitting. The\\n+final refit adds the public validation window as a seventh training outcome and\\n+as observed history for test inference. Positives are organic 80%-complete\\n+plays and organic likes, with gain-weighted positive sampling. The pairwise\\n+softplus ranking loss uses 24 negatives per example, half uniform and half\\n+sampled from square-root popularity. One ensemble component receives three\\n+low-rate fine-tuning epochs on first-discovery-only positives, reconstructed\\n+from sparse first-seen events.\\n+\\n+Public validation graded NDCG@10: **0.024065** over 4,263 scored users. The\\n+popularity-only fallback scored 0.010710.\\n+\\n+## Reproduce the final submission\\n+\\n+From any working directory, run:\\n+\\n+```sh\\n+sh /app/solution/build_submission.sh\\n+```\\n+\\n+The command deterministically refits on `train_* + val_*` and writes\\n+`/app/solution/submission.parquet`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:09:00.552Z Script completed Wall time 0.0 seconds Output: {} ## Step 145 · agent · 2026-08-31T19:09:05.655Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:17.568Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/validation_ensemble_ft_w0p015.parquet /app/solution/validation.parquet\\nsh -n /app/solution/build_submission.sh\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_two_tower.py /app/solution/rerank_saved.py /app/solution/make_popularity.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\nPYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); 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)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nassert all(e.is_eligible(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None))\\nprint(f'FINAL_OK rows={len(x)} users={x.uid.nunique()} path={p}')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output)\n" } ### Tool output · 2026-08-31T19:09:23.681Z Script completed Wall time 6.1 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.024065, "quality_users": 4263 } } FINAL_OK rows=845600 users=8456 path=/app/solution/submission.parquet ## Step 146 · agent · 2026-08-31T19:09:26.476Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:32.257Z { "code": "const p = await tools.update_plan({explanation:\"The selected hybrid ensemble was refit on train+validation, the final submission was written, and both public scoring and all final structural/eligibility invariants passed.\",plan:[\n {step:\"Inspect data schemas, sizes, eligibility, and public evaluator\",status:\"completed\"},\n {step:\"Implement scalable history-derived PyTorch two-tower training and ranking\",status:\"completed\"},\n {step:\"Train baseline, create valid validation/test rankings, and score validation\",status:\"completed\"},\n {step:\"Tune features/objective with public validation and refit on train+validation\",status:\"completed\"},\n {step:\"Verify final artifact and document reproducible submission command\",status:\"completed\"}\n]}); text(p)\n" } ### Tool output · 2026-08-31T19:09:32.348Z Script completed Wall time 0.0 seconds Output: {} ## Step 147 · agent · 2026-08-31T19:09:34.766Z ### Thinking unavailable — no text recorded Implemented and verified the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Public validation NDCG@10: **0.024065** - 845,600 rows for 8,456 users - Zero duplicates or ineligible items; ranks exactly 1–100 - Architecture and methodology: [README.md](/app/solution/README.md) - Reproduction command: `sh /app/solution/build_submission.sh` - Main implementation: [train_two_tower.py](/app/solution/train_two_tower.py) The final 128-dimensional ensemble remains exactly decomposable into independently computed user and item vectors with a dot-product score.