yambda-discovery-ranking-two-tower-base / run-007-gpt-5-6-sol-high-2026-08-31T11-04-49 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T18:25:18.137Z 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 · 2026-08-31T18:25:19.799Z ## 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 · 2026-08-31T18:25:19.800Z 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 · 2026-08-31T18:25:19.800Z 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 · 2026-08-31T18:25:19.800Z 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) - Finances (finances@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": "01a05911-8f31-7d03-81e5-c3d1d09259a1", "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 · 2026-08-31T18:25:19.810Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-08-31T18:25:24.132Z ### Thinking unavailable — no text recorded I’ll build this end to end: inspect the data and evaluation mechanics, establish a valid two-tower baseline, tune it against the public temporal window, then refit on train+validation and preserve the best valid hidden-test submission. I’ll keep every artifact under `/app/solution/` and validate eligibility/schema before handing it off. ### exec · 2026-08-31T18:25:35.202Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect schemas, distributions, eligibility, and evaluator mechanics\",status:\"in_progress\"},\n {step:\"Implement scalable history-based PyTorch two-tower training and ranking\",status:\"pending\"},\n {step:\"Train/tune on train→validation and score public rankings\",status:\"pending\"},\n {step:\"Refit on train+validation, generate and validate final submission\",status:\"pending\"},\n {step:\"Document architecture, objective, score, and command\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,280p' /task/ranking_evaluation.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(p); text(r.output);\n" } ### Tool output · 2026-08-31T18:25:35.273Z Script completed Wall time 0.1 seconds Output: {} /app candidate_catalog.parquet 349352 bytes target_users.parquet 84127 bytes test_eligible_candidates.npz 7736341 bytes test_preference_state.parquet 1092329 bytes train_dislikes.parquet 857213 bytes train_likes.parquet 5374652 bytes train_listens.parquet 319183255 bytes train_undislikes.parquet 155988 bytes train_unlikes.parquet 1855885 bytes val_dislikes.parquet 36571 bytes val_likes.parquet 298484 bytes val_listens.parquet 9947609 bytes val_undislikes.parquet 7845 bytes val_unlikes.parquet 72536 bytes validation_eligible_candidates.npz 7573821 bytes validation_preference_state.parquet 1061761 bytes #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 8 · agent · 2026-08-31T18:25:39.956Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:25:53.903Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && 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,'rowgroups',pf.metadata.num_row_groups,'schema',pf.schema_arrow)\\nprint('\\\\nsmall files samples/counts')\\nfor name in ['candidate_catalog','target_users','validation_preference_state','test_preference_state']:\\n d=pd.read_parquet(D/f'{name}.parquet')\\n print('\\\\n',name,d.shape,d.dtypes.to_dict()); print(d.head());\\n for c in d.columns:\\n print(c,'nunique',d[c].nunique(), 'min', d[c].min() if d[c].dtype.kind in 'iuf' else '', 'max',d[c].max() if d[c].dtype.kind in 'iuf' else '')\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz')\\n print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible count',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),'items ever', (bits.sum(0)>0).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:25:54.043Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ## Step 9 · agent · 2026-08-31T18:25:55.873Z ### Thinking unavailable — no text recorded The default `python` executable is not the task’s pinned environment, so I’m locating the supplied runtime before touching the implementation. The dataset and evaluator are intact; this is just an interpreter-path mismatch. ### exec · 2026-08-31T18:26:01.078Z { "code": "const r = await tools.exec_command({cmd:\"command -v python python3 || true\\nls -la /task | sed -n '1,120p'\\nfind / -path '*/bin/python' -o -path '*/bin/python3' 2>/dev/null | head -30\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:01.356Z Script completed Wall time 0.3 seconds Output: /usr/local/bin/python total 36 dr-xr-xr-x 1 root root 4096 Aug 31 18:22 . drwxr-xr-x 1 root root 4096 Aug 31 18:25 .. -r--r--r-- 1 root root 0 Aug 31 18:08 .keep dr-xr-xr-x 1 root root 4096 Aug 31 18:09 data -r--r--r-- 1 root root 5774 Aug 31 18:08 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 18:08 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 18:08 requirements.txt /opt/task-venv/bin/python /opt/task-venv/bin/python3 /usr/local/bin/python /usr/local/bin/python3 ## Step 10 · agent · 2026-08-31T18:26:02.770Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:26:16.776Z { "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,'rowgroups',pf.metadata.num_row_groups,'cols',pf.schema_arrow.names)\\nprint('\\\\nsmall files')\\nfor name in ['candidate_catalog','target_users','validation_preference_state','test_preference_state']:\\n d=pd.read_parquet(D/f'{name}.parquet')\\n print('\\\\n',name,d.shape,d.dtypes.to_dict()); print(d.head().to_string(index=False))\\n for c in d.columns:\\n print(c,'nunique',d[c].nunique(), 'min', d[c].min() if d[c].dtype.kind in 'iuf' else '', 'max',d[c].max() if d[c].dtype.kind in 'iuf' else '')\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz')\\n print(stage,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=bits.sum(1)\\n print('eligible count',cnt.min(),np.quantile(cnt,[.1,.5,.9]),cnt.max(),'items ever', (bits.sum(0)>0).sum())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:26:19.421Z Script completed Wall time 2.6 seconds Output: candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state'] train_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] validation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state'] small files candidate_catalog (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} item_id positive_listens organic_positive_listens popularity_rank 5862961 26615 20620 1 6901374 25536 17347 2 5635052 24691 17194 3 3542184 24075 17699 4 9378983 24036 18195 5 item_id nunique 30000 min 206 max 9390294 positive_listens nunique 3049 min 163 max 26615 organic_positive_listens nunique 2019 min 0 max 20620 popularity_rank nunique 30000 min 1 max 30000 target_users (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} uid recent_positive_listens history_positive_listens 100 844.0 2604 200 50.0 286 300 168.0 359 600 1985.0 2423 700 1776.0 6114 uid nunique 8456 min 100 max 1000000 recent_positive_listens nunique 3100 min 20.0 max 19592.0 history_positive_listens nunique 4880 min 20 max 26597 validation_preference_state (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} uid item_id state 100 410868 liked 100 732449 liked 100 982903 liked 100 1730273 liked 100 2621343 liked uid nunique 7038 min 100 max 1000000 item_id nunique 28814 min 206 max 9390294 state nunique 2 min max test_preference_state (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} uid item_id state 100 410868 liked 100 732449 liked 100 982903 liked 100 1730273 liked 100 2621343 liked uid nunique 7092 min 100 max 1000000 item_id nunique 28863 min 206 max 9390294 state nunique 2 min max validation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible count 24657 [28038.5 29242. 29852. ] 30000 items ever 30000 test {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible count 24552 [28001. 29220. 29835.] 30000 items ever 30000 ## Step 11 · agent · 2026-08-31T18:26:36.781Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:02.384Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\npf=pq.ParquetFile(D/'train_listens.parquet')\\nfor i in range(pf.metadata.num_row_groups):\\n rg=pf.metadata.row_group(i); vals=[]\\n for j,n in enumerate(pf.schema_arrow.names):\\n s=rg.column(j).statistics\\n vals.append(f'{n}:{s.min}-{s.max}' if s else n)\\n print(i,rg.num_rows,' '.join(vals[:3]))\\n# all public truth stats + event stats\\nT=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nz=np.load(D/'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,it):\\n c=icol.get(int(it)); r=urow.get(int(u)); return c is not None and r is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nli=li[li.uid.isin(T)&(li.is_organic==1)&(li.played_ratio_pct>=80)].drop_duplicates(['uid','item_id'])\\nla=la[la.uid.isin(T)&(la.is_organic==1)].drop_duplicates(['uid','item_id'])\\ntruth={}\\nfor u,it in li[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=1\\nfor u,it in la[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=3\\nprint('truth users',len(truth),'pairs',sum(map(len,truth.values())),'grades',pd.Series([g for x in truth.values() for g in x.values()]).value_counts().to_dict())\\nprs=np.array([rank[i] for x in truth.values() for i in x])\\nprint('truth popularity rank quantiles',np.quantile(prs,[0,.1,.25,.5,.75,.9,.99,1]))\\nprint('relevance per user quantiles',np.quantile([len(x) for x in truth.values()],[0,.1,.5,.9,.99,1]))\\nprint('val target event rows',len(li),len(la),'unique users raw',li.uid.nunique(),la.uid.nunique())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:13.406Z Script running with cell ID 5 Wall time 11.0 seconds Output: ## Step 12 · agent · 2026-08-31T18:27:15.232Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:27:15.937Z { "cell_id": "5", "yield_time_ms": 20000, "max_tokens": 30000 } ### Tool output · 2026-08-31T18:27:32.565Z Script completed Wall time 16.6 seconds Output: ## Step 13 · agent · 2026-08-31T18:27:36.755Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:27:42.602Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pyarrow.parquet as pq\\nfrom pathlib import Path\\npf=pq.ParquetFile('/task/data/train_listens.parquet')\\nfor i in range(pf.metadata.num_row_groups):\\n rg=pf.metadata.row_group(i)\\n print(i,rg.num_rows,[(pf.schema_arrow.names[j],rg.column(j).statistics.min,rg.column(j).statistics.max) for j in range(3)],flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:27:43.114Z Script completed Wall time 0.5 seconds Output: 0 1048576 [('uid', 100, 26500), ('timestamp', 15, 24786690), ('item_id', 206, 9390541)] 1 1048576 [('uid', 26500, 50300), ('timestamp', 185, 24786725), ('item_id', 105, 9390541)] 2 1048576 [('uid', 50300, 73100), ('timestamp', 15320, 24786790), ('item_id', 105, 9390461)] 3 1048576 [('uid', 73100, 98200), ('timestamp', 3625, 24786795), ('item_id', 167, 9390623)] 4 1048576 [('uid', 98200, 122600), ('timestamp', 5735, 24786740), ('item_id', 50, 9390541)] 5 1048576 [('uid', 122600, 147100), ('timestamp', 120, 24786775), ('item_id', 22, 9390541)] 6 1048576 [('uid', 147100, 171600), ('timestamp', 140, 24786750), ('item_id', 50, 9390601)] 7 1048576 [('uid', 171600, 195600), ('timestamp', 220, 24786745), ('item_id', 50, 9390554)] 8 1048576 [('uid', 195600, 216500), ('timestamp', 1890, 24786770), ('item_id', 50, 9390528)] 9 1048576 [('uid', 216500, 241800), ('timestamp', 65, 24786735), ('item_id', 26, 9390541)] 10 1048576 [('uid', 241800, 264900), ('timestamp', 15, 24786720), ('item_id', 71, 9390541)] 11 1048576 [('uid', 264900, 288300), ('timestamp', 350, 24786795), ('item_id', 50, 9390541)] 12 1048576 [('uid', 288300, 312000), ('timestamp', 60, 24786730), ('item_id', 50, 9390541)] 13 1048576 [('uid', 312000, 338000), ('timestamp', 1370, 24786790), ('item_id', 50, 9390601)] 14 1048576 [('uid', 338000, 360500), ('timestamp', 5, 24786770), ('item_id', 26, 9390541)] 15 1048576 [('uid', 360500, 384800), ('timestamp', 30, 24786785), ('item_id', 50, 9390541)] 16 1048576 [('uid', 384800, 407800), ('timestamp', 9145, 24786750), ('item_id', 43, 9390541)] 17 1048576 [('uid', 407800, 428900), ('timestamp', 95, 24786795), ('item_id', 50, 9390581)] 18 1048576 [('uid', 428900, 454100), ('timestamp', 20, 24786760), ('item_id', 108, 9390541)] 19 1048576 [('uid', 454100, 475900), ('timestamp', 0, 24786760), ('item_id', 50, 9390541)] 20 1048576 [('uid', 475900, 502800), ('timestamp', 40, 24786795), ('item_id', 206, 9390541)] 21 1048576 [('uid', 502800, 528400), ('timestamp', 300, 24786790), ('item_id', 206, 9390601)] 22 1048576 [('uid', 528400, 553000), ('timestamp', 45, 24786730), ('item_id', 175, 9390541)] 23 1048576 [('uid', 553000, 577800), ('timestamp', 530, 24786730), ('item_id', 202, 9390541)] 24 1048576 [('uid', 577800, 602700), ('timestamp', 8680, 24786790), ('item_id', 206, 9390541)] 25 1048576 [('uid', 602700, 628600), ('timestamp', 65, 24786785), ('item_id', 43, 9390541)] 26 1048576 [('uid', 628600, 650900), ('timestamp', 20, 24786785), ('item_id', 50, 9390541)] 27 1048576 [('uid', 650900, 671100), ('timestamp', 40, 24786725), ('item_id', 195, 9390601)] 28 1048576 [('uid', 671100, 697400), ('timestamp', 125, 24786770), ('item_id', 26, 9390541)] 29 1048576 [('uid', 697400, 720000), ('timestamp', 7330, 24786785), ('item_id', 206, 9390601)] 30 1048576 [('uid', 720000, 741900), ('timestamp', 30, 24786775), ('item_id', 26, 9390601)] 31 1048576 [('uid', 741900, 766100), ('timestamp', 60, 24786670), ('item_id', 50, 9390601)] 32 1048576 [('uid', 766100, 790200), ('timestamp', 20, 24786795), ('item_id', 142, 9390541)] 33 1048576 [('uid', 790200, 816900), ('timestamp', 355, 24786795), ('item_id', 198, 9390623)] 34 1048576 [('uid', 816900, 839200), ('timestamp', 565, 24786735), ('item_id', 206, 9390576)] 35 1048576 [('uid', 839200, 862300), ('timestamp', 205, 24786785), ('item_id', 50, 9390541)] 36 1048576 [('uid', 862300, 885700), ('timestamp', 50, 24786740), ('item_id', 50, 9390294)] 37 1048576 [('uid', 885700, 913100), ('timestamp', 1190, 24786775), ('item_id', 198, 9390294)] 38 1048576 [('uid', 913100, 940200), ('timestamp', 190, 24786705), ('item_id', 206, 9390541)] 39 1048576 [('uid', 940200, 965500), ('timestamp', 10, 24786760), ('item_id', 50, 9390508)] 40 1048576 [('uid', 965500, 989500), ('timestamp', 230, 24786710), ('item_id', 50, 9390541)] 41 492543 [('uid', 989500, 1000000), ('timestamp', 20, 24786690), ('item_id', 50, 9390508)] ## Step 14 · agent · 2026-08-31T18:27:46.081Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:08.394Z { "code": "const r = await tools.exec_command({cmd:\"free -h && /opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data/'\\nT=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\\nt=time.time()\\nli=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nprint('read',li.shape,time.time()-t,li.memory_usage(deep=True).sum()/1e6,flush=True)\\nli=li[li.uid.isin(T)&(li.is_organic==1)&(li.played_ratio_pct>=80)].drop_duplicates(['uid','item_id'])\\nla=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nla=la[la.uid.isin(T)&(la.is_organic==1)].drop_duplicates(['uid','item_id'])\\nprint('strong/likes',li.shape,la.shape,'users',li.uid.nunique(),la.uid.nunique(),flush=True)\\nz=np.load(D+'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,it):\\n c=icol.get(int(it)); r=urow.get(int(u)); return c is not None and r is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\ntruth={}\\nfor u,it in li[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=1\\nfor u,it in la[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=3\\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nprs=np.array([rank[i] for x in truth.values() for i in x])\\nprint('truth users/pairs',len(truth),sum(map(len,truth.values())),'grades',pd.Series([g for x in truth.values() for g in x.values()]).value_counts().to_dict())\\nprint('popq',np.quantile(prs,[0,.1,.25,.5,.75,.9,.99,1]))\\nprint('peruserq',np.quantile([len(x) for x in truth.values()],[0,.1,.5,.9,.99,1]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:28:08.454Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: free: not found ## Step 15 · agent · 2026-08-31T18:28:10.774Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:28:32.879Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data/'\\nT=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\\nt=time.time(); li=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nprint('read',li.shape,time.time()-t,li.memory_usage(deep=True).sum()/1e6,flush=True)\\nli=li[li.uid.isin(T)&(li.is_organic==1)&(li.played_ratio_pct>=80)].drop_duplicates(['uid','item_id'])\\nla=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); la=la[la.uid.isin(T)&(la.is_organic==1)].drop_duplicates(['uid','item_id'])\\nprint('strong/likes',li.shape,la.shape,'users',li.uid.nunique(),la.uid.nunique(),flush=True)\\nz=np.load(D+'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,it):\\n c=icol.get(int(it)); r=urow.get(int(u)); return c is not None and r is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\ntruth={}\\nfor u,it in li[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=1\\nfor u,it in la[['uid','item_id']].itertuples(index=False,name=None):\\n if elig(u,it):truth.setdefault(int(u),{})[int(it)]=3\\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nprs=np.array([rank[i] for x in truth.values() for i in x])\\nprint('truth users/pairs',len(truth),sum(map(len,truth.values())),'grades',pd.Series([g for x in truth.values() for g in x.values()]).value_counts().to_dict())\\nprint('popq',np.quantile(prs,[0,.1,.25,.5,.75,.9,.99,1]))\\nprint('peruserq',np.quantile([len(x) for x in truth.values()],[0,.1,.5,.9,.99,1]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:28:43.901Z Script running with cell ID 8 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-08-31T18:28:46.064Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:28:46.852Z { "cell_id": "8", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:29:03.062Z Script completed Wall time 16.2 seconds Output: read (1501697, 4) 0.02275252342224121 16.518799 strong/likes (255608, 4) (17195, 3) users 6325 2714 ## Step 17 · agent · 2026-08-31T18:29:22.629Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-08-31T18:29:36.818Z ### Thinking unavailable — no text recorded The benchmark has 8,456 users and 30,000 catalog items; each user retains roughly 29,000 eligible candidates, so request-time scoring can stay a single user-vector–by–precomputed-item-matrix product followed by masking. I’m first measuring the popularity floor, then I’ll train the history encoder on temporal holdouts rather than letting validation behavior leak into features. ### exec · 2026-08-31T18:29:51.181Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution')\\nz=np.load(D/'validation_eligible_candidates.npz')\\nitems=z['item_ids'].astype(np.int64)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').sort_values('popularity_rank')\\npos={int(x):i for i,x in enumerate(items)}\\norder=np.array([pos[int(x)] for x in cat.item_id],dtype=np.int32)\\nrows=[]\\nfor r,u in enumerate(z['uids']):\\n mask=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool)\\n take=order[mask[order]][:100]\\n rows.append(pd.DataFrame({'uid':np.full(100,u,dtype=np.int64),'item_id':items[take],'rank':np.arange(1,101,dtype=np.int64)}))\\npd.concat(rows,ignore_index=True).to_parquet(O/'validation_popularity.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:02.209Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 19 · agent · 2026-08-31T18:30:03.904Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:30:04.679Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:30:21.371Z Script completed Wall time 16.7 seconds Output: ## Step 20 · agent · 2026-08-31T18:30:23.311Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:26.399Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution && test -f /app/solution/validation_popularity.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:26.458Z Script completed Wall time 0.0 seconds Output: total 0 ## Step 21 · agent · 2026-08-31T18:30:35.093Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:46.036Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,time\\nD='/task/data/'; O='/app/solution/'\\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']; uids=z['uids'].astype(np.int64)\\ncat=pd.read_parquet(D+'candidate_catalog.parquet').sort_values('popularity_rank'); pos={int(x):i for i,x in enumerate(items)}; order=np.array([pos[int(x)] for x in cat.item_id],dtype=np.int32)\\ncols=order[:1000]; ok=((packed[:,cols//8] >> (cols%8)) & 1).astype(bool)\\nout=np.empty((len(uids),100),dtype=np.int64)\\nfor r in range(len(uids)): out[r]=items[cols[ok[r]][:100]]\\nframe=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':out.ravel(),'rank':np.tile(np.arange(1,101),len(uids))})\\nframe.to_parquet(O+'validation_popularity.parquet',index=False)\\nprint(frame.shape,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:47.963Z Script completed Wall time 1.9 seconds Output: (845600, 3) ## Step 22 · agent · 2026-08-31T18:30:49.620Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:30:51.850Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:30:55.359Z Script completed Wall time 3.5 seconds Output: { "model": "/app/solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 23 · agent · 2026-08-31T18:31:00.611Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:31:07.050Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,time\\nD='/task/data/'; u=pd.read_parquet(D+'target_users.parquet').uid.astype(int).tolist(); t=time.time()\\nd=pd.read_parquet(D+'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('uid','in',u)])\\nprint(d.shape,d.memory_usage(deep=True).sum()/1e6,'sec',time.time()-t,'users',d.uid.nunique(),'items',d.item_id.nunique())\\nprint(d.head(),d.tail())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:31:10.123Z Script completed Wall time 3.1 seconds Output: (43222297, 5) 648.334587 sec 0.7503514289855957 users 8456 items 839582 uid timestamp item_id is_organic played_ratio_pct 0 100 39420 8326270 0 100 1 100 39420 1441281 0 100 2 100 39625 286361 0 100 3 100 40110 732449 0 100 4 100 40360 3397170 0 46 uid timestamp item_id is_organic played_ratio_pct 43222292 1000000 24598710 413483 0 100 43222293 1000000 24598845 3934184 0 100 43222294 1000000 24599500 7355084 0 100 43222295 1000000 24599500 6682095 0 100 43222296 1000000 24611315 4352630 0 16 ## Step 24 · agent · 2026-08-31T18:31:29.686Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T18:31:48.353Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-08-31T18:32:06.945Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-08-31T18:32:25.602Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-08-31T18:32:44.236Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-08-31T18:33:02.883Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-08-31T18:33:21.543Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-08-31T18:33:38.744Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:33:45.706Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq,pandas as pd,numpy as np\\np=pq.ParquetFile('/task/data/train_listens.parquet'); cat=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy()\\nfor i in [0,20,41]:\\n d=p.read_row_group(i,columns=['timestamp','item_id','played_ratio_pct']).to_pandas()\\n for start in [13000000,17010800]:\\n m=(d.timestamp>=start)&(d.played_ratio_pct>=50)&d.item_id.isin(cat)\\n print(i,start,m.sum(),len(d),m.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:33:47.493Z Script completed Wall time 1.8 seconds Output: 0 13000000 268766 1048576 0.2563152313232422 0 17010800 184937 1048576 0.17636966705322266 20 13000000 272301 1048576 0.2596864700317383 20 17010800 192968 1048576 0.18402862548828125 41 13000000 113228 492543 0.22988449739413613 41 17010800 82389 492543 0.16727270512422265 ## Step 32 · agent · 2026-08-31T18:34:03.790Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:28.374Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/two_tower_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-conditioned PyTorch two-tower ranker.\\n+\\n+The implementation consumes event rows and never materializes a user-item\\n+matrix. The online path is exactly UserTower(history) @ ItemTower(item).T.\\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+import torch.nn.functional as F\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+WINDOW = 7 * 24 * 60 * 60\\n+GAP = 30 * 60\\n+LOOKBACK = 90 * 24 * 60 * 60\\n+MAX_HISTORY = 400\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+ torch.set_num_threads(min(8, max(1, torch.get_num_threads())))\\n+\\n+\\n+@dataclass\\n+class Catalog:\\n+ ids: np.ndarray\\n+ features: np.ndarray\\n+ id_to_col: dict[int, int]\\n+\\n+\\n+def load_catalog() -> Catalog:\\n+ frame = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").sort_values(\\\"popularity_rank\\\")\\n+ ids = frame.item_id.to_numpy(np.int64)\\n+ log_pos = np.log1p(frame.positive_listens.to_numpy(np.float32))\\n+ log_org = np.log1p(frame.organic_positive_listens.to_numpy(np.float32))\\n+ org_rate = frame.organic_positive_listens.to_numpy(np.float32) / np.maximum(\\n+ frame.positive_listens.to_numpy(np.float32), 1\\n+ )\\n+ log_rank = np.log1p(frame.popularity_rank.to_numpy(np.float32))\\n+ x = np.column_stack([log_pos, log_org, org_rate, log_rank]).astype(np.float32)\\n+ x = (x - x.mean(0)) / (x.std(0) + 1e-6)\\n+ return Catalog(ids, x, {int(v): i for i, v in enumerate(ids)})\\n+\\n+\\n+def _cache_events(stage: str, catalog: Catalog) -> Path:\\n+ \\\"\\\"\\\"Create compact recent catalog-positive event arrays, streamed by row group.\\\"\\\"\\\"\\n+ path = OUT / f\\\"events_{stage}.npz\\\"\\n+ if path.exists():\\n+ return path\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ user_index = {int(u): i for i, u in enumerate(targets)}\\n+ keep_from = TRAIN_END - 8 * WINDOW - LOOKBACK - GAP\\n+ chunks: dict[str, list[np.ndarray]] = {k: [] for k in (\\\"u\\\", \\\"t\\\", \\\"i\\\", \\\"o\\\", \\\"r\\\")}\\n+ families = [\\\"train_listens.parquet\\\"] + ([\\\"val_listens.parquet\\\"] if stage == \\\"final\\\" else [])\\n+ for filename in families:\\n+ pf = pq.ParquetFile(DATA / filename)\\n+ for rg in range(pf.metadata.num_row_groups):\\n+ d = pf.read_row_group(\\n+ rg, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ ).to_pandas()\\n+ item = pd.Index(catalog.ids).get_indexer(d.item_id)\\n+ user = d.uid.map(user_index).fillna(-1).to_numpy(np.int32)\\n+ mask = (\\n+ (d.timestamp.to_numpy() >= keep_from)\\n+ & (d.played_ratio_pct.to_numpy() >= 50)\\n+ & (item >= 0)\\n+ & (user >= 0)\\n+ )\\n+ chunks[\\\"u\\\"].append(user[mask].astype(np.int16))\\n+ chunks[\\\"t\\\"].append(d.timestamp.to_numpy()[mask].astype(np.int32))\\n+ chunks[\\\"i\\\"].append(item[mask].astype(np.uint16))\\n+ chunks[\\\"o\\\"].append(d.is_organic.to_numpy()[mask].astype(np.uint8))\\n+ chunks[\\\"r\\\"].append(d.played_ratio_pct.to_numpy()[mask].clip(0, 100).astype(np.uint8))\\n+ arrays = {k: np.concatenate(v) for k, v in chunks.items()}\\n+ order = np.lexsort((arrays[\\\"t\\\"], arrays[\\\"u\\\"]))\\n+ arrays = {k: v[order] for k, v in arrays.items()}\\n+ counts = np.bincount(arrays[\\\"u\\\"].astype(np.int64), minlength=len(targets))\\n+ arrays[\\\"offsets\\\"] = np.r_[0, counts.cumsum()].astype(np.int64)\\n+ arrays[\\\"uids\\\"] = targets\\n+ np.savez(path, **arrays)\\n+ print(f\\\"cached {len(arrays['i']):,} positive catalog listens in {path.name}\\\", flush=True)\\n+ return path\\n+\\n+\\n+def load_likes(include_val: bool, catalog: Catalog, uids: np.ndarray) -> dict[int, tuple[np.ndarray, np.ndarray]]:\\n+ user_index = {int(u): i for i, u in enumerate(uids)}\\n+ frames = []\\n+ names = [\\\"train_likes.parquet\\\"] + ([\\\"val_likes.parquet\\\"] if include_val else [])\\n+ for name in names:\\n+ d = pd.read_parquet(DATA / name, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ d = d[d.uid.isin(user_index) & (d.is_organic == 1)].copy()\\n+ d[\\\"u\\\"] = d.uid.map(user_index).astype(np.int32)\\n+ d[\\\"i\\\"] = pd.Index(catalog.ids).get_indexer(d.item_id)\\n+ frames.append(d[d.i >= 0][[\\\"u\\\", \\\"timestamp\\\", \\\"i\\\"]])\\n+ all_likes = pd.concat(frames, ignore_index=True).sort_values([\\\"u\\\", \\\"timestamp\\\"])\\n+ return {\\n+ int(u): (g.timestamp.to_numpy(np.int32), g.i.to_numpy(np.int32))\\n+ for u, g in all_likes.groupby(\\\"u\\\", sort=False)\\n+ }\\n+\\n+\\n+def _latest_distinct(items: np.ndarray, times: np.ndarray, organic: np.ndarray,\\n+ ratio: np.ndarray, cutoff: int) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Return recent distinct history IDs and recency/quality weights.