yambda-discovery-ranking-two-tower-case-1 / run-010-gpt-5-6-sol-high-2026-08-31T12-19-20 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T19:28:49.247Z 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 · 2026-08-31T19:28:50.988Z ## 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 · 2026-08-31T19:28:50.988Z 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 · 2026-08-31T19:28:50.989Z 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 · 2026-08-31T19:28:50.989Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /tmp/codex-home/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: /tmp/codex-home/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0594b-b659-7ae1-bffe-90669b79c0b8", "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 · 2026-08-31T19:28:50.998Z # Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table. The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above. ## What You Have All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window. The package includes: - `/task/current_model/train_current_model.py`: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data 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. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, 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")`. Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. 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 same 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. ## Deliverable Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`. The ranking must: - include the columns `uid`, `item_id`, and `rank`; only these columns are evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user's `test_eligible_candidates.npz` candidate set. Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate. ## 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. - Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Step 7 · agent · 2026-08-31T19:28:56.861Z ### Thinking unavailable — no text recorded I’ll reproduce the baseline first, inspect its negative construction and validation split, then make a small set of CPU-feasible objective changes and keep only measured improvements. I’ll preserve the chronological protocol, eligible-candidate boundary, and pure two-tower scoring contract, and I’ll keep a valid best artifact under `/app/solution/` as early as possible. ### exec · 2026-08-31T19:29:07.363Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Inspect production code and data scale; reproduce public baseline\",\"status\":\"in_progress\"},\n {step:\"Diagnose negative sampling and implement focused alternatives under /app/solution\",\"status\":\"pending\"},\n {step:\"Run public backtests, select best valid approach, and document results\",\"status\":\"pending\"},\n {step:\"Refit on train+validation, create submission, and verify schema/eligibility\",\"status\":\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/current_model /task | sed -n '1,120p' && du -sh /task/data /task/current_model && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,260p' /task/current_model/two_tower_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:29:07.435Z Script completed Wall time 0.1 seconds Output: /app /task/requirements.txt /task/current_model/two_tower_model.py /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /task/current_model/train_current_model.py /task/evaluate_public.py /task/ranking_evaluation.py /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet 340M /task/data 100K /task/current_model #!/usr/bin/env python3 """Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. """ from __future__ import annotations import argparse import json import random import sys import time from dataclasses import asdict, dataclass from pathlib import Path import numpy as np import pandas as pd import torch from torch import nn from torch.nn import functional as F import two_tower_model as base CACHE_VERSION = 2 @dataclass(frozen=True) class Config: bptt_steps: int = 100 user_batch_size: int = 64 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 loss_microbatch_size: int = 65_536 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 negative_filter_scope: str = "full-training-history" target_weighting: str = "organic-strong" ordinary_target_weight: float = 0.25 organic_strong_target_weight: float = 1.0 organic_like_target_weight: float = 3.0 discovery_horizon_days: int = 7 validation_fraction: float = 0.10 max_train_targets_per_user: int = 128 max_validation_targets_per_user: int = 16 early_stopping_patience: int = 1 early_stopping_min_delta: float = 1.0e-4 seed: int = 29 @dataclass(frozen=True) class CandidateSet: uids: np.ndarray item_ids: np.ndarray packed: np.ndarray @classmethod def read(cls, path: Path) -> "CandidateSet": with np.load(path, allow_pickle=False) as artifact: result = cls( uids=artifact["uids"].astype(np.int64, copy=True), item_ids=artifact["item_ids"].astype(np.int64, copy=True), packed=artifact["packed_eligible"].astype(np.uint8, copy=True), ) expected = (len(result.uids), (len(result.item_ids) + 7) // 8) if result.packed.shape != expected: raise ValueError( f"invalid candidate artifact shape {result.packed.shape}; " f"expected {expected}" ) return result @dataclass(frozen=True) class SequenceData: item_ids: np.ndarray item_features: np.ndarray user_ids: np.ndarray offsets: np.ndarray sequence_timestamps: np.ndarray sequence_items: np.ndarray event_features: np.ndarray activity_features: np.ndarray state_ids: np.ndarray liked_states: np.ndarray disliked_states: np.ndarray state_counts: np.ndarray inference_user_rows: np.ndarray inference_liked_items: np.ndarray inference_disliked_items: np.ndarray inference_user_features: np.ndarray @property def lengths(self) -> np.ndarray: return np.diff(self.offsets) @property def target_count(self) -> int: return int(np.maximum(self.lengths - 1, 0).sum()) @dataclass(frozen=True) class TargetPartition: """A contiguous recent target window and its chronological BCE split.""" window_starts: np.ndarray training_counts: np.ndarray validation_counts: np.ndarray history_training_counts: np.ndarray @property def selected_count(self) -> int: return int(self.training_counts.sum() + self.validation_counts.sum()) def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def seed_everything(seed: int) -> None: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def model_config(config: Config) -> base.TowerConfig: """Map the dense trainer configuration onto the unchanged tower.""" return base.TowerConfig( sequence_length=config.bptt_steps, embedding_dim=config.embedding_dim, hidden_dim=config.hidden_dim, user_hidden_1=config.user_hidden_1, user_hidden_2=config.user_hidden_2, item_hidden_1=config.item_hidden_1, item_hidden_2=config.item_hidden_2, dropout=config.dropout, epochs=config.epochs, learning_rate=config.learning_rate, weight_decay=config.weight_decay, gradient_clip=config.gradient_clip, uniform_negatives=config.uniform_negatives, inbatch_negatives=config.inbatch_negatives, inbatch_correction_power=config.inbatch_correction_power, inbatch_correction_min=config.inbatch_correction_min, inbatch_correction_max=config.inbatch_correction_max, seed=config.seed, ) def append_state_snapshot( active_likes: dict[int, int], active_dislikes: dict[int, int], n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> int: liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items)) disliked_states.append( base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items) ) state_counts.append((len(active_likes), len(active_dislikes))) return len(liked_states) - 1 def causal_activity_features( timestamps: np.ndarray, items: np.ndarray, organic: np.ndarray, completion: np.ndarray, ) -> np.ndarray: """Compute the first four reviewed user features at every event cutoff.""" length = len(items) result = np.zeros((length, 4), dtype=np.float16) if not length: return result left_rows = np.searchsorted( timestamps, timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY, side="left", ) rows = np.arange(length, dtype=np.int64) counts = rows - left_rows + 1 organic_sum = np.cumsum(organic.astype(np.float64)) completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64)) before_organic = np.where( left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0 ) before_completion = np.where( left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0 ) unique_counts = np.empty(length, dtype=np.int32) frequencies: dict[int, int] = {} left = 0 for right, raw_item in enumerate(items): item = int(raw_item) frequencies[item] = frequencies.get(item, 0) + 1 required_left = int(left_rows[right]) while left < required_left: old_item = int(items[left]) remaining = frequencies[old_item] - 1 if remaining: frequencies[old_item] = remaining else: del frequencies[old_item] left += 1 unique_counts[right] = len(frequencies) result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16) result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16) result[:, 2] = ( (organic_sum - before_organic) / np.maximum(counts, 1) ).astype(np.float16) result[:, 3] = ( (completion_sum - before_completion) / np.maximum(counts, 1) / 100.0 ).astype(np.float16) return result def build_sequence_parts( listens: base.ListenEvents, explicit: base.ExplicitEvents, n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None: qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False) items = listens.items[qualifying].astype(np.uint16, copy=False) organic = listens.organic[qualifying].astype(np.float32, copy=False) completion = listens.completion_pct[qualifying].astype(np.float32, copy=False) if not len(items): """Shared public-data primitives for the Yambda two-tower baseline.""" from __future__ import annotations import sys from dataclasses import dataclass from pathlib import Path from typing import Iterator import numpy as np import pandas as pd import pyarrow.parquet as pq import torch from torch import nn DAY = 86_400 TRAIN_END = 24_786_800 VALIDATION_END = 25_393_400 LISTEN_POSITIVE_THRESHOLD = 50 PROFILE_LOOKBACK_DAYS = 120 LIKE_ITEMS = 48 DISLIKE_ITEMS = 24 TOP_N = 100 @dataclass(frozen=True) class TowerConfig: sequence_length: int = 100 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 seed: int = 29 @dataclass(frozen=True) class ListenEvents: timestamps: np.ndarray items: np.ndarray organic: np.ndarray completion_pct: np.ndarray @dataclass(frozen=True) class ExplicitEvents: timestamps: np.ndarray items: np.ndarray channel: np.ndarray active: np.ndarray organic_like: np.ndarray def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def build_item_features(catalog: pd.DataFrame) -> np.ndarray: """Return four standardized features built only from the public catalog.""" positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False) organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False) popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False) features = np.column_stack( [ np.log1p(positives), np.log1p(organic), organic / np.maximum(positives, 1.0), 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1), ] ) means = features.mean(axis=0, keepdims=True) scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6) standardized = ((features - means) / scales).astype(np.float32) if not np.isfinite(standardized).all(): raise RuntimeError("catalog features contain non-finite values") return standardized def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents: if len(parts) == 1: return parts[0] timestamps = np.concatenate([part.timestamps for part in parts]) items = np.concatenate([part.items for part in parts]) organic = np.concatenate([part.organic for part in parts]) completion = np.concatenate([part.completion_pct for part in parts]) order = np.argsort(timestamps, kind="stable") return ListenEvents( timestamps=timestamps[order], items=items[order], organic=organic[order], completion_pct=completion[order], ) def iter_listen_users( path: Path, item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Stream one uid-sorted public listen file one user at a time.""" parquet = pq.ParquetFile(path) pending_uid: int | None = None pending: list[ListenEvents] = [] previous_uid = -1 for batch in parquet.iter_batches( batch_size=750_000, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ): frame = batch.to_pandas() mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 if not np.any(keep): continue uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep] timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep] items = mapped[keep].astype(np.int32, copy=False) organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep] completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep] if np.any(uids[1:] < uids[:-1]): raise RuntimeError(f"{path.name} is not sorted by uid") boundaries = np.r_[ 0, np.flatnonzero(uids[1:] != uids[:-1]) + 1, len(uids), ] for start, end in zip(boundaries[:-1], boundaries[1:]): uid = int(uids[start]) part = ListenEvents( timestamps[start:end].copy(), items[start:end].copy(), organic[start:end].copy(), completion[start:end].copy(), ) if pending_uid is None: pending_uid = uid elif uid != pending_uid: if pending_uid < previous_uid: raise RuntimeError(f"{path.name} user order regressed") yield pending_uid, _combine_listen_chunks(pending) previous_uid = pending_uid pending_uid = uid pending = [] pending.append(part) if pending_uid is not None: yield pending_uid, _combine_listen_chunks(pending) def merge_listen_streams( paths: list[Path], item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Merge uid-sorted public listen files without materializing them.""" streams = [iter(iter_listen_users(path, item_index)) for path in paths] heads: list[tuple[int, ListenEvents] | None] = [ next(stream, None) for stream in streams ] while any(head is not None for head in heads): uid = min(head[0] for head in heads if head is not None) parts: list[ListenEvents] = [] for position, head in enumerate(heads): if head is not None and head[0] == uid: parts.append(head[1]) heads[position] = next(streams[position], None) yield uid, _combine_listen_chunks(parts) def load_explicit_events( data: Path, prefixes: tuple[str, ...], item_index: pd.Index, ) -> dict[int, ExplicitEvents]: """Load public explicit actions as chronological state changes.""" specifications = ( ("likes", 0, 1, 0), ("unlikes", 0, 0, 1), ("dislikes", 1, 1, 0), ("undislikes", 1, 0, 1), ) parts: list[pd.DataFrame] = [] for family, channel, active, tie_priority in specifications: for prefix in prefixes: frame = pd.read_parquet( data / f"{prefix}_{family}.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) mapped = item_index.get_indexer( frame.item_id.to_numpy(dtype=np.int64, copy=False) ) keep = mapped >= 0 if not np.any(keep): continue retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy() retained["item"] = mapped[keep].astype(np.int32, copy=False) retained["channel"] = np.int8(channel) retained["active"] = np.int8(active) retained["tie_priority"] = np.int8(tie_priority) retained["organic_like"] = np.int8( family == "likes" ) * retained.is_organic.astype(np.int8) parts.append(retained.drop(columns="is_organic")) if not parts: return {} events = pd.concat(parts, ignore_index=True) events = events.sort_values( ["uid", "timestamp", "tie_priority"], kind="mergesort" ).reset_index(drop=True) result: dict[int, ExplicitEvents] = {} for uid, rows in events.groupby("uid", sort=False): result[int(uid)] = ExplicitEvents( timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True), items=rows.item.to_numpy(dtype=np.int32, copy=True), channel=rows.channel.to_numpy(dtype=np.int8, copy=True), active=rows.active.to_numpy(dtype=np.int8, copy=True), organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True), ) log(f"loaded explicit events: rows={len(events):,} users={len(result):,}") return result def empty_explicit_events() -> ExplicitEvents: return ExplicitEvents( timestamps=np.empty(0, dtype=np.int64), items=np.empty(0, dtype=np.int32), channel=np.empty(0, dtype=np.int8), active=np.empty(0, dtype=np.int8), organic_like=np.empty(0, dtype=np.int8), ) def pack_active_items( active: dict[int, int], width: int, padding_item: int ) -> np.ndarray: packed = np.full(width, padding_item, dtype=np.uint16) if not active: return packed ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width] packed[: len(ordered)] = np.fromiter( (item for item, _ in ordered), dtype=np.uint16, count=len(ordered) ) return packed def load_inference_state( path: Path, item_index: pd.Index, uids: np.ndarray, n_items: int, ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Pack the task-provided active explicit state.""" state = pd.read_parquet(path, columns=["uid", "item_id", "state"]) mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 ## Step 8 · agent · 2026-08-31T19:29:10.678Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:15.640Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"negative|def train|def main|ArgumentParser|TargetPartition|sample|loss|rank|stage\\\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py && sed -n '260,760p' /task/current_model/train_current_model.py && sed -n '760,1320p' /task/current_model/train_current_model.py && sed -n '260,760p' /task/current_model/two_tower_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:50000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:29:15.701Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 16499) Total output lines: 1441 /task/current_model/two_tower_model.py:40: uniform_negatives: int = 24 /task/current_model/two_tower_model.py:41: inbatch_negatives: int = 8 /task/current_model/two_tower_model.py:73: popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False) /task/current_model/two_tower_model.py:79: 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1), /task/current_model/two_tower_model.py:346:def invalid_negative_mask( /task/current_model/two_tower_model.py:347: negatives: torch.Tensor, /task/current_model/two_tower_model.py:353: invalid = negatives.eq(targets[:, None]) /task/current_model/two_tower_model.py:354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2) /task/current_model/two_tower_model.py:355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2) /task/current_model/two_tower_model.py:356: for column in range(1, negatives.shape[1]): /task/current_model/two_tower_model.py:357: invalid[:, column] |= negatives[:, column, None].eq( /task/current_model/two_tower_model.py:358: negatives[:, :column] /task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives( /task/current_model/two_tower_model.py:374: """Draw uniform and unique-target in-batch negatives.""" /task/current_model/two_tower_model.py:406: negatives_cpu = torch.cat([uniform, inbatch], dim=1) /task/current_model/two_tower_model.py:407: correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32) /task/current_model/two_tower_model.py:412: negatives = negatives_cpu.to(device) /task/current_model/two_tower_model.py:415: invalid = invalid_negative_mask( /task/current_model/two_tower_model.py:416: negatives, targets, context_items, liked_items /task/current_model/two_tower_model.py:419: return negatives, correction /task/current_model/two_tower_model.py:426: negatives[invalid] = replacements /task/current_model/two_tower_model.py:428: invalid = invalid_negative_mask( /task/current_model/two_tower_model.py:429: negatives, targets, context_items, liked_items /task/current_model/two_tower_model.py:432: raise RuntimeError("mixed negative resampling failed false-negative filter") /task/current_model/two_tower_model.py:433: return negatives, correction /task/current_model/train_current_model.py:6:loss. The fast default retains each user's most recent 128 training targets and /task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state /task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy, /task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every /task/current_model/train_current_model.py:55: loss_microbatch_size: int = 65_536 /task/current_model/train_current_model.py:56: uniform_negatives: int = 24 /task/current_model/train_current_model.py:57: inbatch_negatives: int = 8 /task/current_model/train_current_model.py:61: negative_filter_scope: str = "full-training-history" /task/current_model/train_current_model.py:127:class TargetPartition: /task/current_model/train_current_model.py:165: uniform_negatives=config.uniform_negatives, /task/current_model/train_current_model.py:166: inbatch_negatives=config.inbatch_negatives, /task/current_model/train_current_model.py:342: public: Path, stage: str, config: Config /task/current_model/train_current_model.py:346: "popularity_rank" /task/current_model/train_current_model.py:354: if stage == "validation": /task/current_model/train_current_model.py:463: f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} " /task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]: /task/current_model/train_current_model.py:474: "stage": stage, /task/current_model/train_current_model.py:477: "event_time_feature": "exp-negative-gap-over-60-days", /task/current_model/train_current_model.py:482: path: Path, prepared: SequenceData, stage: str, config: Config /task/current_model/train_current_model.py:487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)), /task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData: /task/current_model/train_current_model.py:496: expected = cache_metadata(stage, config) /task/current_model/train_current_model.py:513: stage: str, /task/current_model/train_current_model.py:519: if stage == "validation" /task/current_model/train_current_model.py:524: prepared = load_cache(cache, stage, config) /task/current_model/train_current_model.py:528: prepared, built_eligibility = prepare_data(public, stage, config) /task/current_model/train_current_model.py:530: save_cache(cache, prepared, stage, config) /task/current_model/train_current_model.py:603: f"full-history false-negative filter: users={len(prepared.user_ids):,} " /task/current_model/train_current_model.py:609:def invalid_full_history_negatives( /task/current_model/train_current_model.py:610: negatives: np.ndarray, /task/current_model/train_current_model.py:616: """Return row-level invalid negatives using the complete training prefix.""" /task/current_model/train_current_model.py:617: byte_columns = negatives >> 3 /task/current_model/train_current_model.py:618: bit_columns = negatives & 7 /task/current_model/train_current_model.py:621: invalid |= negatives == targets[:, None] /task/current_model/train_current_model.py:622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) /task/current_model/train_current_model.py:623: for column in range(1, negatives.shape[1]): /task/current_model/train_current_model.py:625: negatives[:, column, None] == negatives[:, :column] /task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives( /task/current_model/train_current_model.py:641: """Draw mixed negatives and reject all known public-training positives.""" /task/current_model/train_current_model.py:647: (batch_size, config.uniform_negatives), /task/current_model/train_current_model.py:653: if config.inbatch_negatives: /task/current_model/train_current_model.py:656: config.inbatch_negatives, max(len(unique_targets) - 1, 0) /task/current_model/train_current_model.py:672: if corrected_count < config.inbatch_negatives: /task/current_model/train_current_model.py:675: (batch_size, config.inbatch_negatives - corrected_count), /task/current_model/train_current_model.py:681: negatives = torch.cat([uniform, inbatch], dim=1).numpy() /task/current_model/train_current_model.py:682: correction = np.ones_like(negatives, dtype=np.float32) /task/current_model/train_current_model.py:685: :, config.uniform_negatives : config.uniform_negatives + corrected_count /task/current_model/train_current_model.py:688: invalid = invalid_full_history_negatives( /task/current_model/train_current_model.py:689: negatives, /task/current_model/train_current_model.py:697: torch.from_numpy(negatives).to(device), /task/current_model/train_current_model.py:706: negatives[invalid] = replacements /task/current_model/train_current_model.py:709: invalid = invalid_full_history_negatives( /task/current_model/train_current_model.py:710: negatives, /task/current_model/train_current_model.py:717: raise RuntimeError("full-history negative resampling failed") /task/current_model/train_current_model.py:719: torch.from_numpy(negatives).to(device), /task/current_model/train_current_model.py:724:def draw_training_negatives( /task/current_model/train_current_model.py:736: if config.negative_filter_scope == "full-training-history": /task/current_model/train_current_model.py:738: raise RuntimeError("full-history negative filter was not built") /task/current_model/train_current_model.py:739: return draw_full_history_filtered_negatives( /task/current_model/train_current_model.py:750: return base.draw_filtered_mixed_negatives( /task/current_model/train_current_model.py:755: config.uniform_negatives, /task/current_model/train_current_model.py:756: config.inbatch_negatives, /task/current_model/train_current_model.py:816: negatives: torch.Tensor, /task/current_model/train_current_model.py:818: all_items = torch.cat([positives, negatives.reshape(-1)]) /task/current_model/train_current_model.py:822: negative_vectors = encoded[inverse[len(positives) :]].reshape( /task/current_model/train_current_model.py:823: len(positives), negatives.shape[1], -1 /task/current_model/train_current_model.py:825: return positive_vectors, negative_vectors /task/current_model/train_current_model.py:845:def corrected_negative_loss( /task/current_model/train_current_model.py:851: total = config.uniform_negatives + config.inbatch_negatives /task/current_model/train_current_model.py:853: if config.uniform_negatives: /task/current_model/train_current_model.py:854: result += config.uniform_negatives * raw[ /task/current_model/train_current_model.py:855: :, : config.uniform_negatives /task/current_model/train_current_model.py:857: if config.inbatch_negatives: /task/current_model/train_current_model.py:858: selected = slice(config.uniform_negatives, total) /task/current_model/train_current_model.py:860: result += config.inbatch_negatives * ( /task/current_model/train_current_model.py:880: raise RuntimeError("chronological target partition produced negative counts") /task/current_model/train_current_model.py:893:) -> TargetPartition: /task/current_model/train_current_model.py:906: return TargetPartition( /task/current_model/train_current_model.py:915: public: Path, stage: str, item_ids: np.ndarray /task/current_model/train_current_model.py:918: prefixes = ("train",) if stage == "validation" else ("train", "val") /task/current_model/train_current_model.py:957: stage: str, /task/current_model/train_current_model.py:978: public, stage, prepared.item_ids /task/current_model/train_current_model.py:981: base.TRAIN_END if stage == "validation" else base.VALIDATION_END /task/current_model/train_current_model.py:1119: negatives: torch.Tensor, /task/current_model/train_current_model.py:1120: negative_weights: torch.Tensor, /task/current_model/train_current_model.py:1121: sample_weights: np.ndarray, /task/current_model/train_current_model.py:1127: total_weight = float(sample_weights.sum()) /task/current_model/train_current_model.py:1129: raise RuntimeError("target block has no positive sample weight") /task/current_model/train_current_model.py:1132: "loss_sum": 0.0, /task/current_model/train_current_model.py:1134: "negative_sum": 0.0, /task/current_model/train_current_model.py:1138: for begin in range(0, target_count, config.loss_microbatch_size): /task/current_model/train_current_model.py:1139: end = min(begin + config.loss_microbatch_size, target_count) /task/current_model/train_current_model.py:1162: positive_vectors, negative_vectors = candidate_vectors( /task/current_model/train_current_model.py:1165: negatives[begin:end], /task/current_model/train_current_model.py:1168: negative_logits = torch.einsum( /task/current_model/train_current_model.py:1169: "bd,bkd->bk", query_vectors, negative_vectors /task/current_model/train_current_model.py:1171: positive_loss = F.softplus(-positive_logits) /task/current_model/train_current_model.py:1172: negative_loss = corrected_negative_loss( /task/current_model/train_current_model.py:1173: negative_logits, negative_weights[begin:end], config /task/current_model/train_current_model.py:1176: sample_weights[begin:end].astype(np.float32, copy=False) /task/current_model/train_current_model.py:1178: loss_sum = ( /task/current_model/train_current_model.py:1179: selected_weights * (positive_loss + negative_loss) / 2.0 /task/current_model/train_current_model.py:1181: if not bool(torch.isfinite(loss_sum)): /task/current_model/train_current_model.py:1182: raise RuntimeError("dense next-item training produced non-finite loss") /task/current_model/train_current_model.py:1184: (loss_sum / total_weight).backward() /task/current_model/train_current_model.py:1188: statistics["loss_sum"] += float(loss_sum.detach().cpu()) /task/current_model/train_current_model.py:1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) /task/current_model/train_current_model.py:1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() /task/current_model/train_current_model.py:1227: negative_count = config.uniform_negatives + config.inbatch_negatives /task/current_model/train_current_model.py:1228: total_loss = 0.0 /task/current_model/train_current_model.py:1232: total_negative = 0.0 /task/current_model/train_current_model.py:1293: negatives, negative_weights = draw_training_negatives( /task/current_model/train_current_model.py:1314: negatives, /task/current_model/train_current_model.py:1315: negative_weights, /task/current_model/train_current_model.py:1327: total_loss += statistics["loss_sum"] /task/current_model/train_current_model.py:1331: total_negative += statistics["negative_sum"] /task/current_model/train_current_model.py:1337: batch_bce = statistics["loss_sum"] / max( /task/current_model/train_current_model.py:1340: running_bce = total_loss / max(total_weight, 1.0e-12) /task/current_model/train_current_model.py:1351: "loss": total_loss / max(total_weight, 1.0e-12), /task/current_model/train_current_model.py:1353: "negative_logit": total_negative / max(total_targets * negative_count, 1), /task/current_model/train_current_model.py:1355: / max(total_targets * negative_count, 1), /task/current_model/train_current_model.py:1388: total_loss = 0.0 /task/current_model/train_current_model.py:1449: negatives, negative_weights = draw_training_negatives( /task/current_model/train_current_model.py:1469: negatives, /task/current_model/train_current_model.py:1470: negative_weights, /task/current_model/train_current_model.py:1475: total_loss += statistics["loss_sum"] /task/current_model/train_current_model.py:1481: "validation_loss": total_loss / max(total_weight, 1.0e-12), /task/current_model/train_current_model.py:1521:def train_model( /task/current_model/train_current_model.py:1523: stage: str, /task/current_model/train_current_model.py:1560: stage, /task/current_model/train_current_model.py:1567: if config.negative_filter_scope == "full-training-history" /task/current_model/train_current_model.py:1571: best_loss = float("inf") /task/current_model/train_current_model.py:1601: "negative_filter_scope", "last-100" /task/current_model/train_current_model.py:1629: stored_filter_scope != config.negative_filter_scope /task/current_model/train_current_model.py:1657: prior.pop("validation_loss", None) /task/current_model/train_current_model.py:1660: f"{stored_filter_scope}->{config.negative_filter_scope}, " /task/current_model/train_current_model.py:1666: if "validation_loss" not in prior: /task/current_model/train_current_model.py:1682: best_loss = float(prior["validation_loss"]) /task/current_model/train_current_model.py:1687: f"loss={best_loss:.6f}" /task/current_model/train_current_model.py:1731: diagnostics["validation_loss"] /task/current_model/train_current_model.py:1732: < best_loss - config.early_stopping_min_delta /task/current_model/train_current_model.py:1735: best_loss = diagnostics["validation_loss"] /task/current_model/train_current_model.py:1754: f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " /task/current_model/train_current_model.py:1755: f"validation_loss={diagnostics['validation_loss']:.5f} " /task/current_model/train_current_model.py:1766: f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" /task/current_model/train_current_model.py:1776: f"loss={best_loss:.6f}" /task/current_model/train_current_model.py:1861:def rank_candidates( /task/current_model/train_current_model.py:1867: ranking_batch_size: int, /task/current_model/train_current_model.py:1882: for start in range(0, len(eligibility.uids), ranking_batch_size): /task/current_model/train_current_model.py:1883: end = min(start + ranking_batch_size, len(eligibility.uids)) /task/current_model/train_current_model.py:1896: (uid, int(prepared.item_ids[column]), rank) /task/current_model/train_current_model.py:1897: for rank, column in enumerate(columns, start=1) /task/current_model/train_current_model.py:1900: log(f"ranked users={end:,}/{len(eligibility.uids):,}") /task/current_model/train_current_model.py:1909: return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics /task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None: /task/current_model/train_current_model.py:1922:def main() -> int: /task/current_model/train_current_model.py:1923: parser = argparse.ArgumentParser(description=__doc__) /task/current_model/train_current_model.py:1925: "--stage", choices=("validation", "submission"), default="validation" /task/current_model/train_current_model.py:1939: "--loss-microbatch-size", /task/current_model/train_current_model.py:1941: default=Config.loss_microbatch_size, /task/current_model/train_current_model.py:1944: "--negative-filter-scope", /task/current_model/train_current_model.py:1946: default=Config.negative_filter_scope, /task/current_model/train_current_model.py:2003: parser.add_argument("--ranking-batch-size", type=int, default=64) /task/current_model/train_current_model.py:2012: or args.loss_microbatch_size < 1 /task/current_model/train_current_model.py:2033: loss_microbatch_size=args.loss_microbatch_size, /task/current_model/train_current_model.py:2034: negative_filter_scope=args.negative_filter_scope, /task/current_model/train_current_model.py:2057: args.stage, /task/current_model/train_current_model.py:2071: "stage": args.stage, /task/current_model/train_current_model.py:2095: args.stage, /task/current_model/train_current_model.py:2102: ranking, embedding_diagnostics = rank_candidates( /task/current_model/train_current_model.py:2108: args.ranking_batch_size, /task/current_model/train_current_model.py:2110: write_ranking(ranking, args.output.resolve()) /task/current_model/train_current_model.py:2115: "stage": args.stage, /task/current_model/train_current_model.py:2117: "rows": len(ranking), /task/current_model/train_current_model.py:2118: "users": int(ranking.uid.nunique()), /task/current_model/train_current_model.py:2126: "sequence_encoder": "one-layer-GRU-dense-timestep-loss", /task/current_model/train_current_model.py:2129: "loss_execution": "gradient-accumulated-microbatches", /task/current_model/train_current_model.py:2135: "objective": "sample-weighted-BCE-at-every-timestep", /task/current_model/train_current_model.py:2136: "negative_sampl…6499 tokens truncated… == "validation" else base.VALIDATION_END ) horizon = config.discovery_horizon_days * base.DAY grade_three = 0 for user_row, uid in enumerate(prepared.user_ids): start = int(prepared.offsets[user_row]) end = int(prepared.offsets[user_row + 1]) if end - start < 2: continue last_training_target = start + int(training_counts[user_row]) first_validation_target = last_training_target + 1 split_timestamp = ( int(prepared.sequence_timestamps[first_validation_target]) if first_validation_target < end else history_end ) user_likes = like_outcomes.get(int(uid), {}) seen = {int(prepared.sequence_items[start])} for position in range(start + 1, end): item = int(prepared.sequence_items[position]) novel = item not in seen seen.add(item) if not novel: # Repeats remain training examples, but they cannot be # eligible discovery outcomes at the corresponding # cutoff and therefore keep only the ordinary weight. weights[position] = config.ordinary_target_weight continue timestamps = user_likes.get(item) if timestamps is None: continue timestamp = int(prepared.sequence_timestamps[position]) upper = min( timestamp + horizon, split_timestamp if position <= last_training_target else history_end, ) outcome = int(np.searchsorted(timestamps, timestamp, side="left")) if outcome < len(timestamps) and int(timestamps[outcome]) < upper: weights[position] = config.organic_like_target_weight grade_three += 1 log( f"grade-3 first-encounter targets={grade_three:,} " f"horizon_days={config.discovery_horizon_days}" ) log( f"target weighting={config.target_weighting} " f"min={weights.min():.3f} mean={weights.mean():.3f} " f"max={weights.max():.3f}" ) return weights def build_dense_block( prepared: SequenceData, target_weights: np.ndarray, users: np.ndarray, window_starts: np.ndarray, process_counts: np.ndarray, score_starts: np.ndarray, start: int, steps: int, config: Config, ) -> tuple[np.ndarray, ...]: """Build one causal sequence block and mark the target rows to score.""" batch_size = len(users) padding_item = len(prepared.item_ids) input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16) event_features = np.zeros((batch_size, steps, 3), dtype=np.float16) activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16) state_ids = np.zeros((batch_size, steps), dtype=np.uint32) targets = np.zeros((batch_size, steps), dtype=np.uint16) weights = np.ones((batch_size, steps), dtype=np.float32) scored = np.zeros((batch_size, steps), dtype=bool) histories = np.full( (batch_size, steps, config.bptt_steps), padding_item, dtype=np.uint16, ) processed = np.zeros(batch_size, dtype=np.int32) lengths = prepared.lengths for row, user_row in enumerate(users): count = min(steps, max(int(process_counts[row]) - start, 0)) if not count: continue offset = int(prepared.offsets[user_row]) window_start = int(window_starts[row]) absolute_start = window_start + start positions = slice( offset + absolute_start, offset + absolute_start + count, ) target_positions = slice( offset + absolute_start + 1, offset + absolute_start + count + 1, ) input_items[row, :count] = prepared.sequence_items[positions] event_features[row, :count] = prepared.event_features[positions] activity_features[row, :count] = prepared.activity_features[positions] state_ids[row, :count] = prepared.state_ids[positions] targets[row, :count] = prepared.sequence_items[target_positions] weights[row, :count] = target_weights[target_positions] score_from = min(count, max(int(score_starts[row]) - start, 0)) scored[row, score_from:count] = True processed[row] = count user_sequence = prepared.sequence_items[ offset : offset + int(lengths[user_row]) ] histories[row, :count] = history_windows( user_sequence, absolute_start, count, config.bptt_steps, padding_item, ) return ( input_items, event_features, activity_features, state_ids, targets, scored, histories, processed, weights, ) def score_target_microbatches( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, sequence_vectors: torch.Tensor, flat_states: np.ndarray, flat_activity: np.ndarray, positive_items: torch.Tensor, negatives: torch.Tensor, negative_weights: torch.Tensor, sample_weights: np.ndarray, device: torch.device, backward: bool, ) -> tuple[dict[str, float], torch.Tensor | None]: """Compute one logical block's exact BCE in bounded-memory slices.""" target_count = len(positive_items) total_weight = float(sample_weights.sum()) if total_weight <= 0: raise RuntimeError("target block has no positive sample weight") sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None statistics = { "loss_sum": 0.0, "positive_sum": 0.0, "negative_sum": 0.0, "pair_correct": 0.0, "weight_sum": 0.0, } for begin in range(0, target_count, config.loss_microbatch_size): end = min(begin + config.loss_microbatch_size, target_count) selected_states = flat_states[begin:end] if backward: sequence_input = ( sequence_vectors[begin:end].detach().requires_grad_(True) ) else: sequence_input = sequence_vectors[begin:end] liked_vectors, disliked_vectors, _ = pool_explicit_states( model, prepared, selected_states, device ) counts = prepared.state_counts[selected_states] numeric = np.zeros((end - begin, 6), dtype=np.float32) numeric[:, :4] = flat_activity[begin:end] numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0 numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0 query_vectors = user_queries( model, sequence_input, liked_vectors, disliked_vectors, torch.from_numpy(numeric).to(device), ) positive_vectors, negative_vectors = candidate_vectors( model, positive_items[begin:end], negatives[begin:end], ) positive_logits = (query_vectors * positive_vectors).sum(dim=1) negative_logits = torch.einsum( "bd,bkd->bk", query_vectors, negative_vectors ) positive_loss = F.softplus(-positive_logits) negative_loss = corrected_negative_loss( negative_logits, negative_weights[begin:end], config ) selected_weights = torch.from_numpy( sample_weights[begin:end].astype(np.float32, copy=False) ).to(device) loss_sum = ( selected_weights * (positive_loss + negative_loss) / 2.0 ).sum() if not bool(torch.isfinite(loss_sum)): raise RuntimeError("dense next-item training produced non-finite loss") if backward: (loss_sum / total_weight).backward() if sequence_input.grad is None: raise RuntimeError("sequence microbatch did not produce a gradient") sequence_gradient[begin:end] = sequence_input.grad statistics["loss_sum"] += float(loss_sum.detach().cpu()) statistics["positive_sum"] += float(positive_logits.detach().sum().cpu()) statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) statistics["pair_correct"] += float( (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() ) statistics["weight_sum"] += float(selected_weights.sum().detach().cpu()) return statistics, sequence_gradient def release_accelerator_cache(device: torch.device) -> None: if device.type == "mps": torch.mps.empty_cache() def run_training_epoch( model: base.SequentialTwoTower, optimizer: torch.optim.Optimizer, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, target_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, generator: torch.Generator, device: torch.device, epoch: int, ) -> dict[str, float]: model.train() train_users = np.flatnonzero(target_counts > 0) sorted_users = train_users[ np.argsort(target_counts[train_users], kind="stable") ] groups = [ sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size) ] group_order = torch.randperm(len(groups), generator=generator).numpy() negative_count = config.uniform_negatives + config.inbatch_negatives total_loss = 0.0 total_targets = 0 total_weight = 0.0 total_positive = 0.0 total_negative = 0.0 total_pair_correct = 0.0 batches = 0 started = time.monotonic() for group_number in group_order: users = groups[int(group_number)] group_counts = target_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_counts.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).to(device), torch.from_numpy(block[1].astype(np.float32)).to(device), hidden, ) hidden = torch.stack( [ sequence_outputs[row, count - 1] if count else hidden[0, row] for row, count in enumerate(block[7]) ] ).unsqueeze(0).detach() flat_scored = block[5].reshape(-1) if not np.any(flat_scored): continue mask = torch.from_numpy(flat_scored).to(device) sequence_vectors = sequence_outputs.reshape( -1, config.embedding_dim )[mask] flat_states = block[3].reshape(-1)[flat_scored] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] negatives, negative_weights = draw_training_negatives( positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) optimizer.zero_grad(set_to_none=True) statistics, sequence_gradient = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True, ) if sequence_gradient is None: keep = mapped >= 0 state = state.loc[keep, ["uid", "state"]].copy() state["item"] = mapped[keep].astype(np.int32, copy=False) user_rows = pd.Index(uids) liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16) disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16) like_counts = np.zeros(len(uids), dtype=np.int32) dislike_counts = np.zeros(len(uids), dtype=np.int32) for value, width, destination, counts in ( ("liked", LIKE_ITEMS, liked, like_counts), ("disliked", DISLIKE_ITEMS, disliked, dislike_counts), ): selected = state[state.state == value] for uid, rows in selected.groupby("uid", sort=False): row = int(user_rows.get_indexer([int(uid)])[0]) if row < 0: continue items = np.sort(rows.item.unique()) counts[row] = len(items) retained = items[:width] destination[row, : len(retained)] = retained.astype(np.uint16) return liked, disliked, like_counts, dislike_counts class SequentialTwoTower(nn.Module): """Order-sensitive user tower and independent catalog-aware item tower.""" def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None: super().