{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-case-1", "attemptId": "sol-max-0-153-4-production-r03-20260924T221413Z", "taskChecksum": "8404d794ce8e18d154cd225cfe6b12bf6b4ee02aaefba1d315aa215c1175a013", "source": { "format": "codex-rollout", "sha256": "5011b9603ca9b2b9bd1fa5de6588c6d1f9970b931417fabedc59cd682edb4bfb", "records": 1193, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 196, "item_completed:AgentMessage": 21, "token_count": 166, "task_complete": 1 } }, "sessionId": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T00:03:13.283Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:13.283Z", "text": "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.\n\n# Personality\n\nAs 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.\n\nYou 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.\n\nConversations 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.\n\n## Writing style\n\nAvoid 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.\n\nIf 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.\n\n## Technical communication\n\nLead 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.\n\nYou 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.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nThe 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.\n\nWhen 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.\n\n## Intermediate commentary\n\nAs 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.\n\nIf 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.\n\nDo 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.\n\nNever 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 \".\n\n## Final answer\n\nIn 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.\n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\n### Visualizations\n\nUse 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.\n\nGood candidates include:\n\n- several exact mappings or repeated-field comparisons;\n- one source, component, or decision affecting three or more downstream consumers or branches;\n- three or more dependent steps, or state that changes across an event sequence;\n- hierarchy, ownership, nesting, or layout;\n- a bug or interaction whose relationships are difficult to explain linearly.\n\nPrefer 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.\n\nUsually 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.\n\n# Rules for getting work done\n\n- 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.\n- When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.\n- 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.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n\n## File editing constraints\n\nUse `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.\n\nYou 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.\n\nNever 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.\n\n## Autonomy and persistence\n\nAdapt accordingly based on the user’s request type. When asked to:\n\n- 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.\n- 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.\n- 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.\n- 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.\n\nYou avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances:\na) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope.\nb) 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).\n\nA 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.\n\nYou 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.\n\nWhen 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.\n\nIf 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.\n\n# Destructive actions\n\nBe cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.\n\nBefore taking a destructive action:\n\n- Make sure the action is clearly within the user's request.\n- Resolve the exact targets with read-only checks when necessary.\n- Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command.\n- When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.\n- Prefer recoverable operations, such as moving files to trash, when practical.\n- If the target or scope is unclear, stop and ask the user.\n\nNever run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.\n\nAfter deleting anything material, briefly tell the user what was removed and whether it can be recovered.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.\n\n### How to use skills\n\n- Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n- Trigger rules: If the user names an available skill (with `$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.\n- 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.\n- How to use a skill:\n 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.\n 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.\n 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.\n 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.\n 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist).\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skills you're using and why. If you skip an obvious skill, say why.\n- Context hygiene:\n - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked.\n - When variants exist, select only the relevant references and note the choice.\n- Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.\n\nWhen 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.\n\nExplicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work.\n\nWhen using a skill the user did not explicitly name, follow this procedure:\n\n- First, tell the user in the commentary channel **why** you are using the skill.\n- Then, use the skill as long as it stays within the scope of the task.\n- 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).\n\nIf 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.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-25T00:03:16.106Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:16.106Z", "text": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\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: r0/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: r0/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: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T00:03:16.106Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T00:03:16.106Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn 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.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-25T00:03:16.107Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:16.107Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll 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.\n\nYou 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.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote 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.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-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." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-25T00:03:16.107Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:16.107Z", "text": "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." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-25T00:03:16.107Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:16.107Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n- GitHub (github@openai-curated-remote)\n- Gmail (gmail@openai-curated-remote)\n- Google Calendar (google-calendar@openai-curated-remote)\n- Google Drive (google-drive@openai-curated-remote)\n- Linear (linear@openai-curated-remote)\n- Notion (notion@openai-curated-remote)\n- OpenAI Developers (openai-developers@openai-curated-remote)\n- Outlook Calendar (outlook-calendar@openai-curated-remote)\n- Outlook Email (outlook-email@openai-curated-remote)\n- SharePoint (sharepoint@openai-curated-remote)\n- Slack (slack@openai-curated-remote)\n- Teams (teams@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-25T00:03:16.107Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T00:03:16.108Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\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: r0/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: r0/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: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/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: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "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": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } }, { "kind": "context", "timestamp": "2026-09-25T00:03:16.109Z", "data": { "type": "turn_context", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "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": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-25T00:03:16.125Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:03:16.125Z", "text": "# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-25T00:03:23.791Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:03:23.791Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:03:25.065Z", "text": "I’ll first reproduce the public baseline and inspect the sampler/objective, then run a small set of targeted negative-sampling experiments. I’ll keep the best valid artifact under `/app/solution/` as I go, refit the winner on `train_* + val_*`, and validate the final submission schema and eligibility." } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T00:03:27.012Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:03:27.012Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:03:29.306Z", "callId": "call_etGfhlwzI0pietunrEeYpjxj", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"find /task/current_model -maxdepth 2 -type f -print && find /task/data -maxdepth 1 -type f -printf '%f\\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py && sed -n '1,260p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":40000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:03:29.374Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:03:29.349Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba44534c87d29bedad1fc32d95e3", "usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 184, "total_tokens": 15293 }, "turn_token_usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 184, "total_tokens": 15293 }, "thread_token_usage": { "input_tokens": 14900, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 184, "total_tokens": 15293 } } }, { "kind": "context", "timestamp": "2026-09-25T00:03:29.368Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0bdff781-0dfb-49f1-aae5-f010fee1e120", "process_id": "60423", "command": [ "/bin/sh", "-lc", "find /task/current_model -maxdepth 2 -type f -print && find /task/data -maxdepth 1 -type f -printf '%f\n' | sort && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py && sed -n '1,260p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "find /task/current_model -maxdepth 2 -type f -print", "path": "current_model" }, { "type": "list_files", "cmd": "find /task/data -maxdepth 1 -type f -printf '%f\n'", "path": "data" }, { "type": "read", "cmd": "sed -n '1,260p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1,300p' /task/current_model/two_tower_model.py", "name": "two_tower_model.py", "path": "/task/current_model/two_tower_model.py" }, { "type": "read", "cmd": "sed -n '1,260p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3090 }, "formatted_output": "/task/current_model/train_current_model.py\n/task/current_model/two_tower_model.py\ncandidate_catalog.parquet\ntarget_users.parquet\ntest_eligible_candidates.npz\ntest_preference_state.parquet\ntrain_dislikes.parquet\ntrain_likes.parquet\ntrain_listens.parquet\ntrain_undislikes.parquet\ntrain_unlikes.parquet\nval_dislikes.parquet\nval_likes.parquet\nval_listens.parquet\nval_undislikes.parquet\nval_unlikes.parquet\nvalidation_eligible_candidates.npz\nvalidation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> int:\n liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n disliked_states.append(\n base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n )\n state_counts.append((len(active_likes), len(active_dislikes)))\n return len(liked_states) - 1\n\n\ndef causal_activity_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n completion: np.ndarray,\n) -> np.ndarray:\n \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n length = len(items)\n result = np.zeros((length, 4), dtype=np.float16)\n if not length:\n return result\n left_rows = np.searchsorted(\n timestamps,\n timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n rows = np.arange(length, dtype=np.int64)\n counts = rows - left_rows + 1\n organic_sum = np.cumsum(organic.astype(np.float64))\n completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n before_organic = np.where(\n left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n before_completion = np.where(\n left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n )\n\n unique_counts = np.empty(length, dtype=np.int32)\n frequencies: dict[int, int] = {}\n left = 0\n for right, raw_item in enumerate(items):\n item = int(raw_item)\n frequencies[item] = frequencies.get(item, 0) + 1\n required_left = int(left_rows[right])\n while left < required_left:\n old_item = int(items[left])\n remaining = frequencies[old_item] - 1\n if remaining:\n frequencies[old_item] = remaining\n else:\n del frequencies[old_item]\n left += 1\n unique_counts[right] = len(frequencies)\n\n result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n result[:, 2] = (\n (organic_sum - before_organic) / np.maximum(counts, 1)\n ).astype(np.float16)\n result[:, 3] = (\n (completion_sum - before_completion)\n / np.maximum(counts, 1)\n / 100.0\n ).astype(np.float16)\n return result\n\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T00:03:38.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:03:38.022Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:03:39.513Z", "callId": "call_rw7OI0mo7tXslXz83Rhfm9Bh", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"rg -n \\\"^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\\\(\\\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:03:39.561Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40: uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41: inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73: popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79: 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347: negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353: invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356: for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357: invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358: negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374: \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406: negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407: correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412: negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419: return negatives, correction\n/task/current_model/two_tower_model.py:426: negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432: raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433: return negatives, correction\n/task/current_model/train_current_model.py:6:loss. The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55: loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56: uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57: inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61: negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67: validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69: max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132: validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137: return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152: return base.TowerConfig(\n/task/current_model/train_current_model.py:165: uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166: inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346: \"popularity_rank\"\n/task/current_model/train_current_model.py:354: if stage == \"validation\":\n/task/current_model/train_current_model.py:357: candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358: state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518: \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519: if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610: negatives: np.ndarray,\n/task/current_model/train_current_model.py:616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617: byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618: bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621: invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623: for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625: negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647: (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672: if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675: (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682: correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689: negatives,\n/task/current_model/train_current_model.py:697: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706: negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710: negatives,\n/task/current_model/train_current_model.py:717: raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736: if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738: raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739: return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750: return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755: config.uniform_negatives,\n/task/current_model/train_current_model.py:756: config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818: all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823: len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825: return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851: total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853: if config.uniform_negatives:\n/task/current_model/train_current_model.py:854: result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855: :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858: selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860: result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873: training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878: or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880: raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882: window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890: validation_fraction: float,\n/task/current_model/train_current_model.py:892: max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897: validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898: if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899: validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901: history_training = totals - validation\n/task/current_model/train_current_model.py:905: window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909: validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991: first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993: int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994: if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120: negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121: sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127: total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129: raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132: \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134: \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138: for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139: end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162: positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165: negatives[begin:end],\n/task/current_model/train_current_model.py:1168: negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171: positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172: negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173: negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176: sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178: loss_sum = (\n/task/current_model/train_current_model.py:1179: selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181: if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184: (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228: total_loss = 0.0\n/task/current_model/train_current_model.py:1232: total_negative = 0.0\n/task/current_model/train_current_model.py:1293: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314: negatives,\n/task/current_model/train_current_model.py:1315: negative_weights,\n/task/current_model/train_current_model.py:1327: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331: total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337: batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355: / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378: totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380: ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388: total_loss = 0.0\n/task/current_model/train_current_model.py:1449: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469: negatives,\n/task/current_model/train_current_model.py:1470: negative_weights,\n/task/current_model/train_current_model.py:1475: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482: \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483: \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484: \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532: config.validation_fraction,\n/task/current_model/train_current_model.py:1534: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537: validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540: f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567: if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571: best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601: \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619: stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624: config.validation_fraction,\n/task/current_model/train_current_model.py:1626: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629: stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657: prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664: \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666: if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668: evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675: validation_counts,\n/task/current_model/train_current_model.py:1682: best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705: validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712: validation_counts,\n/task/current_model/train_current_model.py:1717: diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731: diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732: < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735: best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867: ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882: for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883: end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896: (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897: for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900: log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909: return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923: parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939: \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941: default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944: \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946: default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974: \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976: default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985: \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987: default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003: parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012: or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020: or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023: if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024: parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028: config = Config(\n/task/current_model/train_current_model.py:2033: loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034: negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040: validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063: config.validation_fraction,\n/task/current_model/train_current_model.py:2065: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079: target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102: ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108: args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110: write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117: \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118: \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128: \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135: \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136: \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139: \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149: target_partition.validation_counts.sum()\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:03:39.547Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba519ccc87d281bfd1f0edbe37b9", "usage": { "input_tokens": 21561, "cached_input_tokens": 14720, "cache_write_input_tokens": 0, "output_tokens": 116, "reasoning_output_tokens": 25, "total_tokens": 21677 }, "turn_token_usage": { "input_tokens": 36461, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 509, "reasoning_output_tokens": 209, "total_tokens": 36970 }, "thread_token_usage": { "input_tokens": 36461, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 509, "reasoning_output_tokens": 209, "total_tokens": 36970 } } }, { "kind": "context", "timestamp": "2026-09-25T00:03:39.555Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d2e6c9bf-c2ad-44f7-8b53-1a03828e69be", "process_id": "95658", "command": [ "/bin/sh", "-lc", "rg -n \"^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\(\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "search", "cmd": "rg -n '^(def|class) |negative|sample|loss|validation|rank|argparse|Config'\"\\\\(\" /task/current_model/train_current_model.py /task/current_model/two_tower_model.py", "query": "^(def|class) |negative|sample|loss|validation|rank|argparse|Config\\(", "path": "train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40: uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41: inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73: popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79: 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347: negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353: invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356: for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357: invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358: negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374: \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406: negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407: correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412: negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419: return negatives, correction\n/task/current_model/two_tower_model.py:426: negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432: raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433: return negatives, correction\n/task/current_model/train_current_model.py:6:loss. The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55: loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56: uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57: inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61: negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67: validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69: max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132: validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137: return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152: return base.TowerConfig(\n/task/current_model/train_current_model.py:165: uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166: inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346: \"popularity_rank\"\n/task/current_model/train_current_model.py:354: if stage == \"validation\":\n/task/current_model/train_current_model.py:357: candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358: state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518: \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519: if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610: negatives: np.ndarray,\n/task/current_model/train_current_model.py:616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617: byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618: bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621: invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623: for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625: negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647: (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672: if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675: (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682: correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689: negatives,\n/task/current_model/train_current_model.py:697: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706: negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710: negatives,\n/task/current_model/train_current_model.py:717: raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736: if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738: raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739: return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750: return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755: config.uniform_negatives,\n/task/current_model/train_current_model.py:756: config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818: all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823: len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825: return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851: total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853: if config.uniform_negatives:\n/task/current_model/train_current_model.py:854: result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855: :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858: selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860: result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873: training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878: or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880: raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882: window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890: validation_fraction: float,\n/task/current_model/train_current_model.py:892: max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897: validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898: if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899: validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901: history_training = totals - validation\n/task/current_model/train_current_model.py:905: window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909: validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991: first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993: int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994: if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120: negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121: sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127: total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129: raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132: \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134: \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138: for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139: end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162: positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165: negatives[begin:end],\n/task/current_model/train_current_model.py:1168: negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171: positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172: negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173: negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176: sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178: loss_sum = (\n/task/current_model/train_current_model.py:1179: selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181: if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184: (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228: total_loss = 0.0\n/task/current_model/train_current_model.py:1232: total_negative = 0.0\n/task/current_model/train_current_model.py:1293: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314: negatives,\n/task/current_model/train_current_model.py:1315: negative_weights,\n/task/current_model/train_current_model.py:1327: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331: total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337: batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355: / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378: totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380: ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388: total_loss = 0.0\n/task/current_model/train_current_model.py:1449: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469: negatives,\n/task/current_model/train_current_model.py:1470: negative_weights,\n/task/current_model/train_current_model.py:1475: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482: \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483: \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484: \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532: config.validation_fraction,\n/task/current_model/train_current_model.py:1534: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537: validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540: f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567: if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571: best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601: \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619: stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624: config.validation_fraction,\n/task/current_model/train_current_model.py:1626: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629: stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657: prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664: \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666: if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668: evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675: validation_counts,\n/task/current_model/train_current_model.py:1682: best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705: validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712: validation_counts,\n/task/current_model/train_current_model.py:1717: diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731: diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732: < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735: best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867: ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882: for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883: end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896: (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897: for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900: log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909: return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923: parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939: \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941: default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944: \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946: default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974: \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976: default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985: \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987: default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003: parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012: or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020: or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023: if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024: parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028: config = Config(\n/task/current_model/train_current_model.py:2033: loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034: negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040: validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063: config.validation_fraction,\n/task/current_model/train_current_model.py:2065: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079: target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102: ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108: args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110: write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117: \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118: \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128: \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135: \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136: \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139: \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149: target_partition.validation_counts.sum()\n", "stderr": "", "aggregated_output": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40: uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41: inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73: popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79: 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347: negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353: invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356: for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357: invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358: negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374: \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406: negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407: correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412: negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419: return negatives, correction\n/task/current_model/two_tower_model.py:426: negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432: raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433: return negatives, correction\n/task/current_model/train_current_model.py:6:loss. The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55: loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56: uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57: inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61: negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67: validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69: max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132: validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137: return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152: return base.TowerConfig(\n/task/current_model/train_current_model.py:165: uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166: inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346: \"popularity_rank\"\n/task/current_model/train_current_model.py:354: if stage == \"validation\":\n/task/current_model/train_current_model.py:357: candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358: state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518: \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519: if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610: negatives: np.ndarray,\n/task/current_model/train_current_model.py:616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617: byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618: bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621: invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623: for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625: negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647: (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672: if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675: (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682: correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689: negatives,\n/task/current_model/train_current_model.py:697: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706: negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710: negatives,\n/task/current_model/train_current_model.py:717: raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736: if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738: raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739: return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750: return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755: config.uniform_negatives,\n/task/current_model/train_current_model.py:756: config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818: all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823: len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825: return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851: total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853: if config.uniform_negatives:\n/task/current_model/train_current_model.py:854: result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855: :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858: selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860: result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873: training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878: or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880: raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882: window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890: validation_fraction: float,\n/task/current_model/train_current_model.py:892: max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897: validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898: if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899: validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901: history_training = totals - validation\n/task/current_model/train_current_model.py:905: window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909: validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991: first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993: int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994: if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120: negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121: sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127: total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129: raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132: \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134: \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138: for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139: end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162: positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165: negatives[begin:end],\n/task/current_model/train_current_model.py:1168: negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171: positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172: negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173: negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176: sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178: loss_sum = (\n/task/current_model/train_current_model.py:1179: selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181: if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184: (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228: total_loss = 0.0\n/task/current_model/train_current_model.py:1232: total_negative = 0.0\n/task/current_model/train_current_model.py:1293: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314: negatives,\n/task/current_model/train_current_model.py:1315: negative_weights,\n/task/current_model/train_current_model.py:1327: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331: total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337: batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355: / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378: totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380: ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388: total_loss = 0.0\n/task/current_model/train_current_model.py:1449: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469: negatives,\n/task/current_model/train_current_model.py:1470: negative_weights,\n/task/current_model/train_current_model.py:1475: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482: \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483: \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484: \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532: config.validation_fraction,\n/task/current_model/train_current_model.py:1534: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537: validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540: f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567: if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571: best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601: \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619: stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624: config.validation_fraction,\n/task/current_model/train_current_model.py:1626: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629: stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657: prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664: \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666: if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668: evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675: validation_counts,\n/task/current_model/train_current_model.py:1682: best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705: validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712: validation_counts,\n/task/current_model/train_current_model.py:1717: diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731: diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732: < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735: best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867: ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882: for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883: end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896: (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897: for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900: log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909: return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923: parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939: \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941: default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944: \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946: default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974: \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976: default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985: \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987: default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003: parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012: or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020: or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023: if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024: parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028: config = Config(\n/task/current_model/train_current_model.py:2033: loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034: negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040: validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063: config.validation_fraction,\n/task/current_model/train_current_model.py:2065: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079: target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102: ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108: args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110: write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117: \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118: \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128: \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135: \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136: \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139: \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149: target_partition.validation_counts.sum()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4051 }, "formatted_output": "/task/current_model/two_tower_model.py:27:class TowerConfig:\n/task/current_model/two_tower_model.py:40: uniform_negatives: int = 24\n/task/current_model/two_tower_model.py:41: inbatch_negatives: int = 8\n/task/current_model/two_tower_model.py:49:class ListenEvents:\n/task/current_model/two_tower_model.py:57:class ExplicitEvents:\n/task/current_model/two_tower_model.py:65:def log(message: str) -> None:\n/task/current_model/two_tower_model.py:69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n/task/current_model/two_tower_model.py:73: popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/task/current_model/two_tower_model.py:79: 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/task/current_model/two_tower_model.py:90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n/task/current_model/two_tower_model.py:106:def iter_listen_users(\n/task/current_model/two_tower_model.py:157:def merge_listen_streams(\n/task/current_model/two_tower_model.py:175:def load_explicit_events(\n/task/current_model/two_tower_model.py:228:def empty_explicit_events() -> ExplicitEvents:\n/task/current_model/two_tower_model.py:238:def pack_active_items(\n/task/current_model/two_tower_model.py:251:def load_inference_state(\n/task/current_model/two_tower_model.py:284:class SequentialTwoTower(nn.Module):\n/task/current_model/two_tower_model.py:346:def invalid_negative_mask(\n/task/current_model/two_tower_model.py:347: negatives: torch.Tensor,\n/task/current_model/two_tower_model.py:353: invalid = negatives.eq(targets[:, None])\n/task/current_model/two_tower_model.py:354: invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:355: invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/task/current_model/two_tower_model.py:356: for column in range(1, negatives.shape[1]):\n/task/current_model/two_tower_model.py:357: invalid[:, column] |= negatives[:, column, None].eq(\n/task/current_model/two_tower_model.py:358: negatives[:, :column]\n/task/current_model/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/task/current_model/two_tower_model.py:374: \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/task/current_model/two_tower_model.py:406: negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/task/current_model/two_tower_model.py:407: correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/task/current_model/two_tower_model.py:412: negatives = negatives_cpu.to(device)\n/task/current_model/two_tower_model.py:415: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:416: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:419: return negatives, correction\n/task/current_model/two_tower_model.py:426: negatives[invalid] = replacements\n/task/current_model/two_tower_model.py:428: invalid = invalid_negative_mask(\n/task/current_model/two_tower_model.py:429: negatives, targets, context_items, liked_items\n/task/current_model/two_tower_model.py:432: raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/task/current_model/two_tower_model.py:433: return negatives, correction\n/task/current_model/train_current_model.py:6:loss. The fast default retains each user's most recent 128 training targets and\n/task/current_model/train_current_model.py:7:16 held-out validation targets; passing zero for either cap restores the\n/task/current_model/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/task/current_model/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/task/current_model/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/task/current_model/train_current_model.py:20:import argparse\n/task/current_model/train_current_model.py:41:class Config:\n/task/current_model/train_current_model.py:55: loss_microbatch_size: int = 65_536\n/task/current_model/train_current_model.py:56: uniform_negatives: int = 24\n/task/current_model/train_current_model.py:57: inbatch_negatives: int = 8\n/task/current_model/train_current_model.py:61: negative_filter_scope: str = \"full-training-history\"\n/task/current_model/train_current_model.py:67: validation_fraction: float = 0.10\n/task/current_model/train_current_model.py:69: max_validation_targets_per_user: int = 16\n/task/current_model/train_current_model.py:76:class CandidateSet:\n/task/current_model/train_current_model.py:99:class SequenceData:\n/task/current_model/train_current_model.py:127:class TargetPartition:\n/task/current_model/train_current_model.py:132: validation_counts: np.ndarray\n/task/current_model/train_current_model.py:137: return int(self.training_counts.sum() + self.validation_counts.sum())\n/task/current_model/train_current_model.py:140:def log(message: str) -> None:\n/task/current_model/train_current_model.py:144:def seed_everything(seed: int) -> None:\n/task/current_model/train_current_model.py:150:def model_config(config: Config) -> base.TowerConfig:\n/task/current_model/train_current_model.py:152: return base.TowerConfig(\n/task/current_model/train_current_model.py:165: uniform_negatives=config.uniform_negatives,\n/task/current_model/train_current_model.py:166: inbatch_negatives=config.inbatch_negatives,\n/task/current_model/train_current_model.py:174:def append_state_snapshot(\n/task/current_model/train_current_model.py:190:def causal_activity_features(\n/task/current_model/train_current_model.py:247:def build_sequence_parts(\n/task/current_model/train_current_model.py:314:def final_user_features(\n/task/current_model/train_current_model.py:341:def prepare_data(\n/task/current_model/train_current_model.py:346: \"popularity_rank\"\n/task/current_model/train_current_model.py:354: if stage == \"validation\":\n/task/current_model/train_current_model.py:357: candidate_name = \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:358: state_name = \"validation_preference_state.parquet\"\n/task/current_model/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/task/current_model/train_current_model.py:477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/task/current_model/train_current_model.py:481:def save_cache(\n/task/current_model/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/task/current_model/train_current_model.py:511:def load_or_prepare(\n/task/current_model/train_current_model.py:518: \"validation_eligible_candidates.npz\"\n/task/current_model/train_current_model.py:519: if stage == \"validation\"\n/task/current_model/train_current_model.py:534:def build_item_correction(\n/task/current_model/train_current_model.py:578:def build_known_positive_filter(\n/task/current_model/train_current_model.py:603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/task/current_model/train_current_model.py:609:def invalid_full_history_negatives(\n/task/current_model/train_current_model.py:610: negatives: np.ndarray,\n/task/current_model/train_current_model.py:616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/task/current_model/train_current_model.py:617: byte_columns = negatives >> 3\n/task/current_model/train_current_model.py:618: bit_columns = negatives & 7\n/task/current_model/train_current_model.py:621: invalid |= negatives == targets[:, None]\n/task/current_model/train_current_model.py:622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/task/current_model/train_current_model.py:623: for column in range(1, negatives.shape[1]):\n/task/current_model/train_current_model.py:625: negatives[:, column, None] == negatives[:, :column]\n/task/current_model/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/task/current_model/train_current_model.py:647: (batch_size, config.uniform_negatives),\n/task/current_model/train_current_model.py:653: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/task/current_model/train_current_model.py:672: if corrected_count < config.inbatch_negatives:\n/task/current_model/train_current_model.py:675: (batch_size, config.inbatch_negatives - corrected_count),\n/task/current_model/train_current_model.py:681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/task/current_model/train_current_model.py:682: correction = np.ones_like(negatives, dtype=np.float32)\n/task/current_model/train_current_model.py:685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/task/current_model/train_current_model.py:688: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:689: negatives,\n/task/current_model/train_current_model.py:697: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:706: negatives[invalid] = replacements\n/task/current_model/train_current_model.py:709: invalid = invalid_full_history_negatives(\n/task/current_model/train_current_model.py:710: negatives,\n/task/current_model/train_current_model.py:717: raise RuntimeError(\"full-history negative resampling failed\")\n/task/current_model/train_current_model.py:719: torch.from_numpy(negatives).to(device),\n/task/current_model/train_current_model.py:724:def draw_training_negatives(\n/task/current_model/train_current_model.py:736: if config.negative_filter_scope == \"full-training-history\":\n/task/current_model/train_current_model.py:738: raise RuntimeError(\"full-history negative filter was not built\")\n/task/current_model/train_current_model.py:739: return draw_full_history_filtered_negatives(\n/task/current_model/train_current_model.py:750: return base.draw_filtered_mixed_negatives(\n/task/current_model/train_current_model.py:755: config.uniform_negatives,\n/task/current_model/train_current_model.py:756: config.inbatch_negatives,\n/task/current_model/train_current_model.py:763:def event_sequence(\n/task/current_model/train_current_model.py:774:def pool_explicit_states(\n/task/current_model/train_current_model.py:798:def user_queries(\n/task/current_model/train_current_model.py:813:def candidate_vectors(\n/task/current_model/train_current_model.py:816: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:818: all_items = torch.cat([positives, negatives.reshape(-1)])\n/task/current_model/train_current_model.py:822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/task/current_model/train_current_model.py:823: len(positives), negatives.shape[1], -1\n/task/current_model/train_current_model.py:825: return positive_vectors, negative_vectors\n/task/current_model/train_current_model.py:828:def history_windows(\n/task/current_model/train_current_model.py:845:def corrected_negative_loss(\n/task/current_model/train_current_model.py:851: total = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:853: if config.uniform_negatives:\n/task/current_model/train_current_model.py:854: result += config.uniform_negatives * raw[\n/task/current_model/train_current_model.py:855: :, : config.uniform_negatives\n/task/current_model/train_current_model.py:857: if config.inbatch_negatives:\n/task/current_model/train_current_model.py:858: selected = slice(config.uniform_negatives, total)\n/task/current_model/train_current_model.py:860: result += config.inbatch_negatives * (\n/task/current_model/train_current_model.py:867:def retained_target_window_starts(\n/task/current_model/train_current_model.py:869: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:873: training_region_ends = total_targets - validation_counts\n/task/current_model/train_current_model.py:878: or np.any(validation_counts < 0)\n/task/current_model/train_current_model.py:880: raise RuntimeError(\"chronological target partition produced negative counts\")\n/task/current_model/train_current_model.py:882: window_starts + training_counts + validation_counts, total_targets\n/task/current_model/train_current_model.py:888:def chronological_target_partition(\n/task/current_model/train_current_model.py:890: validation_fraction: float,\n/task/current_model/train_current_model.py:892: max_validation_targets_per_user: int,\n/task/current_model/train_current_model.py:896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n/task/current_model/train_current_model.py:897: validation[(totals >= 2) & (validation == 0)] = 1\n/task/current_model/train_current_model.py:898: if max_validation_targets_per_user:\n/task/current_model/train_current_model.py:899: validation = np.minimum(validation, max_validation_targets_per_user)\n/task/current_model/train_current_model.py:901: history_training = totals - validation\n/task/current_model/train_current_model.py:905: window_starts = retained_target_window_starts(totals, validation, training)\n/task/current_model/train_current_model.py:909: validation_counts=validation,\n/task/current_model/train_current_model.py:914:def load_organic_like_outcomes(\n/task/current_model/train_current_model.py:918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/task/current_model/train_current_model.py:955:def build_target_weights(\n/task/current_model/train_current_model.py:981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/task/current_model/train_current_model.py:991: first_validation_target = last_training_target + 1\n/task/current_model/train_current_model.py:993: int(prepared.sequence_timestamps[first_validation_target])\n/task/current_model/train_current_model.py:994: if first_validation_target < end\n/task/current_model/train_current_model.py:1035:def build_dense_block(\n/task/current_model/train_current_model.py:1111:def score_target_microbatches(\n/task/current_model/train_current_model.py:1119: negatives: torch.Tensor,\n/task/current_model/train_current_model.py:1120: negative_weights: torch.Tensor,\n/task/current_model/train_current_model.py:1121: sample_weights: np.ndarray,\n/task/current_model/train_current_model.py:1127: total_weight = float(sample_weights.sum())\n/task/current_model/train_current_model.py:1129: raise RuntimeError(\"target block has no positive sample weight\")\n/task/current_model/train_current_model.py:1132: \"loss_sum\": 0.0,\n/task/current_model/train_current_model.py:1134: \"negative_sum\": 0.0,\n/task/current_model/train_current_model.py:1138: for begin in range(0, target_count, config.loss_microbatch_size):\n/task/current_model/train_current_model.py:1139: end = min(begin + config.loss_microbatch_size, target_count)\n/task/current_model/train_current_model.py:1162: positive_vectors, negative_vectors = candidate_vectors(\n/task/current_model/train_current_model.py:1165: negatives[begin:end],\n/task/current_model/train_current_model.py:1168: negative_logits = torch.einsum(\n/task/current_model/train_current_model.py:1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n/task/current_model/train_current_model.py:1171: positive_loss = F.softplus(-positive_logits)\n/task/current_model/train_current_model.py:1172: negative_loss = corrected_negative_loss(\n/task/current_model/train_current_model.py:1173: negative_logits, negative_weights[begin:end], config\n/task/current_model/train_current_model.py:1176: sample_weights[begin:end].astype(np.float32, copy=False)\n/task/current_model/train_current_model.py:1178: loss_sum = (\n/task/current_model/train_current_model.py:1179: selected_weights * (positive_loss + negative_loss) / 2.0\n/task/current_model/train_current_model.py:1181: if not bool(torch.isfinite(loss_sum)):\n/task/current_model/train_current_model.py:1182: raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/task/current_model/train_current_model.py:1184: (loss_sum / total_weight).backward()\n/task/current_model/train_current_model.py:1188: statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/task/current_model/train_current_model.py:1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/task/current_model/train_current_model.py:1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/task/current_model/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/task/current_model/train_current_model.py:1203:def run_training_epoch(\n/task/current_model/train_current_model.py:1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n/task/current_model/train_current_model.py:1228: total_loss = 0.0\n/task/current_model/train_current_model.py:1232: total_negative = 0.0\n/task/current_model/train_current_model.py:1293: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1314: negatives,\n/task/current_model/train_current_model.py:1315: negative_weights,\n/task/current_model/train_current_model.py:1327: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1331: total_negative += statistics[\"negative_sum\"]\n/task/current_model/train_current_model.py:1337: batch_bce = statistics[\"loss_sum\"] / max(\n/task/current_model/train_current_model.py:1340: running_bce = total_loss / max(total_weight, 1.0e-12)\n/task/current_model/train_current_model.py:1351: \"loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1355: / max(total_targets * negative_count, 1),\n/task/current_model/train_current_model.py:1364:def evaluate_validation_bce(\n/task/current_model/train_current_model.py:1371: validation_counts: np.ndarray,\n/task/current_model/train_current_model.py:1378: totals = training_counts + validation_counts\n/task/current_model/train_current_model.py:1379: users_with_validation = np.flatnonzero(validation_counts > 0)\n/task/current_model/train_current_model.py:1380: ordered = users_with_validation[\n/task/current_model/train_current_model.py:1381: np.argsort(totals[users_with_validation], kind=\"stable\")\n/task/current_model/train_current_model.py:1388: total_loss = 0.0\n/task/current_model/train_current_model.py:1449: negatives, negative_weights = draw_training_negatives(\n/task/current_model/train_current_model.py:1469: negatives,\n/task/current_model/train_current_model.py:1470: negative_weights,\n/task/current_model/train_current_model.py:1475: total_loss += statistics[\"loss_sum\"]\n/task/current_model/train_current_model.py:1481: \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/task/current_model/train_current_model.py:1482: \"validation_targets\": float(total_targets),\n/task/current_model/train_current_model.py:1483: \"validation_target_weight_sum\": total_weight,\n/task/current_model/train_current_model.py:1484: \"validation_seconds\": time.monotonic() - started,\n/task/current_model/train_current_model.py:1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n/task/current_model/train_current_model.py:1495:def save_epoch_checkpoint(\n/task/current_model/train_current_model.py:1521:def train_model(\n/task/current_model/train_current_model.py:1532: config.validation_fraction,\n/task/current_model/train_current_model.py:1534: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1537: validation_counts = partition.validation_counts\n/task/current_model/train_current_model.py:1540: f\"validation={int(validation_counts.sum()):,} \"\n/task/current_model/train_current_model.py:1567: if config.negative_filter_scope == \"full-training-history\"\n/task/current_model/train_current_model.py:1571: best_loss = float(\"inf\")\n/task/current_model/train_current_model.py:1601: \"negative_filter_scope\", \"last-100\"\n/task/current_model/train_current_model.py:1619: stored_config.get(\"validation_fraction\", 0.10),\n/task/current_model/train_current_model.py:1621: stored_config.get(\"max_validation_targets_per_user\", 0),\n/task/current_model/train_current_model.py:1624: config.validation_fraction,\n/task/current_model/train_current_model.py:1626: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1629: stored_filter_scope != config.negative_filter_scope\n/task/current_model/train_current_model.py:1657: prior.pop(\"validation_loss\", None)\n/task/current_model/train_current_model.py:1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/task/current_model/train_current_model.py:1664: \"recomputing the resume checkpoint's validation BCE\"\n/task/current_model/train_current_model.py:1666: if \"validation_loss\" not in prior:\n/task/current_model/train_current_model.py:1668: evaluate_validation_bce(\n/task/current_model/train_current_model.py:1675: validation_counts,\n/task/current_model/train_current_model.py:1682: best_loss = float(prior[\"validation_loss\"])\n/task/current_model/train_current_model.py:1686: f\"resumed validation-BCE training: epoch={start_epoch} \"\n/task/current_model/train_current_model.py:1687: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1705: validation = evaluate_validation_bce(\n/task/current_model/train_current_model.py:1712: validation_counts,\n/task/current_model/train_current_model.py:1717: diagnostics.update(validation)\n/task/current_model/train_current_model.py:1731: diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1732: < best_loss - config.early_stopping_min_delta\n/task/current_model/train_current_model.py:1735: best_loss = diagnostics[\"validation_loss\"]\n/task/current_model/train_current_model.py:1754: f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/task/current_model/train_current_model.py:1755: f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/task/current_model/train_current_model.py:1758: f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n/task/current_model/train_current_model.py:1760: f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n/task/current_model/train_current_model.py:1766: f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1775: f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n/task/current_model/train_current_model.py:1776: f\"loss={best_loss:.6f}\"\n/task/current_model/train_current_model.py:1782:def inference_queries(\n/task/current_model/train_current_model.py:1861:def rank_candidates(\n/task/current_model/train_current_model.py:1867: ranking_batch_size: int,\n/task/current_model/train_current_model.py:1882: for start in range(0, len(eligibility.uids), ranking_batch_size):\n/task/current_model/train_current_model.py:1883: end = min(start + ranking_batch_size, len(eligibility.uids))\n/task/current_model/train_current_model.py:1896: (uid, int(prepared.item_ids[column]), rank)\n/task/current_model/train_current_model.py:1897: for rank, column in enumerate(columns, start=1)\n/task/current_model/train_current_model.py:1900: log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/task/current_model/train_current_model.py:1909: return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/task/current_model/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/task/current_model/train_current_model.py:1922:def main() -> int:\n/task/current_model/train_current_model.py:1923: parser = argparse.ArgumentParser(description=__doc__)\n/task/current_model/train_current_model.py:1925: \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n/task/current_model/train_current_model.py:1939: \"--loss-microbatch-size\",\n/task/current_model/train_current_model.py:1941: default=Config.loss_microbatch_size,\n/task/current_model/train_current_model.py:1944: \"--negative-filter-scope\",\n/task/current_model/train_current_model.py:1946: default=Config.negative_filter_scope,\n/task/current_model/train_current_model.py:1974: \"--validation-fraction\",\n/task/current_model/train_current_model.py:1976: default=Config.validation_fraction,\n/task/current_model/train_current_model.py:1982: help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:1985: \"--max-validation-targets-per-user\",\n/task/current_model/train_current_model.py:1987: default=Config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:1988: help=\"retain this many final validation targets per user; 0 is uncapped\",\n/task/current_model/train_current_model.py:2003: parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/task/current_model/train_current_model.py:2012: or args.loss_microbatch_size < 1\n/task/current_model/train_current_model.py:2020: or args.max_validation_targets_per_user < 0\n/task/current_model/train_current_model.py:2023: if not 0.0 < args.validation_fraction < 1.0:\n/task/current_model/train_current_model.py:2024: parser.error(\"validation fraction must be strictly between zero and one\")\n/task/current_model/train_current_model.py:2028: config = Config(\n/task/current_model/train_current_model.py:2033: loss_microbatch_size=args.loss_microbatch_size,\n/task/current_model/train_current_model.py:2034: negative_filter_scope=args.negative_filter_scope,\n/task/current_model/train_current_model.py:2040: validation_fraction=args.validation_fraction,\n/task/current_model/train_current_model.py:2042: max_validation_targets_per_user=args.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2063: config.validation_fraction,\n/task/current_model/train_current_model.py:2065: config.max_validation_targets_per_user,\n/task/current_model/train_current_model.py:2078: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2079: target_partition.validation_counts.sum()\n/task/current_model/train_current_model.py:2102: ranking, embedding_diagnostics = rank_candidates(\n/task/current_model/train_current_model.py:2108: args.ranking_batch_size,\n/task/current_model/train_current_model.py:2110: write_ranking(ranking, args.output.resolve())\n/task/current_model/train_current_model.py:2117: \"rows\": len(ranking),\n/task/current_model/train_current_model.py:2118: \"users\": int(ranking.uid.nunique()),\n/task/current_model/train_current_model.py:2126: \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/task/current_model/train_current_model.py:2128: \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n/task/current_model/train_current_model.py:2129: \"loss_execution\": \"gradient-accumulated-microbatches\",\n/task/current_model/train_current_model.py:2135: \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/task/current_model/train_current_model.py:2136: \"negative_sampling\": (\n/task/current_model/train_current_model.py:2139: \"false_negative_filter\": config.negative_filter_scope,\n/task/current_model/train_current_model.py:2148: \"selected_validation_targets\": int(\n/task/current_model/train_current_model.py:2149: target_partition.validation_counts.sum()\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T00:03:47.620Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:03:47.620Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:03:50.039Z", "callId": "call_8Y9M1xLEoBVp1RLzxVbOS8kW", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '247,760p' /task/current_model/train_current_model.py && sed -n '760,1210p' /task/current_model/train_current_model.py && sed -n '1200,1785p' /task/current_model/train_current_model.py && sed -n '1780,2165p' /task/current_model/train_current_model.py && sed -n '284,455p' /task/current_model/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":60000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:03:50.119Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nWarning: truncated output (original token count: 19542)\nTotal output lines: 2086\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_r…9542 tokens truncated… (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:03:50.083Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd1c7da5-4416-4e39-b063-9269b92f5f38", "process_id": "91739", "command": [ "/bin/sh", "-lc", "sed -n '247,760p' /task/current_model/train_current_model.py && sed -n '760,1210p' /task/current_model/train_current_model.py && sed -n '1200,1785p' /task/current_model/train_current_model.py && sed -n '1780,2165p' /task/current_model/train_current_model.py && sed -n '284,455p' /task/current_model/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '247,760p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '760,1210p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1200,1785p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1780,2165p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '284,455p' /task/current_model/two_tower_model.py", "name": "two_tower_model.py", "path": "/task/current_model/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "def build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n", "stderr": "", "aggregated_output": "def build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n )\n ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1970 }, "formatted_output": "Warning: truncated output (original token count: 19542)\nTotal output lines: 2086\n\ndef build_sequence_parts(\n listens: base.ListenEvents,\n explicit: base.ExplicitEvents,\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n items = listens.items[qualifying].astype(np.uint16, copy=False)\n organic = listens.organic[qualifying].astype(np.float32, copy=False)\n completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n if not len(items):\n return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n) -> np.ndarray:\n start = int(\n np.searchsorted(\n timestamps,\n history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n side=\"left\",\n )\n )\n result = np.zeros(6, dtype=np.float32)\n recent_items = items[start:]\n if len(recent_items):\n result[0] = np.log1p(len(recent_items)) / 8.0\n result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n result[2] = float(np.mean(event_features[start:, 0]))\n result[3] = float(np.mean(event_features[start:, 1]))\n result[4] = np.log1p(like_count) / 6.0\n result[5] = np.log1p(dislike_count) / 4.0\n return result\n\n\ndef prepare_data(\n public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n data = public / \"data\"\n catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n \"popularity_rank\"\n )\n item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n item_index = pd.Index(item_ids)\n n_items = len(item_ids)\n if n_items >= np.iinfo(np.uint16).max:\n raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n if stage == \"validation\":\n prefixes = (\"train\",)\n history_end = base.TRAIN_END\n candidate_name = \"validation_eligible_candidates.npz\"\n state_name = \"validation_preference_state.parquet\"\n else:\n prefixes = (\"train\", \"val\")\n history_end = base.VALIDATION_END\n candidate_name = \"test_eligible_candidates.npz\"\n state_name = \"test_preference_state.parquet\"\n eligibility = CandidateSet.read(data / candidate_name)\n if not np.array_equal(eligibility.item_ids, item_ids):\n raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n inference_likes, inference_dislikes, like_counts, dislike_counts = (\n base.load_inference_state(\n data / state_name,\n item_index,\n eligibility.uids,\n n_items,\n )\n )\n inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n # Snapshot zero is the shared empty explicit state.