\\\"\\\"\\\"\\n+ hi = int(np.searchsorted(times, cutoff, side=\\\"left\\\"))\\n+ lo = int(np.searchsorted(times, cutoff - LOOKBACK, side=\\\"left\\\"))\\n+ seen: set[int] = set()\\n+ chosen_i: list[int] = []\\n+ chosen_w: list[float] = []\\n+ chosen_o: list[float] = []\\n+ for p in range(hi - 1, lo - 1, -1):\\n+ it = int(items[p])\\n+ if it in seen:\\n+ continue\\n+ seen.add(it)\\n+ age_days = max(0.0, (cutoff - int(times[p])) / 86400.0)\\n+ recency = math.exp(-age_days / 35.0)\\n+ quality = 0.65 + 0.35 * min(float(ratio[p]), 100.0) / 100.0\\n+ organic_boost = 1.35 if organic[p] else 1.0\\n+ chosen_i.append(it)\\n+ chosen_w.append(recency * quality * organic_boost)\\n+ chosen_o.append(float(organic[p]))\\n+ if len(chosen_i) >= MAX_HISTORY:\\n+ break\\n+ if not chosen_i:\\n+ return np.array([0], np.int32), np.array([0.0], np.float32), np.zeros(5, np.float32)\\n+ ids = np.asarray(chosen_i, np.int32)\\n+ weights = np.asarray(chosen_w, np.float32)\\n+ numeric = np.asarray(\\n+ [math.log1p(hi - lo), math.log1p(len(ids)), float(np.mean(chosen_o)),\\n+ float(np.mean(weights)), float(np.max(weights))], np.float32\\n+ )\\n+ return ids, weights, numeric\\n+\\n+\\n+def build_examples(stage: str, catalog: Catalog):\\n+ \\\"\\\"\\\"Build temporal next-window training examples and exact-cutoff profiles.\\\"\\\"\\\"\\n+ cache = np.load(_cache_events(stage, catalog), allow_pickle=False)\\n+ uids = cache[\\\"uids\\\"].astype(np.int64)\\n+ likes = load_likes(stage == \\\"final\\\", catalog, uids)\\n+ contexts_i: list[np.ndarray] = []\\n+ contexts_w: list[np.ndarray] = []\\n+ numeric: list[np.ndarray] = []\\n+ labels: list[np.ndarray] = []\\n+ label_weights: list[np.ndarray] = []\\n+\\n+ # Validation selection uses only pseudo-windows inside train. Final refit\\n+ # additionally uses the now-observed validation window as a training target.\\n+ cutoffs = []\\n+ for k in range(8):\\n+ outcome_end = TRAIN_END - k * WINDOW\\n+ cutoffs.append((outcome_end - WINDOW - GAP, outcome_end))\\n+ if stage == \\\"final\\\":\\n+ val_listens = pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=[\\\"timestamp\\\"])\\n+ val_end = int(val_listens.timestamp.max())\\n+ cutoffs.insert(0, (TRAIN_END, val_end + 1))\\n+\\n+ infer_cutoff = TRAIN_END if stage == \\\"validation\\\" else int(\\n+ pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=[\\\"timestamp\\\"]).timestamp.max()\\n+ ) + 1\\n+ infer_ids: list[np.ndarray] = []\\n+ infer_w: list[np.ndarray] = []\\n+ infer_num: list[np.ndarray] = []\\n+\\n+ for u in range(len(uids)):\\n+ a, b = int(cache[\\\"offsets\\\"][u]), int(cache[\\\"offsets\\\"][u + 1])\\n+ t = cache[\\\"t\\\"][a:b]\\n+ item = cache[\\\"i\\\"][a:b].astype(np.int32)\\n+ org = cache[\\\"o\\\"][a:b]\\n+ ratio = cache[\\\"r\\\"][a:b]\\n+ li_t, li_i = likes.get(u, (np.empty(0, np.int32), np.empty(0, np.int32)))\\n+ for hist_cut, outcome_end in cutoffs:\\n+ ids, weights, nums = _latest_distinct(item, t, org, ratio, hist_cut)\\n+ start = hist_cut + (GAP if hist_cut != TRAIN_END else GAP)\\n+ lo = int(np.searchsorted(t, start, side=\\\"left\\\"))\\n+ hi = int(np.searchsorted(t, outcome_end, side=\\\"left\\\"))\\n+ good = (org[lo:hi] == 1) & (ratio[lo:hi] >= 80)\\n+ grade: dict[int, float] = {int(x): 1.0 for x in item[lo:hi][good]}\\n+ llo = int(np.searchsorted(li_t, start, side=\\\"left\\\"))\\n+ lhi = int(np.searchsorted(li_t, outcome_end, side=\\\"left\\\"))\\n+ for x in li_i[llo:lhi]:\\n+ grade[int(x)] = 3.0\\n+ # The retrieval service largely removes tracks already consumed.\\n+ prior = set(map(int, item[: int(np.searchsorted(t, hist_cut, side=\\\"left\\\"))]))\\n+ grade = {x: g for x, g in grade.items() if x not in prior}\\n+ if grade:\\n+ contexts_i.append(ids); contexts_w.append(weights); numeric.append(nums)\\n+ labels.append(np.fromiter(grade.keys(), dtype=np.int32))\\n+ label_weights.append(np.fromiter(grade.values(), dtype=np.float32))\\n+ ids, weights, nums = _latest_distinct(item, t, org, ratio, infer_cutoff)\\n+ infer_ids.append(ids); infer_w.append(weights); infer_num.append(nums)\\n+\\n+ # Add cutoff-correct active likes to the inference profile. They remain\\n+ # history features only; eligibility continues to be platform-owned.\\n+ pref_name = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pref = pref[pref.state == \\\"liked\\\"].copy()\\n+ uid_pos = {int(x): i for i, x in enumerate(uids)}\\n+ pref[\\\"u\\\"] = pref.uid.map(uid_pos).fillna(-1).astype(np.int32)\\n+ pref[\\\"i\\\"] = pd.Index(catalog.ids).get_indexer(pref.item_id)\\n+ for u, g in pref[(pref.u >= 0) & (pref.i >= 0)].groupby(\\\"u\\\"):\\n+ old_i, old_w = infer_ids[int(u)], infer_w[int(u)]\\n+ liked = g.i.to_numpy(np.int32)\\n+ existing = set(map(int, old_i))\\n+ liked = np.asarray([x for x in liked if int(x) not in existing], np.int32)\\n+ if len(liked):\\n+ infer_ids[int(u)] = np.r_[liked[-100:], old_i][:MAX_HISTORY]\\n+ infer_w[int(u)] = np.r_[np.full(min(100, len(liked)), 2.5, np.float32), old_w][:MAX_HISTORY]\\n+\\n+ print(f\\\"built {len(labels):,} temporal user contexts; {sum(map(len, labels)):,} distinct targets\\\", flush=True)\\n+ return (uids, contexts_i, contexts_w, np.asarray(numeric, np.float32), labels,\\n+ label_weights, infer_ids, infer_w, np.asarray(infer_num, np.float32))\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int = 64):\\n+ super().__init__()\\n+ self.history = nn.Embedding(n_items, dim)\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim + 5, 128), nn.LayerNorm(128), nn.SiLU(), nn.Dropout(0.08),\\n+ nn.Linear(128, dim),\\n+ )\\n+\\n+ def forward(self, ids: torch.Tensor, weights: torch.Tensor, numeric: torch.Tensor) -> torch.Tensor:\\n+ pooled = (self.history(ids) * weights.unsqueeze(-1)).sum(1)\\n+ pooled = pooled / weights.abs().sum(1, keepdim=True).clamp_min(1e-5)\\n+ return F.normalize(self.net(torch.cat([pooled, numeric], dim=1)), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int = 64):\\n+ super().__init__()\\n+ self.identity = nn.Embedding(n_items, dim)\\n+ self.net = nn.Sequential(\\n+ nn.Linear(dim + 4, 128), nn.LayerNorm(128), nn.SiLU(), nn.Dropout(0.03),\\n+ nn.Linear(128, dim),\\n+ )\\n+\\n+ def forward(self, ids: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ return F.normalize(self.net(torch.cat([self.identity(ids), features], dim=1)), dim=1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int = 64):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, dim)\\n+ self.log_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+ def score(self, user_vec: torch.Tensor, item_vec: torch.Tensor) -> torch.Tensor:\\n+ return user_vec @ item_vec.T\\n+\\n+\\n+def pad_histories(indices: np.ndarray, histories, weights):\\n+ ids = np.zeros((len(indices), MAX_HISTORY), np.int64)\\n+ w = np.zeros((len(indices), MAX_HISTORY), np.float32)\\n+ for row, ix in enumerate(indices):\\n+ n = min(MAX_HISTORY, len(histories[int(ix)]))\\n+ ids[row, :n] = histories[int(ix)][:n]\\n+ w[row, :n] = weights[int(ix)][:n]\\n+ return torch.from_numpy(ids), torch.from_numpy(w)\\n+\\n+\\n+def train_model(model: TwoTower, catalog: Catalog, data, epochs: int, seed: int) -> None:\\n+ _, histories, weights, numeric, labels, label_weights, *_ = data\\n+ item_features = torch.from_numpy(catalog.features)\\n+ popularity = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").sort_values(\\\"popularity_rank\\\").positive_listens.to_numpy(np.float64)\\n+ neg_p = np.sqrt(popularity + 5.0); neg_p /= neg_p.sum()\\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ rng = np.random.default_rng(seed)\\n+ batch_size, n_neg = 192, 24\\n+ order = np.arange(len(labels))\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ rng.shuffle(order)\\n+ running = 0.0\\n+ for start in range(0, len(order), batch_size):\\n+ ix = order[start:start + batch_size]\\n+ h_id, h_w = pad_histories(ix, histories, weights)\\n+ nums = torch.from_numpy(numeric[ix])\\n+ pos = np.asarray([\\n+ rng.choice(labels[int(j)], p=label_weights[int(j)] / label_weights[int(j)].sum())\\n+ for j in ix\\n+ ], np.int64)\\n+ neg = rng.choice(len(catalog.ids), size=(len(ix), n_neg), p=neg_p).astype(np.int64)\\n+ u = model.user_tower(h_id, h_w, nums)\\n+ p = model.item_tower(torch.from_numpy(pos), item_features[pos])\\n+ flat = neg.ravel()\\n+ n = model.item_tower(torch.from_numpy(flat), item_features[flat]).reshape(len(ix), n_neg, -1)\\n+ scale = model.log_scale.exp().clamp(5.0, 40.0)\\n+ pos_score = (u * p).sum(1, keepdim=True)\\n+ neg_score = (u.unsqueeze(1) * n).sum(2)\\n+ # Pairwise sampled-softmax objective; grade-3 targets are sampled\\n+ # three times as often through the target distribution above.\\n+ loss = F.softplus(scale * (neg_score - pos_score)).mean()\\n+ optimizer.zero_grad(set_to_none=True); loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0); optimizer.step()\\n+ running += float(loss) * len(ix)\\n+ print(f\\\"epoch {epoch + 1:02d}/{epochs} loss={running / len(order):.5f} scale={model.log_scale.exp().item():.2f}\\\", flush=True)\\n+\\n+\\n+@torch.no_grad()\\n+def rank(model: TwoTower, catalog: Catalog, data, stage: str, output: Path) -> None:\\n+ uids, *_, infer_ids, infer_w, infer_num = data\\n+ model.eval()\\n+ item_features = torch.from_numpy(catalog.features)\\n+ all_ids = torch.arange(len(catalog.ids))\\n+ item_vec = model.item_tower(all_ids, item_features)\\n+ eligibility = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\", allow_pickle=False)\\n+ # Eligibility item order is currently the catalog ID set but not assumed to\\n+ # match popularity order.\\n+ eligibility_pos = {int(x): i for i, x in enumerate(eligibility[\\\"item_ids\\\"])}\\n+ packed_cols = np.asarray([eligibility_pos[int(x)] for x in catalog.ids], np.int32)\\n+ uid_row = {int(x): i for i, x in enumerate(eligibility[\\\"uids\\\"])}\\n+ top = np.empty((len(uids), 100), np.int64)\\n+ batch_size = 128\\n+ for start in range(0, len(uids), batch_size):\\n+ ix = np.arange(start, min(start + batch_size, len(uids)))\\n+ h_id, h_w = pad_histories(ix, infer_ids, infer_w)\\n+ u = model.user_tower(h_id, h_w, torch.from_numpy(infer_num[ix]))\\n+ scores = model.score(u, item_vec).numpy()\\n+ rows = np.asarray([uid_row[int(uids[j])] for j in ix], np.int32)\\n+ pc = packed_cols\\n+ valid = ((eligibility[\\\"packed_eligible\\\"][rows[:, None], pc[None, :] // 8]\\n+ >> (pc[None, :] % 8)) & 1).astype(bool)\\n+ scores[~valid] = -np.inf\\n+ part = np.argpartition(scores, -100, axis=1)[:, -100:]\\n+ vals = np.take_along_axis(scores, part, axis=1)\\n+ top[ix] = part[np.arange(len(ix))[:, None], np.argsort(-vals, axis=1)]\\n+ frame = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(uids, 100), \\\"item_id\\\": catalog.ids[top.ravel()],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(uids)),\\n+ })\\n+ frame.to_parquet(output, index=False)\\n+ print(f\\\"wrote {len(frame):,} rows to {output}\\\", flush=True)\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\", \\\"final\\\"], default=\\\"validation\\\")\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=8)\\n+ parser.add_argument(\\\"--dim\\\", type=int, default=64)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260831)\\n+ parser.add_argument(\\\"--output\\\", type=Path)\\n+ args = parser.parse_args()\\n+ stage = \\\"final\\\" if args.stage in {\\\"test\\\", \\\"final\\\"} else \\\"validation\\\"\\n+ seed_everything(args.seed)\\n+ catalog = load_catalog()\\n+ data = build_examples(stage, catalog)\\n+ model = TwoTower(len(catalog.ids), args.dim)\\n+ train_model(model, catalog, data, args.epochs, args.seed)\\n+ model_path = OUT / f\\\"two_tower_{stage}.pt\\\"\\n+ torch.save({\\\"model\\\": model.state_dict(), \\\"dim\\\": args.dim, \\\"seed\\\": args.seed}, model_path)\\n+ output = args.output or (OUT / (\\\"validation.parquet\\\" if stage == \\\"validation\\\" else \\\"submission.parquet\\\"))\\n+ rank(model, catalog, data, \\\"validation\\\" if stage == \\\"validation\\\" else \\\"test\\\", output)\\n+ (OUT / f\\\"run_{stage}.json\\\").write_text(json.dumps(vars(args), default=str, indent=2) + \\\"\\\\n\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:37:28.425Z Script completed Wall time 0.0 seconds Output: {} ## Step 33 · agent · 2026-08-31T18:37:30.799Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:34.035Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution && /opt/task-venv/bin/python -m py_compile /app/solution/two_tower_ranker.py && sed -n '1,40p' /app/solution/two_tower_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:37:34.229Z Script completed Wall time 0.2 seconds Output: total 732K -rw-r--r-- 1 agent agent 18K Aug 31 18:37 two_tower_ranker.py -rw-r--r-- 1 agent agent 712K Aug 31 18:30 validation_popularity.parquet #!/usr/bin/env python3 """Train and apply a scalable history-conditioned PyTorch two-tower ranker. The implementation consumes event rows and never materializes a user-item matrix. The online path is exactly UserTower(history) @ ItemTower(item).T. """ from __future__ import annotations import argparse import json import math import random from dataclasses import dataclass from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import torch from torch import nn import torch.nn.functional as F DATA = Path("/task/data") OUT = Path("/app/solution") TRAIN_END = 24_786_800 WINDOW = 7 * 24 * 60 * 60 GAP = 30 * 60 LOOKBACK = 90 * 24 * 60 * 60 MAX_HISTORY = 400 def seed_everything(seed: int) -> None: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.set_num_threads(min(8, max(1, torch.get_num_threads()))) @dataclass ## Step 34 · agent · 2026-08-31T18:37:37.616Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:41.053Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage validation --epochs 5 --dim 64 --seed 20260831 --output /app/solution/validation_twotower_e5.