__init__() n_items, n_item_features = item_features.shape dim = config.embedding_dim self.n_items = n_items self.context_embedding = nn.Embedding( n_items + 1, dim, padding_idx=n_items ) self.event_projection = nn.Sequential( nn.Linear(dim + 3, config.hidden_dim), nn.GELU(), ) self.sequence_encoder = nn.GRU( input_size=config.hidden_dim, hidden_size=dim, num_layers=1, batch_first=True, ) self.user_tower = nn.Sequential( nn.Linear(dim * 3 + 6, config.user_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_1, config.user_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_2, dim), ) self.candidate_embedding = nn.Embedding(n_items, dim) self.register_buffer( "item_features", torch.from_numpy(item_features), persistent=True ) self.item_tower = nn.Sequential( nn.Linear(dim + n_item_features, config.item_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_1, config.item_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_2, dim), ) nn.init.normal_(self.context_embedding.weight, std=0.02) nn.init.normal_(self.candidate_embedding.weight, std=0.02) with torch.no_grad(): self.context_embedding.weight[n_items].zero_() def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor: mask = items.ne(self.n_items) vectors = self.context_embedding(items) return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum( dim=1, keepdim=True ).clamp_min(1) def encode_items(self, items: torch.Tensor) -> torch.Tensor: inputs = torch.cat( [self.candidate_embedding(items), self.item_features[items]], dim=1 ) return self.item_tower(inputs) def invalid_negative_mask( negatives: torch.Tensor, targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, ) -> torch.Tensor: """Identify targets, known positives, active likes, and row duplicates.""" invalid = negatives.eq(targets[:, None]) invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2) invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= negatives[:, column, None].eq( negatives[:, :column] ).any(dim=1) return invalid def draw_filtered_mixed_negatives( targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, n_items: int, uniform_count: int, inbatch_count: int, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw uniform and unique-target in-batch negatives.""" batch_size = len(targets) uniform = torch.randint( n_items, (batch_size, uniform_count), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu") corrected_count = 0 if inbatch_count: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0)) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq(targets.detach().cpu()[:, None]), -1.0, ) columns = torch.topk(priorities, k=corrected_count, dim=1).indices inbatch = unique_targets[columns] if corrected_count < inbatch_count: filler = torch.randint( n_items, (batch_size, inbatch_count - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives_cpu = torch.cat([uniform, inbatch], dim=1) correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32) if corrected_count: correction_cpu[ :, uniform_count : uniform_count + corrected_count ] = item_correction[inbatch[:, :corrected_count]] negatives = negatives_cpu.to(device) correction = correction_cpu.to(device) for _ in range(32): invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if not bool(invalid.any()): return negatives, correction replacements = torch.randint( n_items, (int(invalid.sum().detach().cpu()),), generator=generator, device="cpu", ).to(device) negatives[invalid] = replacements correction[invalid] = 1.0 invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if bool(invalid.any()): raise RuntimeError("mixed negative resampling failed false-negative filter") return negatives, correction ## Step 9 · agent · 2026-08-31T19:29:17.954Z ### Thinking unavailable — no text recorded The baseline’s central limitation is already visible: each positive is contrasted with 24 catalog-uniform negatives and only 8 randomly chosen unique targets from the current training block. Uniform draws are mostly trivial in a 30k-item catalog, while the in-batch component is neither query-adaptive nor candidate-cutoff-aware; its propensity correction also changes weights, not which confusable tracks get learned against. I’m reproducing its score before changing that mechanism. ### exec · 2026-08-31T19:29:26.993Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && /usr/bin/time -f 'ELAPSED=%E MAXRSS_KB=%M' python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/production_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:29:27.080Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"1565b3","wall_time_seconds":0.000003811,"exit_code":0,"original_token_count":10,"output":"/bin/sh: 1: /usr/bin/time: not found\n"} ## Step 10 · agent · 2026-08-31T19:29:32.638Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:35.569Z { "code": "const r = await tools.exec_command({cmd:\"python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/production_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:29:35.783Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"0222b2","wall_time_seconds":0.038482809,"exit_code":0,"original_token_count":43,"output":"Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"} ## Step 11 · agent · 2026-08-31T19:29:37.665Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:41.875Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\npython --version\\npython3 --version\\nsed -n '1,200p' /task/requirements.txt\\nls -la /app /task | sed -n '1,160p'\\nfind / -maxdepth 3 -type f -name 'python*' 2>/dev/null | sed -n '1,100p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:29:41.951Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 Python 3.12.13 # 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 /app: total 16 drwxr-xr-x 1 agent agent 4096 Aug 31 19:25 . drwxr-xr-x 1 root root 4096 Aug 31 19:28 .. drwxr-xr-x 1 agent agent 4096 Aug 31 19:29 solution /task: total 40 dr-xr-xr-x 1 root root 4096 Aug 31 19:25 . drwxr-xr-x 1 root root 4096 Aug 31 19:28 .. -r--r--r-- 1 root root 0 Aug 31 19:23 .keep dr-xr-xr-x 1 root root 4096 Aug 31 19:23 current_model dr-xr-xr-x 1 root root 4096 Aug 31 19:24 data -r--r--r-- 1 root root 5774 Aug 31 19:23 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 31 19:23 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 31 19:23 requirements.txt ## Step 12 · agent · 2026-08-31T19:29:43.983Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:47.726Z { "code": "const r = await tools.exec_command({cmd:\"env | sort | sed -n '1,200p'\\nfind / -maxdepth 4 -type d \\\\( -name site-packages -o -name dist-packages -o -name .venv -o -name venv \\\\) 2>/dev/null | sed -n '1,160p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:29:47.808Z Script completed Wall time 0.1 seconds Output: CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_THREAD_ID=01a0594b-b645-7843-8d58-b5b57069ab8e COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=1151a7ad22c4 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ## Step 13 · agent · 2026-08-31T19:29:50.115Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:56.302Z { "code": "const r = await tools.exec_command({cmd:\"find /opt /usr/local /tmp -type d -name 'numpy' 2>/dev/null | sed -n '1,100p'\\nfind /opt /usr/local /tmp -type f \\\\( -name 'torch*.whl' -o -name 'numpy*.whl' -o -name 'uv' \\\\) 2>/dev/null | sed -n '1,160p'\\nls -la /usr/local/lib/python3.12/site-packages 2>/dev/null | sed -n '1,120p'\\ncommand -v uv || true\\nuv --version 2>/dev/null || true\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:29:56.533Z Script completed Wall time 0.2 seconds Output: /opt/task-venv/lib/python3.12/site-packages/numpy /opt/task-venv/lib/python3.12/site-packages/numpy/_core/include/numpy /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/numpy /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/numpy /opt/task-venv/lib/python3.12/site-packages/pandas/compat/numpy total 20 drwxr-xr-x 4 root root 4096 Aug 5 01:13 . drwxr-xr-x 38 root root 4096 Aug 5 01:13 .. -rw-r--r-- 1 root root 119 Aug 5 01:13 README.txt drwxr-xr-x 5 root root 4096 Aug 5 01:13 pip drwxr-xr-x 2 root root 4096 Aug 5 01:13 pip-25.0.1.dist-info ## Step 14 · agent · 2026-08-31T19:29:58.629Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:01.924Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,100p'\\n/opt/task-venv/bin/python -c 'import numpy,pandas,torch; print(numpy.__version__,pandas.__version__,torch.__version__)'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:30:05.855Z Script completed Wall time 3.9 seconds Output: total 124 dr-xr-xr-x 1 root root 4096 Aug 31 19:25 . dr-xr-xr-x 1 root root 4096 Aug 31 19:25 .. -r--r--r-- 1 root root 9033 Aug 31 19:25 Activate.ps1 -r--r--r-- 1 root root 2158 Aug 31 19:25 activate -r--r--r-- 1 root root 920 Aug 31 19:25 activate.csh -r--r--r-- 1 root root 2195 Aug 31 19:25 activate.fish -r-xr-xr-x 1 root root 302 Aug 31 19:25 chardetect -r-xr-xr-x 1 root root 304 Aug 31 19:25 dotenv -r-xr-xr-x 1 root root 308 Aug 31 19:25 f2py -r-xr-xr-x 1 root root 299 Aug 31 19:25 idna -r-xr-xr-x 1 root root 297 Aug 31 19:25 isympy -r-xr-xr-x 1 root root 305 Aug 31 19:25 jsonschema -r-xr-xr-x 1 root root 311 Aug 31 19:25 jupyter -r-xr-xr-x 1 root root 311 Aug 31 19:25 jupyter-migrate -r-xr-xr-x 1 root root 316 Aug 31 19:25 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Aug 31 19:25 jupyter-trust -r-xr-xr-x 1 root root 311 Aug 31 19:25 jupytext -r-xr-xr-x 1 root root 315 Aug 31 19:25 jupytext-config -r-xr-xr-x 1 root root 301 Aug 31 19:25 kaggle -r-xr-xr-x 1 root root 312 Aug 31 19:25 markdown-it -r-xr-xr-x 1 root root 325 Aug 31 19:25 normalizer -r-xr-xr-x 1 root root 308 Aug 31 19:25 numpy-config -r-xr-xr-x 1 root root 313 Aug 31 19:25 pip -r-xr-xr-x 1 root root 313 Aug 31 19:25 pip3 -r-xr-xr-x 1 root root 313 Aug 31 19:25 pip3.12 lrwxrwxrwx 1 root root 21 Aug 31 19:25 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 19:25 python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 19:25 python3.12 -> python -r-xr-xr-x 1 root root 307 Aug 31 19:25 slugify -r-xr-xr-x 1 root root 321 Aug 31 19:25 torchfrtrace -r-xr-xr-x 1 root root 312 Aug 31 19:25 torchrun -r-xr-xr-x 1 root root 299 Aug 31 19:25 tqdm 2.2.6 2.3.3 2.8.0+cpu ## Step 15 · agent · 2026-08-31T19:30:12.912Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:16.111Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/production_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:30:17.290Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9dae07","wall_time_seconds":1.001908656,"session_id":51338,"original_token_count":0,"output":""} ## Step 16 · agent · 2026-08-31T19:30:18.785Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:20.181Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:30:25.203Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"f60bec","wall_time_seconds":5.001095105,"session_id":51338,"original_token_count":13,"output":"loaded explicit events: rows=720,270 users=8,220\n"} ## Step 17 · agent · 2026-08-31T19:30:28.650Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:32.789Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '560,870p' /app/solution/train_current_model.py\\nsed -n '870,1030p' /app/solution/train_current_model.py\\nsed -n '1510,1810p' /app/solution/train_current_model.py\\nsed -n '1920,2160p' /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:45000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:30:32.919Z Script completed Wall time 0.1 seconds Output: raw = np.power( (1.0 / len(counts)) / probabilities, config.inbatch_correction_power, ) normalization = float(np.sum(counts * raw) / max(total, 1.0)) correction = np.clip( raw / max(normalization, 1.0e-12), config.inbatch_correction_min, config.inbatch_correction_max, ).astype(np.float32) mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0)) log( f"inbatch correction: min={correction.min():.3f} " f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}" ) return torch.from_numpy(correction) def build_known_positive_filter( prepared: SequenceData, training_counts: np.ndarray ) -> np.ndarray: """Pack each user's complete public-training positive-item set.""" width = (len(prepared.item_ids) + 7) // 8 packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8) retained = 0 for user_row, target_count in enumerate(training_counts): if not target_count: continue offset = int(prepared.offsets[user_row]) # T adjacent targets consume sequence positions 0 through T. The # chronological BCE suffix starts at T + 1 and is intentionally absent. items = np.unique( prepared.sequence_items[ offset : offset + int(target_count) + 1 ].astype(np.int64) ) byte_columns = items >> 3 bit_values = np.left_shift( np.uint8(1), (items & 7).astype(np.uint8) ) np.bitwise_or.at(packed[user_row], byte_columns, bit_values) retained += len(items) log( f"full-history false-negative filter: users={len(prepared.user_ids):,} " f"unique_user_items={retained:,} bytes={packed.nbytes:,}" ) return packed def invalid_full_history_negatives( negatives: np.ndarray, targets: np.ndarray, user_rows: np.ndarray, known_positive_packed: np.ndarray, liked_items: np.ndarray, ) -> np.ndarray: """Return row-level invalid negatives using the complete training prefix.""" byte_columns = negatives >> 3 bit_columns = negatives & 7 known = known_positive_packed[user_rows[:, None], byte_columns] invalid = ((known >> bit_columns) & 1).astype(bool) invalid |= negatives == targets[:, None] invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= ( negatives[:, column, None] == negatives[:, :column] ).any(axis=1) return invalid def draw_full_history_filtered_negatives( targets: torch.Tensor, user_rows: np.ndarray, liked_items: torch.Tensor, known_positive_packed: np.ndarray, n_items: int, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw mixed negatives and reject all known public-training positives.""" targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False) liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False) batch_size = len(targets_cpu) uniform = torch.randint( n_items, (batch_size, config.uniform_negatives), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long) corrected_count = 0 if config.inbatch_negatives: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min( config.inbatch_negatives, max(len(unique_targets) - 1, 0) ) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq( torch.from_numpy(targets_cpu)[:, None] ), -1.0, ) columns = torch.topk( priorities, k=corrected_count, dim=1 ).indices inbatch = unique_targets[columns] if corrected_count < config.inbatch_negatives: filler = torch.randint( n_items, (batch_size, config.inbatch_negatives - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives = torch.cat([uniform, inbatch], dim=1).numpy() correction = np.ones_like(negatives, dtype=np.float32) if corrected_count: correction[ :, config.uniform_negatives : config.uniform_negatives + corrected_count ] = item_correction[inbatch[:, :corrected_count]].numpy() for _ in range(32): invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if not np.any(invalid): return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) replacements = torch.randint( n_items, (int(invalid.sum()),), generator=generator, device="cpu", ).numpy() negatives[invalid] = replacements # Rejected in-batch items become ordinary uniform draws. correction[invalid] = 1.0 invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if np.any(invalid): raise RuntimeError("full-history negative resampling failed") return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) def draw_training_negatives( positive_items: torch.Tensor, known_history: torch.Tensor, liked_items: torch.Tensor, user_rows: np.ndarray, known_positive_packed: np.ndarray | None, prepared: SequenceData, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: if config.negative_filter_scope == "full-training-history": if known_positive_packed is None: raise RuntimeError("full-history negative filter was not built") return draw_full_history_filtered_negatives( positive_items, user_rows, liked_items, known_positive_packed, len(prepared.item_ids), config, item_correction, generator, device, ) return base.draw_filtered_mixed_negatives( positive_items, known_history, liked_items, len(prepared.item_ids), config.uniform_negatives, config.inbatch_negatives, item_correction, generator, device, ) def event_sequence( model: base.SequentialTwoTower, items: torch.Tensor, features: torch.Tensor, hidden: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: inputs = torch.cat([model.context_embedding(items), features], dim=-1) projected = model.event_projection(inputs) return model.sequence_encoder(projected, hidden) def pool_explicit_states( model: base.SequentialTwoTower, prepared: SequenceData, state_ids: np.ndarray, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: unique_states, inverse = np.unique(state_ids, return_inverse=True) liked_items = torch.from_numpy( prepared.liked_states[unique_states].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.disliked_states[unique_states].astype(np.int64) ).to(device) liked_vectors = model._mean_context_channel(liked_items) disliked_vectors = model._mean_context_channel(disliked_items) inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device) counts = prepared.state_counts[state_ids] return ( liked_vectors[inverse_rows], disliked_vectors[inverse_rows], torch.from_numpy(counts.astype(np.float32)).to(device), ) def user_queries( model: base.SequentialTwoTower, sequence_vectors: torch.Tensor, liked_vectors: torch.Tensor, disliked_vectors: torch.Tensor, user_features: torch.Tensor, ) -> torch.Tensor: return model.user_tower( torch.cat( [sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1, ) ) def candidate_vectors( model: base.SequentialTwoTower, positives: torch.Tensor, negatives: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: all_items = torch.cat([positives, negatives.reshape(-1)]) unique_items, inverse = torch.unique(all_items, return_inverse=True) encoded = model.encode_items(unique_items) positive_vectors = encoded[inverse[: len(positives)]] negative_vectors = encoded[inverse[len(positives) :]].reshape( len(positives), negatives.shape[1], -1 ) return positive_vectors, negative_vectors def history_windows( sequence: np.ndarray, start: int, count: int, width: int, padding_item: int, ) -> np.ndarray: base_start = max(0, start - width + 1) segment = sequence[base_start : start + count] padded = np.concatenate( [np.full(width - 1, padding_item, dtype=np.uint16), segment] ) windows = np.lib.stride_tricks.sliding_window_view(padded, width) offset = start - base_start return windows[offset : offset + count].copy() def corrected_negative_loss( logits: torch.Tensor, correction: torch.Tensor, config: Config, ) -> torch.Tensor: raw = F.softplus(logits) total = config.uniform_negatives + config.inbatch_negatives result = torch.zeros(len(logits), device=logits.device) if config.uniform_negatives: result += config.uniform_negatives * raw[ :, : config.uniform_negatives ].mean(dim=1) if config.inbatch_negatives: selected = slice(config.uniform_negatives, total) weights = correction[:, selected] result += config.inbatch_negatives * ( (raw[:, selected] * weights).sum(dim=1) / weights.sum(dim=1).clamp_min(1.0e-6) ) return result / total def retained_target_window_starts( total_targets: np.ndarray, validation_counts: np.ndarray, training_counts: np.ndarray, training_counts: np.ndarray, ) -> np.ndarray: """Return the discarded prefix before the retained chronological suffix.""" training_region_ends = total_targets - validation_counts window_starts = training_region_ends - training_counts if ( np.any(window_starts < 0) or np.any(training_counts < 0) or np.any(validation_counts < 0) ): raise RuntimeError("chronological target partition produced negative counts") if not np.array_equal( window_starts + training_counts + validation_counts, total_targets ): raise RuntimeError("chronological target window is not a final contiguous suffix") return window_starts def chronological_target_partition( prepared: SequenceData, validation_fraction: float, max_train_targets_per_user: int, max_validation_targets_per_user: int, ) -> TargetPartition: """Select a recent contiguous window with a final out-of-time BCE suffix.""" totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64) validation = np.floor(totals * validation_fraction).astype(np.int64) validation[(totals >= 2) & (validation == 0)] = 1 if max_validation_targets_per_user: validation = np.minimum(validation, max_validation_targets_per_user) history_training = totals - validation training = history_training.copy() if max_train_targets_per_user: training = np.minimum(training, max_train_targets_per_user) window_starts = retained_target_window_starts(totals, validation, training) return TargetPartition( window_starts=window_starts, training_counts=training, validation_counts=validation, history_training_counts=history_training, ) def load_organic_like_outcomes( public: Path, stage: str, item_ids: np.ndarray ) -> dict[int, dict[int, np.ndarray]]: """Load mapped organic-like timestamps from public fitting history.""" prefixes = ("train",) if stage == "validation" else ("train", "val") frames = [ pd.read_parquet( public / "data" / f"{prefix}_likes.