\n liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n disliked_states = [\n np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n ]\n state_counts: list[tuple[int, int]] = [(0, 0)]\n user_ids: list[int] = []\n offsets = [0]\n timestamp_parts: list[np.ndarray] = []\n item_parts: list[np.ndarray] = []\n event_parts: list[np.ndarray] = []\n activity_parts: list[np.ndarray] = []\n state_parts: list[np.ndarray] = []\n\n listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n started = time.monotonic()\n for seen_users, (uid, listens) in enumerate(\n base.merge_listen_streams(listen_paths, item_index), start=1\n ):\n parts = build_sequence_parts(\n listens,\n explicit_by_user.get(uid, base.empty_explicit_events()),\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n if parts is None:\n continue\n sequence_row = len(user_ids)\n user_ids.append(uid)\n timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n item_parts.append(parts[1])\n event_parts.append(parts[2])\n activity_parts.append(parts[3])\n state_parts.append(parts[4])\n offsets.append(offsets[-1] + len(parts[1]))\n inference_row = inference_rows.get(uid)\n if inference_row is not None:\n inference_sequence_rows[inference_row] = sequence_row\n inference_user_features[inference_row] = final_user_features(\n parts[0],\n parts[1],\n parts[2],\n history_end,\n int(like_counts[inference_row]),\n int(dislike_counts[inference_row]),\n )\n if seen_users % 1_000 == 0:\n log(\n f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n f\"states={len(liked_states):,} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n missing = np.flatnonzero(inference_sequence_rows < 0)\n if len(missing):\n inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n inference_user_features[missing, 5] = (\n np.log1p(dislike_counts[missing]) / 4.0\n )\n log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n prepared = SequenceData(\n item_ids=item_ids,\n item_features=base.build_item_features(catalog),\n user_ids=np.asarray(user_ids, dtype=np.int64),\n offsets=np.asarray(offsets, dtype=np.int64),\n sequence_timestamps=np.concatenate(timestamp_parts),\n sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_r…9542 tokens truncated… (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:03:50.118Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba5bf15487d2929e5193255cebf5", "usage": { "input_tokens": 28176, "cached_input_tokens": 21376, "cache_write_input_tokens": 0, "output_tokens": 151, "reasoning_output_tokens": 8, "total_tokens": 28327 }, "turn_token_usage": { "input_tokens": 64637, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 660, "reasoning_output_tokens": 217, "total_tokens": 65297 }, "thread_token_usage": { "input_tokens": 64637, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 660, "reasoning_output_tokens": 217, "total_tokens": 65297 } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T00:04:00.549Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:00.549Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:04:03.313Z", "callId": "call_xBPYke2o21B4JzZLrW4JjzOV", "name": "exec", "input": { "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 && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:04:03.522Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"476ffa\",\"wall_time_seconds\":0.042024855,\"exit_code\":1,\"original_token_count\":45,\"output\":\"Traceback (most recent call last):\\n File \\\"/task/current_model/train_current_model.py\\\", line 28, in \\n import numpy as np\\nModuleNotFoundError: No module named 'numpy'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:03.361Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba66a36087d2bbc630d5b2234137", "usage": { "input_tokens": 37107, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 254, "reasoning_output_tokens": 96, "total_tokens": 37361 }, "turn_token_usage": { "input_tokens": 101744, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 914, "reasoning_output_tokens": 313, "total_tokens": 102658 }, "thread_token_usage": { "input_tokens": 101744, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 914, "reasoning_output_tokens": 313, "total_tokens": 102658 } } }, { "kind": "context", "timestamp": "2026-09-25T00:04:03.518Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d81a65a3-cc62-4476-b66c-8f190c67fa83", "process_id": "41046", "command": [ "/bin/sh", "-lc", "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 && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "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 && python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 42015145 }, "formatted_output": "Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T00:04:10.709Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:10.709Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:04:11.798Z", "text": "The default shell’s `python` is not the task runtime—it lacks even NumPy. I’m locating the supplied offline environment now, then I’ll rerun the exact baseline there; no model conclusion is being drawn from this setup failure." }, { "kind": "tool", "timestamp": "2026-09-25T00:04:14.182Z", "callId": "call_TFghYC89KV2Ec340XhBVelWg", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:04:14.260Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root 0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:14.228Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c43d6b81-e412-491b-96ca-31d831066c63", "process_id": "44837", "command": [ "/bin/sh", "-lc", "command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; command -v python3; which -a python python3 2>/dev/null || true; find /opt /usr/local /venv /env -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; sed -n '1,160p' /task/requirements.txt; ls -la /app /task | head -80" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root 0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root 0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3360 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n/app:\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\ndrwxr-xr-x 1 agent agent 4096 Sep 25 00:04 solution\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:03 ..\n-r--r--r-- 1 root root 0 Sep 24 23:56 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:56 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 23:57 data\n-r--r--r-- 1 root root 5774 Sep 24 23:56 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:56 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:56 requirements.txt\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:04:14.259Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba73b7a087d299a0704df2ae8fc0", "usage": { "input_tokens": 37464, "cached_input_tokens": 36992, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 29, "total_tokens": 37688 }, "turn_token_usage": { "input_tokens": 139208, "cached_input_tokens": 112256, "cache_write_input_tokens": 0, "output_tokens": 1138, "reasoning_output_tokens": 342, "total_tokens": 140346 }, "thread_token_usage": { "input_tokens": 139208, "cached_input_tokens": 112256, "cache_write_input_tokens": 0, "output_tokens": 1138, "reasoning_output_tokens": 342, "total_tokens": 140346 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T00:04:20.764Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:20.764Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:04:22.812Z", "callId": "call_ACZ5386FHqblX0cZzboIMkQ6", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"python -VV; python -c 'import sys; print(sys.executable); print(\\\"\\\\n\\\".join(sys.path))'; ls -la /usr/local/lib; find / -path '*/site-packages/numpy/__init__.py' -o -path '*/dist-packages/numpy/__init__.py' 2>/dev/null | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:04:23.314Z", "content": "Script completed\nWall time 0.5 seconds\nOutput:\n\nPython 3.12.13 (main, Aug 5 2026, 01:11:46) [GCC 12.2.0]\n/usr/local/bin/python\n\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 6528\ndrwxr-xr-x 1 root root 4096 Sep 25 00:01 .\ndrwxr-xr-x 1 root root 4096 Aug 3 00:00 ..\nlrwxrwxrwx 1 root root 20 Aug 5 01:13 libpython3.12.so -> libpython3.12.so.1.0\n-rwxr-xr-x 1 root root 6642968 Aug 5 01:13 libpython3.12.so.1.0\n-rwxr-xr-x 1 root root 14000 Aug 5 01:13 libpython3.so\ndrwxr-xr-x 2 root root 4096 Aug 5 01:13 pkgconfig\ndrwxr-xr-x 3 root root 4096 Sep 25 00:01 python3.11\ndrwxr-xr-x 38 root root 4096 Aug 5 01:13 python3.12\n/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:22.913Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba7ece9087d2bfbbfce4138cce96", "usage": { "input_tokens": 38594, "cached_input_tokens": 37248, "cache_write_input_tokens": 0, "output_tokens": 191, "reasoning_output_tokens": 68, "total_tokens": 38785 }, "turn_token_usage": { "input_tokens": 177802, "cached_input_tokens": 149504, "cache_write_input_tokens": 0, "output_tokens": 1329, "reasoning_output_tokens": 410, "total_tokens": 179131 }, "thread_token_usage": { "input_tokens": 177802, "cached_input_tokens": 149504, "cache_write_input_tokens": 0, "output_tokens": 1329, "reasoning_output_tokens": 410, "total_tokens": 179131 } } }, { "kind": "context", "timestamp": "2026-09-25T00:04:23.310Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4df99c9f-1797-4c3b-b543-a474d3d71393", "process_id": "76469", "command": [ "/bin/sh", "-lc", "python -VV; python -c 'import sys; print(sys.executable); print(\"\\n\".join(sys.path))'; ls -la /usr/local/lib; find / -path '*/site-packages/numpy/__init__.py' -o -path '*/dist-packages/numpy/__init__.py' 2>/dev/null | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -VV; python -c 'import sys; print(sys.executable); print(\"\\n\".join(sys.path))'; ls -la /usr/local/lib; find / -path '*/site-packages/numpy/__init__.py' -o -path '*/dist-packages/numpy/__init__.py' 2>/dev/null | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 334361933 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/numpy/__init__.py\n" } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T00:04:29.540Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:29.540Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:04:31.203Z", "callId": "call_u73xcJY2cpEtbeagepMtUxw2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -c 'import numpy,pandas,torch,pyarrow; print(numpy.__version__, pandas.__version__, torch.__version__, pyarrow.__version__)' && ls -la /opt/task-venv/bin/python*\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:04:35.172Z", "content": "Script completed\nWall time 4.0 seconds\nOutput:\n\n2.2.6 2.3.3 2.8.0+cpu 18.1.0\nlrwxrwxrwx 1 root root 21 Sep 24 23:58 /opt/task-venv/bin/python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3.12 -> python\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:31.282Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba87b5d087d294972ae2adce5c29", "usage": { "input_tokens": 39142, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 127, "reasoning_output_tokens": 26, "total_tokens": 39269 }, "turn_token_usage": { "input_tokens": 216944, "cached_input_tokens": 187904, "cache_write_input_tokens": 0, "output_tokens": 1456, "reasoning_output_tokens": 436, "total_tokens": 218400 }, "thread_token_usage": { "input_tokens": 216944, "cached_input_tokens": 187904, "cache_write_input_tokens": 0, "output_tokens": 1456, "reasoning_output_tokens": 436, "total_tokens": 218400 } } }, { "kind": "context", "timestamp": "2026-09-25T00:04:35.168Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-80a9976d-16b5-4fe6-a41c-e73bedddb479", "process_id": "67582", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -c 'import numpy,pandas,torch,pyarrow; print(numpy.__version__, pandas.__version__, torch.__version__, pyarrow.__version__)' && ls -la /opt/task-venv/bin/python*" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -c 'import numpy,pandas,torch,pyarrow; print(numpy.__version__, pandas.__version__, torch.__version__, pyarrow.__version__)' && ls -la /opt/task-venv/bin/python*" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.2.6 2.3.3 2.8.0+cpu 18.1.0\nlrwxrwxrwx 1 root root 21 Sep 24 23:58 /opt/task-venv/bin/python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3.12 -> python\n", "stderr": "", "aggregated_output": "2.2.6 2.3.3 2.8.0+cpu 18.1.0\nlrwxrwxrwx 1 root root 21 Sep 24 23:58 /opt/task-venv/bin/python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3.12 -> python\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 801984246 }, "formatted_output": "2.2.6 2.3.3 2.8.0+cpu 18.1.0\nlrwxrwxrwx 1 root root 21 Sep 24 23:58 /opt/task-venv/bin/python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 24 23:58 /opt/task-venv/bin/python3.12 -> python\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T00:04:41.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:41.621Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:04:43.560Z", "callId": "call_SPYLtHw5zRZbyDmJvwYXJ0NI", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:04:44.727Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"3a6e3d\",\"wall_time_seconds\":1.001855524,\"session_id\":74144,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:43.639Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba93634087d2827bcb4f8dae6807", "usage": { "input_tokens": 39418, "cached_input_tokens": 38912, "cache_write_input_tokens": 0, "output_tokens": 151, "reasoning_output_tokens": 34, "total_tokens": 39569 }, "turn_token_usage": { "input_tokens": 256362, "cached_input_tokens": 226816, "cache_write_input_tokens": 0, "output_tokens": 1607, "reasoning_output_tokens": 470, "total_tokens": 257969 }, "thread_token_usage": { "input_tokens": 256362, "cached_input_tokens": 226816, "cache_write_input_tokens": 0, "output_tokens": 1607, "reasoning_output_tokens": 470, "total_tokens": 257969 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T00:04:49.670Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:04:49.670Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:04:50.371Z", "callId": "call_dbhp0l9bA6ILFeTzXS54IuVh", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:04:55.392Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"af47b5\",\"wall_time_seconds\":5.001029982,\"session_id\":74144,\"original_token_count\":29,\"output\":\"loaded explicit events: rows=720,270 users=8,220\\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:04:50.462Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5ba9ceccc87d2931c71009d8431fa", "usage": { "input_tokens": 39629, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 109, "reasoning_output_tokens": 61, "total_tokens": 39738 }, "turn_token_usage": { "input_tokens": 295991, "cached_input_tokens": 266112, "cache_write_input_tokens": 0, "output_tokens": 1716, "reasoning_output_tokens": 531, "total_tokens": 297707 }, "thread_token_usage": { "input_tokens": 295991, "cached_input_tokens": 266112, "cache_write_input_tokens": 0, "output_tokens": 1716, "reasoning_output_tokens": 531, "total_tokens": 297707 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-25T00:05:01.053Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:05:01.053Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:05:03.255Z", "callId": "call_hWD0NwuCSZEv8FusMZOj94Yv", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '798,1038p' /task/current_model/train_current_model.py; sed -n '1035,1210p' /task/current_model/train_current_model.py; sed -n '1203,1368p' /task/current_model/train_current_model.py; sed -n '1364,1782p' /task/current_model/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":50000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:05:03.333Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:05:03.298Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ea3621d-5a60-450d-a0ee-e5e13faaacf0", "process_id": "31190", "command": [ "/bin/sh", "-lc", "sed -n '798,1038p' /task/current_model/train_current_model.py; sed -n '1035,1210p' /task/current_model/train_current_model.py; sed -n '1203,1368p' /task/current_model/train_current_model.py; sed -n '1364,1782p' /task/current_model/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '798,1038p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1035,1210p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1203,1368p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1364,1782p' /task/current_model/train_current_model.py", "name": "train_current_model.py", "path": "/task/current_model/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "def user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n", "stderr": "", "aggregated_output": "def user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4050 }, "formatted_output": "def user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)\n\n history_training = totals - validation\n training = history_training.copy()\n if max_train_targets_per_user:\n training = np.minimum(training, max_train_targets_per_user)\n window_starts = retained_target_window_starts(totals, validation, training)\n return TargetPartition(\n window_starts=window_starts,\n training_counts=training,\n validation_counts=validation,\n history_training_counts=history_training,\n )\n\n\ndef load_organic_like_outcomes(\n public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n frames = [\n pd.read_parquet(\n public / \"data\" / f\"{prefix}_likes.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n for prefix in prefixes\n ]\n likes = pd.concat(frames, ignore_index=True)\n likes = likes[likes.is_organic == 1].copy()\n mapped = pd.Index(item_ids).get_indexer(\n likes.item_id.to_numpy(dtype=np.int64)\n )\n likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n nested: dict[int, dict[int, list[int]]] = {}\n for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n index=False, name=None\n ):\n nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n int(raw_timestamp)\n )\n result = {\n uid: {\n item: np.asarray(timestamps, dtype=np.int64)\n for item, timestamps in items.items()\n }\n for uid, items in nested.items()\n }\n log(\n f\"loaded organic-like outcomes: rows={len(likes):,} \"\n f\"users={len(result):,}\"\n )\n return result\n\n\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\ndef build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()\n for name, value in model.state_dict().items()\n }\n\n\ndef save_epoch_checkpoint(\n checkpoint_dir: Path,\n epoch: int,\n model_state: dict[str, torch.Tensor],\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:05:03.332Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5baa7a3b887d2894b8e2cc1f831b3", "usage": { "input_tokens": 39835, "cached_input_tokens": 39424, "cache_write_input_tokens": 0, "output_tokens": 160, "reasoning_output_tokens": 35, "total_tokens": 39995 }, "turn_token_usage": { "input_tokens": 335826, "cached_input_tokens": 305536, "cache_write_input_tokens": 0, "output_tokens": 1876, "reasoning_output_tokens": 566, "total_tokens": 337702 }, "thread_token_usage": { "input_tokens": 335826, "cached_input_tokens": 305536, "cache_write_input_tokens": 0, "output_tokens": 1876, "reasoning_output_tokens": 566, "total_tokens": 337702 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-25T00:05:11.151Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:05:11.151Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:05:11.851Z", "callId": "call_f8GN9NPrt8qKtRuzdXShtq7X", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:05:16.860Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"8081a3\",\"wall_time_seconds\":5.001527297,\"session_id\":74144,\"original_token_count\":67,\"output\":\"prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:05:11.925Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5baaf9be887d2b7fcc4d3de4619ab", "usage": { "input_tokens": 47712, "cached_input_tokens": 39680, "cache_write_input_tokens": 0, "output_tokens": 222, "reasoning_output_tokens": 174, "total_tokens": 47934 }, "turn_token_usage": { "input_tokens": 383538, "cached_input_tokens": 345216, "cache_write_input_tokens": 0, "output_tokens": 2098, "reasoning_output_tokens": 740, "total_tokens": 385636 }, "thread_token_usage": { "input_tokens": 383538, "cached_input_tokens": 345216, "cache_write_input_tokens": 0, "output_tokens": 2098, "reasoning_output_tokens": 740, "total_tokens": 385636 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-25T00:05:25.384Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:05:25.384Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:05:33.711Z", "callId": "call_atX6qMs10Rc0UPXDyrbvJd3k", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data'\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\\ntgt=pd.read_parquet(D+'/target_users.parquet')\\nprint('catalog', cat.shape, cat.columns.tolist())\\nprint(cat.head().to_string(index=False))\\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\\nfor st in ['validation','test']:\\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n c=masks.sum(1)\\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\\n# public truth rank/profile\\nuids=set(tgt.uid.astype(int))\\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\\nfor name,x in [('strong',strong),('likes',likes)]:\\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:05:36.239Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\ncatalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntargets (8456, 3) uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\n 600 1985.0 2423\n 700 1776.0 6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [ 1 207 996 4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [ 1 442 1648 5554 13506 21728 29989]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:05:33.748Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5babd0ce487d2a837bda37785c9fa", "usage": { "input_tokens": 48091, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 730, "reasoning_output_tokens": 208, "total_tokens": 48821 }, "turn_token_usage": { "input_tokens": 431629, "cached_input_tokens": 392704, "cache_write_input_tokens": 0, "output_tokens": 2828, "reasoning_output_tokens": 948, "total_tokens": 434457 }, "thread_token_usage": { "input_tokens": 431629, "cached_input_tokens": 392704, "cache_write_input_tokens": 0, "output_tokens": 2828, "reasoning_output_tokens": 948, "total_tokens": 434457 } } }, { "kind": "context", "timestamp": "2026-09-25T00:05:36.235Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-18e185bd-0f7f-4832-a7e5-b55a0bc5d0e3", "process_id": "5549", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data'\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\ntgt=pd.read_parquet(D+'/target_users.parquet')\nprint('catalog', cat.shape, cat.columns.tolist())\nprint(cat.head().to_string(index=False))\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\nfor st in ['validation','test']:\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n c=masks.sum(1)\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\n# public truth rank/profile\nuids=set(tgt.uid.astype(int))\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\nfor name,x in [('strong',strong),('likes',likes)]:\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data'\ncat=pd.read_parquet(D+'/candidate_catalog.parquet').sort_values('popularity_rank').reset_index(drop=True)\ntgt=pd.read_parquet(D+'/target_users.parquet')\nprint('catalog', cat.shape, cat.columns.tolist())\nprint(cat.head().to_string(index=False))\nprint('targets', tgt.shape, tgt.head().to_string(index=False))\nfor st in ['validation','test']:\n z=np.load(f'{D}/{st}_eligible_candidates.npz')\n masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n c=masks.sum(1)\n print(st, z['uids'].shape, masks.shape, 'eligible min/mean/max',c.min(),c.mean(),c.max())\n# public truth rank/profile\nuids=set(tgt.uid.astype(int))\nstrong=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nstrong=strong[(strong.uid.isin(uids))&(strong.is_organic==1)&(strong.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nlikes=likes[(likes.uid.isin(uids))&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\nitemrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nprint('val strong raw',len(strong),'likes',len(likes),'users',strong.uid.nunique(),likes.uid.nunique())\nfor name,x in [('strong',strong),('likes',likes)]:\n rr=np.array([itemrank.get(int(i),np.nan) for i in x.item_id]); rr=rr[np.isfinite(rr)]\n print(name,'catalog events',len(rr),'rank quantiles',np.quantile(rr,[0,.1,.25,.5,.75,.9,1]).astype(int))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntargets (8456, 3) uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\n 600 1985.0 2423\n 700 1776.0 6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [ 1 207 996 4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [ 1 442 1648 5554 13506 21728 29989]\n", "stderr": "", "aggregated_output": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntargets (8456, 3) uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\n 600 1985.0 2423\n 700 1776.0 6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [ 1 207 996 4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [ 1 442 1648 5554 13506 21728 29989]\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 359904203 }, "formatted_output": "catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\ntargets (8456, 3) uid recent_positive_listens history_positive_listens\n 100 844.0 2604\n 200 50.0 286\n 300 168.0 359\n 600 1985.0 2423\n 700 1776.0 6114\nvalidation (8456,) (8456, 30000) eligible min/mean/max 24657 29067.306291390727 30000\ntest (8456,) (8456, 30000) eligible min/mean/max 24552 29041.3178807947 30000\nval strong raw 255608 likes 17195 users 6325 2714\nstrong catalog events 170791 rank quantiles [ 1 207 996 4079 11258 20059 30000]\nlikes catalog events 8528 rank quantiles [ 1 442 1648 5554 13506 21728 29989]\n" } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-25T00:05:39.409Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:05:39.409Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:05:45.079Z", "callId": "call_rMYJaa1YfYSjhkw1G9gfFrw3", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet\\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\\nfor gr in [1,3]:\\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":6000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:05:48.192Z", "content": "Script completed\nWall time 3.1 seconds\nOutput:\n\nscored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [ 1 408 1693 5545 13274 21656 30000]\n3 4417 [ 2 618 2107 6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:05:45.128Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bad06a1487d2b6e1da1ff43f82b4", "usage": { "input_tokens": 49199, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 340, "reasoning_output_tokens": 17, "total_tokens": 49539 }, "turn_token_usage": { "input_tokens": 480828, "cached_input_tokens": 440576, "cache_write_input_tokens": 0, "output_tokens": 3168, "reasoning_output_tokens": 965, "total_tokens": 483996 }, "thread_token_usage": { "input_tokens": 480828, "cached_input_tokens": 440576, "cache_write_input_tokens": 0, "output_tokens": 3168, "reasoning_output_tokens": 965, "total_tokens": 483996 } } }, { "kind": "context", "timestamp": "2026-09-25T00:05:48.187Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac13f981-e2c2-49c2-8094-6f3830f8215f", "process_id": "76216", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\nfor gr in [1,3]:\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD='/task/data'; t=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); e=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz')); g=validation_truth(e,t)\nvals=[(u,i,v) for u,z in g.items() for i,v in z.items()]\nprint('scored users',len(g),'relevant pairs',len(vals),'grades',pd.Series([v for _,_,v in vals]).value_counts().to_dict())\ncat=pd.read_parquet(D+'/candidate_catalog.parquet'); ranks=dict(zip(cat.item_id,cat.popularity_rank))\nfor gr in [1,3]:\n x=np.array([ranks[i] for _,i,v in vals if v==gr]); print(gr,len(x),np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int))\nprint('rels/user',pd.Series([len(x) for x in g.values()]).describe(percentiles=[.5,.75,.9,.95,.99]).to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [ 1 408 1693 5545 13274 21656 30000]\n3 4417 [ 2 618 2107 6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n", "stderr": "", "aggregated_output": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [ 1 408 1693 5545 13274 21656 30000]\n3 4417 [ 2 618 2107 6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 907586075 }, "formatted_output": "scored users 4263 relevant pairs 37769 grades {1: 33352, 3: 4417}\n1 33352 [ 1 408 1693 5545 13274 21656 30000]\n3 4417 [ 2 618 2107 6371 14181 21801 29989]\nrels/user {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '75%': 10.0, '90%': 21.0, '95%': 31.0, '99%': 63.0, 'max': 442.0}\n" } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T00:05:52.671Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:05:52.671Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:05:53.317Z", "callId": "call_y90p8MDizMSz71Er55vKnAbb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:05:58.326Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"213d39\",\"wall_time_seconds\":5.00152362,\"session_id\":74144,\"original_token_count\":215,\"output\":\"prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\\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_sequences.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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:05:53.381Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5badc634c87d287d0c1b9fee7b647", "usage": { "input_tokens": 49742, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 49797 }, "turn_token_usage": { "input_tokens": 530570, "cached_input_tokens": 489600, "cache_write_input_tokens": 0, "output_tokens": 3223, "reasoning_output_tokens": 972, "total_tokens": 533793 }, "thread_token_usage": { "input_tokens": 530570, "cached_input_tokens": 489600, "cache_write_input_tokens": 0, "output_tokens": 3223, "reasoning_output_tokens": 972, "total_tokens": 533793 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T00:06:13.921Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:06:13.921Z", "text": "" } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T00:06:19.217Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:06:19.217Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:06:19.646Z", "callId": "call_I1MhxygN2lx7tCbCj2oFuQcb", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:06:24.662Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"8cfdfe\",\"wall_time_seconds\":5.001984843,\"session_id\":74144,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:06:19.688Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bae682bc87d2b9c67b30dcfe3c0a", "usage": { "input_tokens": 50127, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 787, "reasoning_output_tokens": 739, "total_tokens": 50914 }, "turn_token_usage": { "input_tokens": 580697, "cached_input_tokens": 539136, "cache_write_input_tokens": 0, "output_tokens": 4010, "reasoning_output_tokens": 1711, "total_tokens": 584707 }, "thread_token_usage": { "input_tokens": 580697, "cached_input_tokens": 539136, "cache_write_input_tokens": 0, "output_tokens": 4010, "reasoning_output_tokens": 1711, "total_tokens": 584707 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T00:06:28.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:06:28.865Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:06:29.442Z", "callId": "call_EuyetEekBkP4qJFzeTHLaV9k", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:06:34.468Z", "content": "Script completed\nWall time 5.0 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-h /app/solution/* 2>/dev/null | sort -h; ls -lh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:07:19.532Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n0\t/app/solution/production_validation_run.json\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\ntotal 511M\n-rw-r--r-- 1 agent agent 0 Sep 25 00:04 production_validation_run.json\n-r--r--r-- 1 agent agent 79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent 16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:07:19.515Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": 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production_validation_run.json\n-r--r--r-- 1 agent agent 79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent 16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3820 }, "formatted_output": "0\t/app/solution/production_validation_run.json\n16K\t/app/solution/two_tower_model.py\n80K\t/app/solution/train_current_model.py\n511M\t/app/solution/validation_sequences.npz\ntotal 511M\n-rw-r--r-- 1 agent agent 0 Sep 25 00:04 production_validation_run.json\n-r--r--r-- 1 agent agent 79K Sep 25 00:04 train_current_model.py\n-r--r--r-- 1 agent agent 16K Sep 25 00:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 25 00:05 validation_sequences.npz\n" } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T00:07:23.212Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:07:23.212Z", "text": "" }, { "kind": "tool", 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"timestamp": "2026-09-25T00:08:57.468Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:08:59.426Z", "text": "The baseline’s main weakness is now concrete: 24/32 negatives are catalog-uniform (mostly easy tail tracks), while only 8 come from observed positives, and those 8 are inverse-propensity downweighted. I’m adding a fixed-size hard-negative component mined from the item tower after a one-epoch warm-up, mixed with uniform and in-batch examples so compute per training row stays at 32 negatives." }, { "kind": "tool", "timestamp": "2026-09-25T00:09:02.382Z", "callId": "call_218JuyP9E51uuafwBh38fhyK", "name": "exec", "input": { "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 Yambda windowed two-tower retriever.\\n@@\\n-The model is a pure two-tower retriever: no user-ID embedding, no output\\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+The model is a pure two-tower retriever: no user-ID embedding, no output\\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\\n+entropy. After a one-epoch warm-up, the negative mix combines uniform,\\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\\n+keeping the production total of 32 negatives per target. Every selected\\n+adjacent target retains a nonzero weight. Only files under the supplied public\\n+directory are read.\\n@@\\n class Config:\\n@@\\n- epochs: int = 1\\n+ epochs: int = 2\\n@@\\n- uniform_negatives: int = 24\\n+ uniform_negatives: int = 8\\n inbatch_negatives: int = 8\\n+ hard_negatives: int = 16\\n+ hard_negative_pool_size: int = 64\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:09:02.425Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\nExit code: 1\nWall time: 0 seconds\nOutput:\nFailed to write file /app/solution/train_current_model.py\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:02.409Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-bb6237f8-6357-4a38-b98a-708efebac6d3", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n \n@@ -12,6 +12,8 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\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 sample-weighted binary cross\n+entropy. After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target. Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n \"\"\"\n@@ -50,3 +52,3 @@\n dropout: float = 0.05\n- epochs: int = 1\n+ epochs: int = 2\n learning_rate: float = 1.0e-3\n@@ -55,4 +57,6 @@\n loss_microbatch_size: int = 65_536\n- uniform_negatives: int = 24\n+ uniform_negatives: int = 8\n inbatch_negatives: int = 8\n+ hard_negatives: int = 16\n+ hard_negative_pool_size: int = 64\n inbatch_correction_power: float = 0.5\n", "move_path": null } }, "status": "failed", "stdout": "", "stderr": "Failed to write file /app/solution/train_current_model.py\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:02.424Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bb40f9fc87d2bcaad5f90d711fda", "usage": { "input_tokens": 53221, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 4255, "reasoning_output_tokens": 3804, "total_tokens": 57476 }, "turn_token_usage": { "input_tokens": 892847, "cached_input_tokens": 847104, "cache_write_input_tokens": 0, "output_tokens": 10128, "reasoning_output_tokens": 7096, "total_tokens": 902975 }, "thread_token_usage": { "input_tokens": 892847, "cached_input_tokens": 847104, "cache_write_input_tokens": 0, "output_tokens": 10128, "reasoning_output_tokens": 7096, "total_tokens": 902975 } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-25T00:09:07.760Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:07.760Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:07.761Z", "callId": "call_hMl8XbOCqFAgoTRdVnGpS5io", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:09:07.801Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:07.794Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bb9ec30887d2a4a3bea65070aea6", "usage": { "input_tokens": 57530, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 19, "total_tokens": 57617 }, "turn_token_usage": { "input_tokens": 950377, "cached_input_tokens": 900096, "cache_write_input_tokens": 0, "output_tokens": 10215, "reasoning_output_tokens": 7115, "total_tokens": 960592 }, "thread_token_usage": { "input_tokens": 950377, "cached_input_tokens": 900096, "cache_write_input_tokens": 0, "output_tokens": 10215, "reasoning_output_tokens": 7115, "total_tokens": 960592 } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:07.797Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74db8ccd-a452-440f-b05d-a8e80940d624", "process_id": "63161", "command": [ "/bin/sh", "-lc", "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3170 }, "formatted_output": "" } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-25T00:09:10.751Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:10.751Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:17.043Z", "callId": "call_yNVDbPlmLppQXHdJ9UrLrkhD", "name": "exec", "input": { "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 Yambda windowed two-tower retriever.\\n@@\\n-The model is a pure two-tower retriever: no user-ID embedding, no output\\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+The model is a pure two-tower retriever: no user-ID embedding, no output\\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\\n+entropy. After a one-epoch warm-up, the negative mix combines uniform,\\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\\n+keeping the production total of 32 negatives per target. Every selected\\n+adjacent target retains a nonzero weight. Only files under the supplied public\\n+directory are read.\\n@@\\n class Config:\\n@@\\n- epochs: int = 1\\n+ epochs: int = 2\\n@@\\n- uniform_negatives: int = 24\\n+ uniform_negatives: int = 8\\n inbatch_negatives: int = 8\\n+ hard_negatives: int = 16\\n+ hard_negative_pool_size: int = 64\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:09:17.140Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:17.063Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-dbee8b8e-ae38-43f3-9960-176aecf6d27a", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower retriever.\n \n@@ -12,6 +12,8 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\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 sample-weighted binary cross\n+entropy. After a one-epoch warm-up, the negative mix combines uniform,\n+propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n+keeping the production total of 32 negatives per target. Every selected\n+adjacent target retains a nonzero weight. Only files under the supplied public\n+directory are read.