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:37:42.237Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f34f3a","wall_time_seconds":1.000953912,"session_id":79935,"original_token_count":0,"output":""} ## Step 35 · agent · 2026-08-31T18:37:44.203Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:37:45.579Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:37:56.607Z Script running with cell ID 18 Wall time 11.0 seconds Output: ## Step 36 · agent · 2026-08-31T18:37:58.421Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:37:59.215Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:38:15.614Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"82f42e","wall_time_seconds":30.001815185,"session_id":79935,"original_token_count":18,"output":"cached 11,739,837 positive catalog listens in events_validation.npz\r\n"} ## Step 37 · agent · 2026-08-31T18:38:17.045Z ### Thinking unavailable — no text recorded The preprocessing pass retained 11.7 million recent, positive catalog interactions while staying event-based—no user–item matrix is built or factorized. Training examples are now being formed from eight rolling temporal holdouts, which should make the public result a meaningful test of the same retrieval behavior needed for hidden evaluation. ### exec · 2026-08-31T18:38:21.093Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:38:32.123Z Script running with cell ID 19 Wall time 11.0 seconds Output: ## Step 38 · agent · 2026-08-31T18:38:33.547Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:38:34.335Z { "cell_id": "19", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:38:51.128Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"157f66","wall_time_seconds":30.001436489,"session_id":79935,"original_token_count":0,"output":""} ## Step 39 · agent · 2026-08-31T18:38:52.468Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:38:53.870Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:39:04.896Z Script running with cell ID 20 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T18:39:06.369Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:07.180Z { "cell_id": "20", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:39:23.900Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"af598c","wall_time_seconds":30.001052778,"session_id":79935,"original_token_count":0,"output":""} ## Step 41 · agent · 2026-08-31T18:39:27.337Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:39:28.731Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:39:39.758Z Script running with cell ID 21 Wall time 11.0 seconds Output: ## Step 42 · agent · 2026-08-31T18:39:42.779Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:39:43.588Z { "cell_id": "21", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:39:58.763Z Script completed Wall time 15.2 seconds Output: {"chunk_id":"eb7dc5","wall_time_seconds":30.001125312,"session_id":79935,"original_token_count":0,"output":""} ## Step 43 · agent · 2026-08-31T18:40:00.090Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:01.540Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:40:12.566Z Script running with cell ID 22 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T18:40:16.832Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:17.605Z { "cell_id": "22", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:40:31.571Z Script completed Wall time 14.0 seconds Output: {"chunk_id":"e763c1","wall_time_seconds":30.000582963,"session_id":79935,"original_token_count":0,"output":""} ## Step 45 · agent · 2026-08-31T18:40:35.071Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:40:36.450Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:40:47.477Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T18:40:49.189Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:40:49.972Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:41:06.481Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"b7f3c4","wall_time_seconds":30.001461528,"session_id":79935,"original_token_count":0,"output":""} ## Step 47 · agent · 2026-08-31T18:41:08.187Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:10.892Z { "code": "const r = await tools.exec_command({cmd:\"ps -o pid,etime,pcpu,pmem,rss,stat,cmd -p $(pgrep -f 'two_tower_ranker.py' | head -1)\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:41:10.961Z Script completed Wall time 0.1 seconds Output: /bin/sh: 1: pgrep: not found /bin/sh: 1: ps: not found ## Step 48 · agent · 2026-08-31T18:41:12.735Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:14.132Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:41:25.160Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 49 · agent · 2026-08-31T18:41:26.647Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:41:27.406Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:41:44.164Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"db5327","wall_time_seconds":30.001107864,"session_id":79935,"original_token_count":0,"output":""} ## Step 50 · agent · 2026-08-31T18:41:47.312Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:41:48.711Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:41:59.737Z Script running with cell ID 26 Wall time 11.0 seconds Output: ## Step 51 · agent · 2026-08-31T18:42:01.357Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:02.318Z { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:42:18.741Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"00c1ef","wall_time_seconds":30.000702384,"session_id":79935,"original_token_count":0,"output":""} ## Step 52 · agent · 2026-08-31T18:42:23.684Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:25.279Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:42:36.308Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 53 · agent · 2026-08-31T18:42:38.184Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:42:38.953Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:42:55.311Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"2a7319","wall_time_seconds":30.000849553,"session_id":79935,"original_token_count":0,"output":""} ## Step 54 · agent · 2026-08-31T18:42:57.440Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:42:58.818Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:09.844Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 55 · agent · 2026-08-31T18:43:11.995Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:43:12.786Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:43:28.847Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"d230c1","wall_time_seconds":30.00094086,"session_id":79935,"original_token_count":0,"output":""} ## Step 56 · agent · 2026-08-31T18:43:30.569Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:32.099Z { "code": "const r = await tools.write_stdin({session_id:79935,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:32.540Z Script completed Wall time 0.4 seconds Output: {"chunk_id":"242e41","wall_time_seconds":0.315048473,"exit_code":1,"original_token_count":349,"output":"^CTraceback (most recent call last):\r\n File \"/app/solution/two_tower_ranker.py\", line 376, in \r\n main()\r\n File \"/app/solution/two_tower_ranker.py\", line 365, in main\r\n data = build_examples(stage, catalog)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/two_tower_ranker.py\", line 183, in build_examples\r\n t = cache[\"t\"][a:b]\r\n ~~~~~^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1098, in _read1\r\n self._update_crc(data)\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\r\n self._running_crc = crc32(newdata, self._running_crc)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n"} ## Step 57 · agent · 2026-08-31T18:43:37.030Z ### Thinking unavailable — no text recorded I found and stopped a real performance bug before spending training time: NumPy’s archive handle was rereading an entire event array on every user lookup. I’m changing that to a one-time in-memory load; the model logic and data boundaries are unchanged. ### exec · 2026-08-31T18:43:44.094Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/two_tower_ranker.py\\n@@\\n- cache = np.load(_cache_events(stage, catalog), allow_pickle=False)\\n+ # Materialize each member once. Accessing an NpzFile member inside the user\\n+ # loop would repeatedly decode that entire member from the ZIP container.\\n+ with np.load(_cache_events(stage, catalog), allow_pickle=False) as archive:\\n+ cache = {name: archive[name] for name in archive.files}\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T18:43:44.149Z Script completed Wall time 0.0 seconds Output: {} ## Step 58 · agent · 2026-08-31T18:43:45.849Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:49.230Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage validation --epochs 5 --dim 64 --seed 20260831 --output /app/solution/validation_twotower_e5.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:43:50.408Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"424e22","wall_time_seconds":1.000986833,"session_id":96713,"original_token_count":0,"output":""} ## Step 59 · agent · 2026-08-31T18:43:51.814Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:43:53.247Z { "code": "const r = await tools.write_stdin({session_id:96713,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:44:04.273Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 60 · agent · 2026-08-31T18:44:05.890Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:06.655Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:44:23.276Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"3ca480","wall_time_seconds":30.001169242,"session_id":96713,"original_token_count":0,"output":""} ## Step 61 · agent · 2026-08-31T18:44:24.806Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:26.205Z { "code": "const r = await tools.write_stdin({session_id:96713,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:44:37.231Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 62 · agent · 2026-08-31T18:44:39.123Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:44:39.860Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:44:56.234Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"dae792","wall_time_seconds":30.000977347,"session_id":96713,"original_token_count":113,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\nepoch 01/5 loss=0.62742 scale=10.47\r\nepoch 02/5 loss=0.57305 scale=10.06\r\n"} ## Step 63 · agent · 2026-08-31T18:44:57.769Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:44:59.184Z { "code": "const r = await tools.write_stdin({session_id:96713,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:45:10.213Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 64 · agent · 2026-08-31T18:45:12.444Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:45:13.277Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:45:29.216Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"f0ddf6","wall_time_seconds":30.000363299,"session_id":96713,"original_token_count":44,"output":"epoch 03/5 loss=0.52496 scale=9.78\r\nepoch 04/5 loss=0.48511 scale=9.59\r\nepoch 05/5 loss=0.46224 scale=9.43\r\nwrote 845,600 rows to /app/solution/validation_twotower_e5.parquet\r\n"} ## Step 65 · agent · 2026-08-31T18:45:31.062Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:45:33.411Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_e5.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:45:36.881Z Script completed Wall time 3.5 seconds Output: { "model": "/app/solution/validation_twotower_e5.parquet", "scores": { "quality_ndcg@10": 0.011838, "quality_users": 4263 } } ## Step 66 · agent · 2026-08-31T18:45:47.204Z ### Thinking unavailable — no text recorded The first trained model reaches public NDCG@10 of `0.011838`, above the pure-popularity floor of `0.010710`, but the margin is too small to accept. I’m testing calibrated popularity/personalization fusion next, then I’ll use the result to decide whether the next training pass needs stronger collaborative structure or simply better score calibration. ### exec · 2026-08-31T18:46:03.383Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nO='/app/solution/'\\na=pd.read_parquet(O+'validation_twotower_e5.parquet'); b=pd.read_parquet(O+'validation_popularity.parquet')\\nA={int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in a.groupby('uid')}; B={int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in b.groupby('uid')}\\nfor alpha in [.2,.4,.6,.8]:\\n rows=[]\\n for u in A:\\n score={}\\n for r,it in enumerate(A[u],1): score[int(it)]=score.get(int(it),0)+alpha/(20+r)\\n for r,it in enumerate(B[u],1): score[int(it)]=score.get(int(it),0)+(1-alpha)/(20+r)\\n take=sorted(score,key=score.get,reverse=True)[:100]\\n rows.extend((u,it,r) for r,it in enumerate(take,1))\\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O+f'validation_fusion_{alpha:.1f}.parquet',index=False)\\nPY\\nfor f in /app/solution/validation_fusion_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:46:04.568Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"505b96","wall_time_seconds":1.00098951,"session_id":89619,"original_token_count":0,"output":""} ## Step 67 · agent · 2026-08-31T18:46:06.133Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:07.557Z { "code": "const r = await tools.write_stdin({session_id:89619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:46:18.579Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 68 · agent · 2026-08-31T18:46:20.280Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:46:21.029Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:46:27.451Z Script completed Wall time 6.4 seconds Output: {"chunk_id":"d5f976","wall_time_seconds":19.867106293,"exit_code":0,"original_token_count":146,"output":"{\r\n \"model\": \"/app/solution/validation_fusion_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010867,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_fusion_0.4.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01213,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_fusion_0.6.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.013675,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_fusion_0.8.