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) for prefix in prefixes ] likes = pd.concat(frames, ignore_index=True) likes = likes[likes.is_organic == 1].copy() mapped = pd.Index(item_ids).get_indexer( likes.item_id.to_numpy(dtype=np.int64) ) likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy() likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False) likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable") nested: dict[int, dict[int, list[int]]] = {} for raw_uid, raw_timestamp, raw_item in likes.itertuples( index=False, name=None ): nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append( int(raw_timestamp) ) result = { uid: { item: np.asarray(timestamps, dtype=np.int64) for item, timestamps in items.items() } for uid, items in nested.items() } log( f"loaded organic-like outcomes: rows={len(likes):,} " f"users={len(result):,}" ) return result def build_target_weights( public: Path, stage: str, prepared: SequenceData, training_counts: np.ndarray, config: Config, ) -> np.ndarray: """Build nonzero per-token weights without dropping adjacent targets.""" if config.target_weighting == "uniform": weights = np.ones(len(prepared.sequence_items), dtype=np.float32) else: weights = np.full( len(prepared.sequence_items), config.ordinary_target_weight, dtype=np.float32, ) organic_strong = ( (prepared.event_features[:, 0] > 0.5) & (prepared.event_features[:, 1] >= 0.8) ) weights[organic_strong] = config.organic_strong_target_weight if config.target_weighting == "discovery-graded": like_outcomes = load_organic_like_outcomes( public, stage, prepared.item_ids ) history_end = ( base.TRAIN_END if stage == "validation" else base.VALIDATION_END ) horizon = config.discovery_horizon_days * base.DAY grade_three = 0 for user_row, uid in enumerate(prepared.user_ids): start = int(prepared.offsets[user_row]) end = int(prepared.offsets[user_row + 1]) if end - start < 2: continue last_training_target = start + int(training_counts[user_row]) first_validation_target = last_training_target + 1 split_timestamp = ( int(prepared.sequence_timestamps[first_validation_target]) if first_validation_target < end else history_end ) user_likes = like_outcomes.get(int(uid), {}) seen = {int(prepared.sequence_items[start])} for position in range(start + 1, end): item = int(prepared.sequence_items[position]) novel = item not in seen seen.add(item) if not novel: # Repeats remain training examples, but they cannot be # eligible discovery outcomes at the corresponding # cutoff and therefore keep only the ordinary weight. weights[position] = config.ordinary_target_weight continue timestamps = user_likes.get(item) if timestamps is None: continue timestamp = int(prepared.sequence_timestamps[position]) upper = min( timestamp + horizon, split_timestamp if position <= last_training_target else history_end, ) outcome = int(np.searchsorted(timestamps, timestamp, side="left")) if outcome < len(timestamps) and int(timestamps[outcome]) < upper: weights[position] = config.organic_like_target_weight grade_three += 1 log( f"grade-3 first-encounter targets={grade_three:,} " f"horizon_days={config.discovery_horizon_days}" ) log( f"target weighting={config.target_weighting} " f"min={weights.min():.3f} mean={weights.mean():.3f} " f"max={weights.max():.3f}" "diagnostics": diagnostics, "model_state": model_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, path, ) log(f"saved epoch checkpoint: {path}") return path def train_model( public: Path, stage: str, prepared: SequenceData, config: Config, device: torch.device, checkpoint_dir: Path | None, resume_checkpoint: Path | None, ) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]: partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) training_counts = partition.training_counts validation_counts = partition.validation_counts log( f"chronological BCE window: train={int(training_counts.sum()):,} " f"validation={int(validation_counts.sum()):,} " f"selected={partition.selected_count:,} " f"discarded_old_targets={int(partition.window_starts.sum()):,} " f"all={prepared.target_count:,}" ) model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to( device ) optimizer = torch.optim.AdamW( model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay ) generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000) item_correction = build_item_correction( prepared, config, partition.window_starts, training_counts, ) target_weights = build_target_weights( public, stage, prepared, partition.history_training_counts, config, ) known_positive_packed = ( build_known_positive_filter(prepared, partition.history_training_counts) if config.negative_filter_scope == "full-training-history" else None ) history: list[dict[str, float]] = [] best_loss = float("inf") best_epoch = 0 best_state: dict[str, torch.Tensor] | None = None epochs_without_improvement = 0 start_epoch = 0 if resume_checkpoint is not None: payload = torch.load( resume_checkpoint, map_location=device, weights_only=False ) stored_config = payload.get("config", {}) architecture_fields = ( "bptt_steps", "embedding_dim", "hidden_dim", "user_hidden_1", "user_hidden_2", "item_hidden_1", "item_hidden_2", ) mismatches = { name: (stored_config.get(name), getattr(config, name)) for name in architecture_fields if stored_config.get(name) != getattr(config, name) } if mismatches: raise RuntimeError( f"resume checkpoint architecture differs: {mismatches}" ) stored_filter_scope = stored_config.get( "negative_filter_scope", "last-100" ) stored_target_weighting = stored_config.get( "target_weighting", "uniform" ) stored_weight_values = ( stored_config.get("ordinary_target_weight", 1.0), stored_config.get("organic_strong_target_weight", 1.0), stored_config.get("organic_like_target_weight", 3.0), stored_config.get("discovery_horizon_days", 7), ) current_weight_values = ( config.ordinary_target_weight, config.organic_strong_target_weight, config.organic_like_target_weight, config.discovery_horizon_days, ) stored_window_values = ( stored_config.get("validation_fraction", 0.10), stored_config.get("max_train_targets_per_user", 0), stored_config.get("max_validation_targets_per_user", 0), ) current_window_values = ( config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) objective_changed = ( stored_filter_scope != config.negative_filter_scope or stored_target_weighting != config.target_weighting or stored_weight_values != current_weight_values or stored_window_values != current_window_values ) model.load_state_dict(payload["model_state"]) if "optimizer_state" in payload and not objective_changed: optimizer.load_state_dict(payload["optimizer_state"]) for group in optimizer.param_groups: group["lr"] = config.learning_rate group["weight_decay"] = config.weight_decay log(f"restored optimizer state from {resume_checkpoint}") else: log( f"using a fresh AdamW optimizer after loading {resume_checkpoint}" ) if "generator_state" in payload and not objective_changed: generator.set_state(payload["generator_state"]) start_epoch = int(payload["epoch"]) if start_epoch >= config.epochs: raise RuntimeError( f"resume epoch {start_epoch} must be below --epochs {config.epochs}" ) prior = { str(name): float(value) for name, value in payload.get("diagnostics", {}).items() } if objective_changed: prior.pop("validation_loss", None) log( "BCE objective changed: filter " f"{stored_filter_scope}->{config.negative_filter_scope}, " "target weighting " f"{stored_target_weighting}->{config.target_weighting}, " f"target window {stored_window_values}->{current_window_values}; " "recomputing the resume checkpoint's validation BCE" ) if "validation_loss" not in prior: prior.update( evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) ) history.append(prior) best_loss = float(prior["validation_loss"]) best_epoch = start_epoch best_state = snapshot_model(model) log( f"resumed validation-BCE training: epoch={start_epoch} " f"loss={best_loss:.6f}" ) for epoch in range(start_epoch + 1, config.epochs + 1): diagnostics = run_training_epoch( model, optimizer, prepared, config, target_weights, partition.window_starts, training_counts, item_correction, known_positive_packed, generator, device, epoch, ) validation = evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) diagnostics.update(validation) history.append(diagnostics) current_state = snapshot_model(model) if checkpoint_dir is not None: save_epoch_checkpoint( checkpoint_dir, epoch, current_state, config, diagnostics, optimizer, generator, ) improved = ( diagnostics["validation_loss"] < best_loss - config.early_stopping_min_delta ) if improved: best_loss = diagnostics["validation_loss"] best_epoch = epoch best_state = current_state epochs_without_improvement = 0 if checkpoint_dir is not None: torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, "model_state": current_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, checkpoint_dir / "best.pt", ) else: epochs_without_improvement += 1 log( f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " f"validation_loss={diagnostics['validation_loss']:.5f} " f"pair_accuracy={diagnostics['pair_accuracy']:.3f} " f"train_targets={int(diagnostics['targets']):,} " f"validation_targets={int(diagnostics['validation_targets']):,} " f"train_seconds={diagnostics['seconds']:.1f} " f"validation_seconds={diagnostics['validation_seconds']:.1f}" ) release_accelerator_cache(device) if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows lengths = np.where( sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 ) ordered = np.argsort(lengths, kind="stable") result = torch.zeros( len(sequence_rows), config.embedding_dim, dtype=torch.float32 ) for group_start in range(0, len(ordered), config.user_batch_size): output_rows = ordered[group_start : group_start + config.user_batch_size] rows = sequence_rows[output_rows] group_lengths = lengths[output_rows] hidden = torch.zeros( 1, len(output_rows), config.embedding_dim, device=device ) for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps): steps = min( config.bptt_steps, int(group_lengths.max(initial=0)) - start, ) items = np.full( (len(output_rows), steps), len(prepared.item_ids), def main() -> int: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--stage", choices=("validation", "submission"), default="validation" ) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--public-dir", type=Path, required=True) parser.add_argument("--prepared-cache", type=Path) parser.add_argument("--device", choices=("cpu", "mps"), default="cpu") parser.add_argument("--threads", type=int, default=8) parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps) parser.add_argument( "--user-batch-size", type=int, default=Config.user_batch_size ) parser.add_argument("--epochs", type=int, default=Config.epochs) parser.add_argument("--learning-rate", type=float, default=Config.learning_rate) parser.add_argument( "--loss-microbatch-size", type=int, default=Config.loss_microbatch_size, ) parser.add_argument( "--negative-filter-scope", choices=("full-training-history", "last-100"), default=Config.negative_filter_scope, ) parser.add_argument( "--target-weighting", choices=("discovery-graded", "organic-strong", "uniform"), default=Config.target_weighting, ) parser.add_argument( "--ordinary-target-weight", type=float, default=Config.ordinary_target_weight, ) parser.add_argument( "--organic-strong-target-weight", type=float, default=Config.organic_strong_target_weight, ) parser.add_argument( "--organic-like-target-weight", type=float, default=Config.organic_like_target_weight, ) parser.add_argument( "--discovery-horizon-days", type=int, default=Config.discovery_horizon_days, ) parser.add_argument( "--validation-fraction", type=float, default=Config.validation_fraction, ) parser.add_argument( "--max-train-targets-per-user", type=int, default=Config.max_train_targets_per_user, help="retain this many recent pre-validation targets per user; 0 is uncapped", ) parser.add_argument( "--max-validation-targets-per-user", type=int, default=Config.max_validation_targets_per_user, help="retain this many final validation targets per user; 0 is uncapped", ) parser.add_argument( "--early-stopping-patience", type=int, default=Config.early_stopping_patience, ) parser.add_argument( "--early-stopping-min-delta", type=float, default=Config.early_stopping_min_delta, ) parser.add_argument("--checkpoint-dir", type=Path) parser.add_argument("--resume-checkpoint", type=Path) parser.add_argument("--seed", type=int, default=Config.seed) parser.add_argument("--ranking-batch-size", type=int, default=64) parser.add_argument("--prepare-only", action="store_true") args = parser.parse_args() if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1: parser.error("threads, BPTT steps, and user batch size must be positive") if ( args.epochs < 1 or args.learning_rate <= 0 or args.loss_microbatch_size < 1 or args.early_stopping_patience < 1 or args.early_stopping_min_delta < 0 or args.ordinary_target_weight <= 0 or args.organic_strong_target_weight <= 0 or args.organic_like_target_weight <= 0 or args.discovery_horizon_days < 1 or args.max_train_targets_per_user < 0 or args.max_validation_targets_per_user < 0 ): parser.error("training counts, learning rate, and patience are invalid") if not 0.0 < args.validation_fraction < 1.0: parser.error("validation fraction must be strictly between zero and one") public = args.public_dir.resolve() if not (public / "data" / "candidate_catalog.parquet").is_file(): parser.error(f"public task data not found under {public}") config = Config( bptt_steps=args.bptt_steps, user_batch_size=args.user_batch_size, epochs=args.epochs, learning_rate=args.learning_rate, loss_microbatch_size=args.loss_microbatch_size, negative_filter_scope=args.negative_filter_scope, target_weighting=args.target_weighting, ordinary_target_weight=args.ordinary_target_weight, organic_strong_target_weight=args.organic_strong_target_weight, organic_like_target_weight=args.organic_like_target_weight, discovery_horizon_days=args.discovery_horizon_days, validation_fraction=args.validation_fraction, max_train_targets_per_user=args.max_train_targets_per_user, max_validation_targets_per_user=args.max_validation_targets_per_user, early_stopping_patience=args.early_stopping_patience, early_stopping_min_delta=args.early_stopping_min_delta, seed=args.seed, ) torch.set_num_threads(args.threads) torch.set_num_interop_threads(1) seed_everything(config.seed) device = torch.device(args.device) if device.type == "mps" and not torch.backends.mps.is_available(): parser.error("MPS was requested but is unavailable") started = time.monotonic() prepared, eligibility = load_or_prepare( public, args.stage, config, args.prepared_cache.resolve() if args.prepared_cache else None, ) target_partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) if args.prepare_only: print( json.dumps( { "stage": args.stage, "sequence_users": len(prepared.user_ids), "sequence_tokens": len(prepared.sequence_items), "next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int( target_partition.window_starts.sum() ), "target_users": len(eligibility.uids), "runtime_seconds": round(time.monotonic() - started, 3), "config": asdict(config), }, indent=2, ) ) return 0 model, training_history = train_model( public, args.stage, prepared, config, device, args.checkpoint_dir.resolve() if args.checkpoint_dir else None, args.resume_checkpoint.resolve() if args.resume_checkpoint else None, ) ranking, embedding_diagnostics = rank_candidates( model, prepared, eligibility, config, device, args.ranking_batch_size, ) write_ranking(ranking, args.output.resolve()) print( json.dumps( { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": args.stage, "output": str(args.output.resolve()), "rows": len(ranking), "users": int(ranking.uid.nunique()), "runtime_seconds": round(time.monotonic() - started, 3), "public_inputs_only": True, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": ( "every-adjacent-target-within-recent-chronological-window" ), "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": False, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": ( "24-uniform-plus-8-propensity-corrected-inbatch" ), "false_negative_filter": config.negative_filter_scope, "target_weighting": config.target_weighting, }, "config": asdict(config), "sequence_tokens": len(prepared.sequence_items), "all_next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int(target_partition.window_starts.sum()), "training_history": training_history, "embedding_diagnostics": embedding_diagnostics, "torch_version": torch.__version__, "device": str(device), }, indent=2, ) ) return 0 ## Step 18 · agent · 2026-08-31T19:30:36.161Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:37.526Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:30:42.544Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"a8c4f5","wall_time_seconds":5.0007211,"session_id":51338,"original_token_count":50,"output":"prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\n"} ## Step 19 · agent · 2026-08-31T19:31:02.004Z ### Thinking unavailable — no text recorded ## Step 20 · agent · 2026-08-31T19:31:20.659Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-08-31T19:31:39.275Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-08-31T19:31:47.229Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:49.708Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,260p' /task/evaluate_public.py\\nsed -n '1,280p' /task/ranking_evaluation.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:31:49.798Z Script completed Wall time 0.1 seconds Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } ## Step 23 · agent · 2026-08-31T19:31:53.013Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:54.409Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:31:59.461Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"30f8a0","wall_time_seconds":5.002030301,"session_id":51338,"original_token_count":229,"output":"prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.9s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.4s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.4s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 24 · agent · 2026-08-31T19:32:05.005Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:06.366Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:32:11.393Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"971bbd","wall_time_seconds":5.001526969,"session_id":51338,"original_token_count":0,"output":""} ## Step 25 · agent · 2026-08-31T19:32:13.441Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:16.235Z { "code": "const r = await tools.exec_command({cmd:\"tail -n 30 /app/solution/production_validation.log\\nps -eo pid,etime,pcpu,pmem,rss,args | rg 'train_current_model|PID'\",\"workdir\":\"/app/solution\",\"yield_time_ms\":10000,\"max_output_tokens\":5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:32:16.317Z Script completed Wall time 0.1 seconds Output: loaded explicit events: rows=720,270 users=8,220 prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.9s prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.4s prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.4s target users without qualifying catalog sequence=7 prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 /bin/sh: 2: ps: not found /bin/sh: 2: rg: not found ## Step 26 · agent · 2026-08-31T19:32:20.522Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:21.910Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:32:26.993Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"f7466e","wall_time_seconds":5.002340881,"session_id":51338,"original_token_count":0,"output":""} ## Step 27 · agent · 2026-08-31T19:32:32.965Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:53.256Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nD=Path('/task/data'); O=Path('/app/solution')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nwith np.load(D/'validation_eligible_candidates.npz') as z:\\n u=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\nassert np.array_equal(items,cat.item_id.to_numpy())\\nrows=[]\\nfor r,uid in enumerate(u):\\n mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n cols=np.flatnonzero(mask)[:100]\\n rows.extend((int(uid),int(items[c]),k+1) for k,c in enumerate(cols))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/'popularity_validation.parquet',index=False)\\n# Truth popularity diagnostics\\nT=set(u.tolist())\\nls=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nlk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\ntruth=pd.concat([ls[(ls.uid.isin(T))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(grade=1),lk[(lk.uid.isin(T))&(lk.is_organic==1)][['uid','item_id']].assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nrank=pd.Series(np.arange(1,len(items)+1),index=items)\\ntruth['pop_rank']=truth.item_id.map(rank)\\nprint('catalog',len(cat),'users',len(u),'eligible mean',np.unpackbits(packed,axis=1,count=len(items),bitorder='little').sum(1).mean())\\nprint('truth',len(truth),'users',truth.uid.nunique(),'grades',truth.grade.value_counts().to_dict())\\nprint(truth.groupby('grade').pop_rank.describe(percentiles=[.1,.25,.5,.75,.9]).to_string())\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:33:02.446Z Script completed Wall time 9.1 seconds Output: catalog 30000 users 8456 eligible mean 29067.306291390727 truth 266887 users 6511 grades {1: 249692, 3: 17195} count mean std min 10% 25% 50% 75% 90% max grade 1 167201.0 7190.356134 7798.668689 1.0 205.0 988.00 4056.0 11221.0 20049.0 30000.0 3 8528.0 8415.048311 8123.488549 1.0 442.0 1648.75 5554.0 13506.5 21728.7 29989.0 { "model": "/app/solution/popularity_validation.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 28 · agent · 2026-08-31T19:33:05.325Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:06.731Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:33:11.770Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"abe27f","wall_time_seconds":5.001002214,"session_id":51338,"original_token_count":0,"output":""} ## Step 29 · agent · 2026-08-31T19:33:15.661Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:18.956Z { "code": "const r = await tools.exec_command({cmd:\"for f in /proc/[0-9]*/cmdline; do s=$(tr '\\\\000' ' ' < \\\"$f\\\" 2>/dev/null); case \\\"$s\\\" in *train_current_model.py*) echo \\\"$f $s\\\";; esac; done\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:33:19.020Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 4569) Total output lines: 357 /proc/315/cmdline /bin/sh -lc /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 2>&1 | tee /app/solution/production_validation.log /proc/318/cmdline /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet /proc/462/cmdline /bin/sh -c for f in /proc/[0-9]*/cmdline; do s=$(tr '' ' ' < "$f" 2>/dev/null); case "$s" in *train_current_model.py*) echo "$f $s";; esac; done /proc/60/cmdline bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team'"'"'s next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table. The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above. ## What You Have All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window. The package includes: - `/task/current_model/train_current_model.py`: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data 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. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, 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…1569 tokens truncated… sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data 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. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, 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")`. Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. 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 same 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. ## Deliverable Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`. The ranking must: - include the columns `uid`, `item_id`, and `rank`; only these columns are evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user's `test_eligible_candidates.npz` candidate set. Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate. ## 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. - Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Step 30 · agent · 2026-08-31T19:33:21.290Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:23.797Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,80p' /proc/318/status\\ncat /proc/318/stat | cut -d' ' -f14-24\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:33:23.871Z Script completed Wall time 0.1 seconds Output: Name: python Umask: 0022 State: R (running) Tgid: 318 Ngid: 0 Pid: 318 PPid: 315 TracerPid: 0 Uid: 1000 1000 1000 1000 Gid: 1000 1000 1000 1000 FDSize: 64 Groups: 1000 NStgid: 318 NSpid: 318 NSpgid: 315 NSsid: 315 Kthread: 0 VmPeak: 7518428 kB VmSize: 6971628 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 5026444 kB VmRSS: 4468296 kB RssAnon: 4318196 kB RssFile: 150100 kB RssShmem: 0 kB VmData: 5096848 kB VmStk: 136 kB VmExe: 4 kB VmLib: 452620 kB VmPTE: 9660 kB VmSwap: 0 kB HugetlbPages: 0 kB CoreDumping: 0 THP_enabled: 1 untag_mask: 0xffffffffffffffff Threads: 32 SigQ: 0/56399 SigPnd: 0000000000000000 ShdPnd: 0000000000000000 SigBlk: 0000000000000000 SigIgn: 0000000001001000 SigCgt: 0000000100000002 CapInh: 0000000000000000 CapPrm: 0000000000000000 CapEff: 0000000000000000 CapBnd: 00000000a80425fb CapAmb: 0000000000000000 NoNewPrivs: 0 Seccomp: 2 Seccomp_filters: 1 Speculation_Store_Bypass: thread vulnerable SpeculationIndirectBranch: conditional enabled Cpus_allowed: ff Cpus_allowed_list: 0-7 Mems_allowed: 00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000000,00000001 Mems_allowed_list: 0 voluntary_ctxt_switches: 671 nonvoluntary_ctxt_switches: 4727 x86_Thread_features: x86_Thread_features_locked: 70759 5602 0 0 20 0 32 0 64382 7138947072 1117344 ## Step 31 · agent · 2026-08-31T19:33:43.936Z ### Thinking unavailable — no text recorded ## Step 32 · agent · 2026-08-31T19:34:02.515Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-08-31T19:34:09.090Z ### Thinking unavailable — no text recorded A useful data check supports a focused change: each user has about 29,067 eligible tracks, and future relevant tracks span the catalog rather than clustering only at the head (median popularity rank is roughly 4,056 for strong listens and 5,554 for organic likes). A pure popularity ranker reaches only 0.010710 NDCG@10. That makes “more head sampling” alone a poor fix; the better target is query-confusable negatives—shifting capacity from easy uniform examples toward target-distributed examples and emphasizing the highest-scoring negatives already found in each batch. ### exec · 2026-08-31T19:34:15.268Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:34:20.289Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"09331c","wall_time_seconds":5.00135141,"session_id":51338,"original_token_count":0,"output":""} ## Step 34 · agent · 2026-08-31T19:34:23.713Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:34:26.933Z { "code": "const r = await tools.exec_command({cmd:\"cat /proc/318/stat | cut -d' ' -f14-24\\nsed -n '1,35p' /proc/318/status | tail -n 15\\ntail -n 5 /app/solution/production_validation.log\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:34:27.008Z Script completed Wall time 0.0 seconds Output: 101826 8520 0 0 20 0 32 0 64382 7469117440 1171966 VmPin: 0 kB VmHWM: 5316952 kB VmRSS: 4689468 kB RssAnon: 4539368 kB RssFile: 150100 kB RssShmem: 0 kB VmData: 5419280 kB VmStk: 136 kB VmExe: 4 kB VmLib: 452620 kB VmPTE: 10220 kB VmSwap: 0 kB HugetlbPages: 0 kB CoreDumping: 0 THP_enabled: 1 prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133 chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000 target weighting=organic-strong min=0.250 mean=0.575 max=1.000 full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250 ## Step 35 · agent · 2026-08-31T19:34:46.661Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-08-31T19:34:47.907Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:35:30.853Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- uniform_negatives: int = 24\\n- inbatch_negatives: int = 8\\n+ # Spend the fixed 32-negative budget mostly on items drawn from the\\n+ # empirical target distribution. Catalog-uniform negatives are useful for\\n+ # coverage, but most are already separated and contribute almost no useful\\n+ # ranking gradient in a 30k-item retrieval set.\\n+ uniform_negatives: int = 8\\n+ inbatch_negatives: int = 24\\n@@\\n inbatch_correction_max: float = 4.0\\n+ negative_loss: str = \\\"self-adversarial\\\"\\n+ hard_negative_temperature: float = 1.0\\n+ hard_negative_mix: float = 0.5\\n@@\\n def corrected_negative_loss(\\n@@\\n- raw = F.softplus(logits)\\n+ raw = F.softplus(logits)\\n@@\\n- return result / total\\n+ corrected_mean = result / total\\n+ if config.negative_loss == \\\"corrected-mean\\\":\\n+ return corrected_mean\\n+\\n+ # The draw remains stochastic and false-negative filtered, but examples\\n+ # that the current query tower scores highly get more of the negative-loss\\n+ # budget. Detaching the weights implements self-adversarial sampling\\n+ # without differentiating through the selection policy. Mixing with the\\n+ # ordinary corrected mean keeps broad catalog coverage and prevents one\\n+ # extreme logit from monopolizing an update.\\n+ sampling_weights = torch.ones_like(raw)\\n+ if config.inbatch_negatives:\\n+ selected = slice(config.uniform_negatives, total)\\n+ sampling_weights[:, selected] = correction[:, selected]\\n+ hardness = torch.softmax(\\n+ logits.detach() / config.hard_negative_temperature, dim=1\\n+ )\\n+ adversarial_mean = (raw * sampling_weights * hardness).sum(dim=1) / (\\n+ sampling_weights * hardness\\n+ ).sum(dim=1).clamp_min(1.0e-6)\\n+ return (\\n+ (1.0 - config.hard_negative_mix) * corrected_mean\\n+ + config.hard_negative_mix * adversarial_mean\\n+ )\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n objective_changed = (\\n stored_filter_scope != config.negative_filter_scope\\n or stored_target_weighting != config.target_weighting\\n or stored_weight_values != current_weight_values\\n or stored_window_values != current_window_values\\n+ or stored_config.get(\\\"uniform_negatives\\\", 24)\\n+ != config.uniform_negatives\\n+ or stored_config.get(\\\"inbatch_negatives\\\", 8)\\n+ != config.inbatch_negatives\\n+ or stored_config.get(\\\"negative_loss\\\", \\\"corrected-mean\\\")\\n+ != config.negative_loss\\n+ or stored_config.get(\\\"hard_negative_temperature\\\", 1.0)\\n+ != config.hard_negative_temperature\\n+ or stored_config.get(\\\"hard_negative_mix\\\", 0.0)\\n+ != config.hard_negative_mix\\n )\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n@@\\n )\\n+ parser.add_argument(\\n+ \\\"--uniform-negatives\\\", type=int, default=Config.uniform_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--negative-loss\\\",\\n+ choices=(\\\"self-adversarial\\\", \\\"corrected-mean\\\"),\\n+ default=Config.negative_loss,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-temperature\\\",\\n+ type=float,\\n+ default=Config.hard_negative_temperature,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-mix\\\", type=float, default=Config.hard_negative_mix\\n+ )\\n@@\\n or args.loss_microbatch_size < 1\\n+ or args.uniform_negatives < 0\\n+ or args.inbatch_negatives < 0\\n+ or args.uniform_negatives + args.inbatch_negatives < 1\\n+ or args.hard_negative_temperature <= 0\\n+ or not 0.0 <= args.hard_negative_mix <= 1.0\\n@@\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ uniform_negatives=args.uniform_negatives,\\n+ inbatch_negatives=args.inbatch_negatives,\\n+ negative_loss=args.negative_loss,\\n+ hard_negative_temperature=args.hard_negative_temperature,\\n+ hard_negative_mix=args.hard_negative_mix,\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n+ f\\\"{config.uniform_negatives}-uniform-plus-\\\"\\n+ f\\\"{config.inbatch_negatives}-propensity-corrected-inbatch\\\"\\n ),\\n+ \\\"negative_loss\\\": config.negative_loss,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:35:30.933Z Script failed Wall time 0.1 seconds Output: Script error: Exit code: 1 Wall time: 0 seconds Output: Failed to write file /app/solution/train_current_model.py ## Step 37 · agent · 2026-08-31T19:35:33.424Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:35:36.036Z { "code": "const r = await tools.exec_command({cmd:\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && ls -l /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:2000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:35:36.124Z Script completed Wall time 0.1 seconds Output: -rw-r--r-- 1 agent agent 80058 Aug 31 19:29 /app/solution/train_current_model.py ## Step 38 · agent · 2026-08-31T19:35:39.009Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:20.730Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- uniform_negatives: int = 24\\n- inbatch_negatives: int = 8\\n+ # Spend the fixed 32-negative budget mostly on items drawn from the\\n+ # empirical target distribution. Catalog-uniform negatives are useful for\\n+ # coverage, but most are already separated in a 30k-item retrieval set.\\n+ uniform_negatives: int = 8\\n+ inbatch_negatives: int = 24\\n@@\\n inbatch_correction_max: float = 4.0\\n+ negative_loss: str = \\\"self-adversarial\\\"\\n+ hard_negative_temperature: float = 1.0\\n+ hard_negative_mix: float = 0.5\\n@@\\n def corrected_negative_loss(\\n@@\\n- return result / total\\n+ corrected_mean = result / total\\n+ if config.negative_loss == \\\"corrected-mean\\\":\\n+ return corrected_mean\\n+\\n+ # Give more of the fixed loss budget to examples the current user tower\\n+ # finds confusable. The detached weights implement self-adversarial\\n+ # selection without differentiating through the selection policy. Mixing\\n+ # with the corrected mean retains broad catalog coverage.\\n+ sampling_weights = torch.ones_like(raw)\\n+ if config.inbatch_negatives:\\n+ selected = slice(config.uniform_negatives, total)\\n+ sampling_weights[:, selected] = correction[:, selected]\\n+ hardness = torch.softmax(\\n+ logits.detach() / config.hard_negative_temperature, dim=1\\n+ )\\n+ adversarial_mean = (raw * sampling_weights * hardness).sum(dim=1) / (\\n+ sampling_weights * hardness\\n+ ).sum(dim=1).clamp_min(1.0e-6)\\n+ return (\\n+ (1.0 - config.hard_negative_mix) * corrected_mean\\n+ + config.hard_negative_mix * adversarial_mean\\n+ )\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n objective_changed = (\\n stored_filter_scope != config.negative_filter_scope\\n or stored_target_weighting != config.target_weighting\\n or stored_weight_values != current_weight_values\\n or stored_window_values != current_window_values\\n+ or stored_config.get(\\\"uniform_negatives\\\", 24)\\n+ != config.uniform_negatives\\n+ or stored_config.get(\\\"inbatch_negatives\\\", 8)\\n+ != config.inbatch_negatives\\n+ or stored_config.get(\\\"negative_loss\\\", \\\"corrected-mean\\\")\\n+ != config.negative_loss\\n+ or stored_config.get(\\\"hard_negative_temperature\\\", 1.0)\\n+ != config.hard_negative_temperature\\n+ or stored_config.get(\\\"hard_negative_mix\\\", 0.0)\\n+ != config.hard_negative_mix\\n )\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n@@\\n default=Config.loss_microbatch_size,\\n )\\n+ parser.add_argument(\\n+ \\\"--uniform-negatives\\\", type=int, default=Config.uniform_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--negative-loss\\\",\\n+ choices=(\\\"self-adversarial\\\", \\\"corrected-mean\\\"),\\n+ default=Config.negative_loss,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-temperature\\\",\\n+ type=float,\\n+ default=Config.hard_negative_temperature,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-mix\\\", type=float, default=Config.hard_negative_mix\\n+ )\\n@@\\n args.epochs < 1\\n or args.learning_rate <= 0\\n or args.loss_microbatch_size < 1\\n+ or args.uniform_negatives < 0\\n+ or args.inbatch_negatives < 0\\n+ or args.uniform_negatives + args.inbatch_negatives < 1\\n+ or args.hard_negative_temperature <= 0\\n+ or not 0.0 <= args.hard_negative_mix <= 1.0\\n@@\\n learning_rate=args.learning_rate,\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ uniform_negatives=args.uniform_negatives,\\n+ inbatch_negatives=args.inbatch_negatives,\\n+ negative_loss=args.negative_loss,\\n+ hard_negative_temperature=args.hard_negative_temperature,\\n+ hard_negative_mix=args.hard_negative_mix,\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n+ f\\\"{config.uniform_negatives}-uniform-plus-\\\"\\n+ f\\\"{config.inbatch_negatives}-propensity-corrected-inbatch\\\"\\n ),\\n+ \\\"negative_loss\\\": config.negative_loss,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:36:20.817Z Script completed Wall time 0.1 seconds Output: {} ## Step 39 · agent · 2026-08-31T19:36:23.523Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:26.945Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,180p'\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:36:31.085Z Script completed Wall time 4.1 seconds Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output OUTPUT --public-dir PUBLIC_DIR [--prepared-cache PREPARED_CACHE] [--device {cpu,mps}] [--threads THREADS] [--bptt-steps BPTT_STEPS] [--user-batch-size USER_BATCH_SIZE] [--epochs EPOCHS] [--learning-rate LEARNING_RATE] [--loss-microbatch-size LOSS_MICROBATCH_SIZE] [--uniform-negatives UNIFORM_NEGATIVES] [--inbatch-negatives INBATCH_NEGATIVES] [--negative-loss {self-adversarial,corrected-mean}] [--hard-negative-temperature HARD_NEGATIVE_TEMPERATURE] [--hard-negative-mix HARD_NEGATIVE_MIX] [--negative-filter-scope {full-training-history,last-100}] [--target-weighting {discovery-graded,organic-strong,uniform}] [--ordinary-target-weight ORDINARY_TARGET_WEIGHT] [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT] [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT] [--discovery-horizon-days DISCOVERY_HORIZON_DAYS] [--validation-fraction VALIDATION_FRACTION] [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER] [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER] [--early-stopping-patience EARLY_STOPPING_PATIENCE] [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA] [--checkpoint-dir CHECKPOINT_DIR] [--resume-checkpoint RESUME_CHECKPOINT] [--seed SEED] [--ranking-batch-size RANKING_BATCH_SIZE] [--prepare-only] Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. options: -h, --help show this help message and exit --stage {validation,submission} --output OUTPUT --public-dir PUBLIC_DIR --prepared-cache PREPARED_CACHE --device {cpu,mps} --threads THREADS --bptt-steps BPTT_STEPS --user-batch-size USER_BATCH_SIZE --epochs EPOCHS --learning-rate LEARNING_RATE --loss-microbatch-size LOSS_MICROBATCH_SIZE --uniform-negatives UNIFORM_NEGATIVES --inbatch-negatives INBATCH_NEGATIVES --negative-loss {self-adversarial,corrected-mean} --hard-negative-temperature HARD_NEGATIVE_TEMPERATURE --hard-negative-mix HARD_NEGATIVE_MIX --negative-filter-scope {full-training-history,last-100} --target-weighting {discovery-graded,organic-strong,uniform} --ordinary-target-weight ORDINARY_TARGET_WEIGHT --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT --discovery-horizon-days DISCOVERY_HORIZON_DAYS --validation-fraction VALIDATION_FRACTION --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER retain this many recent pre-validation targets per user; 0 is uncapped --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER retain this many final validation targets per user; 0 is uncapped --early-stopping-patience EARLY_STOPPING_PATIENCE --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA --checkpoint-dir CHECKPOINT_DIR --resume-checkpoint RESUME_CHECKPOINT --seed SEED --ranking-batch-size RANKING_BATCH_SIZE --prepare-only ## Step 40 · agent · 2026-08-31T19:36:33.640Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:35.022Z { "code": "const r = await tools.write_stdin({session_id:51338,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:36:35.101Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"fc0085","wall_time_seconds":0.00000719,"exit_code":0,"original_token_count":885,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=204.3s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=217.3 validation_seconds=15.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 365.417,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380443854521,\n \"positive_logit\": 0.7427799617194347,\n \"negative_logit\": -0.6222247057241568,\n \"pair_accuracy\": 0.7547733315257186,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 217.30003871099996,\n \"validation_loss\": 0.49663621489170984,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.614260562000027\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697498202323914,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279486298561096\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 41 · agent · 2026-08-31T19:36:36.763Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:38.954Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:36:42.328Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/production_validation.parquet", "scores": { "quality_ndcg@10": 0.02128, "quality_users": 4263 } } ## Step 42 · agent · 2026-08-31T19:36:45.604Z ### Thinking unavailable — no text recorded Baseline is now measured at 0.021280 NDCG@10 (4,263 scored users), with one epoch taking about 217 seconds for training and 366 seconds end-to-end. I’ve implemented a fixed-cost alternative that keeps 32 negatives but reallocates them from 24/8 to 8 uniform/24 target-distributed in-batch, then mixes the propensity-corrected mean loss with detached self-adversarial weights from current logits. This changes training only: the user/item towers and raw-dot-product serving path are untouched. Next I’m testing it on the identical validation boundary. ## Step 43 · agent · 2026-08-31T19:36:52.010Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:54.454Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '465,550p' /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:6000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:36:54.521Z Script completed Wall time 0.0 seconds Output: inference_disliked_items=inference_dislikes, inference_user_features=inference_user_features, ) log( f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} " f"tokens={len(prepared.sequence_items):,} " f"next_item_targets={prepared.target_count:,} " f"states={len(prepared.liked_states):,}" ) return prepared, eligibility def cache_metadata(stage: str, config: Config) -> dict[str, object]: return { "cache_version": CACHE_VERSION, "stage": stage, "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD, "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS, "event_time_feature": "exp-negative-gap-over-60-days", } def save_cache( path: Path, prepared: SequenceData, stage: str, config: Config ) -> None: path.parent.mkdir(parents=True, exist_ok=True) np.savez( path, metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)), **prepared.__dict__, ) log(f"saved public-derived full-sequence cache: {path}") def load_cache(path: Path, stage: str, config: Config) -> SequenceData: with np.load(path, allow_pickle=False) as artifact: actual = json.loads(str(artifact["metadata"].item())) expected = cache_metadata(stage, config) if actual != expected: raise RuntimeError(f"sequence cache differs: {actual} != {expected}") values = { field: artifact[field].copy() for field in SequenceData.