\n \"\"\"\n@@ -50,3 +52,3 @@\n dropout: float = 0.05\n- epochs: int = 1\n+ epochs: int = 2\n learning_rate: float = 1.0e-3\n@@ -55,4 +57,6 @@\n loss_microbatch_size: int = 65_536\n- uniform_negatives: int = 24\n+ uniform_negatives: int = 8\n inbatch_negatives: int = 8\n+ hard_negatives: int = 16\n+ hard_negative_pool_size: int = 64\n inbatch_correction_power: float = 0.5\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:17.130Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bba4231487d2bf9f637824f3ff87", "usage": { "input_tokens": 57640, "cached_input_tokens": 57344, "cache_write_input_tokens": 0, "output_tokens": 365, "reasoning_output_tokens": 7, "total_tokens": 58005 }, "turn_token_usage": { "input_tokens": 1008017, "cached_input_tokens": 957440, "cache_write_input_tokens": 0, "output_tokens": 10580, "reasoning_output_tokens": 7122, "total_tokens": 1018597 }, "thread_token_usage": { "input_tokens": 1008017, "cached_input_tokens": 957440, "cache_write_input_tokens": 0, "output_tokens": 10580, "reasoning_output_tokens": 7122, "total_tokens": 1018597 } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-25T00:09:23.084Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:23.084Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:23.684Z", "callId": "call_kBfyEVY9rW7SAYJiZ4U5rt54", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '520,765p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":22000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:09:23.796Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:23.728Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbad70d487d2a05e301a62504c08", "usage": { "input_tokens": 58029, "cached_input_tokens": 57472, "cache_write_input_tokens": 0, "output_tokens": 239, "reasoning_output_tokens": 175, "total_tokens": 58268 }, "turn_token_usage": { "input_tokens": 1066046, "cached_input_tokens": 1014912, "cache_write_input_tokens": 0, "output_tokens": 10819, "reasoning_output_tokens": 7297, "total_tokens": 1076865 }, "thread_token_usage": { "input_tokens": 1066046, "cached_input_tokens": 1014912, "cache_write_input_tokens": 0, "output_tokens": 10819, "reasoning_output_tokens": 7297, "total_tokens": 1076865 } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:23.789Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7515276d-f558-4327-8fd6-438650c93262", "process_id": "67927", "command": [ "/bin/sh", "-lc", "sed -n '520,765p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '520,765p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": ") -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n", "stderr": "", "aggregated_output": ") -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3930 }, "formatted_output": ") -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n" } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-25T00:09:26.296Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:26.296Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:44.800Z", "callId": "call_wJ27PND4JGC1z7iF5SPIsRFx", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def build_item_correction(\\n@@\\n return torch.from_numpy(correction)\\n \\n \\n+@torch.no_grad()\\n+def build_hard_negative_lookup(\\n+ model: base.SequentialTwoTower,\\n+ n_items: int,\\n+ pool_size: int,\\n+ device: torch.device,\\n+) -> np.ndarray:\\n+ \\\"\\\"\\\"Mine a compact nearest-neighbor pool in the serving item-vector space.\\n+\\n+ Mining is performed between epochs and never changes the serving score. A\\n+ small row block bounds memory while exact dot products preserve the same\\n+ notion of confusability used by retrieval.\\n+ \\\"\\\"\\\"\\n+ if pool_size < 1 or pool_size >= n_items:\\n+ raise ValueError(\\\"hard-negative pool size must be in [1, n_items)\\\")\\n+ started = time.monotonic()\\n+ model.eval()\\n+ parts: list[torch.Tensor] = []\\n+ for start in range(0, n_items, 2_048):\\n+ rows = torch.arange(\\n+ start,\\n+ min(start + 2_048, n_items),\\n+ dtype=torch.long,\\n+ device=device,\\n+ )\\n+ parts.append(model.encode_items(rows).cpu())\\n+ vectors = torch.cat(parts)\\n+ lookup = np.empty((n_items, pool_size), dtype=np.uint16)\\n+ score_block = 256\\n+ for start in range(0, n_items, score_block):\\n+ end = min(start + score_block, n_items)\\n+ scores = vectors[start:end] @ vectors.T\\n+ local_rows = torch.arange(end - start)\\n+ scores[local_rows, torch.arange(start, end)] = -torch.inf\\n+ lookup[start:end] = torch.topk(\\n+ scores, k=pool_size, dim=1, sorted=True\\n+ ).indices.numpy().astype(np.uint16, copy=False)\\n+ log(\\n+ f\\\"mined item-tower hard-negative lookup: items={n_items:,} \\\"\\n+ f\\\"pool={pool_size} seconds={time.monotonic() - started:.1f}\\\"\\n+ )\\n+ return lookup\\n+\\n+\\n def build_known_positive_filter(\\n@@\\n def draw_full_history_filtered_negatives(\\n@@\\n config: Config,\\n item_correction: torch.Tensor,\\n+ hard_negative_lookup: np.ndarray | None,\\n generator: torch.Generator,\\n@@\\n- negatives = torch.cat([uniform, inbatch], dim=1).numpy()\\n+ hard = torch.empty((batch_size, 0), dtype=torch.long)\\n+ if config.hard_negatives:\\n+ if hard_negative_lookup is None:\\n+ # Epoch one is an intentional baseline warm-up. Keeping the total\\n+ # at 32 also makes its compute directly comparable to production.\\n+ hard = torch.randint(\\n+ n_items,\\n+ (batch_size, config.hard_negatives),\\n+ generator=generator,\\n+ device=\\\"cpu\\\",\\n+ )\\n+ else:\\n+ pool_size = hard_negative_lookup.shape[1]\\n+ priorities = torch.rand(\\n+ (batch_size, pool_size), generator=generator\\n+ )\\n+ columns = torch.topk(\\n+ priorities, k=config.hard_negatives, dim=1\\n+ ).indices.numpy()\\n+ hard = torch.from_numpy(\\n+ hard_negative_lookup[targets_cpu[:, None], columns].astype(\\n+ np.int64, copy=False\\n+ )\\n+ )\\n+\\n+ negatives = torch.cat([uniform, inbatch, hard], dim=1).numpy()\\n@@\\n def draw_training_negatives(\\n@@\\n config: Config,\\n item_correction: torch.Tensor,\\n+ hard_negative_lookup: np.ndarray | None,\\n generator: torch.Generator,\\n@@\\n config,\\n item_correction,\\n+ hard_negative_lookup,\\n generator,\\n@@\\n- return base.draw_filtered_mixed_negatives(\\n+ if config.hard_negatives:\\n+ raise RuntimeError(\\n+ \\\"hard negatives require --negative-filter-scope full-training-history\\\"\\n+ )\\n+ return base.draw_filtered_mixed_negatives(\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:09:44.941Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:44.885Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbb41a3887d2b7dcc773dd716804", "usage": { "input_tokens": 60195, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 1045, "reasoning_output_tokens": 9, "total_tokens": 61240 }, "turn_token_usage": { "input_tokens": 1126241, "cached_input_tokens": 1072768, "cache_write_input_tokens": 0, "output_tokens": 11864, "reasoning_output_tokens": 7306, "total_tokens": 1138105 }, "thread_token_usage": { "input_tokens": 1126241, "cached_input_tokens": 1072768, "cache_write_input_tokens": 0, "output_tokens": 11864, "reasoning_output_tokens": 7306, "total_tokens": 1138105 } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:44.928Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e99f8683-10c8-482f-9baa-84d61efd1a44", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -581,2 +581,46 @@\n \n+@torch.no_grad()\n+def build_hard_negative_lookup(\n+ model: base.SequentialTwoTower,\n+ n_items: int,\n+ pool_size: int,\n+ device: torch.device,\n+) -> np.ndarray:\n+ \"\"\"Mine a compact nearest-neighbor pool in the serving item-vector space.\n+\n+ Mining is performed between epochs and never changes the serving score. A\n+ small row block bounds memory while exact dot products preserve the same\n+ notion of confusability used by retrieval.\n+ \"\"\"\n+ if pool_size < 1 or pool_size >= n_items:\n+ raise ValueError(\"hard-negative pool size must be in [1, n_items)\")\n+ started = time.monotonic()\n+ model.eval()\n+ parts: list[torch.Tensor] = []\n+ for start in range(0, n_items, 2_048):\n+ rows = torch.arange(\n+ start,\n+ min(start + 2_048, n_items),\n+ dtype=torch.long,\n+ device=device,\n+ )\n+ parts.append(model.encode_items(rows).cpu())\n+ vectors = torch.cat(parts)\n+ lookup = np.empty((n_items, pool_size), dtype=np.uint16)\n+ score_block = 256\n+ for start in range(0, n_items, score_block):\n+ end = min(start + score_block, n_items)\n+ scores = vectors[start:end] @ vectors.T\n+ local_rows = torch.arange(end - start)\n+ scores[local_rows, torch.arange(start, end)] = -torch.inf\n+ lookup[start:end] = torch.topk(\n+ scores, k=pool_size, dim=1, sorted=True\n+ ).indices.numpy().astype(np.uint16, copy=False)\n+ log(\n+ f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n+ f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+ )\n+ return lookup\n+\n+\n def build_known_positive_filter(\n@@ -641,2 +685,3 @@\n item_correction: torch.Tensor,\n+ hard_negative_lookup: np.ndarray | None,\n generator: torch.Generator,\n@@ -684,3 +729,28 @@\n \n- negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n+ hard = torch.empty((batch_size, 0), dtype=torch.long)\n+ if config.hard_negatives:\n+ if hard_negative_lookup is None:\n+ # Epoch one is an intentional baseline warm-up. Keeping the total\n+ # at 32 also makes its compute directly comparable to production.\n+ hard = torch.randint(\n+ n_items,\n+ (batch_size, config.hard_negatives),\n+ generator=generator,\n+ device=\"cpu\",\n+ )\n+ else:\n+ pool_size = hard_negative_lookup.shape[1]\n+ priorities = torch.rand(\n+ (batch_size, pool_size), generator=generator\n+ )\n+ columns = torch.topk(\n+ priorities, k=config.hard_negatives, dim=1\n+ ).indices.numpy()\n+ hard = torch.from_numpy(\n+ hard_negative_lookup[targets_cpu[:, None], columns].astype(\n+ np.int64, copy=False\n+ )\n+ )\n+\n+ negatives = torch.cat([uniform, inbatch, hard], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n@@ -736,2 +806,3 @@\n item_correction: torch.Tensor,\n+ hard_negative_lookup: np.ndarray | None,\n generator: torch.Generator,\n@@ -750,2 +821,3 @@\n item_correction,\n+ hard_negative_lookup,\n generator,\n@@ -753,2 +825,6 @@\n )\n+ if config.hard_negatives:\n+ raise RuntimeError(\n+ \"hard negatives require --negative-filter-scope full-training-history\"\n+ )\n return base.draw_filtered_mixed_negatives(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-25T00:09:47.284Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:47.284Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:52.375Z", "callId": "call_Wl5IhR2SHB0E0IpTrdIOlNVo", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def corrected_negative_loss(\\n@@\\n- total = config.uniform_negatives + config.inbatch_negatives\\n+ total = (\\n+ config.uniform_negatives\\n+ + config.inbatch_negatives\\n+ + config.hard_negatives\\n+ )\\n@@\\n if config.inbatch_negatives:\\n- selected = slice(config.uniform_negatives, total)\\n+ selected = slice(\\n+ config.uniform_negatives,\\n+ config.uniform_negatives + config.inbatch_negatives,\\n+ )\\n weights = correction[:, selected]\\n result += config.inbatch_negatives * (\\n (raw[:, selected] * weights).sum(dim=1)\\n / weights.sum(dim=1).clamp_min(1.0e-6)\\n )\\n+ if config.hard_negatives:\\n+ selected = slice(\\n+ config.uniform_negatives + config.inbatch_negatives,\\n+ total,\\n+ )\\n+ result += config.hard_negatives * raw[:, selected].mean(dim=1)\\n return result / total\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:09:52.494Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:52.452Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbc9310c87d2939d57cfdb1850cc", "usage": { "input_tokens": 61264, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 344, "reasoning_output_tokens": 53, "total_tokens": 61608 }, "turn_token_usage": { "input_tokens": 1187505, "cached_input_tokens": 1132800, "cache_write_input_tokens": 0, "output_tokens": 12208, "reasoning_output_tokens": 7359, "total_tokens": 1199713 }, "thread_token_usage": { "input_tokens": 1187505, "cached_input_tokens": 1132800, "cache_write_input_tokens": 0, "output_tokens": 12208, "reasoning_output_tokens": 7359, "total_tokens": 1199713 } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:52.464Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d3e65aa9-9b84-417a-9032-c9d93b607f61", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -930,3 +930,7 @@\n raw = F.softplus(logits)\n- total = config.uniform_negatives + config.inbatch_negatives\n+ total = (\n+ config.uniform_negatives\n+ + config.inbatch_negatives\n+ + config.hard_negatives\n+ )\n result = torch.zeros(len(logits), device=logits.device)\n@@ -937,3 +941,6 @@\n if config.inbatch_negatives:\n- selected = slice(config.uniform_negatives, total)\n+ selected = slice(\n+ config.uniform_negatives,\n+ config.uniform_negatives + config.inbatch_negatives,\n+ )\n weights = correction[:, selected]\n@@ -943,2 +950,8 @@\n )\n+ if config.hard_negatives:\n+ selected = slice(\n+ config.uniform_negatives + config.inbatch_negatives,\n+ total,\n+ )\n+ result += config.hard_negatives * raw[:, selected].mean(dim=1)\n return result / total\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-25T00:09:55.391Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:55.391Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:09:56.903Z", "callId": "call_SoWKJX5WF3PSkufnb0muY2eV", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"rg -n \\\"draw_training_negatives\\\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\\\" /app/solution/train_current_model.py | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":6000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:09:57.010Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n798:def draw_training_negatives(\n821: item_correction,\n837: item_correction,\n1296:def run_training_epoch(\n1320: negative_count = config.uniform_negatives + config.inbatch_negatives\n1386: negatives, negative_weights = draw_training_negatives(\n1394: item_correction,\n1457:def evaluate_validation_bce(\n1542: negatives, negative_weights = draw_training_negatives(\n1550: item_correction,\n1769: item_correction,\n1792: item_correction,\n1806: item_correction,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:09:56.968Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27a3c302-ba6a-4123-ae7f-0545e4d68360", "process_id": "90420", "command": [ "/bin/sh", "-lc", "rg -n \"draw_training_negatives\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\" /app/solution/train_current_model.py | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "search", "cmd": "rg -n \"draw_training_negatives\\\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,\" /app/solution/train_current_model.py", "query": "draw_training_negatives\\(|def run_training_epoch|def evaluate_validation_bce|negative_count =|item_correction,", "path": "train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "798:def draw_training_negatives(\n821: item_correction,\n837: item_correction,\n1296:def run_training_epoch(\n1320: negative_count = config.uniform_negatives + config.inbatch_negatives\n1386: negatives, negative_weights = draw_training_negatives(\n1394: item_correction,\n1457:def evaluate_validation_bce(\n1542: negatives, negative_weights = draw_training_negatives(\n1550: item_correction,\n1769: item_correction,\n1792: item_correction,\n1806: item_correction,\n", "stderr": "", "aggregated_output": "798:def draw_training_negatives(\n821: item_correction,\n837: item_correction,\n1296:def run_training_epoch(\n1320: negative_count = config.uniform_negatives + config.inbatch_negatives\n1386: negatives, negative_weights = draw_training_negatives(\n1394: item_correction,\n1457:def evaluate_validation_bce(\n1542: negatives, negative_weights = draw_training_negatives(\n1550: item_correction,\n1769: item_correction,\n1792: item_correction,\n1806: item_correction,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "798:def draw_training_negatives(\n821: item_correction,\n837: item_correction,\n1296:def run_training_epoch(\n1320: negative_count = config.uniform_negatives + config.inbatch_negatives\n1386: negatives, negative_weights = draw_training_negatives(\n1394: item_correction,\n1457:def evaluate_validation_bce(\n1542: negatives, negative_weights = draw_training_negatives(\n1550: item_correction,\n1769: item_correction,\n1792: item_correction,\n1806: item_correction,\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:09:57.000Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbd0d35487d29e5c8207e2819b34", "usage": { "input_tokens": 61632, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 29, "total_tokens": 61755 }, "turn_token_usage": { "input_tokens": 1249137, "cached_input_tokens": 1193856, "cache_write_input_tokens": 0, "output_tokens": 12331, "reasoning_output_tokens": 7388, "total_tokens": 1261468 }, "thread_token_usage": { "input_tokens": 1249137, "cached_input_tokens": 1193856, "cache_write_input_tokens": 0, "output_tokens": 12331, "reasoning_output_tokens": 7388, "total_tokens": 1261468 } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T00:09:59.330Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:09:59.330Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:01.082Z", "callId": "call_Cn5nWnHTYNjKCH85bzddfXaC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1288,1410p' /app/solution/train_current_model.py; sed -n '1448,1560p' /app/solution/train_current_model.py; sed -n '1680,1820p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":24000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:10:01.187Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:01.164Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbd5ceb487d2ab4dcf1d6a0f0834", "usage": { "input_tokens": 61900, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 115, "reasoning_output_tokens": 9, "total_tokens": 62015 }, "turn_token_usage": { "input_tokens": 1311037, "cached_input_tokens": 1255296, "cache_write_input_tokens": 0, "output_tokens": 12446, "reasoning_output_tokens": 7397, "total_tokens": 1323483 }, "thread_token_usage": { "input_tokens": 1311037, "cached_input_tokens": 1255296, "cache_write_input_tokens": 0, "output_tokens": 12446, "reasoning_output_tokens": 7397, "total_tokens": 1323483 } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:01.178Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e50df1b-9787-4dbd-a185-6fee39a2ce40", "process_id": "18818", "command": [ "/bin/sh", "-lc", "sed -n '1288,1410p' /app/solution/train_current_model.py; sed -n '1448,1560p' /app/solution/train_current_model.py; sed -n '1680,1820p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1288,1410p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1448,1560p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1680,1820p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n", "stderr": "", "aggregated_output": " return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3610 }, "formatted_output": " return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n" } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T00:10:02.939Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:02.939Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:07.371Z", "callId": "call_IMUwbFR3mJOle7IP7fuaTWrW", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def run_training_epoch(\\n@@\\n item_correction: torch.Tensor,\\n known_positive_packed: np.ndarray | None,\\n+ hard_negative_lookup: np.ndarray | None,\\n generator: torch.Generator,\\n@@\\n- negative_count = config.uniform_negatives + config.inbatch_negatives\\n+ negative_count = (\\n+ config.uniform_negatives\\n+ + config.inbatch_negatives\\n+ + config.hard_negatives\\n+ )\\n@@\\n config,\\n item_correction,\\n+ hard_negative_lookup,\\n generator,\\n@@\\n def evaluate_validation_bce(\\n@@\\n item_correction: torch.Tensor,\\n known_positive_packed: np.ndarray | None,\\n+ hard_negative_lookup: np.ndarray | None,\\n device: torch.device,\\n@@\\n config,\\n item_correction,\\n+ hard_negative_lookup,\\n generator,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:10:07.452Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:07.393Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a0c9b9da-e73c-4cd5-acfc-1306331c7d7e", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1305,2 +1305,3 @@\n known_positive_packed: np.ndarray | None,\n+ hard_negative_lookup: np.ndarray | None,\n generator: torch.Generator,\n@@ -1319,3 +1320,7 @@\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n- negative_count = config.uniform_negatives + config.inbatch_negatives\n+ negative_count = (\n+ config.uniform_negatives\n+ + config.inbatch_negatives\n+ + config.hard_negatives\n+ )\n total_loss = 0.0\n@@ -1394,2 +1399,3 @@\n item_correction,\n+ hard_negative_lookup,\n generator,\n@@ -1466,2 +1472,3 @@\n known_positive_packed: np.ndarray | None,\n+ hard_negative_lookup: np.ndarray | None,\n device: torch.device,\n@@ -1550,2 +1557,3 @@\n item_correction,\n+ hard_negative_lookup,\n generator,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:07.445Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbd97ba487d2ba58ac9baf60bc11", "usage": { "input_tokens": 64720, "cached_input_tokens": 61696, "cache_write_input_tokens": 0, "output_tokens": 273, "reasoning_output_tokens": 19, "total_tokens": 64993 }, "turn_token_usage": { "input_tokens": 1375757, "cached_input_tokens": 1316992, "cache_write_input_tokens": 0, "output_tokens": 12719, "reasoning_output_tokens": 7416, "total_tokens": 1388476 }, "thread_token_usage": { "input_tokens": 1375757, "cached_input_tokens": 1316992, "cache_write_input_tokens": 0, "output_tokens": 12719, "reasoning_output_tokens": 7416, "total_tokens": 1388476 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T00:10:11.022Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:11.022Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:12.609Z", "callId": "call_PVlb9KRdsBFbfsoABgCw0ThO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1600,1845p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":26000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:10:12.825Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:12.661Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8dcf4705-177e-4dbc-a283-d1c896f10800", "process_id": "56486", "command": [ "/bin/sh", "-lc", "sed -n '1600,1845p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1600,1845p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n", "stderr": "", "aggregated_output": " config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4190 }, "formatted_output": " config: Config,\n diagnostics: dict[str, float],\n optimizer: torch.optim.Optimizer,\n generator: torch.Generator,\n) -> Path:\n checkpoint_dir.mkdir(parents=True, exist_ok=True)\n path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": model_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n path,\n )\n log(f\"saved epoch checkpoint: {path}\")\n return path\n\n\ndef train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\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 )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:12.821Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbdfcc9487d2b4922f32407e7ff5", "usage": { "input_tokens": 65017, "cached_input_tokens": 64512, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 31, "total_tokens": 65114 }, "turn_token_usage": { "input_tokens": 1440774, "cached_input_tokens": 1381504, "cache_write_input_tokens": 0, "output_tokens": 12816, "reasoning_output_tokens": 7447, "total_tokens": 1453590 }, "thread_token_usage": { "input_tokens": 1440774, "cached_input_tokens": 1381504, "cache_write_input_tokens": 0, "output_tokens": 12816, "reasoning_output_tokens": 7447, "total_tokens": 1453590 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-25T00:10:23.760Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:23.760Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:37.753Z", "callId": "call_OYLVoDTFv7hJQDwPM6z7wCa3", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n epochs_without_improvement = 0\\n start_epoch = 0\\n+ hard_negative_lookup: np.ndarray | None = None\\n@@\\n current_window_values = (\\n@@\\n config.max_validation_targets_per_user,\\n )\\n+ stored_negative_values = (\\n+ stored_config.get(\\\"uniform_negatives\\\", 24),\\n+ stored_config.get(\\\"inbatch_negatives\\\", 8),\\n+ stored_config.get(\\\"hard_negatives\\\", 0),\\n+ stored_config.get(\\\"hard_negative_pool_size\\\", 64),\\n+ )\\n+ current_negative_values = (\\n+ config.uniform_negatives,\\n+ config.inbatch_negatives,\\n+ config.hard_negatives,\\n+ config.hard_negative_pool_size,\\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_negative_values != current_negative_values\\n )\\n model.load_state_dict(payload[\\\"model_state\\\"])\\n+ if config.hard_negatives:\\n+ hard_negative_lookup = build_hard_negative_lookup(\\n+ model,\\n+ len(prepared.item_ids),\\n+ config.hard_negative_pool_size,\\n+ device,\\n+ )\\n@@\\n prior = {\\n@@\\n }\\n- if objective_changed:\\n+ if objective_changed or config.hard_negatives:\\n prior.pop(\\\"validation_loss\\\", None)\\n+ if objective_changed:\\n log(\\n@@\\n \\\"target weighting \\\"\\n f\\\"{stored_target_weighting}->{config.target_weighting}, \\\"\\n f\\\"target window {stored_window_values}->{current_window_values}; \\\"\\n+ f\\\"negative mix {stored_negative_values}->{current_negative_values}; \\\"\\n \\\"recomputing the resume checkpoint's validation BCE\\\"\\n )\\n@@\\n item_correction,\\n known_positive_packed,\\n+ hard_negative_lookup,\\n device,\\n@@\\n \\n for epoch in range(start_epoch + 1, config.epochs + 1):\\n+ if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\\n+ hard_negative_lookup = build_hard_negative_lookup(\\n+ model,\\n+ len(prepared.item_ids),\\n+ config.hard_negative_pool_size,\\n+ device,\\n+ )\\n+ reference = evaluate_validation_bce(\\n+ model,\\n+ prepared,\\n+ config,\\n+ target_weights,\\n+ partition.window_starts,\\n+ training_counts,\\n+ validation_counts,\\n+ item_correction,\\n+ known_positive_packed,\\n+ hard_negative_lookup,\\n+ device,\\n+ )\\n+ best_loss = float(reference[\\\"validation_loss\\\"])\\n+ best_epoch = epoch - 1\\n+ best_state = snapshot_model(model)\\n+ epochs_without_improvement = 0\\n+ log(\\n+ \\\"hard-negative warm-up complete: \\\"\\n+ f\\\"reference_epoch={best_epoch} validation_loss={best_loss:.6f}\\\"\\n+ )\\n diagnostics = run_training_epoch(\\n@@\\n item_correction,\\n known_positive_packed,\\n+ hard_negative_lookup,\\n generator,\\n@@\\n item_correction,\\n known_positive_packed,\\n+ hard_negative_lookup,\\n device,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:10:37.811Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:37.791Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-20dd1e82-05ae-4c84-a3b1-79667700e273", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1676,2 +1676,3 @@\n start_epoch = 0\n+ hard_negative_lookup: np.ndarray | None = None\n \n@@ -1728,2 +1729,14 @@\n )\n+ stored_negative_values = (\n+ stored_config.get(\"uniform_negatives\", 24),\n+ stored_config.get(\"inbatch_negatives\", 8),\n+ stored_config.get(\"hard_negatives\", 0),\n+ stored_config.get(\"hard_negative_pool_size\", 64),\n+ )\n+ current_negative_values = (\n+ config.uniform_negatives,\n+ config.inbatch_negatives,\n+ config.hard_negatives,\n+ config.hard_negative_pool_size,\n+ )\n objective_changed = (\n@@ -1733,4 +1746,12 @@\n or stored_window_values != current_window_values\n+ or stored_negative_values != current_negative_values\n )\n model.load_state_dict(payload[\"model_state\"])\n+ if config.hard_negatives:\n+ hard_negative_lookup = build_hard_negative_lookup(\n+ model,\n+ len(prepared.item_ids),\n+ config.hard_negative_pool_size,\n+ device,\n+ )\n if \"optimizer_state\" in payload and not objective_changed:\n@@ -1756,4 +1777,5 @@\n }\n+ if objective_changed or config.hard_negatives:\n+ prior.pop(\"validation_loss\", None)\n if objective_changed:\n- prior.pop(\"validation_loss\", None)\n log(\n@@ -1764,2 +1786,3 @@\n f\"target window {stored_window_values}->{current_window_values}; \"\n+ f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n@@ -1778,2 +1801,3 @@\n known_positive_packed,\n+ hard_negative_lookup,\n device,\n@@ -1791,2 +1815,30 @@\n for epoch in range(start_epoch + 1, config.epochs + 1):\n+ if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n+ hard_negative_lookup = build_hard_negative_lookup(\n+ model,\n+ len(prepared.item_ids),\n+ config.hard_negative_pool_size,\n+ device,\n+ )\n+ reference = evaluate_validation_bce(\n+ model,\n+ prepared,\n+ config,\n+ target_weights,\n+ partition.window_starts,\n+ training_counts,\n+ validation_counts,\n+ item_correction,\n+ known_positive_packed,\n+ hard_negative_lookup,\n+ device,\n+ )\n+ best_loss = float(reference[\"validation_loss\"])\n+ best_epoch = epoch - 1\n+ best_state = snapshot_model(model)\n+ epochs_without_improvement = 0\n+ log(\n+ \"hard-negative warm-up complete: \"\n+ f\"reference_epoch={best_epoch} validation_loss={best_loss:.6f}\"\n+ )\n diagnostics = run_training_epoch(\n@@ -1801,2 +1853,3 @@\n known_positive_packed,\n+ hard_negative_lookup,\n generator,\n@@ -1815,2 +1868,3 @@\n known_positive_packed,\n+ hard_negative_lookup,\n device,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:37.806Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbe568d887d2b2cb329c073f0562", "usage": { "input_tokens": 66804, "cached_input_tokens": 64896, "cache_write_input_tokens": 0, "output_tokens": 1240, "reasoning_output_tokens": 392, "total_tokens": 68044 }, "turn_token_usage": { "input_tokens": 1507578, "cached_input_tokens": 1446400, "cache_write_input_tokens": 0, "output_tokens": 14056, "reasoning_output_tokens": 7839, "total_tokens": 1521634 }, "thread_token_usage": { "input_tokens": 1507578, "cached_input_tokens": 1446400, "cache_write_input_tokens": 0, "output_tokens": 14056, "reasoning_output_tokens": 7839, "total_tokens": 1521634 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-25T00:10:40.120Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:40.120Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:41.132Z", "callId": "call_VDKyTC2AMFHqdVPxb8JgRDmp", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '2010,2215p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":24000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:10:41.256Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:41.182Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ea55d23c-1f08-4fe5-a828-9ec5cdad6687", "process_id": "54899", "command": [ "/bin/sh", "-lc", "sed -n '2010,2215p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '2010,2215p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n", "stderr": "", "aggregated_output": " )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4540 }, "formatted_output": " )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:41.251Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bbfe2a8887d29fd8de8949ee7f7a", "usage": { "input_tokens": 68068, "cached_input_tokens": 66688, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 20, "total_tokens": 68154 }, "turn_token_usage": { "input_tokens": 1575646, "cached_input_tokens": 1513088, "cache_write_input_tokens": 0, "output_tokens": 14142, "reasoning_output_tokens": 7859, "total_tokens": 1589788 }, "thread_token_usage": { "input_tokens": 1575646, "cached_input_tokens": 1513088, "cache_write_input_tokens": 0, "output_tokens": 14142, "reasoning_output_tokens": 7859, "total_tokens": 1589788 } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-25T00:10:43.707Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:43.707Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:52.988Z", "callId": "call_4nIFc2YMEgfUXW6lS8HjZiDv", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n type=int,\\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+ \\\"--hard-negatives\\\", type=int, default=Config.hard_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-pool-size\\\",\\n+ type=int,\\n+ default=Config.hard_negative_pool_size,\\n+ )\\n@@\\n or args.max_train_targets_per_user < 0\\n or args.max_validation_targets_per_user < 0\\n+ or args.uniform_negatives < 0\\n+ or args.inbatch_negatives < 0\\n+ or args.hard_negatives < 0\\n+ or args.hard_negative_pool_size < args.hard_negatives\\n+ or args.hard_negative_pool_size >= 30_000\\n+ or (\\n+ args.uniform_negatives\\n+ + args.inbatch_negatives\\n+ + args.hard_negatives\\n+ < 1\\n+ )\\n ):\\n parser.error(\\\"training counts, learning rate, and patience are invalid\\\")\\n@@\\n if not 0.0 < args.validation_fraction < 1.0:\\n parser.error(\\\"validation fraction must be strictly between zero and one\\\")\\n+ if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":\\n+ parser.error(\\\"hard negatives require full-training-history filtering\\\")\\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+ hard_negatives=args.hard_negatives,\\n+ hard_negative_pool_size=args.hard_negative_pool_size,\\n negative_filter_scope=args.negative_filter_scope,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:10:53.056Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:53.007Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-587d56a7-a766-4289-8180-b98347300d0c", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2098,2 +2098,16 @@\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+ \"--hard-negatives\", type=int, default=Config.hard_negatives\n+ )\n+ parser.add_argument(\n+ \"--hard-negative-pool-size\",\n+ type=int,\n+ default=Config.hard_negative_pool_size,\n+ )\n+ parser.add_argument(\n \"--negative-filter-scope\",\n@@ -2175,2 +2189,13 @@\n or args.max_validation_targets_per_user < 0\n+ or args.uniform_negatives < 0\n+ or args.inbatch_negatives < 0\n+ or args.hard_negatives < 0\n+ or args.hard_negative_pool_size < args.hard_negatives\n+ or args.hard_negative_pool_size >= 30_000\n+ or (\n+ args.uniform_negatives\n+ + args.inbatch_negatives\n+ + args.hard_negatives\n+ < 1\n+ )\n ):\n@@ -2179,2 +2204,4 @@\n parser.error(\"validation fraction must be strictly between zero and one\")\n+ if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n+ parser.error(\"hard negatives require full-training-history filtering\")\n public = args.public_dir.resolve()\n@@ -2188,2 +2215,6 @@\n loss_microbatch_size=args.loss_microbatch_size,\n+ uniform_negatives=args.uniform_negatives,\n+ inbatch_negatives=args.inbatch_negatives,\n+ hard_negatives=args.hard_negatives,\n+ hard_negative_pool_size=args.hard_negative_pool_size,\n negative_filter_scope=args.negative_filter_scope,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:53.049Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc01802487d2ac5a259eb4b29783", "usage": { "input_tokens": 70013, "cached_input_tokens": 67840, "cache_write_input_tokens": 0, "output_tokens": 581, "reasoning_output_tokens": 52, "total_tokens": 70594 }, "turn_token_usage": { "input_tokens": 1645659, "cached_input_tokens": 1580928, "cache_write_input_tokens": 0, "output_tokens": 14723, "reasoning_output_tokens": 7911, "total_tokens": 1660382 }, "thread_token_usage": { "input_tokens": 1645659, "cached_input_tokens": 1580928, "cache_write_input_tokens": 0, "output_tokens": 14723, "reasoning_output_tokens": 7911, "total_tokens": 1660382 } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-25T00:10:55.057Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:55.057Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:10:56.604Z", "callId": "call_RaD3o5hp7zn9aJR7mFLi3JX8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '2250,2325p' /app/solution/train_current_model.py; rg -n \\\"evaluate_validation_bce\\\\(|run_training_epoch\\\\(|draw_training_negatives\\\\(\\\" /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:10:56.701Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391: negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549: negatives, negative_weights = draw_training_negatives(\n1792: evaluate_validation_bce(\n1823: reference = evaluate_validation_bce(\n1844: diagnostics = run_training_epoch(\n1859: validation = evaluate_validation_bce(\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:10:56.664Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1a6f882c-0b81-4231-ba7c-1b9830a0244b", "process_id": "74270", "command": [ "/bin/sh", "-lc", "sed -n '2250,2325p' /app/solution/train_current_model.py; rg -n \"evaluate_validation_bce\\(|run_training_epoch\\(|draw_training_negatives\\(\" /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '2250,2325p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "search", "cmd": "rg -n \"evaluate_validation_bce\\\\(|run_training_epoch\\\\(|draw_training_negatives\\\\(\" /app/solution/train_current_model.py", "query": "evaluate_validation_bce\\(|run_training_epoch\\(|draw_training_negatives\\(", "path": "train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391: negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549: negatives, negative_weights = draw_training_negatives(\n1792: evaluate_validation_bce(\n1823: reference = evaluate_validation_bce(\n1844: diagnostics = run_training_epoch(\n1859: validation = evaluate_validation_bce(\n", "stderr": "", "aggregated_output": " config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391: negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549: negatives, negative_weights = draw_training_negatives(\n1792: evaluate_validation_bce(\n1823: reference = evaluate_validation_bce(\n1844: diagnostics = run_training_epoch(\n1859: validation = evaluate_validation_bce(\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4581 }, "formatted_output": " config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\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\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n798:def draw_training_negatives(\n1296:def run_training_epoch(\n1391: negatives, negative_weights = draw_training_negatives(\n1463:def evaluate_validation_bce(\n1549: negatives, negative_weights = draw_training_negatives(\n1792: evaluate_validation_bce(\n1823: reference = evaluate_validation_bce(\n1844: diagnostics = run_training_epoch(\n1859: validation = evaluate_validation_bce(\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:10:56.698Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc0d5d8487d2a4537391785b3dbc", "usage": { "input_tokens": 70618, "cached_input_tokens": 69888, "cache_write_input_tokens": 0, "output_tokens": 127, "reasoning_output_tokens": 31, "total_tokens": 70745 }, "turn_token_usage": { "input_tokens": 1716277, "cached_input_tokens": 1650816, "cache_write_input_tokens": 0, "output_tokens": 14850, "reasoning_output_tokens": 7942, "total_tokens": 1731127 }, "thread_token_usage": { "input_tokens": 1716277, "cached_input_tokens": 1650816, "cache_write_input_tokens": 0, "output_tokens": 14850, "reasoning_output_tokens": 7942, "total_tokens": 1731127 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-25T00:10:58.565Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:10:58.565Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:02.136Z", "callId": "call_LXWN52gTtDLtDU4CxPxrnS44", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- \\\"baseline\\\": \\\"yambda-windowed-sequence-two-tower-base\\\",\\n+ \\\"model\\\": \\\"yambda-windowed-sequence-two-tower-hard-negative\\\",\\n@@\\n \\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",\\n- \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n- ),\\n+ \\\"negative_sampling\\\": {\\n+ \\\"uniform\\\": config.uniform_negatives,\\n+ \\\"propensity_corrected_inbatch\\\": config.inbatch_negatives,\\n+ \\\"item_tower_hard\\\": config.hard_negatives,\\n+ \\\"hard_pool\\\": config.hard_negative_pool_size,\\n+ },\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:11:02.245Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:02.202Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-435916ee-0429-4315-9c36-82233ece6c16", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2299,3 +2299,3 @@\n {\n- \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+ \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": args.stage,\n@@ -2321,5 +2321,8 @@\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n- \"negative_sampling\": (\n- \"24-uniform-plus-8-propensity-corrected-inbatch\"\n- ),\n+ \"negative_sampling\": {\n+ \"uniform\": config.uniform_negatives,\n+ \"propensity_corrected_inbatch\": config.inbatch_negatives,\n+ \"item_tower_hard\": config.hard_negatives,\n+ \"hard_pool\": config.hard_negative_pool_size,\n+ },\n \"false_negative_filter\": config.negative_filter_scope,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:11:02.224Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc11075087d285edebe15bd41439", "usage": { "input_tokens": 71436, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 217, "reasoning_output_tokens": 10, "total_tokens": 71653 }, "turn_token_usage": { "input_tokens": 1787713, "cached_input_tokens": 1721216, "cache_write_input_tokens": 0, "output_tokens": 15067, "reasoning_output_tokens": 7952, "total_tokens": 1802780 }, "thread_token_usage": { "input_tokens": 1787713, "cached_input_tokens": 1721216, "cache_write_input_tokens": 0, "output_tokens": 15067, "reasoning_output_tokens": 7952, "total_tokens": 1802780 } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T00:11:04.303Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:04.303Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T00:11:04.610Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17b5a8eb-1a26-4d9a-8f33-1d9a64f48499", "process_id": "74144", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/baseline_checkpoints --output /app/solution/production_validation.parquet > /app/solution/production_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared 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=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\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_sequences.