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.012186,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 69 · agent · 2026-08-31T18:46:41.860Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:46:52.856Z { "code": "const r = await tools.exec_command({cmd:\"cd /task && /opt/task-venv/bin/python - <<'PY'\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking,ranked_items,ndcg_at_k\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); T=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),T)\\nfor name in ['validation_popularity.parquet','validation_twotower_e5.parquet','validation_fusion_0.6.parquet']:\\n r=ranked_items(validate_ranking(read_ranking(Path('/app/solution')/name),T))\\n print(name,'rec10',np.mean([any(x in g for x in r[u][:10]) for u,g in truth.items()]),'rec100',np.mean([any(x in g for x in r[u][:100]) for u,g in truth.items()]),'hits10',sum(sum(x in g for x in r[u][:10]) for u,g in truth.items()),'hits100',sum(sum(x in g for x in r[u]) for u,g in truth.items()))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T18:46:56.904Z Script completed Wall time 4.0 seconds Output: validation_popularity.parquet rec10 0.053718038939713816 rec100 0.18601923528031902 hits10 368 hits100 1814 validation_twotower_e5.parquet rec10 0.0720150129017124 rec100 0.25170068027210885 hits10 416 hits100 2371 validation_fusion_0.6.parquet rec10 0.07694112127609665 rec100 0.2566267886464931 hits10 474 hits100 2582 ## Step 70 · agent · 2026-08-31T18:47:08.101Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:11.830Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage validation --epochs 12 --dim 64 --seed 20260832 --output /app/solution/validation_twotower_e12.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:47:13.009Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ea5c69","wall_time_seconds":1.001077835,"session_id":42598,"original_token_count":0,"output":""} ## Step 71 · agent · 2026-08-31T18:47:14.653Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:16.024Z { "code": "const r = await tools.write_stdin({session_id:42598,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:47:27.048Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 72 · agent · 2026-08-31T18:47:28.786Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:47:29.864Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:47:46.053Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"646981","wall_time_seconds":30.001491714,"session_id":42598,"original_token_count":0,"output":""} ## Step 73 · agent · 2026-08-31T18:47:49.084Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:47:50.486Z { "code": "const r = await tools.write_stdin({session_id:42598,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:48:01.509Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 74 · agent · 2026-08-31T18:48:03.028Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:03.823Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:48:20.513Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"dfd15b","wall_time_seconds":30.00087541,"session_id":42598,"original_token_count":114,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\nepoch 01/12 loss=0.62732 scale=10.73\r\nepoch 02/12 loss=0.57484 scale=10.33\r\n"} ## Step 75 · agent · 2026-08-31T18:48:21.984Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:23.400Z { "code": "const r = await tools.write_stdin({session_id:42598,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:48:34.433Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 76 · agent · 2026-08-31T18:48:37.132Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:48:37.532Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:48:53.438Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"128ed9","wall_time_seconds":30.001648786,"session_id":42598,"original_token_count":38,"output":"epoch 03/12 loss=0.52728 scale=10.12\r\nepoch 04/12 loss=0.48872 scale=9.93\r\nepoch 05/12 loss=0.46761 scale=9.75\r\nepoch 06/12 loss=0.45036 scale=9.64\r\n"} ## Step 77 · agent · 2026-08-31T18:48:55.035Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:48:56.471Z { "code": "const r = await tools.write_stdin({session_id:42598,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:49:07.497Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 78 · agent · 2026-08-31T18:49:09.285Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:10.092Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:49:26.505Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"5be2c2","wall_time_seconds":30.001220023,"session_id":42598,"original_token_count":37,"output":"epoch 07/12 loss=0.43685 scale=9.46\r\nepoch 08/12 loss=0.42420 scale=9.41\r\nepoch 09/12 loss=0.41177 scale=9.38\r\nepoch 10/12 loss=0.40240 scale=9.35\r\n"} ## Step 79 · agent · 2026-08-31T18:49:28.234Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:49:29.633Z { "code": "const r = await tools.write_stdin({session_id:42598,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:49:40.668Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 80 · agent · 2026-08-31T18:49:42.356Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:49:43.164Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:49:52.613Z Script completed Wall time 9.4 seconds Output: {"chunk_id":"8ffcc2","wall_time_seconds":22.94016278,"exit_code":0,"original_token_count":36,"output":"epoch 11/12 loss=0.39206 scale=9.33\r\nepoch 12/12 loss=0.38436 scale=9.26\r\nwrote 845,600 rows to /app/solution/validation_twotower_e12.parquet\r\n"} ## Step 81 · agent · 2026-08-31T18:49:54.205Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:06.758Z { "code": "let r=await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_e12.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nO='/app/solution/'; a=pd.read_parquet(O+'validation_twotower_e12.parquet'); b=pd.read_parquet(O+'validation_popularity.parquet')\\nA={int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in a.groupby('uid')}; B={int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in b.groupby('uid')}\\nfor alpha in [.45,.55,.65,.75]:\\n rows=[]\\n for u in A:\\n s={}\\n for k,(arr,w) in enumerate([(A[u],alpha),(B[u],1-alpha)]):\\n for rr,it in enumerate(arr,1):s[int(it)]=s.get(int(it),0)+w/(20+rr)\\n take=sorted(s,key=s.get,reverse=True)[:100];rows.extend((u,it,rr) for rr,it in enumerate(take,1))\\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O+f'validation_e12_fusion_{alpha:.2f}.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});text(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:07.941Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"3826bd","wall_time_seconds":1.001687432,"session_id":4650,"original_token_count":0,"output":""} ## Step 82 · agent · 2026-08-31T18:50:09.434Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:10.803Z { "code": "const r = await tools.write_stdin({session_id:4650,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:20.305Z Script completed Wall time 9.5 seconds Output: {"chunk_id":"efd318","wall_time_seconds":9.465403043,"exit_code":0,"original_token_count":37,"output":"{\r\n \"model\": \"/app/solution/validation_twotower_e12.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01956,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 83 · agent · 2026-08-31T18:50:22.291Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:25.082Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_e12_fusion_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:26.261Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"18578c","wall_time_seconds":1.000952145,"session_id":26176,"original_token_count":0,"output":""} ## Step 84 · agent · 2026-08-31T18:50:28.092Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:50:29.515Z { "code": "const r = await tools.write_stdin({session_id:26176,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:38.919Z Script completed Wall time 9.4 seconds Output: {"chunk_id":"fa12bf","wall_time_seconds":9.380377449,"exit_code":0,"original_token_count":151,"output":"{\r\n \"model\": \"/app/solution/validation_e12_fusion_0.45.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.015536,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_e12_fusion_0.55.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.020437,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_e12_fusion_0.65.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.020348,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_e12_fusion_0.75.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.020169,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 85 · agent · 2026-08-31T18:50:42.845Z ### Thinking unavailable — no text recorded The longer fit materially improved the public score to `0.019560`; a 55/45 reciprocal-rank blend with popularity reaches `0.020437`. That confirms the learned personalization is useful rather than noise. I’m using the remaining development budget on seed/ensemble stability, then I’ll lock the best recipe and refit with validation incorporated as observed history. ### exec · 2026-08-31T18:50:58.095Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,itertools\\nO='/app/solution/'\\nnames=['validation_twotower_e12.parquet','validation_twotower_e5.parquet','validation_popularity.parquet']\\nDs=[]\\nfor n in names:\\n d=pd.read_parquet(O+n);Ds.append({int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in d.groupby('uid')})\\nfor ws in [(0.6,.2,.2),(.65,.15,.2),(.7,.1,.2),(.7,.2,.1),(.75,.15,.1)]:\\n rows=[]\\n for u in Ds[0]:\\n s={}\\n for D,w in zip(Ds,ws):\\n for rr,it in enumerate(D[u],1):s[int(it)]=s.get(int(it),0)+w/(20+rr)\\n take=sorted(s,key=s.get,reverse=True)[:100];rows.extend((u,it,rr) for rr,it in enumerate(take,1))\\n tag='_'.join(map(str,ws));pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O+f'validation_ens_{tag}.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:50:59.276Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d64bd2","wall_time_seconds":1.001142385,"session_id":77523,"original_token_count":0,"output":""} ## Step 86 · agent · 2026-08-31T18:51:00.767Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:02.196Z { "code": "const r = await tools.write_stdin({session_id:77523,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:12.908Z Script completed Wall time 10.7 seconds Output: {"chunk_id":"888e05","wall_time_seconds":10.682495177,"exit_code":0,"original_token_count":0,"output":""} ## Step 87 · agent · 2026-08-31T18:51:14.372Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:17.299Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_ens_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:18.479Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"82682b","wall_time_seconds":1.000699596,"session_id":82624,"original_token_count":0,"output":""} ## Step 88 · agent · 2026-08-31T18:51:21.168Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:22.555Z { "code": "const r = await tools.write_stdin({session_id:82624,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:33.576Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T18:51:35.316Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:51:36.092Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:51:36.132Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"075724","wall_time_seconds":12.202269848,"exit_code":0,"original_token_count":190,"output":"{\r\n \"model\": \"/app/solution/validation_ens_0.65_0.15_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.020201,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_ens_0.6_0.2_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.02024,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_ens_0.75_0.15_0.1.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.019966,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_ens_0.7_0.1_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.019957,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_ens_0.7_0.2_0.1.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.019894,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 90 · agent · 2026-08-31T18:51:41.504Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:45.017Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage validation --epochs 12 --dim 64 --seed 314159 --output /app/solution/validation_twotower_e12_seed2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:51:46.200Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"76f1d1","wall_time_seconds":1.001601838,"session_id":38068,"original_token_count":0,"output":""} ## Step 91 · agent · 2026-08-31T18:51:51.916Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:51:53.053Z { "code": "const r = await tools.write_stdin({session_id:38068,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:52:04.080Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 92 · agent · 2026-08-31T18:52:05.990Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:06.864Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:52:23.083Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"da9c5e","wall_time_seconds":30.0012201,"session_id":38068,"original_token_count":0,"output":""} ## Step 93 · agent · 2026-08-31T18:52:24.677Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:26.042Z { "code": "const r = await tools.write_stdin({session_id:38068,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:52:37.068Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 94 · agent · 2026-08-31T18:52:38.799Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:52:40.676Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:52:56.071Z Script completed Wall time 15.4 seconds Output: {"chunk_id":"b06b1d","wall_time_seconds":30.000816185,"session_id":38068,"original_token_count":114,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\nepoch 01/12 loss=0.62718 scale=10.65\r\nepoch 02/12 loss=0.57432 scale=10.12\r\n"} ## Step 95 · agent · 2026-08-31T18:52:57.633Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:52:59.059Z { "code": "const r = await tools.write_stdin({session_id:38068,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:53:10.086Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T18:53:11.778Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:12.561Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:53:29.092Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"1e2e1f","wall_time_seconds":30.001588649,"session_id":38068,"original_token_count":37,"output":"epoch 03/12 loss=0.52569 scale=9.94\r\nepoch 04/12 loss=0.48273 scale=9.72\r\nepoch 05/12 loss=0.46400 scale=9.55\r\nepoch 06/12 loss=0.44530 scale=9.48\r\n"} ## Step 97 · agent · 2026-08-31T18:53:30.605Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:53:32.054Z { "code": "const r = await tools.write_stdin({session_id:38068,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:53:43.083Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T18:53:44.821Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:53:45.652Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:54:02.091Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"ac3765","wall_time_seconds":30.001352401,"session_id":38068,"original_token_count":37,"output":"epoch 07/12 loss=0.43477 scale=9.34\r\nepoch 08/12 loss=0.42434 scale=9.23\r\nepoch 09/12 loss=0.41164 scale=9.25\r\nepoch 10/12 loss=0.40293 scale=9.22\r\n"} ## Step 99 · agent · 2026-08-31T18:54:03.496Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:04.885Z { "code": "const r = await tools.write_stdin({session_id:38068,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:54:15.917Z Script running with cell ID 58 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T18:54:18.180Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:18.532Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:54:24.689Z Script completed Wall time 6.2 seconds Output: {"chunk_id":"8f5da2","wall_time_seconds":19.767216848,"exit_code":0,"original_token_count":38,"output":"epoch 11/12 loss=0.39591 scale=9.25\r\nepoch 12/12 loss=0.38596 scale=9.19\r\nwrote 845,600 rows to /app/solution/validation_twotower_e12_seed2.parquet\r\n"} ## Step 101 · agent · 2026-08-31T18:54:26.759Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:39.787Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_twotower_e12_seed2.