__dataclass_fields__ } prepared = SequenceData(**values) log( f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} " f"targets={prepared.target_count:,}" ) return prepared def load_or_prepare( public: Path, stage: str, config: Config, cache: Path | None, ) -> tuple[SequenceData, CandidateSet]: candidate_name = ( "validation_eligible_candidates.npz" if stage == "validation" else "test_eligible_candidates.npz" ) eligibility = CandidateSet.read(public / "data" / candidate_name) if cache is not None and cache.is_file(): prepared = load_cache(cache, stage, config) if not np.array_equal(prepared.item_ids, eligibility.item_ids): raise RuntimeError("cached catalog differs from candidate artifact") return prepared, eligibility prepared, built_eligibility = prepare_data(public, stage, config) if cache is not None: save_cache(cache, prepared, stage, config) return prepared, built_eligibility def build_item_correction( prepared: SequenceData, config: Config, target_starts: np.ndarray, target_counts: np.ndarray, ) -> torch.Tensor: """Estimate in-batch propensities from the selected training targets.""" counts = np.zeros(len(prepared.item_ids), dtype=np.float64) for user_row, count in enumerate(target_counts): if not count: continue ## Step 44 · agent · 2026-08-31T19:36:59.730Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:03.393Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --output /app/solution/improved_validation.parquet 2>&1 | tee /app/solution/improved_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:37:04.569Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"758e6b","wall_time_seconds":1.001243351,"session_id":85133,"original_token_count":0,"output":""} ## Step 45 · agent · 2026-08-31T19:37:07.793Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:09.219Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:37:14.241Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"73afa8","wall_time_seconds":5.000921621,"session_id":85133,"original_token_count":13,"output":"loaded explicit events: rows=720,270 users=8,220\n"} ## Step 46 · agent · 2026-08-31T19:37:28.599Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:30.093Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:37:35.111Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"86d398","wall_time_seconds":5.001221564,"session_id":85133,"original_token_count":66,"output":"prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s\n"} ## Step 47 · agent · 2026-08-31T19:37:39.553Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:40.977Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:37:45.998Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"75d2e4","wall_time_seconds":5.001387922,"session_id":85133,"original_token_count":51,"output":"prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.9s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.4s\n"} ## Step 48 · agent · 2026-08-31T19:37:47.509Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:48.906Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:37:53.928Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"955728","wall_time_seconds":5.001504206,"session_id":85133,"original_token_count":17,"output":"prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s\n"} ## Step 49 · agent · 2026-08-31T19:37:55.495Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:56.861Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:38:01.892Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"5b433c","wall_time_seconds":5.001353694,"session_id":85133,"original_token_count":164,"output":"prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.4s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 50 · agent · 2026-08-31T19:38:03.428Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:05.005Z { "code": "const r = await tools.write_stdin({session_id:85133,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:3000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:38:05.495Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"193ab1","wall_time_seconds":0.440858004,"exit_code":130,"original_token_count":0,"output":""} ## Step 51 · agent · 2026-08-31T19:38:06.920Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:11.449Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution/improved_checkpoints && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/improved_checkpoints --output /app/solution/improved_validation.parquet 2>&1 | tee /app/solution/improved_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:38:12.625Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"152b77","wall_time_seconds":1.000281233,"session_id":27745,"original_token_count":0,"output":""} ## Step 52 · agent · 2026-08-31T19:38:18.996Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:21.740Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1800,1925p' /app/solution/train_current_model.py\\nsed -n '330,465p' /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:18000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:38:21.824Z Script completed Wall time 0.1 seconds Output: if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows lengths = np.where( sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 ) ordered = np.argsort(lengths, kind="stable") result = torch.zeros( len(sequence_rows), config.embedding_dim, dtype=torch.float32 ) for group_start in range(0, len(ordered), config.user_batch_size): output_rows = ordered[group_start : group_start + config.user_batch_size] rows = sequence_rows[output_rows] group_lengths = lengths[output_rows] hidden = torch.zeros( 1, len(output_rows), config.embedding_dim, device=device ) for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps): steps = min( config.bptt_steps, int(group_lengths.max(initial=0)) - start, ) items = np.full( (len(output_rows), steps), len(prepared.item_ids), dtype=np.uint16, ) features = np.zeros((len(output_rows), steps, 3), dtype=np.float16) counts = np.zeros(len(output_rows), dtype=np.int32) for local_row, sequence_row in enumerate(rows): if sequence_row < 0: continue count = min(steps, max(int(group_lengths[local_row]) - start, 0)) if not count: continue offset = int(prepared.offsets[sequence_row]) selected = slice(offset + start, offset + start + count) items[local_row, :count] = prepared.sequence_items[selected] features[local_row, :count] = prepared.event_features[selected] counts[local_row] = count outputs, _ = event_sequence( model, torch.from_numpy(items.astype(np.int64)).to(device), torch.from_numpy(features.astype(np.float32)).to(device), hidden, ) next_rows: list[torch.Tensor] = [] for local_row, count in enumerate(counts): next_rows.append( outputs[local_row, count - 1] if count else hidden[0, local_row] ) hidden = torch.stack(next_rows).unsqueeze(0) liked_items = torch.from_numpy( prepared.inference_liked_items[output_rows].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.inference_disliked_items[output_rows].astype(np.int64) ).to(device) queries = user_queries( model, hidden.squeeze(0), model._mean_context_channel(liked_items), model._mean_context_channel(disliked_items), torch.from_numpy( prepared.inference_user_features[output_rows].astype(np.float32) ).to(device), ) result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu() return result @torch.inference_mode() def rank_candidates( model: base.SequentialTwoTower, prepared: SequenceData, eligibility: CandidateSet, config: Config, device: torch.device, ranking_batch_size: int, ) -> tuple[pd.DataFrame, dict[str, float]]: model.eval() queries = inference_queries(model, prepared, config, device) item_parts: list[torch.Tensor] = [] for start in range(0, len(prepared.item_ids), 2_048): rows = torch.arange( start, min(start + 2_048, len(prepared.item_ids)), dtype=torch.long, device=device, ) item_parts.append(model.encode_items(rows).cpu()) items = torch.cat(item_parts) rows: list[tuple[int, int, int]] = [] for start in range(0, len(eligibility.uids), ranking_batch_size): end = min(start + ranking_batch_size, len(eligibility.uids)) scores = queries[start:end] @ items.T eligible = np.unpackbits( eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), timestamps, history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY, side="left", ) ) result = np.zeros(6, dtype=np.float32) recent_items = items[start:] if len(recent_items): result[0] = np.log1p(len(recent_items)) / 8.0 result[1] = np.log1p(len(np.unique(recent_items))) / 6.0 result[2] = float(np.mean(event_features[start:, 0])) result[3] = float(np.mean(event_features[start:, 1])) result[4] = np.log1p(like_count) / 6.0 result[5] = np.log1p(dislike_count) / 4.0 return result def prepare_data( public: Path, stage: str, config: Config ) -> tuple[SequenceData, CandidateSet]: data = public / "data" catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values( "popularity_rank" ) item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True) item_index = pd.Index(item_ids) n_items = len(item_ids) if n_items >= np.iinfo(np.uint16).max: raise RuntimeError("uint16 item storage is too small for this catalog") if stage == "validation": prefixes = ("train",) history_end = base.TRAIN_END candidate_name = "validation_eligible_candidates.npz" state_name = "validation_preference_state.parquet" else: prefixes = ("train", "val") history_end = base.VALIDATION_END candidate_name = "test_eligible_candidates.npz" state_name = "test_preference_state.parquet" eligibility = CandidateSet.read(data / candidate_name) if not np.array_equal(eligibility.item_ids, item_ids): raise RuntimeError("catalog and candidate artifact item order differ") explicit_by_user = base.load_explicit_events(data, prefixes, item_index) inference_likes, inference_dislikes, like_counts, dislike_counts = ( base.load_inference_state( data / state_name, item_index, eligibility.uids, n_items, ) ) inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)} inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32) inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32) # Snapshot zero is the shared empty explicit state. liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)] disliked_states = [ np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16) ] state_counts: list[tuple[int, int]] = [(0, 0)] user_ids: list[int] = [] offsets = [0] timestamp_parts: list[np.ndarray] = [] item_parts: list[np.ndarray] = [] event_parts: list[np.ndarray] = [] activity_parts: list[np.ndarray] = [] state_parts: list[np.ndarray] = [] listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes] started = time.monotonic() for seen_users, (uid, listens) in enumerate( base.merge_listen_streams(listen_paths, item_index), start=1 ): parts = build_sequence_parts( listens, explicit_by_user.get(uid, base.empty_explicit_events()), n_items, liked_states, disliked_states, state_counts, ) if parts is None: continue sequence_row = len(user_ids) user_ids.append(uid) timestamp_parts.append(parts[0].astype(np.uint32, copy=False)) item_parts.append(parts[1]) event_parts.append(parts[2]) activity_parts.append(parts[3]) state_parts.append(parts[4]) offsets.append(offsets[-1] + len(parts[1])) inference_row = inference_rows.get(uid) if inference_row is not None: inference_sequence_rows[inference_row] = sequence_row inference_user_features[inference_row] = final_user_features( parts[0], parts[1], parts[2], history_end, int(like_counts[inference_row]), int(dislike_counts[inference_row]), ) if seen_users % 1_000 == 0: log( f"prepared users={seen_users:,} tokens={offsets[-1]:,} " f"states={len(liked_states):,} " f"elapsed={time.monotonic() - started:.1f}s" ) missing = np.flatnonzero(inference_sequence_rows < 0) if len(missing): inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0 inference_user_features[missing, 5] = ( np.log1p(dislike_counts[missing]) / 4.0 ) log(f"target users without qualifying catalog sequence={len(missing):,}") prepared = SequenceData( item_ids=item_ids, item_features=base.build_item_features(catalog), user_ids=np.asarray(user_ids, dtype=np.int64), offsets=np.asarray(offsets, dtype=np.int64), sequence_timestamps=np.concatenate(timestamp_parts), sequence_items=np.concatenate(item_parts), event_features=np.concatenate(event_parts), activity_features=np.concatenate(activity_parts), state_ids=np.concatenate(state_parts), liked_states=np.stack(liked_states), disliked_states=np.stack(disliked_states), state_counts=np.asarray(state_counts, dtype=np.int32), inference_user_rows=inference_sequence_rows, inference_liked_items=inference_likes, inference_disliked_items=inference_dislikes, ## Step 53 · agent · 2026-08-31T19:38:25.934Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:28.000Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1890,1945p' /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:38:28.114Z Script completed Wall time 0.1 seconds Output: prepared.inference_user_features[output_rows].astype(np.float32) ).to(device), ) result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu() return result @torch.inference_mode() def rank_candidates( model: base.SequentialTwoTower, prepared: SequenceData, eligibility: CandidateSet, config: Config, device: torch.device, ranking_batch_size: int, ) -> tuple[pd.DataFrame, dict[str, float]]: model.eval() queries = inference_queries(model, prepared, config, device) item_parts: list[torch.Tensor] = [] for start in range(0, len(prepared.item_ids), 2_048): rows = torch.arange( start, min(start + 2_048, len(prepared.item_ids)), dtype=torch.long, device=device, ) item_parts.append(model.encode_items(rows).cpu()) items = torch.cat(item_parts) rows: list[tuple[int, int, int]] = [] for start in range(0, len(eligibility.uids), ranking_batch_size): end = min(start + ranking_batch_size, len(eligibility.uids)) scores = queries[start:end] @ items.T eligible = np.unpackbits( eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder="little", ).astype(bool, copy=False) scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf) selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy() for local_row, columns in enumerate(selected): uid = int(eligibility.uids[start + local_row]) rows.extend( (uid, int(prepared.item_ids[column]), rank) for rank, column in enumerate(columns, start=1) ) if end == len(eligibility.uids) or end % 1_024 == 0: log(f"ranked users={end:,}/{len(eligibility.uids):,}") query_norm = torch.linalg.vector_norm(queries, dim=1).numpy() item_norm = torch.linalg.vector_norm(items, dim=1).numpy() diagnostics = { "query_norm_mean": float(query_norm.mean()), "query_norm_std": float(query_norm.std()), "item_norm_mean": float(item_norm.mean()), "item_norm_std": float(item_norm.std()), } ## Step 54 · agent · 2026-08-31T19:38:30.705Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:32.121Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:38:37.177Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"1d254d","wall_time_seconds":5.001077425,"session_id":27745,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 55 · agent · 2026-08-31T19:38:45.298Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:52.073Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data'; O='/app/solution'\\ncat=pd.read_parquet(f'{D}/candidate_catalog.parquet')\\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nfor name in ['production_validation.parquet','popularity_validation.parquet']:\\n x=pd.read_parquet(f'{O}/{name}'); x=x[x['rank']<=10].copy(); x['pop']=x.item_id.map(rank)\\n print(name, x['pop'].describe(percentiles=[.1,.25,.5,.75,.9]).to_dict())\\nPY\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:38:54.340Z Script completed Wall time 2.2 seconds Output: production_validation.parquet {'count': 84560.0, 'mean': 2597.0539025543994, 'std': 5715.185504100687, 'min': 1.0, '10%': 5.0, '25%': 14.0, '50%': 111.0, '75%': 2124.0, '90%': 7317.0, 'max': 29868.0} popularity_validation.parquet {'count': 84560.0, 'mean': 17.020068590350046, 'std': 19.375080290167947, 'min': 1.0, '10%': 3.0, '25%': 6.0, '50%': 11.0, '75%': 20.0, '90%': 40.0, 'max': 250.0} ## Step 56 · agent · 2026-08-31T19:39:06.506Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:18.673Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport sys, numpy as np, pandas as pd\\nsys.path.insert(0,'/task')\\nimport evaluate_public as ev\\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\\nD='/task/data'; x=pd.read_parquet('production_validation.parquet')\\ncat=pd.read_parquet(f'{D}/candidate_catalog.parquet')\\npop=pd.Series(cat.popularity_rank.to_numpy(),index=cat.item_id.to_numpy())\\nx['logpop']=np.log1p(x.item_id.map(pop).to_numpy())\\nelig=CandidateSet(__import__('pathlib').Path(D)/'validation_eligible_candidates.npz')\\ntargets=set(pd.read_parquet(f'{D}/target_users.parquet').uid.astype(int)); truth=ev.validation_truth(elig,targets)\\nfor alpha in [-2,-1,-.5,0,.25,.5,.75,1,1.5,2,3,4,5]:\\n y=x.copy(); y['s']=-y['rank']+alpha*y['logpop']; y=y.sort_values(['uid','s'],ascending=[True,False]); y['rank']=y.groupby('uid').cumcount()+1\\n q=score_rankings(ranked_items(y),truth)['quality_ndcg@10']\\n print(alpha,q)\\nPY\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:39:28.854Z Script completed Wall time 10.2 seconds Output: ## Step 57 · agent · 2026-08-31T19:39:35.972Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:39.619Z { "code": "const r = await tools.exec_command({cmd:\"for f in /proc/[0-9]*/cmdline; do s=$(tr '\\\\000' ' ' < \\\"$f\\\" 2>/dev/null); case \\\"$s\\\" in *production_validation.parquet*) echo \\\"$f $s\\\";; esac; done | sed -n '1,20p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:39:39.739Z Script completed Wall time 0.1 seconds Output: /proc/60/cmdline bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team'"'"'s next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed ## Step 58 · agent · 2026-08-31T19:39:43.450Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:44.905Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:39:49.933Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"51a47a","wall_time_seconds":5.001118341,"session_id":27745,"original_token_count":0,"output":""} ## Step 59 · agent · 2026-08-31T19:40:02.172Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:03.577Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:40:08.601Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"f0eebc","wall_time_seconds":5.001183263,"session_id":27745,"original_token_count":0,"output":""} ## Step 60 · agent · 2026-08-31T19:40:10.316Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:15.141Z { "code": "const r = await tools.exec_command({cmd:\"for f in /proc/[0-9]*/cmdline; do s=$(tr '\\\\000' ' ' < \\\"$f\\\" 2>/dev/null); case \\\"$s\\\" in *improved_validation.parquet*) p=${f%/cmdline}; echo \\\"$p\\\"; cat \\\"$p/stat\\\" | cut -d' ' -f14-24; sed -n '16,25p' \\\"$p/status\\\";; esac; done\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:40:15.438Z Script completed Wall time 0.1 seconds Output: /proc/639 0 0 0 0 20 0 1 0 111916 2654208 406 NSsid: 639 Kthread: 0 VmPeak: 2592 kB VmSize: 2592 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 1812 kB VmRSS: 1812 kB RssAnon: 124 kB RssFile: 1688 kB /proc/643 60561 4353 0 0 20 0 23 0 111916 6793273344 1245109 NSsid: 639 Kthread: 0 VmPeak: 6634056 kB VmSize: 6634056 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 5040532 kB VmRSS: 5000364 kB RssAnon: 4861028 kB RssFile: 139336 kB /proc/773 0 0 0 1 20 0 1 0 124287 2654208 385 NSsid: 773 Kthread: 0 VmPeak: 2592 kB VmSize: 2592 kB VmLck: 0 kB VmPin: 0 kB VmHWM: 1688 kB VmRSS: 1688 kB RssAnon: 148 kB RssFile: 1540 kB ## Step 61 · agent · 2026-08-31T19:40:20.856Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:22.229Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:40:33.250Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 62 · agent · 2026-08-31T19:40:36.519Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:40:37.305Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:40:52.260Z Script completed Wall time 15.0 seconds Output: {"chunk_id":"cbc637","wall_time_seconds":30.003584068,"session_id":27745,"original_token_count":0,"output":""} ## Step 63 · agent · 2026-08-31T19:40:53.820Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:55.217Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:41:06.240Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 64 · agent · 2026-08-31T19:41:07.832Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:08.795Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:41:25.254Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"e59ee2","wall_time_seconds":30.001929814,"session_id":27745,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.633467 running_bce=0.626976 elapsed=179.0s\n"} ## Step 65 · agent · 2026-08-31T19:41:27.340Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:41:28.716Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:41:39.752Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 66 · agent · 2026-08-31T19:41:42.514Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:43.291Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:41:58.765Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"2ff9b4","wall_time_seconds":30.001898908,"session_id":27745,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/improved_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.62610 validation_loss=0.61428 pair_accuracy=0.685 train_targets=1,052,953 validation_targets=130,103 train_seconds=190.5 validation_seconds=12.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.614283\n"} ## Step 67 · agent · 2026-08-31T19:42:00.215Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:42:01.631Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:42:12.657Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 68 · agent · 2026-08-31T19:42:14.239Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:15.032Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:42:31.661Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"d910a2","wall_time_seconds":30.001459358,"session_id":27745,"original_token_count":0,"output":""} ## Step 69 · agent · 2026-08-31T19:42:34.516Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:42:35.902Z { "code": "const r = await tools.write_stdin({session_id:27745,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:42:46.942Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 70 · agent · 2026-08-31T19:42:48.417Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:49.228Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:43:04.216Z Script completed Wall time 15.0 seconds Output: {"chunk_id":"49ea8b","wall_time_seconds":28.26923203,"exit_code":0,"original_token_count":839,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/improved_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 288.348,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"8-uniform-plus-24-propensity-corrected-inbatch\",\n \"negative_loss\": \"self-adversarial\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 8,\n \"inbatch_negatives\": 24,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_loss\": \"self-adversarial\",\n \"hard_negative_temperature\": 1.0,\n \"hard_negative_mix\": 0.5,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.6260950342916247,\n \"positive_logit\": 0.24608733355265647,\n \"negative_logit\": -0.3620728148957655,\n \"pair_accuracy\": 0.6851729137008015,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 190.50535212,\n \"validation_loss\": 0.6142827711081374,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 12.49514246700005\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.3063349723815918,\n \"query_norm_std\": 0.5731095671653748,\n \"item_norm_mean\": 1.8118765354156494,\n \"item_norm_std\": 0.8034171462059021\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 71 · agent · 2026-08-31T19:43:06.362Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:43:08.592Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/improved_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:43:11.926Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/improved_validation.parquet", "scores": { "quality_ndcg@10": 0.017985, "quality_users": 4263 } } ## Step 72 · agent · 2026-08-31T19:43:18.912Z ### Thinking unavailable — no text recorded The first hard-negative setting is not acceptable: it dropped public NDCG to 0.017985. Its diagnostics show why—positive/negative separation and pair accuracy both fell sharply, so the self-adversarial term was too strong this early in one-epoch CPU training. I’m retaining the baseline artifact and isolating the two changes now: first test the 8/24 sampling allocation with the stable corrected-mean loss, then only keep a milder hard weighting if it adds value. ### exec · 2026-08-31T19:43:27.996Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution/more_inbatch_checkpoints && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/more_inbatch_checkpoints --negative-loss corrected-mean --output /app/solution/more_inbatch_validation.parquet 2>&1 | tee /app/solution/more_inbatch_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:43:29.173Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a6662c","wall_time_seconds":1.001196558,"session_id":12574,"original_token_count":0,"output":""} ## Step 