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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\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=218.6 validation_seconds=17.1\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", "stderr": "", "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared 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=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\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_sequences.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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\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=218.6 validation_seconds=17.1\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", "exit_code": 0, "duration": { "secs": 380, "nanos": 885933313 }, "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared 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=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.9s\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_sequences.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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\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=218.6 validation_seconds=17.1\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" } } }, { "kind": "tool", "timestamp": "2026-09-25T00:11:06.056Z", "callId": "call_13UULgPgH1uMUKDiYuKYcqZf", "name": "exec", "input": { "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 && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:11:10.256Z", "content": "Script completed\nWall time 4.2 seconds\nOutput:\n\nusage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-negatives HARD_NEGATIVES]\n [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-negatives HARD_NEGATIVES\n --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:06.139Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc1676e087d2b9538c3c1269238e", "usage": { "input_tokens": 71677, "cached_input_tokens": 71296, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 16, "total_tokens": 71799 }, "turn_token_usage": { "input_tokens": 1859390, "cached_input_tokens": 1792512, "cache_write_input_tokens": 0, "output_tokens": 15189, "reasoning_output_tokens": 7968, "total_tokens": 1874579 }, "thread_token_usage": { "input_tokens": 1859390, "cached_input_tokens": 1792512, "cache_write_input_tokens": 0, "output_tokens": 15189, "reasoning_output_tokens": 7968, "total_tokens": 1874579 } } }, { "kind": "context", "timestamp": "2026-09-25T00:11:10.252Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-00356ba2-8b8d-4af1-87a4-a9a8f9237fda", "process_id": "92247", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-negatives HARD_NEGATIVES]\n [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-negatives HARD_NEGATIVES\n --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n", "stderr": "", "aggregated_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-negatives HARD_NEGATIVES]\n [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-negatives HARD_NEGATIVES\n --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 32217844 }, "formatted_output": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--uniform-negatives UNIFORM_NEGATIVES]\n [--inbatch-negatives INBATCH_NEGATIVES]\n [--hard-negatives HARD_NEGATIVES]\n [--hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower retriever. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, and sample-weighted binary\ncross entropy. After a one-epoch warm-up, the negative mix combines uniform,\npropensity-corrected in-batch, and item-tower-neighbor hard negatives while\nkeeping the production total of 32 negatives per target. Every selected\nadjacent target retains a nonzero weight. Only files under the supplied public\ndirectory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --uniform-negatives UNIFORM_NEGATIVES\n --inbatch-negatives INBATCH_NEGATIVES\n --hard-negatives HARD_NEGATIVES\n --hard-negative-pool-size HARD_NEGATIVE_POOL_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-25T00:11:13.004Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:13.004Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:13.714Z", "callId": "call_OdjIDSLB3A1t8DHWu50AwYvY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":74144,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:11:13.795Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"243e5a\",\"wall_time_seconds\":0.00000225,\"exit_code\":0,\"original_token_count\":155,\"output\":\"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.2s\\nsaved epoch checkpoint: /app/solution/baseline_checkpoints/epoch-01.pt\\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=218.6 validation_seconds=17.1\\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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:13.794Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc1e768087d2ae6466bde804e585", "usage": { "input_tokens": 72807, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 39, "total_tokens": 72894 }, "turn_token_usage": { "input_tokens": 1932197, "cached_input_tokens": 1864064, "cache_write_input_tokens": 0, "output_tokens": 15276, "reasoning_output_tokens": 8007, "total_tokens": 1947473 }, "thread_token_usage": { "input_tokens": 1932197, "cached_input_tokens": 1864064, "cache_write_input_tokens": 0, "output_tokens": 15276, "reasoning_output_tokens": 8007, "total_tokens": 1947473 } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-25T00:11:16.010Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:16.010Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:17.792Z", "callId": "call_25hlgL3Ly8awSFkFdKioCH5m", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:11:21.166Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\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\": 376.675,\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_loss\": 0.4966362165382877,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.11805218400002\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570281028747559,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:17.877Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc220ae887d2a47ea4685c6ee00d", "usage": { "input_tokens": 73184, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 120, "reasoning_output_tokens": 13, "total_tokens": 73304 }, "turn_token_usage": { "input_tokens": 2005381, "cached_input_tokens": 1936640, "cache_write_input_tokens": 0, "output_tokens": 15396, "reasoning_output_tokens": 8020, "total_tokens": 2020777 }, "thread_token_usage": { "input_tokens": 2005381, "cached_input_tokens": 1936640, "cache_write_input_tokens": 0, "output_tokens": 15396, "reasoning_output_tokens": 8020, "total_tokens": 2020777 } } }, { "kind": "context", "timestamp": "2026-09-25T00:11:21.153Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-238036ff-81c4-4a31-b946-97661394c632", "process_id": "84134", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet && sed -n '1,260p' /app/solution/production_validation_run.json && ls -lh /app/solution/baseline_checkpoints" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\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\": 376.675,\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_loss\": 0.4966362165382877,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.11805218400002\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570281028747559,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\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\": 376.675,\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_loss\": 0.4966362165382877,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.11805218400002\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570281028747559,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 197006522 }, "formatted_output": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\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\": 376.675,\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_loss\": 0.4966362165382877,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.11805218400002\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.603016972541809,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570281028747559,\n \"item_norm_std\": 0.8279485702514648\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 25 00:09 epoch-01.pt\n" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T00:11:24.751Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:24.751Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:11:26.430Z", "text": "The exact public baseline is `0.021280` NDCG@10 over 4,263 scored users. I’ve preserved its ranking and checkpoint. The first controlled experiment will warm-start that exact model and change only the negative mix: 8 uniform, 8 corrected in-batch, and 16 item-tower-neighbor negatives drawn from each positive’s top-64 confusable items." }, { "kind": "tool", "timestamp": "2026-09-25T00:11:28.623Z", "callId": "call_AVQlFeHpJfqIHPBYXFai7RUv", "name": "exec", "input": { "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_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/hard_checkpoints --output /app/solution/hard_validation.parquet > /app/solution/hard_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:11:29.790Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"c27bd2\",\"wall_time_seconds\":1.001959384,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:28.697Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc295c5487d2802a10041418b4f6", "usage": { "input_tokens": 74377, "cached_input_tokens": 72960, "cache_write_input_tokens": 0, "output_tokens": 355, "reasoning_output_tokens": 135, "total_tokens": 74732 }, "turn_token_usage": { "input_tokens": 2079758, "cached_input_tokens": 2009600, "cache_write_input_tokens": 0, "output_tokens": 15751, "reasoning_output_tokens": 8155, "total_tokens": 2095509 }, "thread_token_usage": { "input_tokens": 2079758, "cached_input_tokens": 2009600, "cache_write_input_tokens": 0, "output_tokens": 15751, "reasoning_output_tokens": 8155, "total_tokens": 2095509 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-25T00:11:31.729Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:31.729Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:32.428Z", "callId": "call_crSk5QbFnX7LR8tOLQPuMuF9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:11:37.446Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"024e1f\",\"wall_time_seconds\":5.001871626,\"session_id\":71321,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:32.507Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc320a2887d2aecd605b455060e4", "usage": { "input_tokens": 74790, "cached_input_tokens": 74240, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 18, "total_tokens": 74856 }, "turn_token_usage": { "input_tokens": 2154548, "cached_input_tokens": 2083840, "cache_write_input_tokens": 0, "output_tokens": 15817, "reasoning_output_tokens": 8173, "total_tokens": 2170365 }, "thread_token_usage": { "input_tokens": 2154548, "cached_input_tokens": 2083840, "cache_write_input_tokens": 0, "output_tokens": 15817, "reasoning_output_tokens": 8173, "total_tokens": 2170365 } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-25T00:11:39.179Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:39.179Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:39.890Z", "callId": "call_kZZFFKncf4o2mYVlpt7nAYlH", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:11:44.904Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"807658\",\"wall_time_seconds\":5.000906454,\"session_id\":71321,\"original_token_count\":107,\"output\":\"mined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:39.978Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc39bdc487d2b158cf3aac34f548", "usage": { "input_tokens": 75048, "cached_input_tokens": 74624, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 14, "total_tokens": 75110 }, "turn_token_usage": { "input_tokens": 2229596, "cached_input_tokens": 2158464, "cache_write_input_tokens": 0, "output_tokens": 15879, "reasoning_output_tokens": 8187, "total_tokens": 2245475 }, "thread_token_usage": { "input_tokens": 2229596, "cached_input_tokens": 2158464, "cache_write_input_tokens": 0, "output_tokens": 15879, "reasoning_output_tokens": 8187, "total_tokens": 2245475 } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-25T00:11:47.035Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:11:47.035Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:11:47.620Z", "callId": "call_ZZpXfuOIIXhprKU2K9HwXwTI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:11:52.652Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"f71153\",\"wall_time_seconds\":5.002023595,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:11:47.712Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc411a3887d2b8590891027de41b", "usage": { "input_tokens": 75293, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 21, "total_tokens": 75362 }, "turn_token_usage": { "input_tokens": 2304889, "cached_input_tokens": 2233344, "cache_write_input_tokens": 0, "output_tokens": 15948, "reasoning_output_tokens": 8208, "total_tokens": 2320837 }, "thread_token_usage": { "input_tokens": 2304889, "cached_input_tokens": 2233344, "cache_write_input_tokens": 0, "output_tokens": 15948, "reasoning_output_tokens": 8208, "total_tokens": 2320837 } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-25T00:12:05.188Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:12:05.188Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:12:11.798Z", "callId": "call_8LppJGSKgrROSCBfsnmfpA25", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\\nhits=[]\\nfor uid,item,rank in r.itertuples(index=False,name=None):\\n gr=g.get(int(uid),{}).get(int(item),0)\\n if gr: hits.append((int(uid),int(item),int(rank),gr))\\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\\n# candidate global popularity rank recall among truth and ranked\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\\nh['poprank']=[pr[x] for x in h.item]\\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:12:15.693Z", "content": "Script completed\nWall time 3.9 seconds\nOutput:\n\ntop100 hits 2619 users 1079 top10 hits 644 top10 users 378\n count mean median\ngrade \n1 2314 37.986603 32.0\n3 305 38.062295 32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:12:11.848Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc48d9e487d29ed8b30f48e241e7", "usage": { "input_tokens": 75419, "cached_input_tokens": 75136, "cache_write_input_tokens": 0, "output_tokens": 909, "reasoning_output_tokens": 509, "total_tokens": 76328 }, "turn_token_usage": { "input_tokens": 2380308, "cached_input_tokens": 2308480, "cache_write_input_tokens": 0, "output_tokens": 16857, "reasoning_output_tokens": 8717, "total_tokens": 2397165 }, "thread_token_usage": { "input_tokens": 2380308, "cached_input_tokens": 2308480, "cache_write_input_tokens": 0, "output_tokens": 16857, "reasoning_output_tokens": 8717, "total_tokens": 2397165 } } }, { "kind": "context", "timestamp": "2026-09-25T00:12:15.658Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-62d5aab7-83e0-4bed-9f7d-c0b56119257d", "process_id": "72033", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nhits=[]\nfor uid,item,rank in r.itertuples(index=False,name=None):\n gr=g.get(int(uid),{}).get(int(item),0)\n if gr: hits.append((int(uid),int(item),int(rank),gr))\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\n# candidate global popularity rank recall among truth and ranked\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\nh['poprank']=[pr[x] for x in h.item]\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nhits=[]\nfor uid,item,rank in r.itertuples(index=False,name=None):\n gr=g.get(int(uid),{}).get(int(item),0)\n if gr: hits.append((int(uid),int(item),int(rank),gr))\nh=pd.DataFrame(hits,columns=['uid','item','rank','grade'])\nprint('top100 hits',len(h),'users',h.uid.nunique(),'top10 hits',len(h[h['rank']<=10]),'top10 users',h[h['rank']<=10].uid.nunique())\nprint(h.groupby('grade')['rank'].agg(['count','mean','median']).to_string())\nprint('grade top10',h[h['rank']<=10].grade.value_counts().to_dict())\n# candidate global popularity rank recall among truth and ranked\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id,cat.popularity_rank))\nh['poprank']=[pr[x] for x in h.item]\nprint('hit poprank',h.groupby('grade').poprank.median().to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n count mean median\ngrade \n1 2314 37.986603 32.0\n3 305 38.062295 32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n", "stderr": "", "aggregated_output": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n count mean median\ngrade \n1 2314 37.986603 32.0\n3 305 38.062295 32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 682882347 }, "formatted_output": "top100 hits 2619 users 1079 top10 hits 644 top10 users 378\n count mean median\ngrade \n1 2314 37.986603 32.0\n3 305 38.062295 32.0\ngrade top10 {1: 564, 3: 80}\nhit poprank {1: 312.5, 3: 618.0}\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-25T00:12:22.212Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:12:22.212Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:12:26.596Z", "callId": "call_bNAGg6YfKgszxH5pO01zhhdn", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n for start in range(0, n_items, score_block):\\n@@\\n lookup[start:end] = torch.topk(\\n scores, k=pool_size, dim=1, sorted=True\\n ).indices.numpy().astype(np.uint16, copy=False)\\n+ sampled = lookup[:, :: max(pool_size // 8, 1)].reshape(-1).astype(np.int64)\\n log(\\n f\\\"mined item-tower hard-negative lookup: items={n_items:,} \\\"\\n- f\\\"pool={pool_size} seconds={time.monotonic() - started:.1f}\\\"\\n+ f\\\"pool={pool_size} unique={len(np.unique(lookup)):,} \\\"\\n+ f\\\"sampled_popularity_rank_median={float(np.median(sampled + 1)):.0f} \\\"\\n+ f\\\"seconds={time.monotonic() - started:.1f}\\\"\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:12:26.683Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:12:26.621Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a5a4be26-9777-4930-bf18-01552e032abd", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -618,5 +618,8 @@\n ).indices.numpy().astype(np.uint16, copy=False)\n+ sampled = lookup[:, :: max(pool_size // 8, 1)].reshape(-1).astype(np.int64)\n log(\n f\"mined item-tower hard-negative lookup: items={n_items:,} \"\n- f\"pool={pool_size} seconds={time.monotonic() - started:.1f}\"\n+ f\"pool={pool_size} unique={len(np.unique(lookup)):,} \"\n+ f\"sampled_popularity_rank_median={float(np.median(sampled + 1)):.0f} \"\n+ f\"seconds={time.monotonic() - started:.1f}\"\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:12:26.659Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc5fe05887d2a3c42ff270b90913", "usage": { "input_tokens": 76448, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 257, "total_tokens": 76961 }, "turn_token_usage": { "input_tokens": 2456756, "cached_input_tokens": 2383744, "cache_write_input_tokens": 0, "output_tokens": 17370, "reasoning_output_tokens": 8974, "total_tokens": 2474126 }, "thread_token_usage": { "input_tokens": 2456756, "cached_input_tokens": 2383744, "cache_write_input_tokens": 0, "output_tokens": 17370, "reasoning_output_tokens": 8974, "total_tokens": 2474126 } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-25T00:12:30.432Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:12:30.432Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:12:31.105Z", "callId": "call_8n7Bkcy9LdQZ0WQMN4RknSM1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:12:36.122Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"176898\",\"wall_time_seconds\":5.001526965,\"session_id\":71321,\"original_token_count\":14,\"output\":\"resumed validation-BCE training: epoch=1 loss=0.749658\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:12:31.212Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc6b1a3887d283327a9861f72f6d", "usage": { "input_tokens": 76985, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 22, "total_tokens": 77055 }, "turn_token_usage": { "input_tokens": 2533741, "cached_input_tokens": 2460032, "cache_write_input_tokens": 0, "output_tokens": 17440, "reasoning_output_tokens": 8996, "total_tokens": 2551181 }, "thread_token_usage": { "input_tokens": 2533741, "cached_input_tokens": 2460032, "cache_write_input_tokens": 0, "output_tokens": 17440, "reasoning_output_tokens": 8996, "total_tokens": 2551181 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-25T00:12:38.931Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:12:38.931Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:12:42.281Z", "callId": "call_AyPNtQtiz8R4uimkHriyNNQz", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\\nfor k in [1,10,100]:\\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:12:44.654Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n1 poprank q [ 1 4 4 22 2395 7317 29868] unique 161\n10 poprank q [ 1 5 14 111 2124 7317 29868] unique 592\n100 poprank q [ 1 33 102 413 2627 9774 29992] unique 2575\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:12:42.459Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc7460b887d2aecdcfd1545b0b08", "usage": { "input_tokens": 77127, "cached_input_tokens": 76800, "cache_write_input_tokens": 0, "output_tokens": 275, "reasoning_output_tokens": 80, "total_tokens": 77402 }, "turn_token_usage": { "input_tokens": 2610868, "cached_input_tokens": 2536832, "cache_write_input_tokens": 0, "output_tokens": 17715, "reasoning_output_tokens": 9076, "total_tokens": 2628583 }, "thread_token_usage": { "input_tokens": 2610868, "cached_input_tokens": 2536832, "cache_write_input_tokens": 0, "output_tokens": 17715, "reasoning_output_tokens": 9076, "total_tokens": 2628583 } } }, { "kind": "context", "timestamp": "2026-09-25T00:12:44.650Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a8cdf90-09a1-4628-9521-4e9b290660df", "process_id": "60781", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\nfor k in [1,10,100]:\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank)); r=pd.read_parquet('/app/solution/production_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\nfor k in [1,10,100]:\n x=p[r['rank'].to_numpy()<=k]; print(k, 'poprank q',np.quantile(x,[0,.1,.25,.5,.75,.9,1]).astype(int), 'unique',len(np.unique(x)))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1 poprank q [ 1 4 4 22 2395 7317 29868] unique 161\n10 poprank q [ 1 5 14 111 2124 7317 29868] unique 592\n100 poprank q [ 1 33 102 413 2627 9774 29992] unique 2575\n", "stderr": "", "aggregated_output": "1 poprank q [ 1 4 4 22 2395 7317 29868] unique 161\n10 poprank q [ 1 5 14 111 2124 7317 29868] unique 592\n100 poprank q [ 1 33 102 413 2627 9774 29992] unique 2575\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 195806433 }, "formatted_output": "1 poprank q [ 1 4 4 22 2395 7317 29868] unique 161\n10 poprank q [ 1 5 14 111 2124 7317 29868] unique 592\n100 poprank q [ 1 33 102 413 2627 9774 29992] unique 2575\n" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-25T00:12:59.160Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:12:59.160Z", "text": "" } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-25T00:13:00.728Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:13:00.728Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:13:01.801Z", "text": "While the hard-negative epoch trains, I’m placing a schema- and eligibility-valid popularity fallback at the required submission path. It is only deadline insurance and will be replaced by the refitted two-tower ranking once the winning objective is selected." }, { "kind": "tool", "timestamp": "2026-09-25T00:13:09.287Z", "callId": "call_dhHrJgAPIN2Dwtc9YnCgN3SU", "name": "exec", "input": { "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\\nout=Path('/app/solution/submission.parquet')\\nwith np.load('/task/data/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 chosen=np.flatnonzero(eligible)[:100]\\n rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\\nprint(out, len(rows), len(uids))\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys,pandas as pd,numpy as np\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:13:16.599Z", "content": "Script completed\nWall time 7.3 seconds\nOutput:\n\n/app/solution/submission.parquet 845600 8456\n{'rows': 845600, 'users': 8456, 'eligible': True}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:13:09.385Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc7cd4d087d29022b7c2304cf710", "usage": { "input_tokens": 77525, "cached_input_tokens": 76928, "cache_write_input_tokens": 0, "output_tokens": 1088, "reasoning_output_tokens": 608, "total_tokens": 78613 }, "turn_token_usage": { "input_tokens": 2688393, "cached_input_tokens": 2613760, "cache_write_input_tokens": 0, "output_tokens": 18803, "reasoning_output_tokens": 9684, "total_tokens": 2707196 }, "thread_token_usage": { "input_tokens": 2688393, "cached_input_tokens": 2613760, "cache_write_input_tokens": 0, "output_tokens": 18803, "reasoning_output_tokens": 9684, "total_tokens": 2707196 } } }, { "kind": "context", "timestamp": "2026-09-25T00:13:16.590Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a3271ac1-84ae-42fa-8365-9bb121868af1", "process_id": "82307", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nout=Path('/app/solution/submission.parquet')\nwith np.load('/task/data/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 chosen=np.flatnonzero(eligible)[:100]\n rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\nprint(out, len(rows), len(uids))\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nout=Path('/app/solution/submission.parquet')\nwith np.load('/task/data/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 chosen=np.flatnonzero(eligible)[:100]\n rows.extend((int(uid),int(items[col]),rank) for rank,col in enumerate(chosen,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False,compression='zstd')\nprint(out, len(rows), len(uids))\nPY\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); f=pd.read_parquet('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(f,targets); e=CandidateSet(D/'test_eligible_candidates.npz'); assert all(e.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print({'rows':len(f),'users':f.uid.nunique(),'eligible':True})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/submission.parquet 845600 8456\n{'rows': 845600, 'users': 8456, 'eligible': True}\n", "stderr": "", "aggregated_output": "/app/solution/submission.parquet 845600 8456\n{'rows': 845600, 'users': 8456, 'eligible': True}\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 121715301 }, "formatted_output": "/app/solution/submission.parquet 845600 8456\n{'rows': 845600, 'users': 8456, 'eligible': True}\n" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T00:13:18.857Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:13:18.857Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:13:19.565Z", "callId": "call_oauSX8vmTeNcfoimAVQVPbuX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:13:24.592Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"05b90a\",\"wall_time_seconds\":5.001427367,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:13:19.658Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bc9cc7cc87d2bb1d97fff01c45a0", "usage": { "input_tokens": 78669, "cached_input_tokens": 77312, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 78730 }, "turn_token_usage": { "input_tokens": 2767062, "cached_input_tokens": 2691072, "cache_write_input_tokens": 0, "output_tokens": 18864, "reasoning_output_tokens": 9697, "total_tokens": 2785926 }, "thread_token_usage": { "input_tokens": 2767062, "cached_input_tokens": 2691072, "cache_write_input_tokens": 0, "output_tokens": 18864, "reasoning_output_tokens": 9697, "total_tokens": 2785926 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T00:13:38.679Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:13:38.679Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:13:39.039Z", "callId": "call_bGLtVBpWjW7DrvsiQ2RG83zQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:13:44.048Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"9248a1\",\"wall_time_seconds\":5.001960591,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:13:39.089Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bca4c41487d2a7f633bf3949da48", "usage": { "input_tokens": 78788, "cached_input_tokens": 78464, "cache_write_input_tokens": 0, "output_tokens": 548, "reasoning_output_tokens": 500, "total_tokens": 79336 }, "turn_token_usage": { "input_tokens": 2845850, "cached_input_tokens": 2769536, "cache_write_input_tokens": 0, "output_tokens": 19412, "reasoning_output_tokens": 10197, "total_tokens": 2865262 }, "thread_token_usage": { "input_tokens": 2845850, "cached_input_tokens": 2769536, "cache_write_input_tokens": 0, "output_tokens": 19412, "reasoning_output_tokens": 10197, "total_tokens": 2865262 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T00:13:47.003Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:13:47.003Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:13:50.902Z", "callId": "call_21FRFTe6dfUanTp64B5675TS", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nwith np.load('/task/data/validation_eligible_candidates.npz',allow_pickle=False) as z:\\n u=z['uids']; items=z['item_ids']; p=z['packed_eligible']\\n rows=[]\\n for r,uid in enumerate(u):\\n ok=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\\n ix=np.flatnonzero(ok)[:100]\\n rows.extend((int(uid),int(items[i]),j) for j,i in enumerate(ix,1))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\\nPY\\nPYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:13:59.492Z", "content": "Script completed\nWall time 8.6 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/popularity_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:13:50.993Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bcb867b887d299b5004c82d3f3dd", "usage": { "input_tokens": 79394, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 327, "reasoning_output_tokens": 83, "total_tokens": 79721 }, "turn_token_usage": { "input_tokens": 2925244, "cached_input_tokens": 2780672, "cache_write_input_tokens": 0, "output_tokens": 19739, "reasoning_output_tokens": 10280, "total_tokens": 2944983 }, "thread_token_usage": { "input_tokens": 2925244, "cached_input_tokens": 2780672, "cache_write_input_tokens": 0, "output_tokens": 19739, "reasoning_output_tokens": 10280, "total_tokens": 2944983 } } }, { "kind": "context", "timestamp": "2026-09-25T00:13:59.447Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fa6de5c9-21ab-456f-aad6-9a2d0166fd16", "process_id": "81819", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz',allow_pickle=False) as z:\n u=z['uids']; items=z['item_ids']; p=z['packed_eligible']\n rows=[]\n for r,uid in enumerate(u):\n ok=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\n ix=np.flatnonzero(ok)[:100]\n rows.extend((int(uid),int(items[i]),j) for j,i in enumerate(ix,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\nPYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nwith np.load('/task/data/validation_eligible_candidates.npz',allow_pickle=False) as z:\n u=z['uids']; items=z['item_ids']; p=z['packed_eligible']\n rows=[]\n for r,uid in enumerate(u):\n ok=np.unpackbits(p[r],count=len(items),bitorder='little').astype(bool)\n ix=np.flatnonzero(ok)[:100]\n rows.extend((int(uid),int(items[i]),j) for j,i in enumerate(ix,1))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\nPYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/popularity_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/popularity_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 325045177 }, "formatted_output": "{\n \"model\": \"/app/solution/popularity_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T00:14:10.812Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:10.812Z", "text": "" } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T00:14:13.300Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:13.300Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:14:15.038Z", "text": "The preserved baseline also shows the expected failure mode: its top-10 recommendations have median catalog popularity rank 111, whereas public relevant items have median rank around 5,500. The hard pool is designed to make those high-scoring distractors contribute gradient instead of letting easy uniform tail negatives dominate. I’m also preparing a pairwise variant so the same mined examples can train the ordering directly if pointwise BCE plateaus." }, { "kind": "tool", "timestamp": "2026-09-25T00:14:22.519Z", "callId": "call_up2at5wKjKbIoxS8ygPzDrBT", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n hard_negatives: int = 16\\n hard_negative_pool_size: int = 64\\n+ loss_objective: str = \\\"pairwise\\\"\\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+ raw = F.softplus(\\n+ logits\\n+ if positive_logits is None\\n+ else logits - positive_logits[:, None]\\n+ )\\n@@\\n- positive_loss = F.softplus(-positive_logits)\\n- negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n- )\\n+ if config.loss_objective == \\\"pointwise-bce\\\":\\n+ positive_loss = F.softplus(-positive_logits)\\n+ negative_loss = corrected_negative_loss(\\n+ negative_logits, negative_weights[begin:end], config\\n+ )\\n+ row_loss = (positive_loss + negative_loss) / 2.0\\n+ elif config.loss_objective == \\\"pairwise\\\":\\n+ row_loss = corrected_negative_loss(\\n+ negative_logits,\\n+ negative_weights[begin:end],\\n+ config,\\n+ positive_logits,\\n+ )\\n+ else:\\n+ raise RuntimeError(f\\\"unknown loss objective {config.loss_objective}\\\")\\n@@\\n- loss_sum = (\\n- selected_weights * (positive_loss + negative_loss) / 2.0\\n- ).sum()\\n+ loss_sum = (selected_weights * row_loss).sum()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:14:22.649Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:14:22.532Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-76d09eb0-96ce-4791-8811-bf75caaa20a2", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -61,2 +61,3 @@\n hard_negative_pool_size: int = 64\n+ loss_objective: str = \"pairwise\"\n inbatch_correction_power: float = 0.5\n@@ -931,4 +932,9 @@\n config: Config,\n+ positive_logits: torch.Tensor | None = None,\n ) -> torch.Tensor:\n- raw = F.softplus(logits)\n+ raw = F.softplus(\n+ logits\n+ if positive_logits is None\n+ else logits - positive_logits[:, None]\n+ )\n total = (\n@@ -1266,6 +1272,17 @@\n )\n- positive_loss = F.softplus(-positive_logits)\n- negative_loss = corrected_negative_loss(\n- negative_logits, negative_weights[begin:end], config\n- )\n+ if config.loss_objective == \"pointwise-bce\":\n+ positive_loss = F.softplus(-positive_logits)\n+ negative_loss = corrected_negative_loss(\n+ negative_logits, negative_weights[begin:end], config\n+ )\n+ row_loss = (positive_loss + negative_loss) / 2.0\n+ elif config.loss_objective == \"pairwise\":\n+ row_loss = corrected_negative_loss(\n+ negative_logits,\n+ negative_weights[begin:end],\n+ config,\n+ positive_logits,\n+ )\n+ else:\n+ raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n selected_weights = torch.from_numpy(\n@@ -1273,5 +1290,3 @@\n ).to(device)\n- loss_sum = (\n- selected_weights * (positive_loss + negative_loss) / 2.0\n- ).sum()\n+ loss_sum = (selected_weights * row_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:14:22.644Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bcc7b6a487d29b78d79f90c7c123", "usage": { "input_tokens": 79790, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 1169, "reasoning_output_tokens": 652, "total_tokens": 80959 }, "turn_token_usage": { "input_tokens": 3005034, "cached_input_tokens": 2859264, "cache_write_input_tokens": 0, "output_tokens": 20908, "reasoning_output_tokens": 10932, "total_tokens": 3025942 }, "thread_token_usage": { "input_tokens": 3005034, "cached_input_tokens": 2859264, "cache_write_input_tokens": 0, "output_tokens": 20908, "reasoning_output_tokens": 10932, "total_tokens": 3025942 } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T00:14:24.815Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:24.815Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:14:28.057Z", "callId": "call_fv9UEm8UyQ3hYW0ZvlZYreuO", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--hard-negative-pool-size\\\",\\n type=int,\\n default=Config.hard_negative_pool_size,\\n )\\n+ parser.add_argument(\\n+ \\\"--loss-objective\\\",\\n+ choices=(\\\"pointwise-bce\\\", \\\"pairwise\\\"),\\n+ default=Config.loss_objective,\\n+ )\\n@@\\n hard_negatives=args.hard_negatives,\\n hard_negative_pool_size=args.hard_negative_pool_size,\\n+ loss_objective=args.loss_objective,\\n negative_filter_scope=args.negative_filter_scope,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:14:28.134Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:14:28.076Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-eb73f0e7-40aa-4e1e-bbb8-82c1d3838c93", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2130,2 +2130,7 @@\n parser.add_argument(\n+ \"--loss-objective\",\n+ choices=(\"pointwise-bce\", \"pairwise\"),\n+ default=Config.loss_objective,\n+ )\n+ parser.add_argument(\n \"--negative-filter-scope\",\n@@ -2237,2 +2242,3 @@\n hard_negative_pool_size=args.hard_negative_pool_size,\n+ loss_objective=args.loss_objective,\n negative_filter_scope=args.negative_filter_scope,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:14:28.113Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bcdf09f487d2b6b41c13e4983da5", "usage": { "input_tokens": 80983, "cached_input_tokens": 79616, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 24, "total_tokens": 81187 }, "turn_token_usage": { "input_tokens": 3086017, "cached_input_tokens": 2938880, "cache_write_input_tokens": 0, "output_tokens": 21112, "reasoning_output_tokens": 10956, "total_tokens": 3107129 }, "thread_token_usage": { "input_tokens": 3086017, "cached_input_tokens": 2938880, "cache_write_input_tokens": 0, "output_tokens": 21112, "reasoning_output_tokens": 10956, "total_tokens": 3107129 } } } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-25T00:14:29.959Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:29.959Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:14:33.620Z", "callId": "call_5fNbuaT6fWBDJHyPJL9lcejS", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n current_negative_values = (\\n@@\\n config.hard_negative_pool_size,\\n )\\n+ stored_loss_objective = stored_config.get(\\n+ \\\"loss_objective\\\", \\\"pointwise-bce\\\"\\n+ )\\n objective_changed = (\\n@@\\n or stored_window_values != current_window_values\\n or stored_negative_values != current_negative_values\\n+ or stored_loss_objective != config.loss_objective\\n@@\\n f\\\"target window {stored_window_values}->{current_window_values}; \\\"\\n f\\\"negative mix {stored_negative_values}->{current_negative_values}; \\\"\\n+ f\\\"loss {stored_loss_objective}->{config.loss_objective}; \\\"\\n \\\"recomputing the resume checkpoint's validation BCE\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:14:33.747Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:14:33.696Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e9df2089-50fc-4f36-80aa-d8939430a970", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1759,2 +1759,5 @@\n )\n+ stored_loss_objective = stored_config.get(\n+ \"loss_objective\", \"pointwise-bce\"\n+ )\n objective_changed = (\n@@ -1765,2 +1768,3 @@\n or stored_negative_values != current_negative_values\n+ or stored_loss_objective != config.loss_objective\n )\n@@ -1805,2 +1809,3 @@\n f\"negative mix {stored_negative_values}->{current_negative_values}; \"\n+ f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:14:33.722Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bce468dc87d28d56b7052711f991", "usage": { "input_tokens": 81211, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, "output_tokens": 238, "reasoning_output_tokens": 26, "total_tokens": 81449 }, "turn_token_usage": { "input_tokens": 3167228, "cached_input_tokens": 3019648, "cache_write_input_tokens": 0, "output_tokens": 21350, "reasoning_output_tokens": 10982, "total_tokens": 3188578 }, "thread_token_usage": { "input_tokens": 3167228, "cached_input_tokens": 3019648, "cache_write_input_tokens": 0, "output_tokens": 21350, "reasoning_output_tokens": 10982, "total_tokens": 3188578 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T00:14:35.952Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:35.952Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:14:37.457Z", "callId": "call_pGnNNOPFyYICMgmmMeJh3hTB", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- \\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",\\n+ \\\"objective\\\": (\\n+ f\\\"sample-weighted-{config.loss_objective}-at-every-timestep\\\"\\n+ ),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:14:37.584Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:14:37.522Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-5ad577f5-428d-4ade-aa29-8b18e841c982", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2349,3 +2349,5 @@\n \"score\": \"raw-dot-product\",\n- \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n+ \"objective\": (\n+ f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n+ ),\n \"negative_sampling\": {\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:14:37.570Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bcea397487d286842375872ea833", "usage": { "input_tokens": 81473, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 130, "reasoning_output_tokens": 33, "total_tokens": 81603 }, "turn_token_usage": { "input_tokens": 3248701, "cached_input_tokens": 3100672, "cache_write_input_tokens": 0, "output_tokens": 21480, "reasoning_output_tokens": 11015, "total_tokens": 3270181 }, "thread_token_usage": { "input_tokens": 3248701, "cached_input_tokens": 3100672, "cache_write_input_tokens": 0, "output_tokens": 21480, "reasoning_output_tokens": 11015, "total_tokens": 3270181 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T00:14:43.594Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:43.594Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:14:44.758Z", "callId": "call_vmfT95ZcemVDUlqrrTiv68gt", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && rg -n \\\"loss_objective|hard_negative_lookup\\\" /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:14:44.990Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n62: loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690: hard_negative_lookup: np.ndarray | None,\n736: if hard_negative_lookup is None:\n746: pool_size = hard_negative_lookup.shape[1]\n754: hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811: hard_negative_lookup: np.ndarray | None,\n826: hard_negative_lookup,\n1273: if config.loss_objective == \"pointwise-bce\":\n1279: elif config.loss_objective == \"pairwise\":\n1287: raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324: hard_negative_lookup: np.ndarray | None,\n1418: hard_negative_lookup,\n1491: hard_negative_lookup: np.ndarray | None,\n1576: hard_negative_lookup,\n1695: hard_negative_lookup: np.ndarray | None = None\n1760: stored_loss_objective = stored_config.get(\n1761: \"loss_objective\", \"pointwise-bce\"\n1769: or stored_loss_objective != config.loss_objective\n1773: hard_negative_lookup = build_hard_negative_lookup(\n1810: f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825: hard_negative_lookup,\n1839: if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840: hard_negative_lookup = build_hard_negative_lookup(\n1856: hard_negative_lookup,\n1877: hard_negative_lookup,\n1892: hard_negative_lookup,\n2138: default=Config.loss_objective,\n2248: loss_objective=args.loss_objective,\n2351: f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:14:44.849Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bcedf21c87d285544e7a56084ba8", "usage": { "input_tokens": 81627, "cached_input_tokens": 81280, "cache_write_input_tokens": 0, "output_tokens": 250, "reasoning_output_tokens": 162, "total_tokens": 81877 }, "turn_token_usage": { "input_tokens": 3330328, "cached_input_tokens": 3181952, "cache_write_input_tokens": 0, "output_tokens": 21730, "reasoning_output_tokens": 11177, "total_tokens": 3352058 }, "thread_token_usage": { "input_tokens": 3330328, "cached_input_tokens": 3181952, "cache_write_input_tokens": 0, "output_tokens": 21730, "reasoning_output_tokens": 11177, "total_tokens": 3352058 } } }, { "kind": "context", "timestamp": "2026-09-25T00:14:44.977Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8a3374d2-047a-4a50-926e-dfc2c8ae2ab8", "process_id": "62244", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && rg -n \"loss_objective|hard_negative_lookup\" /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && rg -n \"loss_objective|hard_negative_lookup\" /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "62: loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690: hard_negative_lookup: np.ndarray | None,\n736: if hard_negative_lookup is None:\n746: pool_size = hard_negative_lookup.shape[1]\n754: hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811: hard_negative_lookup: np.ndarray | None,\n826: hard_negative_lookup,\n1273: if config.loss_objective == \"pointwise-bce\":\n1279: elif config.loss_objective == \"pairwise\":\n1287: raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324: hard_negative_lookup: np.ndarray | None,\n1418: hard_negative_lookup,\n1491: hard_negative_lookup: np.ndarray | None,\n1576: hard_negative_lookup,\n1695: hard_negative_lookup: np.ndarray | None = None\n1760: stored_loss_objective = stored_config.get(\n1761: \"loss_objective\", \"pointwise-bce\"\n1769: or stored_loss_objective != config.loss_objective\n1773: hard_negative_lookup = build_hard_negative_lookup(\n1810: f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825: hard_negative_lookup,\n1839: if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840: hard_negative_lookup = build_hard_negative_lookup(\n1856: hard_negative_lookup,\n1877: hard_negative_lookup,\n1892: hard_negative_lookup,\n2138: default=Config.loss_objective,\n2248: loss_objective=args.loss_objective,\n2351: f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n", "stderr": "", "aggregated_output": "62: loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690: hard_negative_lookup: np.ndarray | None,\n736: if hard_negative_lookup is None:\n746: pool_size = hard_negative_lookup.shape[1]\n754: hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811: hard_negative_lookup: np.ndarray | None,\n826: hard_negative_lookup,\n1273: if config.loss_objective == \"pointwise-bce\":\n1279: elif config.loss_objective == \"pairwise\":\n1287: raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324: hard_negative_lookup: np.ndarray | None,\n1418: hard_negative_lookup,\n1491: hard_negative_lookup: np.ndarray | None,\n1576: hard_negative_lookup,\n1695: hard_negative_lookup: np.ndarray | None = None\n1760: stored_loss_objective = stored_config.get(\n1761: \"loss_objective\", \"pointwise-bce\"\n1769: or stored_loss_objective != config.loss_objective\n1773: hard_negative_lookup = build_hard_negative_lookup(\n1810: f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825: hard_negative_lookup,\n1839: if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840: hard_negative_lookup = build_hard_negative_lookup(\n1856: hard_negative_lookup,\n1877: hard_negative_lookup,\n1892: hard_negative_lookup,\n2138: default=Config.loss_objective,\n2248: loss_objective=args.loss_objective,\n2351: f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 19526810 }, "formatted_output": "62: loss_objective: str = \"pairwise\"\n584:def build_hard_negative_lookup(\n690: hard_negative_lookup: np.ndarray | None,\n736: if hard_negative_lookup is None:\n746: pool_size = hard_negative_lookup.shape[1]\n754: hard_negative_lookup[targets_cpu[:, None], columns].astype(\n811: hard_negative_lookup: np.ndarray | None,\n826: hard_negative_lookup,\n1273: if config.loss_objective == \"pointwise-bce\":\n1279: elif config.loss_objective == \"pairwise\":\n1287: raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n1324: hard_negative_lookup: np.ndarray | None,\n1418: hard_negative_lookup,\n1491: hard_negative_lookup: np.ndarray | None,\n1576: hard_negative_lookup,\n1695: hard_negative_lookup: np.ndarray | None = None\n1760: stored_loss_objective = stored_config.get(\n1761: \"loss_objective\", \"pointwise-bce\"\n1769: or stored_loss_objective != config.loss_objective\n1773: hard_negative_lookup = build_hard_negative_lookup(\n1810: f\"loss {stored_loss_objective}->{config.loss_objective}; \"\n1825: hard_negative_lookup,\n1839: if config.hard_negatives and epoch > 1 and hard_negative_lookup is None:\n1840: hard_negative_lookup = build_hard_negative_lookup(\n1856: hard_negative_lookup,\n1877: hard_negative_lookup,\n1892: hard_negative_lookup,\n2138: default=Config.loss_objective,\n2248: loss_objective=args.loss_objective,\n2351: f\"sample-weighted-{config.loss_objective}-at-every-timestep\"\n" } } } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-25T00:14:46.645Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:14:46.645Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:14:47.337Z", "callId": "call_OUVMXkjjWEiq9wIDAwZ8EgSX", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:14:52.411Z", "content": "Script completed\nWall time 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"2026-09-25T00:15:11.272Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:15:11.938Z", "callId": "call_laIJOHNI15FCFwXYVKMbrlqx", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"df -h /app 2>/dev/null || true; du -sh /app/solution\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:15:12.066Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 16G 81G 17% /\n802M\t/app/solution\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:15:12.042Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": 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"df -h /app 2>/dev/null || true; du -sh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Filesystem Size Used Avail Use% Mounted on\noverlay 96G 16G 81G 17% /\n802M\t/app/solution\n", "stderr": "", "aggregated_output": "Filesystem Size Used Avail Use% Mounted on\noverlay 96G 16G 81G 17% /\n802M\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4260 }, "formatted_output": "Filesystem Size Used Avail Use% Mounted on\noverlay 96G 16G 81G 17% /\n802M\t/app/solution\n" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-25T00:15:14.177Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:15:14.177Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:15:14.877Z", "callId": "call_8BG9ZwDZOGuDUlF05NnXmYfK", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:15:19.915Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"91bb1d\",\"wall_time_seconds\":5.00183554,\"session_id\":71321,\"original_token_count\":24,\"output\":\"epoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:15:14.979Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd105e5087d282fdcc72257c0bf1", "usage": { "input_tokens": 83180, "cached_input_tokens": 82816, "cache_write_input_tokens": 0, 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"thread_token_usage": { "input_tokens": 3744534, "cached_input_tokens": 3593344, "cache_write_input_tokens": 0, "output_tokens": 22581, "reasoning_output_tokens": 11769, "total_tokens": 3767115 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-25T00:15:29.461Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:15:29.461Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:15:30.145Z", "callId": "call_0z0VRmPJ38JTNZaQ1sQHxiBD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:15:35.180Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"28220e\",\"wall_time_seconds\":5.001909972,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": 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"blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:15:36.919Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:15:37.572Z", "callId": "call_smiNyjYd9bfA3fjfshmxZsx9", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:15:42.610Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"4f697e\",\"wall_time_seconds\":5.001426667,\"session_id\":71321,\"original_token_count\":75,\"output\":\"saved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\\n\"}", "isError": null } }, { "kind": 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"2026-09-25T00:15:46.421Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:15:46.421Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:15:48.090Z", "callId": "call_2Mn1LIe8FvQphuwOjmiWaDV5", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:15:53.107Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"bbc8af\",\"wall_time_seconds\":5.001197764,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:15:48.223Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": 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"input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:16:03.755Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"ad1285\",\"wall_time_seconds\":5.001963,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:15:58.822Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd39544487d281c5882f69130a9a", "usage": { "input_tokens": 83918, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 196, "reasoning_output_tokens": 148, "total_tokens": 84114 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"01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd4e205087d2a4207ca64bd9b53d", "usage": { "input_tokens": 84441, "cached_input_tokens": 83968, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 56, "total_tokens": 84545 }, "turn_token_usage": { "input_tokens": 4247883, "cached_input_tokens": 4094592, "cache_write_input_tokens": 0, "output_tokens": 23289, "reasoning_output_tokens": 12189, "total_tokens": 4271172 }, "thread_token_usage": { "input_tokens": 4247883, "cached_input_tokens": 4094592, "cache_write_input_tokens": 0, "output_tokens": 23289, "reasoning_output_tokens": 12189, "total_tokens": 4271172 } } } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-25T00:16:25.342Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:16:25.342Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:16:26.038Z", "callId": "call_UCLkRQsB0XecxHonnWhZWaMr", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:16:56.059Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e75ae2\",\"wall_time_seconds\":30.000818385,\"session_id\":71321,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:16:26.147Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd56571087d285a64ae97e31c8cd", "usage": { "input_tokens": 84604, "cached_input_tokens": 84224, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 32, "total_tokens": 84684 }, "turn_token_usage": { "input_tokens": 4332487, "cached_input_tokens": 4178816, "cache_write_input_tokens": 0, "output_tokens": 23369, "reasoning_output_tokens": 12221, "total_tokens": 4355856 }, "thread_token_usage": { "input_tokens": 4332487, "cached_input_tokens": 4178816, "cache_write_input_tokens": 0, "output_tokens": 23369, "reasoning_output_tokens": 12221, "total_tokens": 4355856 } } }, { "kind": "context", "timestamp": "2026-09-25T00:17:01.311Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f987029a-c409-4087-bd7f-3807b715e3d9", "process_id": "71321", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/hard_checkpoints --output /app/solution/hard_validation.parquet > /app/solution/hard_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/hard_checkpoints --output /app/solution/hard_validation.parquet > /app/solution/hard_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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\nmined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.749658\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\nsaved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\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", "stderr": "", "aggregated_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\nmined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.749658\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\nsaved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\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", "exit_code": 0, "duration": { "secs": 332, "nanos": 523521147 }, "formatted_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\nmined item-tower hard-negative lookup: items=30,000 pool=64 seconds=1.9\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64)->(8, 8, 16, 64); recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.749658\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.431630 running_bce=0.475074 elapsed=199.7s\nsaved epoch checkpoint: /app/solution/hard_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47361 validation_loss=0.43771 pair_accuracy=0.846 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=13.4\nrestored best validation-BCE checkpoint: epoch=2 loss=0.437707\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" } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-25T00:17:02.936Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:02.936Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:17:02.942Z", "text": "The pointwise hard-negative epoch has converged cleanly: its held-out objective fell from `0.74966` to `0.43771`, with pair accuracy rising to `0.846`. The model is now computing the full public ranking; the public NDCG—not this internal loss—will decide whether it remains the leader." } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-25T00:17:02.948Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:02.948Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:17:02.950Z", "callId": "call_VuymKhePvql1HEZW8gj2PUwQ", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":71321,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:17:03.004Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"f0b3df\",\"wall_time_seconds\":0.0000023,\"exit_code\":0,\"original_token_count\":57,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:17:03.002Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd7850e087d281f4a2a935275b13", "usage": { "input_tokens": 84742, "cached_input_tokens": 84480, "cache_write_input_tokens": 0, "output_tokens": 174, "reasoning_output_tokens": 50, "total_tokens": 84916 }, "turn_token_usage": { "input_tokens": 4417229, "cached_input_tokens": 4263296, "cache_write_input_tokens": 0, "output_tokens": 23543, "reasoning_output_tokens": 12271, "total_tokens": 4440772 }, "thread_token_usage": { "input_tokens": 4417229, "cached_input_tokens": 4263296, "cache_write_input_tokens": 0, "output_tokens": 23543, "reasoning_output_tokens": 12271, "total_tokens": 4440772 } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-25T00:17:04.404Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:04.404Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:17:06.124Z", "callId": "call_NUAWb3ZVNlV2jv4YLdTaZ08J", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet && sed -n '1,260p' /app/solution/hard_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:17:09.524Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0109,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 328.196,\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\": {\n \"uniform\": 8,\n \"propensity_corrected_inbatch\": 8,\n \"item_tower_hard\": 16,\n \"hard_pool\": 64\n },\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\": 2,\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\": 8,\n \"hard_negatives\": 16,\n \"hard_negative_pool_size\": 64,\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 14.392733400999987,\n \"validation_loss\": 0.7496576998776704\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.47361474907688017,\n \"positive_logit\": 0.8771473302352399,\n 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"duration": { "secs": 3, "nanos": 206733767 }, "formatted_output": "{\n \"model\": \"/app/solution/hard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0109,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 328.196,\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\": 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\"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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 14.392733400999987,\n \"validation_loss\": 0.7496576998776704\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.47361474907688017,\n \"positive_logit\": 0.8771473302352399,\n \"negative_logit\": -1.7027217965618686,\n \"pair_accuracy\": 0.8463514931340715,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 212.8691298199999,\n \"validation_loss\": 0.43770692986345666,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 13.397850344000062\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.5817008018493652,\n \"query_norm_std\": 0.42488959431648254,\n \"item_norm_mean\": 2.509742498397827,\n \"item_norm_std\": 0.9850362539291382\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-25T00:17:12.370Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:12.370Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:17:14.605Z", "text": "That first hard-negative design is rejected: public NDCG fell to `0.010900` despite a much better sampled validation loss. Item-to-item nearest neighbors are too semantically close to the positive; treating many as absolute negatives destroys useful collaborative neighborhoods. This is a useful diagnostic—the internal sampled BCE is not a reliable selector when the negative distribution changes. I’m switching to user-query hard mining with a gentler pairwise loss, which penalizes only violations relative to the positive rather than forcing every neighbor’s absolute logit below zero." } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-25T00:17:24.568Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:24.568Z", "text": "" } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-25T00:17:33.656Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:33.656Z", "text": "" } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-25T00:17:43.066Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:43.066Z", "text": "" } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-25T00:17:47.236Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:47.236Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:17:49.172Z", "callId": "call_JS9257Xus6rVmatoEmH8VnIb", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- uniform_negatives: int = 8\\n- inbatch_negatives: int = 8\\n- hard_negatives: int = 16\\n+ uniform_negatives: int = 16\\n+ inbatch_negatives: int = 48\\n+ hard_negatives: int = 0\\n hard_negative_pool_size: int = 64\\n+ query_random_negatives: int = 8\\n+ query_hard_negatives: int = 24\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:17:49.217Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:17:49.184Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-95a58540-97d6-4ada-b852-135cc93d9ecd", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -57,6 +57,8 @@\n loss_microbatch_size: int = 65_536\n- uniform_negatives: int = 8\n- inbatch_negatives: int = 8\n- hard_negatives: int = 16\n+ uniform_negatives: int = 16\n+ inbatch_negatives: int = 48\n+ hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n+ query_random_negatives: int = 8\n+ query_hard_negatives: int = 24\n loss_objective: str = \"pairwise\"\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:17:49.215Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bd85b76887d293c5e3d1243f24af", "usage": { "input_tokens": 86419, "cached_input_tokens": 84864, "cache_write_input_tokens": 0, "output_tokens": 2137, "reasoning_output_tokens": 1870, "total_tokens": 88556 }, "turn_token_usage": { "input_tokens": 4588729, "cached_input_tokens": 4432768, "cache_write_input_tokens": 0, "output_tokens": 25780, "reasoning_output_tokens": 14147, "total_tokens": 4614509 }, "thread_token_usage": { "input_tokens": 4588729, "cached_input_tokens": 4432768, "cache_write_input_tokens": 0, "output_tokens": 25780, "reasoning_output_tokens": 14147, "total_tokens": 4614509 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-25T00:17:50.952Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:50.952Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:17:54.692Z", "callId": "call_pZCOZt3O4D6fD5rcjxNUvA2a", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-entropy. After a one-epoch warm-up, the negative mix combines uniform,\\n-propensity-corrected in-batch, and item-tower-neighbor hard negatives while\\n-keeping the production total of 32 negatives per target. Every selected\\n-adjacent target retains a nonzero weight. Only files under the supplied public\\n-directory are read.\\n+entropy. The improved sampler draws a 64-item mixture of uniform and\\n+propensity-corrected in-batch candidates, keeps eight random examples for\\n+coverage, and mines the 24 highest-scoring remaining examples per user query.\\n+The loss therefore retains the production total of 32 negatives per target.\\n+Every selected adjacent target retains a nonzero weight. Only files under the\\n+supplied public directory are read.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:17:54.772Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:17:54.703Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d036be12-9323-4847-a3c1-ddfa5f93c292", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -13,7 +13,8 @@\n normalization, raw dot-product retrieval, and sample-weighted binary cross\n-entropy. After a one-epoch warm-up, the negative mix combines uniform,\n-propensity-corrected in-batch, and item-tower-neighbor hard negatives while\n-keeping the production total of 32 negatives per target. Every selected\n-adjacent target retains a nonzero weight. Only files under the supplied public\n-directory are read.\n+entropy. The improved sampler draws a 64-item mixture of uniform and\n+propensity-corrected in-batch candidates, keeps eight random examples for\n+coverage, and mines the 24 highest-scoring remaining examples per user query.\n+The loss therefore retains the production total of 32 negatives per target.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n \"\"\"\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:17:54.770Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdad8dc087d282b20af39c8b8061", "usage": { "input_tokens": 88580, "cached_input_tokens": 86272, "cache_write_input_tokens": 0, "output_tokens": 231, "reasoning_output_tokens": 15, "total_tokens": 88811 }, "turn_token_usage": { "input_tokens": 4677309, "cached_input_tokens": 4519040, "cache_write_input_tokens": 0, "output_tokens": 26011, "reasoning_output_tokens": 14162, "total_tokens": 4703320 }, "thread_token_usage": { "input_tokens": 4677309, "cached_input_tokens": 4519040, "cache_write_input_tokens": 0, "output_tokens": 26011, "reasoning_output_tokens": 14162, "total_tokens": 4703320 } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-25T00:17:56.524Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:17:56.524Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:17:58.783Z", "callId": "call_qCQ0naoZuC2OKh6kf77XXpuX", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n raw = F.softplus(\\n logits\\n if positive_logits is None\\n else logits - positive_logits[:, None]\\n )\\n+ if config.query_hard_negatives:\\n+ return (raw * correction).sum(dim=1) / correction.sum(\\n+ dim=1\\n+ ).clamp_min(1.0e-6)\\n total = (\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:17:58.893Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:17:58.794Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8cab8322-74f3-41a7-8149-58f17d33b575", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -942,2 +942,6 @@\n )\n+ if config.query_hard_negatives:\n+ return (raw * correction).sum(dim=1) / correction.sum(\n+ dim=1\n+ ).clamp_min(1.0e-6)\n total = (\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:17:58.892Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdb30cf887d2940adc91097ad26d", "usage": { "input_tokens": 88835, "cached_input_tokens": 88448, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 8, "total_tokens": 88977 }, "turn_token_usage": { "input_tokens": 4766144, "cached_input_tokens": 4607488, "cache_write_input_tokens": 0, "output_tokens": 26153, "reasoning_output_tokens": 14170, "total_tokens": 4792297 }, "thread_token_usage": { "input_tokens": 4766144, "cached_input_tokens": 4607488, "cache_write_input_tokens": 0, "output_tokens": 26153, "reasoning_output_tokens": 14170, "total_tokens": 4792297 } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-25T00:18:00.567Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:18:00.567Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:18:01.604Z", "callId": "call_iwRKItTB3H1MFCtSvxjWcFq8", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1245,1305p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":8000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:18:01.699Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n if config.loss_objective == \"pointwise-bce\":\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n row_loss = (positive_loss + negative_loss) / 2.0\n elif config.loss_objective == \"pairwise\":\n row_loss = corrected_negative_loss(\n negative_logits,\n negative_weights[begin:end],\n config,\n positive_logits,\n )\n else:\n raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * row_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:18:01.641Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a847c5c5-9b79-4e90-b84d-2c536c74f0f9", "process_id": "85359", "command": [ "/bin/sh", "-lc", "sed -n '1245,1305p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1245,1305p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n if config.loss_objective == \"pointwise-bce\":\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n row_loss = (positive_loss + negative_loss) / 2.0\n elif config.loss_objective == \"pairwise\":\n row_loss = corrected_negative_loss(\n negative_logits,\n negative_weights[begin:end],\n config,\n positive_logits,\n )\n else:\n raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * row_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n", "stderr": "", "aggregated_output": " \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n if config.loss_objective == \"pointwise-bce\":\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n row_loss = (positive_loss + negative_loss) / 2.0\n elif config.loss_objective == \"pairwise\":\n row_loss = corrected_negative_loss(\n negative_logits,\n negative_weights[begin:end],\n config,\n positive_logits,\n )\n else:\n raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * row_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6930 }, "formatted_output": " \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n if config.loss_objective == \"pointwise-bce\":\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n row_loss = (positive_loss + negative_loss) / 2.0\n elif config.loss_objective == \"pairwise\":\n row_loss = corrected_negative_loss(\n negative_logits,\n negative_weights[begin:end],\n config,\n positive_logits,\n )\n else:\n raise RuntimeError(f\"unknown loss objective {config.loss_objective}\")\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * row_loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:18:01.697Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdb733bc87d2ad08860ce7864a23", "usage": { "input_tokens": 89001, "cached_input_tokens": 88704, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 19, "total_tokens": 89086 }, "turn_token_usage": { "input_tokens": 4855145, "cached_input_tokens": 4696192, "cache_write_input_tokens": 0, "output_tokens": 26238, "reasoning_output_tokens": 14189, "total_tokens": 4881383 }, "thread_token_usage": { "input_tokens": 4855145, "cached_input_tokens": 4696192, "cache_write_input_tokens": 0, "output_tokens": 26238, "reasoning_output_tokens": 14189, "total_tokens": 4881383 } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-25T00:18:06.728Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:18:06.728Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:18:12.991Z", "callId": "call_cfiyt2wqK4jJrzuIFfCYiAy7", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n negative_logits = torch.einsum(\\n \\\"bd,bkd->bk\\\", query_vectors, negative_vectors\\n )\\n+ selected_negative_weights = negative_weights[begin:end]\\n+ if config.query_hard_negatives:\\n+ random_count = config.query_random_negatives\\n+ hard_columns = torch.topk(\\n+ negative_logits[:, random_count:],\\n+ k=config.query_hard_negatives,\\n+ dim=1,\\n+ sorted=False,\\n+ ).indices + random_count\\n+ random_columns = torch.arange(\\n+ random_count, device=device\\n+ )[None, :].expand(end - begin, -1)\\n+ selected_columns = torch.cat(\\n+ [random_columns, hard_columns], dim=1\\n+ )\\n+ negative_logits = negative_logits.gather(1, selected_columns)\\n+ selected_negative_weights = selected_negative_weights.gather(\\n+ 1, selected_columns\\n+ )\\n if config.loss_objective == \\\"pointwise-bce\\\":\\n positive_loss = F.softplus(-positive_logits)\\n negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n+ negative_logits, selected_negative_weights, config\\n )\\n@@\\n negative_logits,\\n- negative_weights[begin:end],\\n+ selected_negative_weights,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:18:13.084Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:18:13.002Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-756c4cdf-2b43-4493-aa54-8dffdc125c99", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1279,2 +1279,21 @@\n )\n+ selected_negative_weights = negative_weights[begin:end]\n+ if config.query_hard_negatives:\n+ random_count = config.query_random_negatives\n+ hard_columns = torch.topk(\n+ negative_logits[:, random_count:],\n+ k=config.query_hard_negatives,\n+ dim=1,\n+ sorted=False,\n+ ).indices + random_count\n+ random_columns = torch.arange(\n+ random_count, device=device\n+ )[None, :].expand(end - begin, -1)\n+ selected_columns = torch.cat(\n+ [random_columns, hard_columns], dim=1\n+ )\n+ negative_logits = negative_logits.gather(1, selected_columns)\n+ selected_negative_weights = selected_negative_weights.gather(\n+ 1, selected_columns\n+ )\n if config.loss_objective == \"pointwise-bce\":\n@@ -1282,3 +1301,3 @@\n negative_loss = corrected_negative_loss(\n- negative_logits, negative_weights[begin:end], config\n+ negative_logits, selected_negative_weights, config\n )\n@@ -1288,3 +1307,3 @@\n negative_logits,\n- negative_weights[begin:end],\n+ selected_negative_weights,\n config,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:18:13.083Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdb9f27487d29a25d1b676b2fb59", "usage": { "input_tokens": 89651, "cached_input_tokens": 88832, "cache_write_input_tokens": 0, "output_tokens": 531, "reasoning_output_tokens": 178, "total_tokens": 90182 }, "turn_token_usage": { "input_tokens": 4944796, "cached_input_tokens": 4785024, "cache_write_input_tokens": 0, "output_tokens": 26769, "reasoning_output_tokens": 14367, "total_tokens": 4971565 }, "thread_token_usage": { "input_tokens": 4944796, "cached_input_tokens": 4785024, "cache_write_input_tokens": 0, "output_tokens": 26769, "reasoning_output_tokens": 14367, "total_tokens": 4971565 } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-25T00:18:16.812Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:18:16.812Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:18:19.060Z", "callId": "call_WL9XfzC6aZvTjztONepnjRek", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n negative_count = (\\n- config.uniform_negatives\\n- + config.inbatch_negatives\\n- + config.hard_negatives\\n+ config.query_random_negatives + config.query_hard_negatives\\n+ if config.query_hard_negatives\\n+ else config.uniform_negatives\\n+ + config.inbatch_negatives\\n+ + config.hard_negatives\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:18:19.157Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:18:19.071Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f51680bd-9906-4240-91bc-344a0e6a5db2", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1365,3 +1365,5 @@\n negative_count = (\n- config.uniform_negatives\n+ config.query_random_negatives + config.query_hard_negatives\n+ if config.query_hard_negatives\n+ else config.uniform_negatives\n + config.inbatch_negatives\n", "move_path": null } }, "status": "completed", "stdout": "Success. 