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nO='/app/solution/'; names=['validation_twotower_e12.parquet','validation_twotower_e12_seed2.parquet','validation_popularity.parquet'];Ds=[]\\nfor n in names:\\n d=pd.read_parquet(O+n);Ds.append({int(u):g.sort_values('rank').item_id.to_numpy(np.int64) for u,g in d.groupby('uid')})\\nfor ws in [(.45,.45,.1),(.4,.4,.2),(.35,.35,.3),(.5,.4,.1),(.5,.3,.2)]:\\n rows=[]\\n for u in Ds[0]:\\n s={}\\n for D,w in zip(Ds,ws):\\n for rr,it in enumerate(D[u],1):s[int(it)]=s.get(int(it),0)+w/(20+rr)\\n take=sorted(s,key=s.get,reverse=True)[:100];rows.extend((u,it,rr) for rr,it in enumerate(take,1))\\n tag='_'.join(map(str,ws));pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O+f'validation_seedens_{tag}.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:54:40.966Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"6d6f75","wall_time_seconds":1.000898292,"session_id":51447,"original_token_count":0,"output":""} ## Step 102 · agent · 2026-08-31T18:54:42.620Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:54:44.026Z { "code": "const r = await tools.write_stdin({session_id:51447,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:54:55.051Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 103 · agent · 2026-08-31T18:54:57.485Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:54:58.331Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:54:58.457Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"6ac93c","wall_time_seconds":14.400955089,"exit_code":0,"original_token_count":39,"output":"{\r\n \"model\": \"/app/solution/validation_twotower_e12_seed2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.018272,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 104 · agent · 2026-08-31T18:55:01.685Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:04.554Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_seedens_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:55:05.735Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"4d9a41","wall_time_seconds":1.000907183,"session_id":77194,"original_token_count":0,"output":""} ## Step 105 · agent · 2026-08-31T18:55:07.431Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:55:08.846Z { "code": "const r = await tools.write_stdin({session_id:77194,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:55:19.867Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T18:55:21.616Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:55:22.378Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T18:55:22.466Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"43b931","wall_time_seconds":13.242591466,"exit_code":0,"original_token_count":195,"output":"{\r\n \"model\": \"/app/solution/validation_seedens_0.35_0.35_0.3.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.023048,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_seedens_0.45_0.45_0.1.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.022111,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_seedens_0.4_0.4_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.022283,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_seedens_0.5_0.3_0.2.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.022759,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_seedens_0.5_0.4_0.1.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.02213,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 107 · agent · 2026-08-31T18:55:42.358Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-08-31T18:55:58.561Z ### Thinking unavailable — no text recorded Two independent fits are complementary: their exact two-tower rankings plus popularity reach `0.023048` on public validation. For the final artifact I’m preserving the latency contract by combining tower outputs as concatenated vectors—weighted sums of dot products are themselves one dot product—rather than introducing a request-time reranker. I’m now refitting both components with validation treated as observed history. ### exec · 2026-08-31T18:56:05.154Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage final --epochs 12 --dim 64 --seed 20260832 --output /app/solution/submission_model1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:56:06.337Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b441cf","wall_time_seconds":1.001027758,"session_id":84526,"original_token_count":0,"output":""} ## Step 109 · agent · 2026-08-31T18:56:07.976Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:09.387Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:56:20.414Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 110 · agent · 2026-08-31T18:56:22.691Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:23.514Z { "cell_id": "64", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:56:39.418Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"dd1b36","wall_time_seconds":30.001576627,"session_id":84526,"original_token_count":16,"output":"cached 12,390,132 positive catalog listens in events_final.npz\r\n"} ## Step 111 · agent · 2026-08-31T18:56:41.730Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:56:42.452Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:56:53.478Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 112 · agent · 2026-08-31T18:56:55.079Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:56:55.988Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:57:12.484Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"7c8153","wall_time_seconds":30.001474142,"session_id":84526,"original_token_count":95,"output":"built 41,339 temporal user contexts; 459,859 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\n"} ## Step 113 · agent · 2026-08-31T18:57:14.766Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:16.194Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:57:27.223Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 114 · agent · 2026-08-31T18:57:29.090Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:57:29.876Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:57:46.232Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"3c3183","wall_time_seconds":30.001493918,"session_id":84526,"original_token_count":38,"output":"epoch 01/12 loss=0.62793 scale=10.74\r\nepoch 02/12 loss=0.56610 scale=10.38\r\nepoch 03/12 loss=0.51382 scale=10.13\r\nepoch 04/12 loss=0.48195 scale=9.91\r\n"} ## Step 115 · agent · 2026-08-31T18:57:48.776Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:57:50.169Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:58:01.199Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 116 · agent · 2026-08-31T18:58:03.791Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:04.629Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:58:20.205Z Script completed Wall time 15.6 seconds Output: {"chunk_id":"33168f","wall_time_seconds":30.002026528,"session_id":84526,"original_token_count":28,"output":"epoch 05/12 loss=0.46109 scale=9.67\r\nepoch 06/12 loss=0.44714 scale=9.53\r\nepoch 07/12 loss=0.43266 scale=9.36\r\n"} ## Step 117 · agent · 2026-08-31T18:58:21.890Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:23.335Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:58:34.364Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 118 · agent · 2026-08-31T18:58:36.570Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:58:37.403Z { "cell_id": "68", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:58:53.369Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"f75928","wall_time_seconds":30.000760426,"session_id":84526,"original_token_count":37,"output":"epoch 08/12 loss=0.42160 scale=9.26\r\nepoch 09/12 loss=0.41045 scale=9.21\r\nepoch 10/12 loss=0.39709 scale=9.17\r\nepoch 11/12 loss=0.38806 scale=9.07\r\n"} ## Step 119 · agent · 2026-08-31T18:58:59.862Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:58:59.865Z { "code": "const r = await tools.write_stdin({session_id:84526,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:59:09.563Z Script completed Wall time 9.7 seconds Output: {"chunk_id":"98920e","wall_time_seconds":9.672074877,"exit_code":0,"original_token_count":25,"output":"epoch 12/12 loss=0.37623 scale=9.06\r\nwrote 845,600 rows to /app/solution/submission_model1.parquet\r\n"} ## Step 120 · agent · 2026-08-31T18:59:11.969Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:16.138Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_seed1.pt\\n/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage final --epochs 12 --dim 64 --seed 314159 --output /app/solution/submission_model2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:59:17.321Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ed20fb","wall_time_seconds":1.001102857,"session_id":36830,"original_token_count":0,"output":""} ## Step 121 · agent · 2026-08-31T18:59:18.804Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:20.214Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T18:59:31.239Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 122 · agent · 2026-08-31T18:59:33.002Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T18:59:33.789Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T18:59:50.243Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"a67d15","wall_time_seconds":30.001112027,"session_id":36830,"original_token_count":0,"output":""} ## Step 123 · agent · 2026-08-31T18:59:57.084Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T18:59:58.472Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:00:09.497Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 124 · agent · 2026-08-31T19:00:11.613Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:12.453Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:00:28.502Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"f83eeb","wall_time_seconds":30.001343035,"session_id":36830,"original_token_count":104,"output":"built 41,339 temporal user contexts; 459,859 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\nepoch 01/12 loss=0.62358 scale=10.47\r\n"} ## Step 125 · agent · 2026-08-31T19:00:30.244Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:00:31.660Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:00:42.688Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 126 · agent · 2026-08-31T19:00:44.800Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:00:45.618Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:01:01.695Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"490c77","wall_time_seconds":30.001194268,"session_id":36830,"original_token_count":38,"output":"epoch 02/12 loss=0.56823 scale=10.11\r\nepoch 03/12 loss=0.51131 scale=9.88\r\nepoch 04/12 loss=0.48031 scale=9.67\r\nepoch 05/12 loss=0.45918 scale=9.64\r\n"} ## Step 127 · agent · 2026-08-31T19:01:03.287Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:04.694Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:01:15.721Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 128 · agent · 2026-08-31T19:01:18.679Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:19.480Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:01:34.727Z Script completed Wall time 15.2 seconds Output: {"chunk_id":"2ecb98","wall_time_seconds":30.001069577,"session_id":36830,"original_token_count":37,"output":"epoch 06/12 loss=0.44613 scale=9.54\r\nepoch 07/12 loss=0.43697 scale=9.46\r\nepoch 08/12 loss=0.42194 scale=9.39\r\nepoch 09/12 loss=0.41273 scale=9.42\r\n"} ## Step 129 · agent · 2026-08-31T19:01:36.650Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:01:38.032Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:01:49.063Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 130 · agent · 2026-08-31T19:01:50.837Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:01:51.633Z { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:02:08.067Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"e9d4e4","wall_time_seconds":30.001039894,"session_id":36830,"original_token_count":28,"output":"epoch 10/12 loss=0.40074 scale=9.38\r\nepoch 11/12 loss=0.38974 scale=9.48\r\nepoch 12/12 loss=0.37892 scale=9.40\r\n"} ## Step 131 · agent · 2026-08-31T19:02:11.217Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:12.608Z { "code": "const r = await tools.write_stdin({session_id:36830,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:02:12.728Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"3fe33d","wall_time_seconds":0.000002421,"exit_code":0,"original_token_count":16,"output":"wrote 845,600 rows to /app/solution/submission_model2.parquet\r\n"} ## Step 132 · agent · 2026-08-31T19:02:32.950Z ### Thinking unavailable — no text recorded ## Step 133 · agent · 2026-08-31T19:02:48.407Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:34.425Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Apply fitted towers, optionally as an exact concatenated-vector ensemble.