73 · agent · 2026-08-31T19:43:48.758Z ### Thinking unavailable — no text recorded ## Step 74 · agent · 2026-08-31T19:44:00.404Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:24.824Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n negative_loss: str = \\\"self-adversarial\\\"\\n hard_negative_temperature: float = 1.0\\n hard_negative_mix: float = 0.5\\n+ training_objective: str = \\\"bce\\\"\\n@@\\n def corrected_negative_loss(\\n logits: torch.Tensor,\\n correction: torch.Tensor,\\n config: Config,\\n+ positive_logits: torch.Tensor | None = None,\\n ) -> torch.Tensor:\\n- raw = F.softplus(logits)\\n+ if config.training_objective == \\\"pairwise\\\":\\n+ if positive_logits is None:\\n+ raise RuntimeError(\\\"pairwise loss requires positive logits\\\")\\n+ selection_logits = logits - positive_logits[:, None]\\n+ raw = F.softplus(selection_logits)\\n+ else:\\n+ selection_logits = logits\\n+ raw = F.softplus(logits)\\n@@\\n hardness = torch.softmax(\\n- logits.detach() / config.hard_negative_temperature, dim=1\\n+ selection_logits.detach() / config.hard_negative_temperature, dim=1\\n@@\\n negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n+ negative_logits,\\n+ negative_weights[begin:end],\\n+ config,\\n+ positive_logits,\\n )\\n@@\\n- loss_sum = (\\n- selected_weights * (positive_loss + negative_loss) / 2.0\\n- ).sum()\\n+ if config.training_objective == \\\"pairwise\\\":\\n+ loss_sum = (selected_weights * negative_loss).sum()\\n+ else:\\n+ loss_sum = (\\n+ selected_weights * (positive_loss + negative_loss) / 2.0\\n+ ).sum()\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n or stored_config.get(\\\"hard_negative_mix\\\", 0.0)\\n != config.hard_negative_mix\\n+ or stored_config.get(\\\"training_objective\\\", \\\"bce\\\")\\n+ != config.training_objective\\n )\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--hard-negative-mix\\\", type=float, default=Config.hard_negative_mix\\n )\\n+ parser.add_argument(\\n+ \\\"--training-objective\\\",\\n+ choices=(\\\"bce\\\", \\\"pairwise\\\"),\\n+ default=Config.training_objective,\\n+ )\\n@@\\n hard_negative_temperature=args.hard_negative_temperature,\\n hard_negative_mix=args.hard_negative_mix,\\n+ training_objective=args.training_objective,\\n@@\\n- \\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",\\n+ \\\"objective\\\": (\\n+ f\\\"sample-weighted-{config.training_objective}-at-every-timestep\\\"\\n+ ),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:44:24.881Z Script completed Wall time 0.0 seconds Output: {} ## Step 75 · agent · 2026-08-31T19:44:27.940Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:30.054Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:44:30.352Z Script completed Wall time 0.3 seconds Output: ## Step 76 · agent · 2026-08-31T19:44:32.361Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:34.826Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:44:45.873Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T19:44:47.606Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:44:48.399Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:45:04.883Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"d226e9","wall_time_seconds":30.001921052,"session_id":12574,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 78 · agent · 2026-08-31T19:45:07.695Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:45:09.151Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:45:20.169Z Script running with cell ID 58 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T19:45:22.486Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:45:23.493Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:45:39.173Z Script completed Wall time 15.7 seconds Output: {"chunk_id":"de55d8","wall_time_seconds":30.001755538,"session_id":12574,"original_token_count":0,"output":""} ## Step 80 · agent · 2026-08-31T19:45:40.868Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:45:42.078Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:45:53.134Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T19:45:55.907Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:45:56.675Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:46:12.138Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"7501eb","wall_time_seconds":30.001617484,"session_id":12574,"original_token_count":0,"output":""} ## Step 82 · agent · 2026-08-31T19:46:13.951Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:15.364Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:46:26.388Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 83 · agent · 2026-08-31T19:46:28.170Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:46:28.936Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:46:45.385Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"35be19","wall_time_seconds":30.000768751,"session_id":12574,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.552496 running_bce=0.565818 elapsed=169.3s\n"} ## Step 84 · agent · 2026-08-31T19:46:48.457Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:49.841Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:47:00.887Z Script running with cell ID 61 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T19:47:02.990Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:47:03.772Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:47:19.896Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"ec3157","wall_time_seconds":30.001360116,"session_id":12574,"original_token_count":77,"output":"saved epoch checkpoint: /app/solution/more_inbatch_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56380 validation_loss=0.52553 pair_accuracy=0.685 train_targets=1,052,953 validation_targets=130,103 train_seconds=180.8 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.525533\n"} ## Step 86 · agent · 2026-08-31T19:47:21.486Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:22.834Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:47:33.860Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T19:47:35.740Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:47:36.544Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:47:52.862Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"9395ca","wall_time_seconds":30.001985135,"session_id":12574,"original_token_count":0,"output":""} ## Step 88 · agent · 2026-08-31T19:47:54.302Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:55.695Z { "code": "const r = await tools.write_stdin({session_id:12574,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:48:06.738Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T19:48:08.421Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:48:09.221Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:48:09.303Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"557e59","wall_time_seconds":13.38600947,"exit_code":0,"original_token_count":840,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/more_inbatch_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 276.749,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"8-uniform-plus-24-propensity-corrected-inbatch\",\n \"negative_loss\": \"corrected-mean\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 8,\n \"inbatch_negatives\": 24,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_loss\": \"corrected-mean\",\n \"hard_negative_temperature\": 1.0,\n \"hard_negative_mix\": 0.5,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5637978504676405,\n \"positive_logit\": 0.5812679772549766,\n \"negative_logit\": -0.3587096033388017,\n \"pair_accuracy\": 0.6852083794338398,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 180.81290498900012,\n \"validation_loss\": 0.5255333822060628,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 12.403299938000146\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.545462727546692,\n \"query_norm_std\": 0.7970317006111145,\n \"item_norm_mean\": 1.8892877101898193,\n \"item_norm_std\": 0.8114685416221619\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 90 · agent · 2026-08-31T19:48:10.793Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:48:13.072Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/more_inbatch_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:48:16.416Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/more_inbatch_validation.parquet", "scores": { "quality_ndcg@10": 0.019215, "quality_users": 4263 } } ## Step 91 · agent · 2026-08-31T19:48:19.937Z ### Thinking unavailable — no text recorded The allocation-only ablation scores 0.019215, so the degradation is not caused only by the adversarial weighting: oversampling batch targets itself over-penalizes popular-but-relevant tracks. That rejects the 8/24 direction. The more promising objective change is now a pairwise logistic loss: keep propensity-corrected false-negative filtering, but train each positive to beat sampled negatives directly instead of spending half the loss forcing absolute positive/negative calibration. I’m testing that ranking-aligned objective next. ### exec · 2026-08-31T19:48:29.675Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution/pairwise_checkpoints && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/pairwise_checkpoints --training-objective pairwise --negative-loss corrected-mean --uniform-negatives 24 --inbatch-negatives 8 --output /app/solution/pairwise_validation.parquet 2>&1 | tee /app/solution/pairwise_validation.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:48:30.851Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"40deff","wall_time_seconds":1.001260338,"session_id":62251,"original_token_count":0,"output":""} ## Step 92 · agent · 2026-08-31T19:48:50.526Z ### Thinking unavailable — no text recorded ## Step 93 · agent · 2026-08-31T19:48:54.521Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:48:55.928Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:49:06.992Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 94 · agent · 2026-08-31T19:49:08.725Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:49:09.485Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:49:25.993Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"52cd74","wall_time_seconds":30.000787493,"session_id":62251,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 95 · agent · 2026-08-31T19:49:27.487Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:49:28.899Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:49:39.961Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T19:49:41.811Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:49:42.597Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:49:58.959Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"c700ca","wall_time_seconds":30.001231624,"session_id":62251,"original_token_count":0,"output":""} ## Step 97 · agent · 2026-08-31T19:50:01.263Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:50:02.932Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:50:13.949Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T19:50:15.687Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:50:16.513Z { "cell_id": "68", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:50:32.971Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"97e664","wall_time_seconds":30.002040128,"session_id":62251,"original_token_count":0,"output":""} ## Step 99 · agent · 2026-08-31T19:50:35.439Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:50:36.821Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:50:47.842Z Script running with cell ID 69 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T19:50:49.584Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:50:50.940Z { "cell_id": "69", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:51:06.856Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"be24ac","wall_time_seconds":30.001798639,"session_id":62251,"original_token_count":0,"output":""} ## Step 101 · agent · 2026-08-31T19:51:08.387Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:51:09.795Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:51:20.838Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T19:51:22.635Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:51:23.589Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:51:39.837Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"fd0c70","wall_time_seconds":30.002025346,"session_id":62251,"original_token_count":0,"output":""} ## Step 103 · agent · 2026-08-31T19:51:44.339Z ### Thinking unavailable — no text recorded The pairwise run is still within the same fixed 32-negative budget and is progressing normally; it is slightly slower because every negative now contributes a positive–negative margin. Regardless of its outcome, the original 0.021280 ranking remains preserved, so no experiment can displace the best artifact without a measured gain. ### exec · 2026-08-31T19:51:48.452Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:51:59.497Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T19:52:01.471Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:52:02.063Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:52:18.504Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"df6961","wall_time_seconds":30.001809896,"session_id":62251,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.438960 running_bce=0.436776 elapsed=196.8s\n"} ## Step 105 · agent · 2026-08-31T19:52:20.621Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:52:22.049Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:52:33.092Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T19:52:35.305Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:52:36.143Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:52:52.104Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"fe168f","wall_time_seconds":30.000688148,"session_id":62251,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/pairwise_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.43457 validation_loss=0.37468 pair_accuracy=0.759 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.3 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.374678\n"} ## Step 107 · agent · 2026-08-31T19:52:54.189Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:52:55.526Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:53:06.561Z Script running with cell ID 73 Wall time 11.0 seconds Output: ## Step 108 · agent · 2026-08-31T19:53:08.540Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:53:09.326Z { "cell_id": "73", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:53:25.569Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"7e645f","wall_time_seconds":30.001939508,"session_id":62251,"original_token_count":0,"output":""} ## Step 109 · agent · 2026-08-31T19:53:27.325Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:28.808Z { "code": "const r = await tools.write_stdin({session_id:62251,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:53:39.832Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 110 · agent · 2026-08-31T19:53:41.754Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:53:42.572Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:53:44.525Z Script completed Wall time 2.0 seconds Output: {"chunk_id":"bab8cb","wall_time_seconds":15.690089116,"exit_code":0,"original_token_count":850,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 310.48,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"negative_loss\": \"corrected-mean\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_loss\": \"corrected-mean\",\n \"hard_negative_temperature\": 1.0,\n \"hard_negative_mix\": 0.5,\n \"training_objective\": \"pairwise\",\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.4345719331787778,\n \"positive_logit\": 0.9072138249181301,\n \"negative_logit\": -0.4947895908564299,\n \"pair_accuracy\": 0.759422221362207,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 210.31189397000003,\n \"validation_loss\": 0.37467752265494014,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.806733069000074\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.4167587757110596,\n \"query_norm_std\": 0.5332574844360352,\n \"item_norm_mean\": 1.7329493761062622,\n \"item_norm_std\": 0.9284447431564331\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 111 · agent · 2026-08-31T19:53:46.025Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:48.371Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:53:51.701Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/pairwise_validation.parquet", "scores": { "quality_ndcg@10": 0.025707, "quality_users": 4263 } } ## Step 112 · agent · 2026-08-31T19:54:06.214Z ### Thinking unavailable — no text recorded The pairwise objective is the measured winner: public NDCG@10 rises from 0.021280 to 0.025707, a 20.8% relative improvement, while pair accuracy also improves from 0.755 to 0.759. I’m locking that configuration—24 uniform plus 8 propensity-corrected in-batch negatives, full-history false-negative filtering, and corrected pairwise logistic loss—and moving to the required train+validation refit. I’ll install the final file only after a separate eligibility/schema check passes. ### exec · 2026-08-31T19:54:25.825Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-\"\"\"Train the Yambda windowed two-tower baseline.\\n+\\\"\\\"\\\"Train the improved Yambda windowed two-tower retriever.\\n@@\\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\\n-selected adjacent target retains a nonzero weight. Only files under the supplied\\n-public directory are read.\\n+normalization, raw dot-product retrieval, and 24 uniform plus eight\\n+propensity-corrected unique in-batch negatives. Unlike the production BCE,\\n+the selected negative examples enter a pairwise logistic ranking loss, so each\\n+update directly trains the positive score to exceed the sampled negatives.\\n+Every selected adjacent target retains a nonzero weight. Only files under the\\n+supplied public directory are read.\\n@@\\n- # Spend the fixed 32-negative budget mostly on items drawn from the\\n- # empirical target distribution. Catalog-uniform negatives are useful for\\n- # coverage, but most are already separated in a 30k-item retrieval set.\\n- uniform_negatives: int = 8\\n- inbatch_negatives: int = 24\\n+ uniform_negatives: int = 24\\n+ inbatch_negatives: int = 8\\n@@\\n- negative_loss: str = \\\"self-adversarial\\\"\\n+ negative_loss: str = \\\"corrected-mean\\\"\\n@@\\n- hard_negative_mix: float = 0.5\\n- training_objective: str = \\\"bce\\\"\\n+ hard_negative_mix: float = 0.0\\n+ training_objective: str = \\\"pairwise\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:54:25.922Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Unexpected string ## Step 113 · agent · 2026-08-31T19:54:29.743Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:54:43.712Z { "code": "const patch = String.raw`*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the improved Yambda windowed two-tower retriever.\n@@\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and 24 uniform plus eight\n+propensity-corrected unique in-batch negatives. Unlike the production BCE,\n+the selected negative examples enter a pairwise logistic ranking loss, so each\n+update directly trains the positive score to exceed the sampled negatives.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n@@\n- # Spend the fixed 32-negative budget mostly on items drawn from the\n- # empirical target distribution. Catalog-uniform negatives are useful for\n- # coverage, but most are already separated in a 30k-item retrieval set.\n- uniform_negatives: int = 8\n- inbatch_negatives: int = 24\n+ uniform_negatives: int = 24\n+ inbatch_negatives: int = 8\n@@\n- negative_loss: str = \"self-adversarial\"\n+ negative_loss: str = \"corrected-mean\"\n@@\n- hard_negative_mix: float = 0.5\n- training_objective: str = \"bce\"\n+ hard_negative_mix: float = 0.0\n+ training_objective: str = \"pairwise\"\n*** End Patch`;\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:54:43.789Z Script completed Wall time 0.0 seconds Output: {} ## Step 114 · agent · 2026-08-31T19:54:47.138Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:03.004Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd, sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); O=Path('/app/solution')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nwith np.load(D/'test_eligible_candidates.npz',allow_pickle=False) as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible']\\nrows=[]\\nfor row,uid in enumerate(uids):\\n eligible=np.unpackbits(packed[row],count=len(items),bitorder='little').astype(bool)\\n selected=np.flatnonzero(eligible)[:100]\\n if len(selected)!=100: raise RuntimeError(f'only {len(selected)} candidates for {uid}')\\n rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(selected,1))\\nframe=pd.DataFrame(rows,columns=['uid','item_id','rank'])\\nvalidate_ranking(frame,targets)\\nframe.to_parquet(O/'submission.parquet',index=False)\\ncheck=CandidateSet(D/'test_eligible_candidates.npz')\\nassert all(check.is_eligible(u,i) for u,i in frame[['uid','item_id']].itertuples(index=False,name=None))\\nprint(f'fallback valid rows={len(frame):,} users={frame.uid.nunique():,}')\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/validation_prepared.npz\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T19:55:06.887Z Script completed Wall time 3.9 seconds Output: fallback valid rows=845,600 users=8,456 -rw-r--r-- 1 agent agent 719K Aug 31 19:55 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 511M Aug 31 19:37 /app/solution/validation_prepared.npz ## Step 115 · agent · 2026-08-31T19:55:09.582Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:14.361Z { "code": "const r = await tools.exec_command({cmd:\"mkdir -p /app/solution/final_checkpoints && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/final_candidate.parquet 2>&1 | tee /app/solution/final_refit.log\",workdir:\"/app/solution\",yield_time_ms:1000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:55:15.540Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"276704","wall_time_seconds":1.001872996,"session_id":10407,"original_token_count":0,"output":""} ## Step 116 · agent · 2026-08-31T19:55:21.588Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:27.025Z { "code": "const p = await tools.update_plan({explanation:\"Baseline and three focused negative-objective ablations are complete. Pairwise corrected loss is the measured winner; final train+validation refit is running, with a structurally valid fallback submission already in place.\",plan:[\n {step:\"Inspect production code and data scale; reproduce public baseline\",\"status\":\"completed\"},\n {step:\"Diagnose negative sampling and implement focused alternatives under /app/solution\",\"status\":\"completed\"},\n {step:\"Run public backtests, select best valid approach, and document results\",\"status\":\"completed\"},\n {step:\"Refit on train+validation, create submission, and verify schema/eligibility\",\"status\":\"in_progress\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T19:55:27.163Z Script completed Wall time 0.0 seconds Output: {} ## Step 117 · agent · 2026-08-31T19:55:29.274Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:30.690Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:55:41.708Z Script running with cell ID 81 Wall time 11.0 seconds Output: ## Step 118 · agent · 2026-08-31T19:55:44.244Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:55:44.804Z { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:56:00.711Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"dd2139","wall_time_seconds":30.001395189,"session_id":10407,"original_token_count":130,"output":"loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.2s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.6s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.2s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.1s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=36.8s\n"} ## Step 119 · agent · 2026-08-31T19:56:02.270Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:56:03.702Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:56:14.723Z Script running with cell ID 82 Wall time 11.0 seconds Output: ## Step 120 · agent · 2026-08-31T19:56:17.690Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:56:19.261Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:56:33.735Z Script completed Wall time 14.5 seconds Output: {"chunk_id":"cc4522","wall_time_seconds":30.002435178,"session_id":10407,"original_token_count":181,"output":"prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.3s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.2s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"} ## Step 121 · agent · 2026-08-31T19:56:35.830Z ### Thinking unavailable — no text recorded The final refit has correctly switched boundaries: it loaded `train_* + val_*`, built 20.7M observed sequence tokens, used the test-cutoff preference state and eligible candidates, and selected its internal chronological training/validation suffix entirely within that observed history. No hidden outcomes are involved. A fully valid fallback submission remains in place while this model finishes. ### exec · 2026-08-31T19:56:40.304Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:56:51.347Z Script running with cell ID 83 Wall time 11.0 seconds Output: ## Step 122 · agent · 2026-08-31T19:56:54.381Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:56:55.180Z { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:57:10.365Z Script completed Wall time 15.2 seconds Output: {"chunk_id":"2e6553","wall_time_seconds":30.002003814,"session_id":10407,"original_token_count":0,"output":""} ## Step 123 · agent · 2026-08-31T19:57:14.766Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:57:16.126Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:57:27.159Z Script running with cell ID 84 Wall time 11.0 seconds Output: ## Step 124 · agent · 2026-08-31T19:57:29.089Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:57:30.268Z { "cell_id": "84", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:57:46.152Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"1d6cf9","wall_time_seconds":30.000983367,"session_id":10407,"original_token_count":0,"output":""} ## Step 125 · agent · 2026-08-31T19:57:48.003Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:57:49.229Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:58:00.280Z Script running with cell ID 85 Wall time 11.0 seconds Output: ## Step 126 · agent · 2026-08-31T19:58:02.260Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:58:02.995Z { "cell_id": "85", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:58:19.298Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"068e2a","wall_time_seconds":30.001719747,"session_id":10407,"original_token_count":0,"output":""} ## Step 127 · agent · 2026-08-31T19:58:21.164Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:58:22.539Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:58:33.576Z Script running with cell ID 86 Wall time 11.0 seconds Output: ## Step 128 · agent · 2026-08-31T19:58:36.225Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:58:37.019Z { "cell_id": "86", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:58:52.593Z Script completed Wall time 15.6 seconds Output: {"chunk_id":"65ad04","wall_time_seconds":30.001287296,"session_id":10407,"original_token_count":0,"output":""} ## Step 129 · agent · 2026-08-31T19:58:54.779Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:58:56.158Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:59:07.198Z Script running with cell ID 87 Wall time 11.0 seconds Output: ## Step 130 · agent · 2026-08-31T19:59:09.056Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:59:10.398Z { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T19:59:26.209Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"426992","wall_time_seconds":30.001975484,"session_id":10407,"original_token_count":0,"output":""} ## Step 131 · agent · 2026-08-31T19:59:28.241Z ### Thinking unavailable — no text recorded Training remains healthy; the pairwise refit is simply in its CPU-intensive epoch and has not emitted its next periodic batch log yet. The final model is using the measured 24/8 sampling mix—not either of the rejected aggressive-hard-negative settings. ### exec · 2026-08-31T19:59:31.855Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T19:59:42.889Z Script running with cell ID 88 Wall time 11.0 seconds Output: ## Step 132 · agent · 2026-08-31T19:59:44.730Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:59:45.542Z { "cell_id": "88", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T20:00:01.902Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"ea52b5","wall_time_seconds":30.001691652,"session_id":10407,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.334134 running_bce=0.439095 elapsed=199.7s\n"} ## Step 133 · agent · 2026-08-31T20:00:03.665Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:00:05.097Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T20:00:16.145Z Script running with cell ID 89 Wall time 11.0 seconds Output: ## Step 134 · agent · 2026-08-31T20:00:18.007Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:00:18.813Z { "cell_id": "89", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T20:00:35.154Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"744924","wall_time_seconds":30.001991346,"session_id":10407,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.43487 validation_loss=0.37903 pair_accuracy=0.758 train_targets=1,067,606 validation_targets=131,997 train_seconds=214.3 validation_seconds=16.3\nrestored best validation-BCE checkpoint: epoch=1 loss=0.379033\n"} ## Step 135 · agent · 2026-08-31T20:00:38.228Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:00:39.639Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T20:00:50.661Z Script running with cell ID 90 Wall time 11.0 seconds Output: ## Step 136 · agent · 2026-08-31T20:00:52.544Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:00:53.382Z { "cell_id": "90", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T20:01:09.671Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"a29e4b","wall_time_seconds":30.0014538,"session_id":10407,"original_token_count":0,"output":""} ## Step 137 · agent · 2026-08-31T20:01:12.884Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:01:14.863Z { "code": "const r = await tools.write_stdin({session_id:10407,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(JSON.stringify(r));\n" } ### Tool output · 2026-08-31T20:01:25.912Z Script running with cell ID 91 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T20:01:28.066Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:01:28.822Z { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 12000 } ### Tool output · 2026-08-31T20:01:28.892Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"e36b26","wall_time_seconds":13.266270837,"exit_code":0,"original_token_count":849,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/final_candidate.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 369.456,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-pairwise-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"negative_loss\": \"corrected-mean\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_loss\": \"corrected-mean\",\n \"hard_negative_temperature\": 1.0,\n \"hard_negative_mix\": 0.0,\n \"training_objective\": \"pairwise\",\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20712971,\n \"all_next_item_targets\": 20703818,\n \"selected_training_targets\": 1067606,\n \"selected_validation_targets\": 131997,\n \"discarded_old_targets\": 19504215,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.43486819283980155,\n \"positive_logit\": 0.8567554155875343,\n \"negative_logit\": -0.5406865185658274,\n \"pair_accuracy\": 0.7584716119523495,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 214.26273486799982,\n \"validation_loss\": 0.37903303406332534,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 16.27803837500005\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.3882863521575928,\n \"query_norm_std\": 0.5058810114860535,\n \"item_norm_mean\": 1.8689460754394531,\n \"item_norm_std\": 0.955160915851593\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 139 · agent · 2026-08-31T20:01:34.833Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:01:52.290Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd, sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); path=Path('/app/solution/final_candidate.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nraw=pd.read_parquet(path)\\nframe=validate_ranking(raw,targets)\\ncs=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(int(u),int(i)) for u,i in frame[['uid','item_id']].itertuples(index=False,name=None) if not cs.is_eligible(int(u),int(i))]\\nif bad: raise RuntimeError(f'ineligible rows: {bad[:5]} total={len(bad)}')\\nassert list(frame.columns)==['uid','item_id','rank']\\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\\nprint({\\n 'rows':len(frame),'users':frame.uid.nunique(),\\n 'items_per_user_min':int(frame.groupby('uid').item_id.nunique().min()),\\n 'items_per_user_max':int(frame.groupby('uid').item_id.nunique().max()),\\n 'rank_min':int(frame['rank'].min()),'rank_max':int(frame['rank'].max()),\\n 'ineligible_rows':len(bad),'all_integer':True\\n})\\nPY\\nmv /app/solution/final_candidate.parquet /app/solution/submission.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/submission.parquet')\\nprint({'regular_file':p.is_file() and not p.is_symlink(),'bytes':p.stat().st_size})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T20:01:55.265Z Script completed Wall time 3.0 seconds Output: {'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'ineligible_rows': 0, 'all_integer': True} {'regular_file': True, 'bytes': 1208689} -rw-r--r-- 1 agent agent 1.2M Aug 31 20:01 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 83K Aug 31 19:54 /app/solution/train_current_model.py -rw-r--r-- 1 agent agent 16K Aug 31 19:29 /app/solution/two_tower_model.py ## Step 140 · agent · 2026-08-31T20:02:07.007Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:02:32.183Z { "code": "const patch = String.raw`*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Improved music-discovery two-tower\n+\n+## Result\n+\n+The production reproduction scored **0.021280 NDCG@10** on the public\n+validation backtest (4,263 scored users). The selected model scored\n+**0.025707**, a **20.8% relative improvement**.\n+\n+| Training objective / negative use | Public NDCG@10 |\n+| --- | ---: |\n+| Production: 24 uniform + 8 corrected in-batch, pointwise BCE | 0.021280 |\n+| 8 uniform + 24 corrected in-batch, pointwise BCE | 0.019215 |\n+| 8/24 plus strong self-adversarial weighting | 0.017985 |\n+| **Selected: 24/8, corrected pairwise logistic loss** | **0.025707** |\n+\n+## Diagnosis and change\n+\n+The sampler's false-negative filtering is sound, but the production pointwise\n+BCE uses each sampled negative mainly to calibrate an absolute negative logit.\n+That is indirect for indexed retrieval, where only the positive/negative score\n+ordering matters. Simply drawing more target-distributed in-batch negatives\n+also over-penalized popular tracks, and aggressive one-epoch hard weighting\n+destabilized positive/negative separation.\n+\n+The selected objective preserves the mature 24 uniform + 8\n+propensity-corrected in-batch sampler and complete training-history\n+false-negative filter. It changes how each negative is used:\n+\n+`softplus(score(user, negative) - score(user, positive))`\n+\n+Losses retain the existing target weights and in-batch propensity corrections.\n+This directly trains the positive to outrank sampled negatives while remaining\n+within the same 32-negative CPU budget.\n+\n+The serving contract is unchanged: item vectors are independently precomputed,\n+one user vector is computed from history, and ranking is a batched raw dot\n+product. There is no user-ID table, cross-encoder, or per-candidate sequence\n+computation.\n+\n+## Reproduction\n+\n+Public validation:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+ --stage validation \\\n+ --public-dir /task \\\n+ --output /app/solution/pairwise_validation.parquet\n+\n+/opt/task-venv/bin/python /task/evaluate_public.py \\\n+ /app/solution/pairwise_validation.parquet\n+```\n+\n+Final refit and ranking:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+ --stage submission \\\n+ --public-dir /task \\\n+ --output /app/solution/submission.parquet\n+```\n+\n+The checked-in `submission.parquet` was refit from `train_* + val_*` using the\n+test-cutoff preference state and candidate set. It contains 845,600 rows for\n+8,456 users, exactly 100 distinct eligible items and ranks 1–100 per user.\n*** End Patch`;\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T20:02:32.320Z Script failed Wall time 0.0 seconds Output: Script error: SyntaxError: Unexpected identifier 'softplus' ## Step 141 · agent · 2026-08-31T20:02:40.625Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:03:05.453Z { "code": "const patch = String.raw`*** Begin Patch\n*** Add File: /app/solution/README.md\n+# Improved music-discovery two-tower\n+\n+## Result\n+\n+The production reproduction scored **0.021280 NDCG@10** on the public\n+validation backtest (4,263 scored users). The selected model scored\n+**0.025707**, a **20.8% relative improvement**.\n+\n+| Training objective / negative use | Public NDCG@10 |\n+| --- | ---: |\n+| Production: 24 uniform + 8 corrected in-batch, pointwise BCE | 0.021280 |\n+| 8 uniform + 24 corrected in-batch, pointwise BCE | 0.019215 |\n+| 8/24 plus strong self-adversarial weighting | 0.017985 |\n+| **Selected: 24/8, corrected pairwise logistic loss** | **0.025707** |\n+\n+## Diagnosis and change\n+\n+The sampler's false-negative filtering is sound, but the production pointwise\n+BCE uses each sampled negative mainly to calibrate an absolute negative logit.\n+That is indirect for indexed retrieval, where only the positive/negative score\n+ordering matters. Simply drawing more target-distributed in-batch negatives\n+also over-penalized popular tracks, and aggressive one-epoch hard weighting\n+destabilized positive/negative separation.\n+\n+The selected objective preserves the mature 24 uniform + 8\n+propensity-corrected in-batch sampler and complete training-history\n+false-negative filter. It changes how each negative is used:\n+\n+**softplus(score(user, negative) - score(user, positive))**\n+\n+Losses retain the existing target weights and in-batch propensity corrections.\n+This directly trains the positive to outrank sampled negatives while remaining\n+within the same 32-negative CPU budget.\n+\n+The serving contract is unchanged: item vectors are independently precomputed,\n+one user vector is computed from history, and ranking is a batched raw dot\n+product. There is no user-ID table, cross-encoder, or per-candidate sequence\n+computation.\n+\n+## Reproduction\n+\n+Public validation:\n+\n+~~~sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+ --stage validation \\\n+ --public-dir /task \\\n+ --output /app/solution/pairwise_validation.parquet\n+\n+/opt/task-venv/bin/python /task/evaluate_public.py \\\n+ /app/solution/pairwise_validation.parquet\n+~~~\n+\n+Final refit and ranking:\n+\n+~~~sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+ --stage submission \\\n+ --public-dir /task \\\n+ --output /app/solution/submission.parquet\n+~~~\n+\n+The checked-in **submission.parquet** was refit from train plus validation\n+events using the test-cutoff preference state and candidate set. It contains\n+845,600 rows for 8,456 users, exactly 100 distinct eligible items and ranks\n+1-100 per user.\n*** End Patch`;\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T20:03:05.510Z Script completed Wall time 0.0 seconds Output: {} ## Step 142 · agent · 2026-08-31T20:03:10.598Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:03:24.013Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\\nrg -n \\\"uniform_negatives:|inbatch_negatives:|negative_loss:|hard_negative_mix:|training_objective:|def corrected_negative_loss|training_objective ==\\\" /app/solution/train_current_model.py\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/pairwise_validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,sys\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,validate_ranking\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet')\\nf=validate_ranking(pd.read_parquet(P),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\\nc=CandidateSet(D/'test_eligible_candidates.npz')\\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint('FINAL_OK',len(f),f.uid.nunique(),P.is_file(),not P.is_symlink(),list(f.columns),f.dtypes.astype(str).to_dict())\\nprint('submission_csv_exists',Path('/app/solution/submission.csv').exists())\\nPY\\nls -lh /app/solution/README.md /app/solution/submission.parquet /app/solution/pairwise_validation.parquet\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:7000});\ntext(r.output);\n" } ### Tool output · 2026-08-31T20:03:30.108Z Script completed Wall time 6.1 seconds Output: /bin/sh: 2: rg: not found { "model": "/app/solution/pairwise_validation.parquet", "scores": { "quality_ndcg@10": 0.025707, "quality_users": 4263 } } FINAL_OK 845600 8456 True True ['uid', 'item_id', 'rank'] {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'} submission_csv_exists False -rw-r--r-- 1 agent agent 2.5K Aug 31 20:03 /app/solution/README.md -rw-r--r-- 1 agent agent 1.2M Aug 31 19:53 /app/solution/pairwise_validation.parquet -rw-r--r-- 1 agent agent 1.2M Aug 31 20:01 /app/solution/submission.parquet ## Step 143 · agent · 2026-08-31T20:03:34.821Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:03:44.171Z { "code": "const r = await tools.exec_command({cmd:\"grep -nE \\\"uniform_negatives:|inbatch_negatives:|negative_loss:|hard_negative_mix:|training_objective:\\\" /app/solution/train_current_model.py | sed -n '1,20p'\\nsed -n '45,80p' /app/solution/train_current_model.py\",workdir:\"/app/solution\",yield_time_ms:10000,max_output_tokens:3000});\nconst p = await tools.update_plan({explanation:\"The pairwise model was refit on train+validation, ranked only test-eligible candidates, atomically installed as submission.parquet, and passed the final structural, dtype, user-set, rank, uniqueness, eligibility, and regular-file checks.\",plan:[\n {step:\"Inspect production code and data scale; reproduce public baseline\",\"status\":\"completed\"},\n {step:\"Diagnose negative sampling and implement focused alternatives under /app/solution\",\"status\":\"completed\"},\n {step:\"Run public backtests, select best valid approach, and document results\",\"status\":\"completed\"},\n {step:\"Refit on train+validation, create submission, and verify schema/eligibility\",\"status\":\"completed\"}\n]});\ntext(r.output);\ntext(p);\n" } ### Tool output · 2026-08-31T20:03:44.260Z Script completed Wall time 0.0 seconds Output: 58: uniform_negatives: int = 24 59: inbatch_negatives: int = 8 63: negative_loss: str = "corrected-mean" 65: hard_negative_mix: float = 0.0 66: training_objective: str = "pairwise" 659: if config.inbatch_negatives: 678: if corrected_count < config.inbatch_negatives: 867: if config.uniform_negatives: 871: if config.inbatch_negatives: 887: if config.inbatch_negatives: user_batch_size: int = 64 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 loss_microbatch_size: int = 65_536 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 negative_loss: str = "corrected-mean" hard_negative_temperature: float = 1.0 hard_negative_mix: float = 0.0 training_objective: str = "pairwise" negative_filter_scope: str = "full-training-history" target_weighting: str = "organic-strong" ordinary_target_weight: float = 0.25 organic_strong_target_weight: float = 1.0 organic_like_target_weight: float = 3.0 discovery_horizon_days: int = 7 validation_fraction: float = 0.10 max_train_targets_per_user: int = 128 max_validation_targets_per_user: int = 16 early_stopping_patience: int = 1 early_stopping_min_delta: float = 1.0e-4 seed: int = 29 {} ## Step 144 · agent · 2026-08-31T20:03:47.591Z ### Thinking unavailable — no text recorded Completed and validated. - Final artifact: [submission.parquet](/app/solution/submission.parquet) - Improved trainer: [train_current_model.py](/app/solution/train_current_model.py) - Model primitives: [two_tower_model.py](/app/solution/two_tower_model.py) - Experiment record: [README.md](/app/solution/README.md) Public NDCG@10 improved from **0.021280** to **0.025707**—a **20.8% relative gain**—using a propensity-corrected pairwise logistic objective. Final submission checks passed: - 8,456 exact target users - 100 distinct items and ranks 1–100 per user - 845,600 integer-valued rows - Zero ineligible items - Regular Parquet file, no competing CSV - Refit on `train_* + val_*` with the test-cutoff state and candidates