\\\"\\\"\\\"\\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+import two_tower_ranker as tt\\n+\\n+\\n+@torch.no_grad()\\n+def build(stage: str, checkpoints: list[Path], weights: list[float],\\n+ biases: list[float], output: Path) -> None:\\n+ catalog = tt.load_catalog()\\n+ internal_stage = \\\"validation\\\" if stage == \\\"validation\\\" else \\\"final\\\"\\n+ data = tt.build_examples(internal_stage, catalog)\\n+ uids, *_, infer_ids, infer_w, infer_num = data\\n+ models = []\\n+ item_vectors = []\\n+ features = torch.from_numpy(catalog.features)\\n+ ids = torch.arange(len(catalog.ids))\\n+ for path in checkpoints:\\n+ saved = torch.load(path, map_location=\\\"cpu\\\", weights_only=True)\\n+ model = tt.TwoTower(len(catalog.ids), int(saved[\\\"dim\\\"]))\\n+ model.load_state_dict(saved[\\\"model\\\"])\\n+ model.eval()\\n+ models.append(model)\\n+ item_vectors.append(model.item_tower(ids, features))\\n+\\n+ eligible_stage = \\\"validation\\\" if stage == \\\"validation\\\" else \\\"test\\\"\\n+ eligibility = np.load(tt.DATA / f\\\"{eligible_stage}_eligible_candidates.npz\\\", allow_pickle=False)\\n+ item_pos = {int(x): i for i, x in enumerate(eligibility[\\\"item_ids\\\"])}\\n+ packed_cols = np.asarray([item_pos[int(x)] for x in catalog.ids], np.int32)\\n+ uid_row = {int(x): i for i, x in enumerate(eligibility[\\\"uids\\\"])}\\n+ # A fixed item-side popularity coordinate and constant user-side coordinate\\n+ # preserve a single maximum-inner-product retrieval operation.\\n+ popularity = catalog.features[:, 0].astype(np.float32)\\n+ tops = {b: np.empty((len(uids), 100), np.int64) for b in biases}\\n+ for start in range(0, len(uids), 128):\\n+ ix = np.arange(start, min(start + 128, len(uids)))\\n+ h_id, h_w = tt.pad_histories(ix, infer_ids, infer_w)\\n+ nums = torch.from_numpy(infer_num[ix])\\n+ base = np.zeros((len(ix), len(catalog.ids)), np.float32)\\n+ for model, item_vec, weight in zip(models, item_vectors, weights):\\n+ user_vec = model.user_tower(h_id, h_w, nums)\\n+ base += float(weight) * (user_vec @ item_vec.T).numpy()\\n+ rows = np.asarray([uid_row[int(uids[j])] for j in ix], np.int32)\\n+ pc = packed_cols\\n+ valid = ((eligibility[\\\"packed_eligible\\\"][rows[:, None], pc[None, :] // 8]\\n+ >> (pc[None, :] % 8)) & 1).astype(bool)\\n+ for bias in biases:\\n+ score = base + float(bias) * popularity[None, :]\\n+ score[~valid] = -np.inf\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ val = np.take_along_axis(score, part, axis=1)\\n+ tops[bias][ix] = part[np.arange(len(ix))[:, None,], np.argsort(-val, axis=1)]\\n+\\n+ for bias, top in tops.items():\\n+ path = output if len(biases) == 1 else output.with_name(f\\\"{output.stem}_bias{bias:g}{output.suffix}\\\")\\n+ frame = pd.DataFrame({\\n+ \\\"uid\\\": np.repeat(uids, 100),\\n+ \\\"item_id\\\": catalog.ids[top.ravel()],\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(uids)),\\n+ })\\n+ frame.to_parquet(path, index=False)\\n+ print(f\\\"wrote bias={bias:g}: {path}\\\", flush=True)\\n+\\n+\\n+def main() -> None:\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], default=\\\"test\\\")\\n+ p.add_argument(\\\"--checkpoint\\\", type=Path, action=\\\"append\\\", required=True)\\n+ p.add_argument(\\\"--weight\\\", type=float, action=\\\"append\\\")\\n+ p.add_argument(\\\"--pop-bias\\\", type=float, action=\\\"append\\\", default=[])\\n+ p.add_argument(\\\"--output\\\", type=Path, default=tt.OUT / \\\"submission.parquet\\\")\\n+ args = p.parse_args()\\n+ weights = args.weight or [1.0 / len(args.checkpoint)] * len(args.checkpoint)\\n+ if len(weights) != len(args.checkpoint):\\n+ raise SystemExit(\\\"supply one --weight for each --checkpoint\\\")\\n+ build(args.stage, args.checkpoint, weights, args.pop_bias or [0.03], args.output)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:03:34.498Z Script completed Wall time 0.0 seconds Output: {} ## Step 134 · agent · 2026-08-31T19:03:38.639Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:44.990Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/two_tower_final.pt /app/solution/two_tower_final_seed2.pt\\n/opt/task-venv/bin/python -m py_compile /app/solution/build_submission.py\\n/opt/task-venv/bin/python -u /app/solution/build_submission.py --stage validation --checkpoint /app/solution/two_tower_validation.pt --pop-bias 0 --pop-bias 0.01 --pop-bias 0.02 --pop-bias 0.03 --pop-bias 0.05 --pop-bias 0.08 --output /app/solution/validation_exact.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:03:46.175Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"110c3e","wall_time_seconds":1.001123137,"session_id":50279,"original_token_count":0,"output":""} ## Step 135 · agent · 2026-08-31T19:03:47.953Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:49.396Z { "code": "const r = await tools.write_stdin({session_id:50279,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:04:00.422Z Script running with cell ID 79 Wall time 11.0 seconds Output: ## Step 136 · agent · 2026-08-31T19:04:02.246Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:03.063Z { "cell_id": "79", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:04:19.426Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"8666e1","wall_time_seconds":30.001264508,"session_id":50279,"original_token_count":0,"output":""} ## Step 137 · agent · 2026-08-31T19:04:20.976Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:22.359Z { "code": "const r = await tools.write_stdin({session_id:50279,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:04:33.383Z Script running with cell ID 80 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T19:04:35.177Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:35.964Z { "cell_id": "80", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:04:45.750Z Script completed Wall time 9.8 seconds Output: {"chunk_id":"bd9e33","wall_time_seconds":23.361760509,"exit_code":0,"original_token_count":114,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\nwrote bias=0: /app/solution/validation_exact_bias0.parquet\r\nwrote bias=0.01: /app/solution/validation_exact_bias0.01.parquet\r\nwrote bias=0.02: /app/solution/validation_exact_bias0.02.parquet\r\nwrote bias=0.03: /app/solution/validation_exact_bias0.03.parquet\r\nwrote bias=0.05: /app/solution/validation_exact_bias0.05.parquet\r\nwrote bias=0.08: /app/solution/validation_exact_bias0.08.parquet\r\n"} ## Step 139 · agent · 2026-08-31T19:04:47.416Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:50.193Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_exact_bias*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:04:51.375Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"3e8244","wall_time_seconds":1.001100621,"session_id":80898,"original_token_count":0,"output":""} ## Step 140 · agent · 2026-08-31T19:04:54.677Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:56.064Z { "code": "const r = await tools.write_stdin({session_id:80898,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:05:07.088Z Script running with cell ID 82 Wall time 11.0 seconds Output: ## Step 141 · agent · 2026-08-31T19:05:10.015Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:10.944Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:05:11.061Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"199092","wall_time_seconds":14.956967694,"exit_code":0,"original_token_count":224,"output":"{\r\n \"model\": \"/app/solution/validation_exact_bias0.01.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.020048,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_bias0.02.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.021497,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_bias0.03.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.021319,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_bias0.05.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.02154,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_bias0.08.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.019777,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_bias0.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.018272,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 142 · agent · 2026-08-31T19:05:16.209Z ### Thinking unavailable — no text recorded The exact dot-product calibration check peaks at public NDCG@10 `0.021540` with a `0.05` fixed popularity coordinate. That stays fully MIPS-compatible: two 64-D tower pairs are concatenated with one scalar popularity dimension, yielding a single 129-D user/item dot product. I’m generating that final test ranking now and will run independent schema and eligibility checks afterward. ### exec · 2026-08-31T19:05:23.951Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/build_submission.py --stage test --checkpoint /app/solution/two_tower_final_seed1.pt --checkpoint /app/solution/two_tower_final_seed2.pt --weight 0.5 --weight 0.5 --pop-bias 0.05 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:05:25.140Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e9a1c2","wall_time_seconds":1.000454307,"session_id":91110,"original_token_count":0,"output":""} ## Step 143 · agent · 2026-08-31T19:05:27.649Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:29.089Z { "code": "const r = await tools.write_stdin({session_id:91110,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:05:40.112Z Script running with cell ID 84 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T19:05:41.840Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:42.673Z { "cell_id": "84", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:05:59.115Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"e97dc6","wall_time_seconds":30.000985801,"session_id":91110,"original_token_count":0,"output":""} ## Step 145 · agent · 2026-08-31T19:06:05.246Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:05.259Z { "code": "const r = await tools.write_stdin({session_id:91110,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:06:16.280Z Script running with cell ID 85 Wall time 11.0 seconds Output: ## Step 146 · agent · 2026-08-31T19:06:19.239Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:19.716Z { "cell_id": "85", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:06:26.027Z Script completed Wall time 6.3 seconds Output: {"chunk_id":"a6fa3a","wall_time_seconds":20.742068318,"exit_code":0,"original_token_count":29,"output":"built 41,339 temporal user contexts; 459,859 distinct targets\r\nwrote bias=0.05: /app/solution/submission.parquet\r\n"} ## Step 147 · agent · 2026-08-31T19:06:45.808Z ### Thinking unavailable — no text recorded ## Step 148 · agent · 2026-08-31T19:06:50.656Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:55.060Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_seed2.pt\\n/opt/task-venv/bin/python -u /app/solution/two_tower_ranker.py --stage validation --epochs 12 --dim 64 --seed 20260832 --output /app/solution/validation_twotower_e12_replay.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:06:56.243Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ec6762","wall_time_seconds":1.001030375,"session_id":47812,"original_token_count":0,"output":""} ## Step 149 · agent · 2026-08-31T19:06:57.876Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:59.335Z { "code": "const r = await tools.write_stdin({session_id:47812,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:07:10.361Z Script running with cell ID 87 Wall time 11.0 seconds Output: ## Step 150 · agent · 2026-08-31T19:07:12.300Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:13.108Z { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:07:29.365Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"0156a9","wall_time_seconds":30.001407345,"session_id":47812,"original_token_count":0,"output":""} ## Step 151 · agent · 2026-08-31T19:07:37.531Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:38.994Z { "code": "const r = await tools.write_stdin({session_id:47812,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:07:50.015Z Script running with cell ID 88 Wall time 11.0 seconds Output: ## Step 152 · agent · 2026-08-31T19:07:52.674Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:53.509Z { "cell_id": "88", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:08:09.021Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"2438b1","wall_time_seconds":30.001176999,"session_id":47812,"original_token_count":114,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\n/app/solution/two_tower_ranker.py:317: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n running += float(loss) * len(ix)\r\nepoch 01/12 loss=0.62732 scale=10.73\r\nepoch 02/12 loss=0.57484 scale=10.33\r\n"} ## Step 153 · agent · 2026-08-31T19:08:10.817Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:12.271Z { "code": "const r = await tools.write_stdin({session_id:47812,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:08:23.304Z Script running with cell ID 89 Wall time 11.0 seconds Output: ## Step 154 · agent · 2026-08-31T19:08:26.357Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:27.192Z { "cell_id": "89", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:08:42.308Z Script completed Wall time 15.1 seconds Output: {"chunk_id":"06b1d1","wall_time_seconds":30.00106922,"session_id":47812,"original_token_count":38,"output":"epoch 03/12 loss=0.52728 scale=10.12\r\nepoch 04/12 loss=0.48872 scale=9.93\r\nepoch 05/12 loss=0.46761 scale=9.75\r\nepoch 06/12 loss=0.45036 scale=9.64\r\n"} ## Step 155 · agent · 2026-08-31T19:08:44.473Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:45.871Z { "code": "const r = await tools.write_stdin({session_id:47812,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:08:56.898Z Script running with cell ID 90 Wall time 11.0 seconds Output: ## Step 156 · agent · 2026-08-31T19:08:58.943Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:59.752Z { "cell_id": "90", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:09:15.905Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"aafc49","wall_time_seconds":30.000612768,"session_id":47812,"original_token_count":37,"output":"epoch 07/12 loss=0.43685 scale=9.46\r\nepoch 08/12 loss=0.42420 scale=9.41\r\nepoch 09/12 loss=0.41177 scale=9.38\r\nepoch 10/12 loss=0.40240 scale=9.35\r\n"} ## Step 157 · agent · 2026-08-31T19:09:17.838Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:19.572Z { "code": "const r = await tools.write_stdin({session_id:47812,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:09:30.600Z Script running with cell ID 91 Wall time 11.0 seconds Output: ## Step 158 · agent · 2026-08-31T19:09:33.393Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:09:34.567Z { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:09:35.945Z Script completed Wall time 1.4 seconds Output: {"chunk_id":"2585ab","wall_time_seconds":16.339397158,"exit_code":0,"original_token_count":38,"output":"epoch 11/12 loss=0.39206 scale=9.33\r\nepoch 12/12 loss=0.38436 scale=9.26\r\nwrote 845,600 rows to /app/solution/validation_twotower_e12_replay.parquet\r\n"} ## Step 159 · agent · 2026-08-31T19:09:38.642Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:45.608Z { "code": "const r = await tools.exec_command({cmd:\"cp /app/solution/two_tower_validation.pt /app/solution/two_tower_validation_seed1.pt\\n/opt/task-venv/bin/python -u /app/solution/build_submission.py --stage validation --checkpoint /app/solution/two_tower_validation_seed1.pt --checkpoint /app/solution/two_tower_validation_seed2.pt --weight 0.5 --weight 0.5 --pop-bias 0.03 --pop-bias 0.04 --pop-bias 0.05 --pop-bias 0.06 --pop-bias 0.07 --output /app/solution/validation_exact_ensemble.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:09:46.785Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"27a101","wall_time_seconds":1.000376088,"session_id":30303,"original_token_count":0,"output":""} ## Step 160 · agent · 2026-08-31T19:09:48.591Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:09:51.837Z { "code": "const r = await tools.write_stdin({session_id:30303,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:10:02.862Z Script running with cell ID 93 Wall time 11.0 seconds Output: ## Step 161 · agent · 2026-08-31T19:10:04.879Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:10:05.741Z { "cell_id": "93", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:10:21.865Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"48f144","wall_time_seconds":30.001702129,"session_id":30303,"original_token_count":0,"output":""} ## Step 162 · agent · 2026-08-31T19:10:23.444Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:24.924Z { "code": "const r = await tools.write_stdin({session_id:30303,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:10:35.946Z Script running with cell ID 94 Wall time 11.0 seconds Output: ## Step 163 · agent · 2026-08-31T19:10:38.561Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:10:39.388Z { "cell_id": "94", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:10:46.152Z Script completed Wall time 6.8 seconds Output: {"chunk_id":"f3d57c","wall_time_seconds":21.201876006,"exit_code":0,"original_token_count":110,"output":"built 36,491 temporal user contexts; 409,942 distinct targets\r\nwrote bias=0.03: /app/solution/validation_exact_ensemble_bias0.03.parquet\r\nwrote bias=0.04: /app/solution/validation_exact_ensemble_bias0.04.parquet\r\nwrote bias=0.05: /app/solution/validation_exact_ensemble_bias0.05.parquet\r\nwrote bias=0.06: /app/solution/validation_exact_ensemble_bias0.06.parquet\r\nwrote bias=0.07: /app/solution/validation_exact_ensemble_bias0.07.parquet\r\n"} ## Step 164 · agent · 2026-08-31T19:10:48.042Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:50.941Z { "code": "const r = await tools.exec_command({cmd:\"for f in /app/solution/validation_exact_ensemble_bias*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:10:52.119Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"8d82af","wall_time_seconds":1.001076362,"session_id":24033,"original_token_count":0,"output":""} ## Step 165 · agent · 2026-08-31T19:10:53.791Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:55.213Z { "code": "const r = await tools.write_stdin({session_id:24033,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:11:06.239Z Script running with cell ID 96 Wall time 11.0 seconds Output: ## Step 166 · agent · 2026-08-31T19:11:07.955Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:11:08.775Z { "cell_id": "96", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:11:08.846Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ffcb1b","wall_time_seconds":13.093749059,"exit_code":0,"original_token_count":199,"output":"{\r\n \"model\": \"/app/solution/validation_exact_ensemble_bias0.03.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.024204,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_ensemble_bias0.04.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.023823,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_ensemble_bias0.05.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.023691,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_ensemble_bias0.06.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.023204,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_exact_ensemble_bias0.07.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.022257,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n"} ## Step 167 · agent · 2026-08-31T19:11:15.772Z ### Thinking unavailable — no text recorded The replayed two-seed validation confirms the deployable exact-dot model at `0.024204` public NDCG@10, with the optimum at popularity coordinate `0.03`. I’m replacing the provisional `0.05` submission with that selected calibration, then doing the final contract audit and documentation. ### exec · 2026-08-31T19:11:22.915Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u /app/solution/build_submission.py --stage test --checkpoint /app/solution/two_tower_final_seed1.pt --checkpoint /app/solution/two_tower_final_seed2.pt --weight 0.5 --weight 0.5 --pop-bias 0.03 --output /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:11:24.092Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"1ded59","wall_time_seconds":1.000476434,"session_id":14156,"original_token_count":0,"output":""} ## Step 168 · agent · 2026-08-31T19:11:25.730Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:27.552Z { "code": "const r = await tools.write_stdin({session_id:14156,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:11:38.573Z Script running with cell ID 98 Wall time 11.0 seconds Output: ## Step 169 · agent · 2026-08-31T19:11:40.400Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:11:41.213Z { "cell_id": "98", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:11:57.579Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"7e8413","wall_time_seconds":30.002505138,"session_id":14156,"original_token_count":0,"output":""} ## Step 170 · agent · 2026-08-31T19:12:09.418Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:12:09.420Z { "code": "const r = await tools.write_stdin({session_id:14156,chars:\"\",yield_time_ms:30000,max_output_tokens:10000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:12:20.441Z Script running with cell ID 99 Wall time 11.0 seconds Output: ## Step 171 · agent · 2026-08-31T19:12:22.633Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:12:23.438Z { "cell_id": "99", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:12:25.087Z Script completed Wall time 1.6 seconds Output: {"chunk_id":"09f2d3","wall_time_seconds":15.636782773,"exit_code":0,"original_token_count":29,"output":"built 41,339 temporal user contexts; 459,859 distinct targets\r\nwrote bias=0.03: /app/solution/submission.parquet\r\n"} ## Step 172 · agent · 2026-08-31T19:12:39.867Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:13:06.929Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/two_tower_ranker.py\\n@@\\n- running += float(loss) * len(ix)\\n+ running += float(loss.detach()) * len(ix)\\n*** Add File: /app/solution/README.md\\n+# CPU PyTorch two-tower discovery ranker\\n+\\n+## Result\\n+\\n+The final file is `submission.parquet`. The selected exact-dot model obtained\\n+public validation NDCG@10 **0.024204** over 4,263 scored users. Validation used\\n+train events only as history; the final fit then incorporated validation as\\n+observed history and as one additional temporal training window.\\n+\\n+## Model\\n+\\n+`two_tower_ranker.py` defines two independent PyTorch neural towers. The user\\n+tower has no user-ID parameter: it embeds up to 400 distinct tracks from the\\n+last 90 days, pools them with recency, completion, and organic-event weights,\\n+adds five history statistics, and applies a LayerNorm/SiLU MLP to a normalized\\n+64-D vector. Active likes are additional cutoff-correct history inputs. The\\n+item tower combines a trainable catalog-item embedding with four catalog\\n+popularity/organic features and applies its own LayerNorm/SiLU MLP to a\\n+normalized 64-D vector.\\n+\\n+Training uses eight rolling seven-day next-window targets, always separated\\n+from history by the 30-minute gap. Organic 80%-complete listens are grade-1\\n+targets and organic likes are grade-3 targets. A sampled pairwise-softmax loss\\n+uses 24 popularity-smoothed negatives per positive; likes are sampled three\\n+times as often. Processing is event-stream/bag based and never constructs or\\n+decomposes a global user-item matrix.\\n+\\n+The final robust fit concatenates two seeded tower pairs plus one fixed\\n+popularity coordinate. Its deployable vectors are\\n+\\n+`U = [sqrt(.5) U1, sqrt(.5) U2, .03]`\\n+\\n+`V = [sqrt(.5) V1, sqrt(.5) V2, standardized_log_popularity]`.\\n+\\n+Thus the only final score is exactly the 129-D inner product `U @ V`; all item\\n+vectors can be computed offline and stored in a MIPS index. Request time needs\\n+one history-based user-tower evaluation and one nearest-neighbor lookup.\\n+\\n+## Commands\\n+\\n+Apply the saved final towers and recreate the submission:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_submission.py \\\\\\n+ --stage test \\\\\\n+ --checkpoint /app/solution/two_tower_final_seed1.pt \\\\\\n+ --checkpoint /app/solution/two_tower_final_seed2.pt \\\\\\n+ --weight 0.5 --weight 0.5 --pop-bias 0.03 \\\\\\n+ --output /app/solution/submission.parquet\\n+```\\n+\\n+Train a validation-stage model and reproduce a ranking with one seed:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/two_tower_ranker.py \\\\\\n+ --stage validation --epochs 12 --dim 64 --seed 20260832 \\\\\\n+ --output /app/solution/validation.parquet\\n+/opt/task-venv/bin/python /task/evaluate_public.py \\\\\\n+ /app/solution/validation.parquet\\n+```\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:13:06.959Z Script completed Wall time 0.0 seconds Output: {} ## Step 173 · agent · 2026-08-31T19:13:15.940Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:13:33.322Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,torch\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); S=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(pd.read_parquet(S),targets)\\nz=np.load(D/'test_eligible_candidates.npz')\\nurow={int(u):i for i,u in enumerate(z['uids'])}; icol={int(i):c for c,i in enumerate(z['item_ids'])}\\nrows=np.fromiter((urow[int(u)] for u in f.uid),dtype=np.int32,count=len(f))\\ncols=np.fromiter((icol[int(i)] for i in f.item_id),dtype=np.int32,count=len(f))\\nelig=((z['packed_eligible'][rows,cols//8]>>(cols%8))&1).astype(bool)\\nassert elig.all()\\nassert list(f.columns)==['uid','item_id','rank']\\nassert f.shape==(len(targets)*100,3)\\nprint({'rows':len(f),'users':f.uid.nunique(),'items':f.item_id.nunique(),'all_test_eligible':bool(elig.all()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'dtypes':{k:str(v) for k,v in f.dtypes.items()}})\\nPY\\n/opt/task-venv/bin/python -m py_compile /app/solution/two_tower_ranker.py /app/solution/build_submission.py\\nsha256sum /app/solution/submission.parquet\\nls -lh /app/solution/submission.parquet /app/solution/two_tower_final_seed1.pt /app/solution/two_tower_final_seed2.pt /app/solution/two_tower_ranker.py /app/solution/build_submission.py /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:13:38.494Z Script completed Wall time 5.2 seconds Output: {'rows': 845600, 'users': 8456, 'items': 4683, 'all_test_eligible': True, 'rank_min': 1, 'rank_max': 100, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}} ef0e33d8d58d62fd75cb16e710d84ad183e6c267be1acf1653549899c22410e2 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 2.5K Aug 31 19:13 /app/solution/README.md -rw-r--r-- 1 agent agent 4.0K Aug 31 19:03 /app/solution/build_submission.py -rw-r--r-- 1 agent agent 1.5M Aug 31 19:12 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 15M Aug 31 18:59 /app/solution/two_tower_final_seed1.pt -rw-r--r-- 1 agent agent 15M Aug 31 19:03 /app/solution/two_tower_final_seed2.pt -rw-r--r-- 1 agent agent 18K Aug 31 19:13 /app/solution/two_tower_ranker.py ## Step 174 · agent · 2026-08-31T19:13:41.277Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:13:46.583Z { "code": "const p = await tools.update_plan({explanation:\"Completed temporal development, exact-dot calibration, train+validation refit, final generation, and independent structural/eligibility audit.\",plan:[\n {step:\"Inspect schemas, distributions, eligibility, and evaluator mechanics\",status:\"completed\"},\n {step:\"Implement scalable history-based PyTorch two-tower training and ranking\",status:\"completed\"},\n {step:\"Train/tune on train→validation and score public rankings\",status:\"completed\"},\n {step:\"Refit on train+validation, generate and validate final submission\",status:\"completed\"},\n {step:\"Document architecture, objective, score, and command\",status:\"completed\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T19:13:46.645Z Script completed Wall time 0.0 seconds Output: {} ## Step 175 · agent · 2026-08-31T19:13:48.499Z ### Thinking unavailable — no text recorded Implemented and validated the complete solution. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/model code: [two_tower_ranker.py](/app/solution/two_tower_ranker.py) - Exact-dot ensemble application: [build_submission.py](/app/solution/build_submission.py) - Documentation: [README.md](/app/solution/README.md) - Public validation NDCG@10: **0.024204** - Final artifact: 845,600 rows, 8,456 users, ranks 1–100, no duplicates, all items test-eligible - Final scoring remains one 129-dimensional user/item dot product suitable for offline item indexing and MIPS retrieval.