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Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:18:28.889Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdcb76d887d2a494d7ffd50c75d5", "usage": { "input_tokens": 90421, "cached_input_tokens": 89984, "cache_write_input_tokens": 0, "output_tokens": 456, "reasoning_output_tokens": 118, "total_tokens": 90877 }, "turn_token_usage": { "input_tokens": 5125423, "cached_input_tokens": 4964480, "cache_write_input_tokens": 0, "output_tokens": 27416, "reasoning_output_tokens": 14543, "total_tokens": 5152839 }, "thread_token_usage": { "input_tokens": 5125423, "cached_input_tokens": 4964480, "cache_write_input_tokens": 0, "output_tokens": 27416, "reasoning_output_tokens": 14543, "total_tokens": 5152839 } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-25T00:18:32.470Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:18:32.470Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:18:35.805Z", "callId": "call_2fUW5OHZWsNBj7vorrAb34J4", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n stored_negative_values = (\\n stored_config.get(\\\"uniform_negatives\\\", 24),\\n stored_config.get(\\\"inbatch_negatives\\\", 8),\\n stored_config.get(\\\"hard_negatives\\\", 0),\\n stored_config.get(\\\"hard_negative_pool_size\\\", 64),\\n+ stored_config.get(\\\"query_random_negatives\\\", 0),\\n+ stored_config.get(\\\"query_hard_negatives\\\", 0),\\n )\\n@@\\n config.hard_negatives,\\n config.hard_negative_pool_size,\\n+ config.query_random_negatives,\\n+ config.query_hard_negatives,\\n )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:18:35.911Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:18:35.816Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2fc9c1df-5e8f-49cc-b6d2-b9292aacf8ca", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -1780,2 +1780,4 @@\n stored_config.get(\"hard_negative_pool_size\", 64),\n+ stored_config.get(\"query_random_negatives\", 0),\n+ stored_config.get(\"query_hard_negatives\", 0),\n )\n@@ -1786,2 +1788,4 @@\n config.hard_negative_pool_size,\n+ config.query_random_negatives,\n+ config.query_hard_negatives,\n )\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:18:35.910Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdd545e487d29abcfbb228fa9176", "usage": { "input_tokens": 90901, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 225, "reasoning_output_tokens": 33, "total_tokens": 91126 }, "turn_token_usage": { "input_tokens": 5216324, "cached_input_tokens": 5054720, "cache_write_input_tokens": 0, "output_tokens": 27641, "reasoning_output_tokens": 14576, "total_tokens": 5243965 }, "thread_token_usage": { "input_tokens": 5216324, "cached_input_tokens": 5054720, "cache_write_input_tokens": 0, "output_tokens": 27641, "reasoning_output_tokens": 14576, "total_tokens": 5243965 } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-25T00:18:38.062Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:18:38.062Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:18:41.494Z", "callId": "call_g1cBkryxzGWST4ky8jBepdc2", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n \\\"negative_sampling\\\": {\\n- \\\"uniform\\\": config.uniform_negatives,\\n- \\\"propensity_corrected_inbatch\\\": config.inbatch_negatives,\\n- \\\"item_tower_hard\\\": config.hard_negatives,\\n- \\\"hard_pool\\\": config.hard_negative_pool_size,\\n+ \\\"uniform_pool\\\": config.uniform_negatives,\\n+ \\\"propensity_corrected_inbatch_pool\\\": (\\n+ config.inbatch_negatives\\n+ ),\\n+ \\\"item_neighbor_pool\\\": config.hard_negatives,\\n+ \\\"random_retained\\\": config.query_random_negatives,\\n+ \\\"query_hard_retained\\\": config.query_hard_negatives,\\n },\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:18:41.573Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:18:41.504Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-73c13235-ad1d-44b1-bcb1-a89af770deda", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2407,6 +2407,9 @@\n \"negative_sampling\": {\n- \"uniform\": config.uniform_negatives,\n- \"propensity_corrected_inbatch\": config.inbatch_negatives,\n- \"item_tower_hard\": config.hard_negatives,\n- \"hard_pool\": config.hard_negative_pool_size,\n+ \"uniform_pool\": config.uniform_negatives,\n+ \"propensity_corrected_inbatch_pool\": (\n+ config.inbatch_negatives\n+ ),\n+ \"item_neighbor_pool\": config.hard_negatives,\n+ \"random_retained\": config.query_random_negatives,\n+ \"query_hard_retained\": config.query_hard_negatives,\n },\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:18:41.571Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bddc33b487d2a35607fc82241a61", "usage": { "input_tokens": 91150, "cached_input_tokens": 90752, "cache_write_input_tokens": 0, "output_tokens": 207, "reasoning_output_tokens": 8, "total_tokens": 91357 }, "turn_token_usage": { "input_tokens": 5307474, "cached_input_tokens": 5145472, "cache_write_input_tokens": 0, "output_tokens": 27848, "reasoning_output_tokens": 14584, "total_tokens": 5335322 }, "thread_token_usage": { "input_tokens": 5307474, "cached_input_tokens": 5145472, "cache_write_input_tokens": 0, "output_tokens": 27848, 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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_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/query_pairwise_checkpoints --output /app/solution/query_pairwise_validation.parquet > /app/solution/query_pairwise_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:19:01.443Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a5dd37\",\"wall_time_seconds\":1.000754974,\"session_id\":75600,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:19:00.365Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdeb39ac87d29632564fa4508be7", "usage": { "input_tokens": 91620, "cached_input_tokens": 91264, "cache_write_input_tokens": 0, "output_tokens": 450, "reasoning_output_tokens": 315, "total_tokens": 92070 }, "turn_token_usage": { "input_tokens": 5490475, "cached_input_tokens": 5327744, "cache_write_input_tokens": 0, "output_tokens": 28514, "reasoning_output_tokens": 15026, "total_tokens": 5518989 }, "thread_token_usage": { "input_tokens": 5490475, "cached_input_tokens": 5327744, "cache_write_input_tokens": 0, "output_tokens": 28514, "reasoning_output_tokens": 15026, "total_tokens": 5518989 } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-25T00:19:03.873Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:19:03.873Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:19:04.597Z", "callId": "call_I5lwY9z8GRwUaEEaGCGBAeN0", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:19:09.606Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"44358b\",\"wall_time_seconds\":5.000932994,\"session_id\":75600,\"original_token_count\":49,\"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\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:19:04.733Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdf5a44087d294bff41a288b1ccc", "usage": { "input_tokens": 92128, "cached_input_tokens": 91392, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 92183 }, "turn_token_usage": { "input_tokens": 5582603, "cached_input_tokens": 5419136, "cache_write_input_tokens": 0, "output_tokens": 28569, "reasoning_output_tokens": 15033, "total_tokens": 5611172 }, "thread_token_usage": { "input_tokens": 5582603, "cached_input_tokens": 5419136, "cache_write_input_tokens": 0, "output_tokens": 28569, "reasoning_output_tokens": 15033, "total_tokens": 5611172 } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-25T00:19:11.210Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:19:11.210Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:19:11.950Z", "callId": "call_l2ejBpXrfD8MI5jofE4rEk9J", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:19:16.979Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"c3b8b2\",\"wall_time_seconds\":5.001888686,\"session_id\":75600,\"original_token_count\":154,\"output\":\"inbatch 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\\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:19:12.063Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bdfdcaf887d28552be90a465e88e", "usage": { "input_tokens": 92301, "cached_input_tokens": 91904, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 6, "total_tokens": 92355 }, "turn_token_usage": { "input_tokens": 5674904, "cached_input_tokens": 5511040, "cache_write_input_tokens": 0, "output_tokens": 28623, "reasoning_output_tokens": 15039, "total_tokens": 5703527 }, "thread_token_usage": { "input_tokens": 5674904, "cached_input_tokens": 5511040, "cache_write_input_tokens": 0, "output_tokens": 28623, "reasoning_output_tokens": 15039, "total_tokens": 5703527 } } } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-25T00:19:19.551Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:19:19.551Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:19:20.284Z", "callId": "call_Z05UEIzfbJdoN4zej7igQFI2", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:19:50.316Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"8bfa44\",\"wall_time_seconds\":30.000853666,\"session_id\":75600,\"original_token_count\":14,\"output\":\"resumed validation-BCE training: epoch=1 loss=0.690025\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:19:20.411Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be054d2087d280dd84a0c9fefcf3", "usage": { "input_tokens": 92613, "cached_input_tokens": 92160, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 13, "total_tokens": 92674 }, "turn_token_usage": { "input_tokens": 5767517, "cached_input_tokens": 5603200, "cache_write_input_tokens": 0, "output_tokens": 28684, "reasoning_output_tokens": 15052, "total_tokens": 5796201 }, "thread_token_usage": { "input_tokens": 5767517, "cached_input_tokens": 5603200, "cache_write_input_tokens": 0, "output_tokens": 28684, "reasoning_output_tokens": 15052, "total_tokens": 5796201 } } } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-25T00:19:52.569Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:19:52.569Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:19:56.032Z", "callId": "call_QW7RwzFM81NupLroHW4pOfz4", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank));\\nfor name in ['production','hard']:\\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\\n print(name,'top10 popularity rank',np.quantile(p[r['rank'].to_numpy()<=10],[.1,.25,.5,.75,.9]).astype(int),'unique',len(np.unique(p[r['rank'].to_numpy()<=10])))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:19:58.566Z", "content": "Script completed\nWall time 2.5 seconds\nOutput:\n\nproduction top10 popularity rank [ 5 14 111 2124 7317] unique 592\nhard top10 popularity rank [ 195 377 1342 3595 8300] unique 595\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:19:56.147Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be26827c87d2b0c70c54c5e30aad", "usage": { "input_tokens": 92748, "cached_input_tokens": 92416, "cache_write_input_tokens": 0, "output_tokens": 248, "reasoning_output_tokens": 49, "total_tokens": 92996 }, "turn_token_usage": { "input_tokens": 5860265, "cached_input_tokens": 5695616, "cache_write_input_tokens": 0, "output_tokens": 28932, "reasoning_output_tokens": 15101, "total_tokens": 5889197 }, "thread_token_usage": { "input_tokens": 5860265, "cached_input_tokens": 5695616, "cache_write_input_tokens": 0, "output_tokens": 28932, "reasoning_output_tokens": 15101, "total_tokens": 5889197 } } }, { "kind": "context", "timestamp": "2026-09-25T00:19:58.562Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c4bbd5a-7d4c-4776-9a84-e1811f761bcf", "process_id": "65755", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank));\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\n print(name,'top10 popularity rank',np.quantile(p[r['rank'].to_numpy()<=10],[.1,.25,.5,.75,.9]).astype(int),'unique',len(np.unique(p[r['rank'].to_numpy()<=10])))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; cat=pd.read_parquet(D+'/candidate_catalog.parquet'); rank=dict(zip(cat.item_id,cat.popularity_rank));\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); p=np.array([rank[x] for x in r.item_id]);\n print(name,'top10 popularity rank',np.quantile(p[r['rank'].to_numpy()<=10],[.1,.25,.5,.75,.9]).astype(int),'unique',len(np.unique(p[r['rank'].to_numpy()<=10])))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "production top10 popularity rank [ 5 14 111 2124 7317] unique 592\nhard top10 popularity rank [ 195 377 1342 3595 8300] unique 595\n", "stderr": "", "aggregated_output": "production top10 popularity rank [ 5 14 111 2124 7317] unique 592\nhard top10 popularity rank [ 195 377 1342 3595 8300] unique 595\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 349005810 }, "formatted_output": "production top10 popularity rank [ 5 14 111 2124 7317] unique 592\nhard top10 popularity rank [ 195 377 1342 3595 8300] unique 595\n" } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-25T00:20:01.731Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:20:01.731Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:20:09.584Z", "callId": "call_C7WwXp7bRYCzb1VlO8HXWGJ6", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet\\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\\nfor name in ['production','hard']:\\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:20:13.200Z", "content": "Script completed\nWall time 3.6 seconds\nOutput:\n\nproduction top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:20:09.741Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be2ee18487d2a931defe94a4e13a", "usage": { "input_tokens": 93070, "cached_input_tokens": 92544, "cache_write_input_tokens": 0, "output_tokens": 349, "reasoning_output_tokens": 83, "total_tokens": 93419 }, "turn_token_usage": { "input_tokens": 5953335, "cached_input_tokens": 5788160, "cache_write_input_tokens": 0, "output_tokens": 29281, "reasoning_output_tokens": 15184, "total_tokens": 5982616 }, "thread_token_usage": { "input_tokens": 5953335, "cached_input_tokens": 5788160, "cache_write_input_tokens": 0, "output_tokens": 29281, "reasoning_output_tokens": 15184, "total_tokens": 5982616 } } }, { "kind": "context", "timestamp": "2026-09-25T00:20:13.197Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5f55a5b-84f3-4a6f-8652-00a90eb81f3a", "process_id": "45349", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nD=Path('/task/data'); t=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),t)\nfor name in ['production','hard']:\n r=pd.read_parquet(f'/app/solution/{name}_validation.parquet'); h=[(u,i,k,g.get(u,{}).get(i,0)) for u,i,k in r.itertuples(index=False,name=None) if g.get(u,{}).get(i,0)]; x=pd.DataFrame(h,columns=['u','i','rank','grade']); y=x[x['rank']<=10]; print(name,'top100',len(x),x.u.nunique(),'top10',len(y),y.u.nunique(),y.grade.value_counts().to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n", "stderr": "", "aggregated_output": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 423067156 }, "formatted_output": "production top100 2619 1079 top10 644 378 {1: 564, 3: 80}\nhard top100 1803 962 top10 357 283 {1: 312, 3: 45}\n" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-25T00:20:23.688Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:20:23.688Z", "text": "" } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-25T00:20:30.830Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:20:30.830Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:20:35.652Z", "callId": "call_hnw5VNbaEtAb2deAtToTZZhK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nr=pd.read_parquet('/app/solution/production_validation.parquet')\\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\\n ranked={}\\n for uid,x in r.groupby('uid',sort=False):\\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:20:45.868Z", "content": "Script completed\nWall time 10.2 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:20:35.736Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be3d6c1887d2999681c6b3ac482c", "usage": { "input_tokens": 93495, "cached_input_tokens": 92928, "cache_write_input_tokens": 0, "output_tokens": 1182, "reasoning_output_tokens": 870, "total_tokens": 94677 }, "turn_token_usage": { "input_tokens": 6046830, "cached_input_tokens": 5881088, "cache_write_input_tokens": 0, "output_tokens": 30463, "reasoning_output_tokens": 16054, "total_tokens": 6077293 }, "thread_token_usage": { "input_tokens": 6046830, "cached_input_tokens": 5881088, "cache_write_input_tokens": 0, "output_tokens": 30463, "reasoning_output_tokens": 16054, "total_tokens": 6077293 } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-25T00:20:51.088Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:20:51.088Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-25T00:20:54.155Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7824beac-6128-4b49-9c58-fea695309f60", "process_id": "78927", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\n ranked={}\n for uid,x in r.groupby('uid',sort=False):\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\nfor beta in [-16,-8,-4,-2,-1,0,1,2,4,8,16]:\n ranked={}\n for uid,x in r.groupby('uid',sort=False):\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id]))\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n", "stderr": "", "aggregated_output": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 296955606 }, "formatted_output": "-16 0.011951\n-8 0.013554\n-4 0.015507\n-2 0.018381\n-1 0.019763\n0 0.02128\n1 0.021974\n2 0.02239\n4 0.021683\n8 0.020468\n16 0.020423\n" } } }, { "kind": "tool", "timestamp": "2026-09-25T00:20:56.659Z", "callId": "call_4epmWalcQqW8S3cI5rn3nMQC", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nr=pd.read_parquet('/app/solution/production_validation.parquet')\\ngroups=list(r.groupby('uid',sort=False))\\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\\n ranked={}\\n for uid,x in groups:\\n vals=np.array([pr[int(i)] for i in x.item_id])\\n s=-x['rank'].to_numpy(float)+beta*np.log(vals)\\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\\n print(beta,score_rankings(ranked,g),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:21:07.875Z", "content": "Script completed\nWall time 11.2 seconds\nOutput:\n\n{\"chunk_id\":\"ccc58f\",\"wall_time_seconds\":11.044941463,\"exit_code\":0,\"original_token_count\":125,\"output\":\"-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:20:56.765Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be5e629087d283f885642b2c7878", "usage": { "input_tokens": 94700, "cached_input_tokens": 93312, "cache_write_input_tokens": 0, "output_tokens": 471, "reasoning_output_tokens": 154, "total_tokens": 95171 }, "turn_token_usage": { "input_tokens": 6141530, "cached_input_tokens": 5974400, "cache_write_input_tokens": 0, "output_tokens": 30934, "reasoning_output_tokens": 16208, "total_tokens": 6172464 }, "thread_token_usage": { "input_tokens": 6141530, "cached_input_tokens": 5974400, "cache_write_input_tokens": 0, "output_tokens": 30934, "reasoning_output_tokens": 16208, "total_tokens": 6172464 } } }, { "kind": "context", "timestamp": "2026-09-25T00:21:07.872Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd8b46b8-8da0-43d6-a954-d13906f7f111", "process_id": "62118", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\ngroups=list(r.groupby('uid',sort=False))\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\n ranked={}\n for uid,x in groups:\n vals=np.array([pr[int(i)] for i in x.item_id])\n s=-x['rank'].to_numpy(float)+beta*np.log(vals)\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nr=pd.read_parquet('/app/solution/production_validation.parquet')\ngroups=list(r.groupby('uid',sort=False))\nfor beta in [-8,-4,-2,-1,0,1,2,4,8]:\n ranked={}\n for uid,x in groups:\n vals=np.array([pr[int(i)] for i in x.item_id])\n s=-x['rank'].to_numpy(float)+beta*np.log(vals)\n ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n", "stderr": "", "aggregated_output": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 45157838 }, "formatted_output": "-8 {'quality_ndcg@10': 0.013554, 'quality_users': 4263}\n-4 {'quality_ndcg@10': 0.015507, 'quality_users': 4263}\n-2 {'quality_ndcg@10': 0.018381, 'quality_users': 4263}\n-1 {'quality_ndcg@10': 0.019763, 'quality_users': 4263}\n0 {'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n1 {'quality_ndcg@10': 0.021974, 'quality_users': 4263}\n2 {'quality_ndcg@10': 0.02239, 'quality_users': 4263}\n4 {'quality_ndcg@10': 0.021683, 'quality_users': 4263}\n8 {'quality_ndcg@10': 0.020468, 'quality_users': 4263}\n" } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-25T00:21:13.335Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:21:13.335Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:21:19.050Z", "callId": "call_H0LDPo9UcEiuNeDBVOxxbArk", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\\n ranked={}\\n for uid,x in groups:\\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:21:30.317Z", "content": "Script completed\nWall time 11.2 seconds\nOutput:\n\n{\"chunk_id\":\"33e13b\",\"wall_time_seconds\":11.014744139,\"exit_code\":0,\"original_token_count\":29,\"output\":\"1.25 0.02194\\n1.5 0.021972\\n1.75 0.022182\\n2 0.02239\\n2.25 0.022501\\n2.5 0.022716\\n2.75 0.022671\\n3 0.022366\\n3.5 0.021785\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:21:19.197Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be74198c87d283c2c7c007a4a8aa", "usage": { "input_tokens": 95437, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 555, "reasoning_output_tokens": 228, "total_tokens": 95992 }, "turn_token_usage": { "input_tokens": 6236967, "cached_input_tokens": 6068864, "cache_write_input_tokens": 0, "output_tokens": 31489, "reasoning_output_tokens": 16436, "total_tokens": 6268456 }, "thread_token_usage": { "input_tokens": 6236967, "cached_input_tokens": 6068864, "cache_write_input_tokens": 0, "output_tokens": 31489, "reasoning_output_tokens": 16436, "total_tokens": 6268456 } } }, { "kind": "context", "timestamp": "2026-09-25T00:21:30.307Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-23e5bb67-e33b-43c8-9ca7-adf92398e9b6", "process_id": "58474", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\n ranked={}\n for uid,x in groups:\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pr=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int))); r=pd.read_parquet('/app/solution/production_validation.parquet'); groups=list(r.groupby('uid',sort=False))\nfor beta in [1.25,1.5,1.75,2,2.25,2.5,2.75,3,3.5]:\n ranked={}\n for uid,x in groups:\n s=-x['rank'].to_numpy(float)+beta*np.log(np.array([pr[int(i)] for i in x.item_id])); ranked[int(uid)]=x.item_id.to_numpy()[np.argsort(-s,kind='stable')].astype(int).tolist()\n print(beta,score_rankings(ranked,g)['quality_ndcg@10'],flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n", "stderr": "", "aggregated_output": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 15031376 }, "formatted_output": "1.25 0.02194\n1.5 0.021972\n1.75 0.022182\n2 0.02239\n2.25 0.022501\n2.5 0.022716\n2.75 0.022671\n3 0.022366\n3.5 0.021785\n" } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-25T00:21:40.893Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:21:40.893Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:21:41.752Z", "callId": "call_d8eivf5ifNGJxMWkaegNZlYi", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:21:46.775Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"b7c9fa\",\"wall_time_seconds\":5.001939951,\"session_id\":75600,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:21:41.951Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be8a888c87d2a3ef278ff9d1a215", "usage": { "input_tokens": 96125, "cached_input_tokens": 95232, "cache_write_input_tokens": 0, "output_tokens": 558, "reasoning_output_tokens": 510, "total_tokens": 96683 }, "turn_token_usage": { "input_tokens": 6333092, "cached_input_tokens": 6164096, "cache_write_input_tokens": 0, "output_tokens": 32047, "reasoning_output_tokens": 16946, "total_tokens": 6365139 }, "thread_token_usage": { "input_tokens": 6333092, "cached_input_tokens": 6164096, "cache_write_input_tokens": 0, "output_tokens": 32047, "reasoning_output_tokens": 16946, "total_tokens": 6365139 } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-25T00:21:50.403Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:21:50.403Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:21:51.120Z", "callId": "call_WqlJTg4gDTNXU23XyrzmvBDe", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:22:21.135Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"63a532\",\"wall_time_seconds\":30.001633423,\"session_id\":75600,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:21:51.230Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5be9c195487d2bf925c632ad003d4", "usage": { "input_tokens": 96742, "cached_input_tokens": 96000, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 18, "total_tokens": 96808 }, "turn_token_usage": { "input_tokens": 6429834, "cached_input_tokens": 6260096, "cache_write_input_tokens": 0, "output_tokens": 32113, "reasoning_output_tokens": 16964, "total_tokens": 6461947 }, "thread_token_usage": { "input_tokens": 6429834, "cached_input_tokens": 6260096, "cache_write_input_tokens": 0, "output_tokens": 32113, "reasoning_output_tokens": 16964, "total_tokens": 6461947 } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-25T00:22:23.044Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:22:23.044Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:22:24.870Z", "text": "A cheap diagnostic confirms the sampler diagnosis independently: mildly demoting globally popular items within the baseline’s existing top 100 raises public NDCG from `0.021280` to `0.022716` (+6.7%). That means the baseline’s easy-negative mix is indeed leaving a popularity shortcut. I’ll use this only as a calibration check; the active experiment is training query-specific hard negatives so the correction remains inside the two-tower objective." } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-25T00:22:25.726Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:22:25.726Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:22:27.126Z", "callId": "call_k6R8RhHbXRkimNCLOTVj390c", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:22:27.379Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:22:27.209Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e7d6d593-75df-4cfb-b02c-056fc7c91dba", "process_id": "50649", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/query_pairwise_checkpoints 2>/dev/null || true; du -sh /app/solution/query_pairwise_checkpoints 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4000 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:22:27.355Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bebd64f887d2a38e8f293f0948a3", "usage": { "input_tokens": 96865, "cached_input_tokens": 96512, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 68, "total_tokens": 97118 }, "turn_token_usage": { "input_tokens": 6526699, "cached_input_tokens": 6356608, "cache_write_input_tokens": 0, "output_tokens": 32366, "reasoning_output_tokens": 17032, "total_tokens": 6559065 }, "thread_token_usage": { "input_tokens": 6526699, "cached_input_tokens": 6356608, "cache_write_input_tokens": 0, "output_tokens": 32366, "reasoning_output_tokens": 17032, "total_tokens": 6559065 } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-25T00:22:40.542Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:22:40.542Z", "text": "" } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-25T00:22:44.383Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:22:44.383Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:23:03.150Z", "callId": "call_QGUMUnHQt7KZ9FWl17pEFvbs", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/sweep_popularity_bias.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Development-only sweep of a static item-side popularity calibration.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+import train_current_model as trainer\\n+import two_tower_model as base\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--checkpoint\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--prepared-cache\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--public-dir\\\", type=Path, required=True)\\n+ parser.add_argument(\\\"--output-dir\\\", type=Path, required=True)\\n+ parser.add_argument(\\n+ \\\"--biases\\\",\\n+ type=float,\\n+ nargs=\\\"+\\\",\\n+ default=(0.0, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5),\\n+ )\\n+ parser.add_argument(\\\"--threads\\\", type=int, default=8)\\n+ args = parser.parse_args()\\n+\\n+ torch.set_num_threads(args.threads)\\n+ torch.set_num_interop_threads(1)\\n+ config = trainer.Config()\\n+ prepared = trainer.load_cache(args.prepared_cache, \\\"validation\\\", config)\\n+ eligibility = trainer.CandidateSet.read(\\n+ args.public_dir / \\\"data\\\" / \\\"validation_eligible_candidates.npz\\\"\\n+ )\\n+ payload = torch.load(args.checkpoint, map_location=\\\"cpu\\\", weights_only=False)\\n+ model = base.SequentialTwoTower(\\n+ prepared.item_features, trainer.model_config(config)\\n+ )\\n+ model.load_state_dict(payload[\\\"model_state\\\"])\\n+ model.eval()\\n+\\n+ with torch.inference_mode():\\n+ queries = trainer.inference_queries(\\n+ model, prepared, config, torch.device(\\\"cpu\\\")\\n+ )\\n+ item_parts = []\\n+ for start in range(0, len(prepared.item_ids), 2_048):\\n+ rows = torch.arange(start, min(start + 2_048, len(prepared.item_ids)))\\n+ item_parts.append(model.encode_items(rows))\\n+ items = torch.cat(item_parts)\\n+\\n+ selections = {\\n+ bias: np.empty((len(eligibility.uids), base.TOP_N), dtype=np.uint16)\\n+ for bias in args.biases\\n+ }\\n+ item_popularity = torch.from_numpy(prepared.item_features[:, 0])\\n+ for start in range(0, len(eligibility.uids), 64):\\n+ end = min(start + 64, len(eligibility.uids))\\n+ raw_scores = queries[start:end] @ items.T\\n+ eligible = np.unpackbits(\\n+ eligibility.packed[start:end],\\n+ axis=1,\\n+ count=len(prepared.item_ids),\\n+ bitorder=\\\"little\\\",\\n+ ).astype(bool, copy=False)\\n+ mask = torch.from_numpy(eligible)\\n+ for bias in args.biases:\\n+ scores = raw_scores - float(bias) * item_popularity\\n+ scores = scores.masked_fill(~mask, -torch.inf)\\n+ selections[bias][start:end] = torch.topk(\\n+ scores, k=base.TOP_N, dim=1\\n+ ).indices.numpy().astype(np.uint16)\\n+\\n+ args.output_dir.mkdir(parents=True, exist_ok=True)\\n+ for bias, selected in selections.items():\\n+ frame = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(eligibility.uids, base.TOP_N),\\n+ \\\"item_id\\\": prepared.item_ids[selected.reshape(-1)],\\n+ \\\"rank\\\": np.tile(np.arange(1, base.TOP_N + 1), len(eligibility.uids)),\\n+ }\\n+ )\\n+ label = str(bias).replace(\\\"-\\\", \\\"m\\\").replace(\\\".\\\", \\\"p\\\")\\n+ frame.to_parquet(\\n+ args.output_dir / f\\\"bias_{label}.parquet\\\",\\n+ index=False,\\n+ compression=\\\"zstd\\\",\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:23:03.302Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:23:03.225Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-75fd9d27-2d4e-4d67-ba07-dcc343b91dbd", "changes": { "/app/solution/sweep_popularity_bias.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Development-only sweep of a static item-side popularity calibration.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nimport train_current_model as trainer\nimport two_tower_model as base\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--checkpoint\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--output-dir\", type=Path, required=True)\n parser.add_argument(\n \"--biases\",\n type=float,\n nargs=\"+\",\n default=(0.0, 0.025, 0.05, 0.1, 0.2, 0.3, 0.5),\n )\n parser.add_argument(\"--threads\", type=int, default=8)\n args = parser.parse_args()\n\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n config = trainer.Config()\n prepared = trainer.load_cache(args.prepared_cache, \"validation\", config)\n eligibility = trainer.CandidateSet.read(\n args.public_dir / \"data\" / \"validation_eligible_candidates.npz\"\n )\n payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n model = base.SequentialTwoTower(\n prepared.item_features, trainer.model_config(config)\n )\n model.load_state_dict(payload[\"model_state\"])\n model.eval()\n\n with torch.inference_mode():\n queries = trainer.inference_queries(\n model, prepared, config, torch.device(\"cpu\")\n )\n item_parts = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(start, min(start + 2_048, len(prepared.item_ids)))\n item_parts.append(model.encode_items(rows))\n items = torch.cat(item_parts)\n\n selections = {\n bias: np.empty((len(eligibility.uids), base.TOP_N), dtype=np.uint16)\n for bias in args.biases\n }\n item_popularity = torch.from_numpy(prepared.item_features[:, 0])\n for start in range(0, len(eligibility.uids), 64):\n end = min(start + 64, len(eligibility.uids))\n raw_scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n mask = torch.from_numpy(eligible)\n for bias in args.biases:\n scores = raw_scores - float(bias) * item_popularity\n scores = scores.masked_fill(~mask, -torch.inf)\n selections[bias][start:end] = torch.topk(\n scores, k=base.TOP_N, dim=1\n ).indices.numpy().astype(np.uint16)\n\n args.output_dir.mkdir(parents=True, exist_ok=True)\n for bias, selected in selections.items():\n frame = pd.DataFrame(\n {\n \"uid\": np.repeat(eligibility.uids, base.TOP_N),\n \"item_id\": prepared.item_ids[selected.reshape(-1)],\n \"rank\": np.tile(np.arange(1, base.TOP_N + 1), len(eligibility.uids)),\n }\n )\n label = str(bias).replace(\"-\", \"m\").replace(\".\", \"p\")\n frame.to_parquet(\n args.output_dir / f\"bias_{label}.parquet\",\n index=False,\n compression=\"zstd\",\n )\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/sweep_popularity_bias.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:23:03.279Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bec3e2bc87d2bbb1bf3a2b5d426c", "usage": { "input_tokens": 97141, "cached_input_tokens": 96640, "cache_write_input_tokens": 0, "output_tokens": 1774, "reasoning_output_tokens": 713, "total_tokens": 98915 }, "turn_token_usage": { "input_tokens": 6623840, "cached_input_tokens": 6453248, "cache_write_input_tokens": 0, "output_tokens": 34140, "reasoning_output_tokens": 17745, "total_tokens": 6657980 }, "thread_token_usage": { "input_tokens": 6623840, "cached_input_tokens": 6453248, "cache_write_input_tokens": 0, "output_tokens": 34140, 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tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:24:44.948Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d8493e\",\"wall_time_seconds\":30.001845655,\"session_id\":75600,\"original_token_count\":77,\"output\":\"saved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:24:15.120Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": 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"input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:25:17.869Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"b45968\",\"wall_time_seconds\":30.00168489,\"session_id\":75600,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:24:47.953Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bf4d444487d28a070dbf293ba1fc", "usage": { "input_tokens": 99498, "cached_input_tokens": 99072, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 32, "total_tokens": 99578 }, "turn_token_usage": { "input_tokens": 7020625, "cached_input_tokens": 6847104, "cache_write_input_tokens": 0, "output_tokens": 34482, "reasoning_output_tokens": 17895, "total_tokens": 7055107 }, "thread_token_usage": { "input_tokens": 7020625, "cached_input_tokens": 6847104, "cache_write_input_tokens": 0, "output_tokens": 34482, "reasoning_output_tokens": 17895, "total_tokens": 7055107 } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-25T00:25:19.797Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:25:19.797Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:25:20.853Z", "callId": "call_dA4YEFRNdaizY0CLnICeQPwa", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:25:50.885Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"190994\",\"wall_time_seconds\":30.001915164,\"session_id\":75600,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:25:20.956Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bf6e1e1887d2a2bdc828b586432f", "usage": { "input_tokens": 99635, "cached_input_tokens": 99328, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 8, "total_tokens": 99691 }, "turn_token_usage": { "input_tokens": 7120260, "cached_input_tokens": 6946432, "cache_write_input_tokens": 0, "output_tokens": 34538, "reasoning_output_tokens": 17903, "total_tokens": 7154798 }, "thread_token_usage": { "input_tokens": 7120260, "cached_input_tokens": 6946432, "cache_write_input_tokens": 0, "output_tokens": 34538, "reasoning_output_tokens": 17903, "total_tokens": 7154798 } } }, { "kind": "context", "timestamp": "2026-09-25T00:25:57.214Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4288b575-f5dd-4b46-af8a-1e3105983dee", "process_id": "75600", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/query_pairwise_checkpoints --output /app/solution/query_pairwise_validation.parquet > /app/solution/query_pairwise_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/query_pairwise_checkpoints --output /app/solution/query_pairwise_validation.parquet > /app/solution/query_pairwise_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.690025\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.542237 running_bce=0.547834 elapsed=273.1s\nsaved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\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", "stderr": "", "aggregated_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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.690025\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.542237 running_bce=0.547834 elapsed=273.1s\nsaved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\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", "exit_code": 0, "duration": { "secs": 416, "nanos": 772255436 }, "formatted_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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 48, 0, 64, 8, 24); loss pointwise-bce->pairwise; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.690025\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.542237 running_bce=0.547834 elapsed=273.1s\nsaved epoch checkpoint: /app/solution/query_pairwise_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.54652 validation_loss=0.52864 pair_accuracy=0.623 train_targets=1,052,953 validation_targets=130,103 train_seconds=289.0 validation_seconds=17.8\nrestored best validation-BCE checkpoint: epoch=2 loss=0.528635\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" } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-25T00:26:00.288Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:00.288Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:26:00.293Z", "text": "The query-mined pairwise epoch also converged internally (`0.6900 → 0.5286`) and kept exactly 32 loss negatives from a 64-item pool. Ranking inference is underway. I’m treating the earlier loss/quality mismatch as a hard guardrail: only public NDCG can promote this checkpoint." } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-25T00:26:00.297Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:00.297Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:26:00.298Z", "callId": "call_qFRRjexNcXAHD4HAvQbIqaxI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":75600,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:26:00.843Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"ec469f\",\"wall_time_seconds\":0.00000141,\"exit_code\":0,\"original_token_count\":57,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:00.841Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bf8f22fc87d29ef2f81e521d0511", "usage": { "input_tokens": 99747, "cached_input_tokens": 99456, "cache_write_input_tokens": 0, "output_tokens": 155, "reasoning_output_tokens": 33, "total_tokens": 99902 }, "turn_token_usage": { "input_tokens": 7220007, "cached_input_tokens": 7045888, "cache_write_input_tokens": 0, "output_tokens": 34693, "reasoning_output_tokens": 17936, "total_tokens": 7254700 }, "thread_token_usage": { "input_tokens": 7220007, "cached_input_tokens": 7045888, "cache_write_input_tokens": 0, "output_tokens": 34693, "reasoning_output_tokens": 17936, "total_tokens": 7254700 } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-25T00:26:02.812Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:02.812Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:26:04.387Z", "callId": "call_yseOAVaBSlxYtdmRy1U7W69i", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/query_pairwise_validation.parquet && sed -n '1,260p' /app/solution/query_pairwise_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:26:07.768Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/query_pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020273,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/query_pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 412.496,\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\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 48,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 8,\n \"query_hard_retained\": 24\n },\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\": 2,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 48,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 8,\n \"query_hard_negatives\": 24,\n \"loss_objective\": \"pairwise\",\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.021699814000158,\n \"validation_loss\": 0.6900251174726226\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.5465189422087784,\n \"positive_logit\": 0.8022641865617705,\n \"negative_logit\": 0.13663982700910554,\n \"pair_accuracy\": 0.6231171999130065,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 288.951905763,\n \"validation_loss\": 0.5286354844009227,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.83157643300001\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.3673927783966064,\n \"query_norm_std\": 0.5522235631942749,\n \"item_norm_mean\": 1.6735552549362183,\n \"item_norm_std\": 0.5771517157554626\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:04.510Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bf992bb087d2a5c6398318da2090", "usage": { "input_tokens": 100065, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 102, "reasoning_output_tokens": 6, "total_tokens": 100167 }, "turn_token_usage": { "input_tokens": 7320072, "cached_input_tokens": 7145472, "cache_write_input_tokens": 0, "output_tokens": 34795, "reasoning_output_tokens": 17942, "total_tokens": 7354867 }, "thread_token_usage": { "input_tokens": 7320072, "cached_input_tokens": 7145472, "cache_write_input_tokens": 0, "output_tokens": 34795, "reasoning_output_tokens": 17942, "total_tokens": 7354867 } } }, { "kind": "context", "timestamp": "2026-09-25T00:26:07.763Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0fcb3bdf-4117-4803-ab7b-25787eaa595c", "process_id": "49592", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/query_pairwise_validation.parquet && sed -n '1,260p' /app/solution/query_pairwise_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/query_pairwise_validation.parquet && sed -n '1,260p' /app/solution/query_pairwise_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/query_pairwise_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020273,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/query_pairwise_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 412.496,\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\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 48,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 8,\n \"query_hard_retained\": 24\n },\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\": 2,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 48,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 8,\n \"query_hard_negatives\": 24,\n \"loss_objective\": \"pairwise\",\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\": 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\"users\": 8456,\n \"runtime_seconds\": 412.496,\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\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 48,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 8,\n \"query_hard_retained\": 24\n },\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\": 2,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 48,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 8,\n \"query_hard_negatives\": 24,\n \"loss_objective\": \"pairwise\",\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 18.021699814000158,\n \"validation_loss\": 0.6900251174726226\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.5465189422087784,\n \"positive_logit\": 0.8022641865617705,\n \"negative_logit\": 0.13663982700910554,\n \"pair_accuracy\": 0.6231171999130065,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 288.951905763,\n \"validation_loss\": 0.5286354844009227,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 17.83157643300001\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.3673927783966064,\n \"query_norm_std\": 0.5522235631942749,\n \"item_norm_mean\": 1.6735552549362183,\n \"item_norm_std\": 0.5771517157554626\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-25T00:26:16.417Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:26:16.417Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:26:18.991Z", "text": "Query-aware mining is much safer but still slightly below baseline (`0.020273` vs. `0.021280`), so it is not promoted. The lesson is that 24 mined examples are too dominant under one full fine-tuning epoch. I’m keeping the baseline checkpoint as leader and testing two conservative corrections next: fewer hard examples at a lower learning rate, and an item-side popularity calibration expressed as part of the two-tower dot product." }, { "kind": "tool", "timestamp": "2026-09-25T00:26:20.792Z", "callId": "call_9GJlWeYkXJcGZHc6nAvLLwZO", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep --biases 0 0.01 0.02 0.03 0.05 0.075 0.1 0.15 0.2 0.3 0.5\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:26:21.958Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"a25cee\",\"wall_time_seconds\":1.000660067,\"session_id\":69840,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:26:20.974Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bf9ff29c87d2bca9898646ff86f1", "usage": { "input_tokens": 101450, "cached_input_tokens": 99840, "cache_write_input_tokens": 0, "output_tokens": 638, "reasoning_output_tokens": 386, "total_tokens": 102088 }, "turn_token_usage": { "input_tokens": 7421522, "cached_input_tokens": 7245312, "cache_write_input_tokens": 0, "output_tokens": 35433, "reasoning_output_tokens": 18328, "total_tokens": 7456955 }, "thread_token_usage": { "input_tokens": 7421522, "cached_input_tokens": 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"2026-09-25T00:27:00.069Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:27:00.069Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:27:01.259Z", "callId": "call_RNcDuln5VWMKu85Neiqms37V", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":69840,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:27:31.280Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"acff39\",\"wall_time_seconds\":30.001487877,\"session_id\":69840,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:27:01.354Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": 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"input": { "code": "const r = await tools.write_stdin({\"session_id\":69840,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:27:50.179Z", "content": "Script completed\nWall time 15.6 seconds\nOutput:\n\n{\"chunk_id\":\"32eff6\",\"wall_time_seconds\":15.5412018,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:27:34.731Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5bff3b16c87d286fa3a861e8bb675", "usage": { "input_tokens": 102558, "cached_input_tokens": 102272, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 102613 }, "turn_token_usage": { "input_tokens": 7728669, "cached_input_tokens": 7550848, "cache_write_input_tokens": 0, "output_tokens": 35766, "reasoning_output_tokens": 18517, "total_tokens": 7764435 }, "thread_token_usage": { "input_tokens": 7728669, "cached_input_tokens": 7550848, "cache_write_input_tokens": 0, "output_tokens": 35766, "reasoning_output_tokens": 18517, "total_tokens": 7764435 } } }, { "kind": "context", "timestamp": "2026-09-25T00:27:50.176Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b433e18c-0192-45f0-844a-5c996557b7b1", "process_id": "69840", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep --biases 0 0.01 0.02 0.03 0.05 0.075 0.1 0.15 0.2 0.3 0.5" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep --biases 0 0.01 0.02 0.03 0.05 0.075 0.1 0.15 0.2 0.3 0.5" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "exit_code": 0, "duration": { "secs": 89, "nanos": 218753051 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n" } } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-09-25T00:27:52.871Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:27:52.871Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:27:54.336Z", "callId": "call_MBnY8yfB18zMBr4U01WNXhr1", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/bias_sweep/*.parquet; do PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:24.511Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p01.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p02.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021671,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021649,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021809,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p075.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022222,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022596,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p15.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023208,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:27:54.447Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": 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"tool", "timestamp": "2026-09-25T00:28:30.886Z", "callId": "call_iFwWgwtNHc0FCP28xwM5rt7f", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p3.parquet && PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p5.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:28:37.633Z", "content": "Script completed\nWall time 6.7 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:31.067Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c028c63487d2a128ebb72692350a", "usage": { "input_tokens": 103322, "cached_input_tokens": 102528, "cache_write_input_tokens": 0, "output_tokens": 227, "reasoning_output_tokens": 109, "total_tokens": 103549 }, "turn_token_usage": { "input_tokens": 7934660, "cached_input_tokens": 7755776, "cache_write_input_tokens": 0, "output_tokens": 36146, "reasoning_output_tokens": 18692, "total_tokens": 7970806 }, "thread_token_usage": { "input_tokens": 7934660, "cached_input_tokens": 7755776, "cache_write_input_tokens": 0, "output_tokens": 36146, "reasoning_output_tokens": 18692, "total_tokens": 7970806 } } }, { "kind": "context", "timestamp": "2026-09-25T00:28:31.085Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-464adad9-0eff-4927-a554-cf084a982f34", "process_id": "3192", "command": [ "/bin/sh", "-lc", "for f in /app/solution/bias_sweep/*.parquet; do PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/bias_sweep/*.parquet; do PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p01.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p02.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021671,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021649,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021809,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p075.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022222,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022596,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p15.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023208,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p01.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p02.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021671,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021649,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021809,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p075.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022222,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022596,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p15.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023208,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 583733746 }, "formatted_output": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p01.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p02.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021671,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021649,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021809,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p075.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022222,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022596,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p15.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023208,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:28:37.629Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-46c1bd70-558d-4975-bdae-ea1de9a2bbfb", "process_id": "72789", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p3.parquet && PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p5.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p3.parquet && PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/bias_sweep/bias_0p5.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 567938737 }, "formatted_output": "{\n \"model\": \"/app/solution/bias_sweep/bias_0p3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.022594,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/bias_sweep/bias_0p5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020468,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 134, "role": "agent", "timestamp": "2026-09-25T00:28:40.919Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:40.919Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:28:42.718Z", "text": "The exact dot-product calibration is materially better: subtracting `0.20 ×` the standardized log-popularity item feature raises public NDCG to `0.023208` (+9.1%) while leaving user computation and indexed retrieval unchanged. This is representable by one appended constant user dimension and one precomputed item dimension. I’m narrowing that coefficient and then testing whether conservative hard-negative fine-tuning adds value beyond it." }, { "kind": "tool", "timestamp": "2026-09-25T00:28:45.305Z", "callId": "call_NHHNkHszMsmQslokiDbeFU5B", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:28:46.472Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"3fc9bb\",\"wall_time_seconds\":1.000850432,\"session_id\":11678,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:45.529Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c035d82c87d2a463c13289461894", "usage": { "input_tokens": 103678, "cached_input_tokens": 103168, "cache_write_input_tokens": 0, "output_tokens": 355, "reasoning_output_tokens": 108, "total_tokens": 104033 }, "turn_token_usage": { "input_tokens": 8038338, "cached_input_tokens": 7858944, "cache_write_input_tokens": 0, "output_tokens": 36501, "reasoning_output_tokens": 18800, "total_tokens": 8074839 }, "thread_token_usage": { "input_tokens": 8038338, "cached_input_tokens": 7858944, "cache_write_input_tokens": 0, "output_tokens": 36501, "reasoning_output_tokens": 18800, "total_tokens": 8074839 } } } ] }, { "id": 135, "role": "agent", "timestamp": "2026-09-25T00:28:51.252Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:28:51.252Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:28:51.952Z", "callId": "call_MvxRPqIKNC2kp7OjLaeZHeh8", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":11678,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:29:21.990Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1608b8\",\"wall_time_seconds\":30.001231441,\"session_id\":11678,\"original_token_count\":17,\"output\":\"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:28:52.119Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c03eb5d087d280e74489a205d51f", "usage": { "input_tokens": 104091, "cached_input_tokens": 103552, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 9, "total_tokens": 104148 }, "turn_token_usage": { "input_tokens": 8142429, "cached_input_tokens": 7962496, "cache_write_input_tokens": 0, "output_tokens": 36558, "reasoning_output_tokens": 18809, "total_tokens": 8178987 }, "thread_token_usage": { "input_tokens": 8142429, "cached_input_tokens": 7962496, "cache_write_input_tokens": 0, "output_tokens": 36558, "reasoning_output_tokens": 18809, "total_tokens": 8178987 } } } ] }, { "id": 136, "role": "agent", "timestamp": "2026-09-25T00:29:23.967Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:23.967Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:29:24.673Z", "callId": "call_Pp11hwAjm2dp7w9GxFEaWAeq", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":11678,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:29:54.706Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e28ec7\",\"wall_time_seconds\":30.000864887,\"session_id\":11678,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:24.836Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c062487887d290769a5cdbdad3f1", "usage": { "input_tokens": 104226, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 104281 }, "turn_token_usage": { "input_tokens": 8246655, "cached_input_tokens": 8066432, "cache_write_input_tokens": 0, "output_tokens": 36613, "reasoning_output_tokens": 18816, "total_tokens": 8283268 }, "thread_token_usage": { "input_tokens": 8246655, "cached_input_tokens": 8066432, "cache_write_input_tokens": 0, "output_tokens": 36613, "reasoning_output_tokens": 18816, "total_tokens": 8283268 } } } ] }, { "id": 137, "role": "agent", "timestamp": "2026-09-25T00:29:56.271Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:29:56.271Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:29:56.966Z", "callId": "call_kwnbnqotv4sgLdlHK56YZTUV", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":11678,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:30:10.855Z", "content": "Script completed\nWall time 13.9 seconds\nOutput:\n\n{\"chunk_id\":\"4f020d\",\"wall_time_seconds\":13.860632111,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:29:57.136Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c082fbd087d2bcb3282960cf9ac3", "usage": { "input_tokens": 104339, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 104394 }, "turn_token_usage": { "input_tokens": 8350994, "cached_input_tokens": 8170496, "cache_write_input_tokens": 0, "output_tokens": 36668, "reasoning_output_tokens": 18823, "total_tokens": 8387662 }, "thread_token_usage": { "input_tokens": 8350994, "cached_input_tokens": 8170496, "cache_write_input_tokens": 0, "output_tokens": 36668, "reasoning_output_tokens": 18823, "total_tokens": 8387662 } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:10.852Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1a4706dd-d43a-4b36-b1ae-2cde1ea7863d", "process_id": "11678", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/sweep_popularity_bias.py --checkpoint /app/solution/baseline_checkpoints/best.pt --prepared-cache /app/solution/validation_sequences.npz --public-dir /task --output-dir /app/solution/bias_sweep_fine --biases 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23 0.24 0.25" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n", "exit_code": 0, "duration": { "secs": 85, "nanos": 381134306 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\n" } } } ] }, { "id": 138, "role": "agent", "timestamp": "2026-09-25T00:30:14.504Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:14.504Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:18.287Z", "callId": "call_7vD77Sk5NycQVA277QOhVBBa", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,score_rankings\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:30:27.630Z", "content": "Script completed\nWall time 9.3 seconds\nOutput:\n\nbias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:18.471Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0930c1487d296edd2b857ad0cf9", "usage": { "input_tokens": 104451, "cached_input_tokens": 104192, "cache_write_input_tokens": 0, "output_tokens": 295, "reasoning_output_tokens": 69, "total_tokens": 104746 }, "turn_token_usage": { "input_tokens": 8455445, "cached_input_tokens": 8274688, "cache_write_input_tokens": 0, "output_tokens": 36963, "reasoning_output_tokens": 18892, "total_tokens": 8492408 }, "thread_token_usage": { "input_tokens": 8455445, "cached_input_tokens": 8274688, "cache_write_input_tokens": 0, "output_tokens": 36963, "reasoning_output_tokens": 18892, "total_tokens": 8492408 } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:27.619Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a93285eb-1343-4755-8391-970fdb3224e0", "process_id": "17645", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,score_rankings\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(CandidateSet(D/'validation_eligible_candidates.npz'),targets)\nfor f in sorted(Path('/app/solution/bias_sweep_fine').glob('*.parquet')):\n x=pd.read_parquet(f).sort_values(['uid','rank']); ranked={int(u):g.item_id.astype(int).tolist() for u,g in x.groupby('uid',sort=False)}; print(f.stem,score_rankings(ranked,truth)['quality_ndcg@10'])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n", "stderr": "", "aggregated_output": "bias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 163713173 }, "formatted_output": "bias_0p16 0.023157\nbias_0p17 0.023061\nbias_0p18 0.023004\nbias_0p19 0.023064\nbias_0p2 0.023208\nbias_0p21 0.023069\nbias_0p22 0.02278\nbias_0p23 0.022631\nbias_0p24 0.022761\nbias_0p25 0.022735\n" } } } ] }, { "id": 139, "role": "agent", "timestamp": "2026-09-25T00:30:37.935Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:37.935Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:41.207Z", "callId": "call_83FQnQjbYwC1fS78JeAjVq5d", "name": "exec", "input": { "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_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:30:42.375Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"bf2259\",\"wall_time_seconds\":1.000984635,\"session_id\":90974,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:41.332Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0a3d74887d2bf386c2a24046d2e", "usage": { "input_tokens": 104879, "cached_input_tokens": 104320, "cache_write_input_tokens": 0, "output_tokens": 682, "reasoning_output_tokens": 493, "total_tokens": 105561 }, "turn_token_usage": { "input_tokens": 8560324, "cached_input_tokens": 8379008, "cache_write_input_tokens": 0, "output_tokens": 37645, "reasoning_output_tokens": 19385, "total_tokens": 8597969 }, "thread_token_usage": { "input_tokens": 8560324, "cached_input_tokens": 8379008, "cache_write_input_tokens": 0, "output_tokens": 37645, "reasoning_output_tokens": 19385, "total_tokens": 8597969 } } } ] }, { "id": 140, "role": "agent", "timestamp": "2026-09-25T00:30:44.266Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:44.266Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:49.373Z", "callId": "call_Bhd99Nzou158aXZ1SDFV79kT", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n query_hard_negatives: int = 24\\n loss_objective: str = \\\"pairwise\\\"\\n+ popularity_bias: float = 0.20\\n@@\\n def rank_candidates(\\n@@\\n- scores = queries[start:end] @ items.T\\n+ scores = queries[start:end] @ items.T\\n+ # This is still a pure dot product: it is equivalent to appending 1\\n+ # to every user vector and -bias * popularity to each item vector.\\n+ if config.popularity_bias:\\n+ scores -= config.popularity_bias * torch.from_numpy(\\n+ prepared.item_features[:, 0]\\n+ )\\n@@\\n parser.add_argument(\\n \\\"--loss-objective\\\",\\n@@\\n default=Config.loss_objective,\\n )\\n+ parser.add_argument(\\n+ \\\"--popularity-bias\\\", type=float, default=Config.popularity_bias\\n+ )\\n@@\\n query_hard_negatives=args.query_hard_negatives,\\n loss_objective=args.loss_objective,\\n+ popularity_bias=args.popularity_bias,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:30:49.519Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:49.439Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f1a4f9dc-eee2-403b-a300-c5c8e231243e", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -65,2 +65,3 @@\n loss_objective: str = \"pairwise\"\n+ popularity_bias: float = 0.20\n inbatch_correction_power: float = 0.5\n@@ -2094,2 +2095,8 @@\n scores = queries[start:end] @ items.T\n+ # This is still a pure dot product: it is equivalent to appending 1\n+ # to every user vector and -bias * popularity to each item vector.\n+ if config.popularity_bias:\n+ scores -= config.popularity_bias * torch.from_numpy(\n+ prepared.item_features[:, 0]\n+ )\n eligible = np.unpackbits(\n@@ -2182,2 +2189,5 @@\n parser.add_argument(\n+ \"--popularity-bias\", type=float, default=Config.popularity_bias\n+ )\n+ parser.add_argument(\n \"--negative-filter-scope\",\n@@ -2302,2 +2312,3 @@\n loss_objective=args.loss_objective,\n+ popularity_bias=args.popularity_bias,\n negative_filter_scope=args.negative_filter_scope,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:49.518Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0b2aaa087d28cbb82fa1537f66a", "usage": { "input_tokens": 105618, "cached_input_tokens": 104704, "cache_write_input_tokens": 0, "output_tokens": 308, "reasoning_output_tokens": 13, "total_tokens": 105926 }, "turn_token_usage": { "input_tokens": 8665942, "cached_input_tokens": 8483712, "cache_write_input_tokens": 0, "output_tokens": 37953, "reasoning_output_tokens": 19398, "total_tokens": 8703895 }, "thread_token_usage": { "input_tokens": 8665942, "cached_input_tokens": 8483712, "cache_write_input_tokens": 0, "output_tokens": 37953, "reasoning_output_tokens": 19398, "total_tokens": 8703895 } } } ] }, { "id": 141, "role": "agent", "timestamp": "2026-09-25T00:30:52.611Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:52.611Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:53.750Z", "callId": "call_57UYDOxmHCYBYRSx4dIO75JY", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n or args.query_hard_negatives < 0\\n+ or not np.isfinite(args.popularity_bias)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:30:53.906Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:53.776Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-222aab34-17ba-47ed-b0dd-5801b2a4f5e1", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2276,2 +2276,3 @@\n or args.query_hard_negatives < 0\n+ or not np.isfinite(args.popularity_bias)\n or args.query_random_negatives > args.uniform_negatives\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:53.890Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0b9d0b087d2a537c1884185d11d", "usage": { "input_tokens": 105950, "cached_input_tokens": 105472, "cache_write_input_tokens": 0, "output_tokens": 137, "reasoning_output_tokens": 65, "total_tokens": 106087 }, "turn_token_usage": { "input_tokens": 8771892, "cached_input_tokens": 8589184, "cache_write_input_tokens": 0, "output_tokens": 38090, "reasoning_output_tokens": 19463, "total_tokens": 8809982 }, "thread_token_usage": { "input_tokens": 8771892, "cached_input_tokens": 8589184, "cache_write_input_tokens": 0, "output_tokens": 38090, "reasoning_output_tokens": 19463, "total_tokens": 8809982 } } } ] }, { "id": 142, "role": "agent", "timestamp": "2026-09-25T00:30:55.886Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:30:55.886Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:30:57.512Z", "callId": "call_WucDa56uoIS8zErfkYRGvSTv", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n \\\"normalization\\\": \\\"none\\\",\\n- \\\"score\\\": \\\"raw-dot-product\\\",\\n+ \\\"score\\\": \\\"augmented-dot-product-with-item-popularity-bias\\\",\\n+ \\\"popularity_bias\\\": config.popularity_bias,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:30:57.633Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:30:57.537Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-fc2030e9-cf27-49f9-93e8-085cbf54a6fd", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2414,3 +2414,4 @@\n \"normalization\": \"none\",\n- \"score\": \"raw-dot-product\",\n+ \"score\": \"augmented-dot-product-with-item-popularity-bias\",\n+ \"popularity_bias\": config.popularity_bias,\n \"objective\": (\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:30:57.623Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0be4f8887d28f3699470225ff18", "usage": { "input_tokens": 106111, "cached_input_tokens": 105728, "cache_write_input_tokens": 0, "output_tokens": 106, "reasoning_output_tokens": 8, "total_tokens": 106217 }, "turn_token_usage": { "input_tokens": 8878003, "cached_input_tokens": 8694912, "cache_write_input_tokens": 0, "output_tokens": 38196, "reasoning_output_tokens": 19471, "total_tokens": 8916199 }, "thread_token_usage": { "input_tokens": 8878003, "cached_input_tokens": 8694912, "cache_write_input_tokens": 0, "output_tokens": 38196, "reasoning_output_tokens": 19471, "total_tokens": 8916199 } } } ] }, { "id": 143, "role": "agent", "timestamp": "2026-09-25T00:31:01.801Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:01.801Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:31:02.520Z", "callId": "call_rjS2mIKPAXKpMTZXguosKjvE", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90974,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:31:07.566Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"1bbc65\",\"wall_time_seconds\":5.000800646,\"session_id\":90974,\"original_token_count\":217,\"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\\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\\nresumed validation-BCE training: epoch=1 loss=0.514172\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:31:02.692Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0c227ec87d2a6548205231ef017", "usage": { "input_tokens": 106241, "cached_input_tokens": 105984, "cache_write_input_tokens": 0, "output_tokens": 159, "reasoning_output_tokens": 111, "total_tokens": 106400 }, "turn_token_usage": { "input_tokens": 8984244, "cached_input_tokens": 8800896, "cache_write_input_tokens": 0, "output_tokens": 38355, "reasoning_output_tokens": 19582, "total_tokens": 9022599 }, "thread_token_usage": { "input_tokens": 8984244, "cached_input_tokens": 8800896, "cache_write_input_tokens": 0, "output_tokens": 38355, "reasoning_output_tokens": 19582, "total_tokens": 9022599 } } } ] }, { "id": 144, "role": "agent", "timestamp": "2026-09-25T00:31:14.664Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:14.664Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:31:15.517Z", "callId": "call_W2zH7GnQ4ymnTjwfAko2LGRK", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"date -u +%H:%M:%S\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:31:15.669Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n00:31:15\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:31:15.577Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-609ed3da-1612-44ad-8de5-ff08b70594a2", "process_id": "52502", "command": [ "/bin/sh", "-lc", "date -u +%H:%M:%S" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "date -u +%H:%M:%S" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "00:31:15\n", "stderr": "", "aggregated_output": "00:31:15\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6491 }, "formatted_output": "00:31:15\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:31:15.662Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c0cbc67887d2ae8acf1c1113c5cd", "usage": { "input_tokens": 106736, "cached_input_tokens": 106112, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 311, "total_tokens": 107104 }, "turn_token_usage": { "input_tokens": 9090980, "cached_input_tokens": 8907008, "cache_write_input_tokens": 0, "output_tokens": 38723, "reasoning_output_tokens": 19893, "total_tokens": 9129703 }, "thread_token_usage": { "input_tokens": 9090980, "cached_input_tokens": 8907008, "cache_write_input_tokens": 0, "output_tokens": 38723, "reasoning_output_tokens": 19893, "total_tokens": 9129703 } } } ] }, { "id": 145, "role": "agent", "timestamp": "2026-09-25T00:31:20.893Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:20.893Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:31:22.752Z", "text": "The coefficient search is stable enough to select `0.20` (`0.023208`, +9.1%). A conservative 16-uniform/16-in-batch fine-tune is now running at one-third the original learning rate; unlike the rejected miners, it changes only the mix of genuine observed-positive negatives. In parallel I’m building the cutoff-correct `train+val` sequence cache so the final refit can start immediately after this decision." }, { "kind": "tool", "timestamp": "2026-09-25T00:31:24.553Z", "callId": "call_lmhupHAv6bjAeh9EigZAxG1Y", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --prepare-only --output /app/solution/unused_prepare_only.parquet > /app/solution/submission_prepare.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:31:25.743Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"845eab\",\"wall_time_seconds\":1.00170292,\"session_id\":30598,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": 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"2026-09-25T00:31:28.629Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:31:28.629Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:31:29.320Z", "callId": "call_PBKKrRt1pjzlyblg577X5Fb1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":30598,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:31:34.344Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"f76971\",\"wall_time_seconds\":5.001879848,\"session_id\":30598,\"original_token_count\":13,\"output\":\"loaded explicit events: rows=746,504 users=8,307\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:31:29.455Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": 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"callId": "call_NjQVsJBxRvjoXhQ69TVxDIGI", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":30598,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:32:06.688Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"40091e\",\"wall_time_seconds\":30.002340554,\"session_id\":30598,\"original_token_count\":67,\"output\":\"prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:31:36.807Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": 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"callId": "call_SAgWdrEKHgEEAMsAczbkXA1a", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":30598,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:32:39.208Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1eccc4\",\"wall_time_seconds\":30.001894695,\"session_id\":30598,\"original_token_count\":68,\"output\":\"prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:32:09.326Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c106f43487d280da21a20e3300ac", "usage": { "input_tokens": 107963, "cached_input_tokens": 107520, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 14, "total_tokens": 108025 }, "turn_token_usage": { "input_tokens": 9521439, "cached_input_tokens": 9335424, "cache_write_input_tokens": 0, "output_tokens": 39331, "reasoning_output_tokens": 20149, "total_tokens": 9560770 }, "thread_token_usage": { "input_tokens": 9521439, "cached_input_tokens": 9335424, "cache_write_input_tokens": 0, "output_tokens": 39331, "reasoning_output_tokens": 20149, "total_tokens": 9560770 } } } ] }, { "id": 149, "role": "agent", "timestamp": "2026-09-25T00:32:41.655Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:32:41.655Z", "text": "" }, { "kind": "tool", "timestamp": 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"turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c127663c87d2ab59f1da5443512e", "usage": { "input_tokens": 108182, "cached_input_tokens": 107776, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 22, "total_tokens": 108252 }, "turn_token_usage": { "input_tokens": 9629621, "cached_input_tokens": 9443200, "cache_write_input_tokens": 0, "output_tokens": 39401, "reasoning_output_tokens": 20171, "total_tokens": 9669022 }, "thread_token_usage": { "input_tokens": 9629621, "cached_input_tokens": 9443200, "cache_write_input_tokens": 0, "output_tokens": 39401, "reasoning_output_tokens": 20171, "total_tokens": 9669022 } } }, { "kind": "context", "timestamp": "2026-09-25T00:32:46.541Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ccfe1735-1649-40ba-8336-9bd33a6d1c78", 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tokens=9,087,187 states=153,283 elapsed=31.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\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_sequences.npz\n", "stderr": "", "aggregated_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=31.2s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=38.6s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=45.6s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=54.0s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=62.2s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=69.6s\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_sequences.npz\n", "exit_code": 0, "duration": { "secs": 81, "nanos": 805277780 }, "formatted_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.4s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=15.0s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=23.1s\nprepared users=4,000 tokens=9,087,187 states=153,283 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tools.write_stdin({\"session_id\":90974,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:32:53.909Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"da2d0b\",\"wall_time_seconds\":5.001216852,\"session_id\":90974,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:32:48.984Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c12ebdfc87d2bf637d5da6bc0182", "usage": { "input_tokens": 108394, "cached_input_tokens": 108032, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 10, "total_tokens": 108452 }, "turn_token_usage": { 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train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:17.622Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c1c1cedc87d28b5fd4919e25de43", "usage": { "input_tokens": 109231, "cached_input_tokens": 108928, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 53, "total_tokens": 109332 }, "turn_token_usage": { "input_tokens": 10391234, "cached_input_tokens": 10202368, "cache_write_input_tokens": 0, "output_tokens": 40019, "reasoning_output_tokens": 20349, "total_tokens": 10431253 }, "thread_token_usage": { "input_tokens": 10391234, "cached_input_tokens": 10202368, "cache_write_input_tokens": 0, "output_tokens": 40019, "reasoning_output_tokens": 20349, "total_tokens": 10431253 } } } ] }, { "id": 158, "role": "agent", "timestamp": "2026-09-25T00:35:53.039Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:35:53.039Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:35:53.757Z", "callId": "call_sR73cI0Xt0sAuta1JBJFd0Gx", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90974,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:36:23.792Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"bb55c9\",\"wall_time_seconds\":30.001828023,\"session_id\":90974,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:35:53.913Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c1e3cdc087d299628e6af2a82240", "usage": { "input_tokens": 109515, "cached_input_tokens": 109056, "cache_write_input_tokens": 0, "output_tokens": 273, "reasoning_output_tokens": 225, "total_tokens": 109788 }, "turn_token_usage": { "input_tokens": 10500749, "cached_input_tokens": 10311424, "cache_write_input_tokens": 0, "output_tokens": 40292, "reasoning_output_tokens": 20574, "total_tokens": 10541041 }, "thread_token_usage": { "input_tokens": 10500749, "cached_input_tokens": 10311424, "cache_write_input_tokens": 0, "output_tokens": 40292, "reasoning_output_tokens": 20574, "total_tokens": 10541041 } } } ] }, { "id": 159, "role": "agent", "timestamp": "2026-09-25T00:36:25.565Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:36:25.565Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:36:26.272Z", "callId": "call_8odNYl8r5p26tqLX8rUMbk3t", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90974,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:36:56.304Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"4c6aa0\",\"wall_time_seconds\":30.00104711,\"session_id\":90974,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:36:26.430Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": 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"exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":90974,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:37:00.796Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"3a367e\",\"wall_time_seconds\":1.2970973670000001,\"exit_code\":0,\"original_token_count\":57,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:36:59.567Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c228918c87d289df6bc5cc07db80", "usage": { "input_tokens": 109960, "cached_input_tokens": 109696, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 20, "total_tokens": 110028 }, "turn_token_usage": { "input_tokens": 10720555, "cached_input_tokens": 10530432, "cache_write_input_tokens": 0, "output_tokens": 40415, "reasoning_output_tokens": 20601, "total_tokens": 10760970 }, "thread_token_usage": { "input_tokens": 10720555, "cached_input_tokens": 10530432, "cache_write_input_tokens": 0, "output_tokens": 40415, "reasoning_output_tokens": 20601, "total_tokens": 10760970 } } }, { "kind": "context", "timestamp": "2026-09-25T00:37:00.792Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5fd9bc01-9878-4c3a-80ae-9a497e66e243", "process_id": "90974", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --resume-checkpoint /app/solution/baseline_checkpoints/best.pt --checkpoint-dir /app/solution/mixed16_checkpoints --output /app/solution/mixed16_validation.parquet --uniform-negatives 16 --inbatch-negatives 16 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --learning-rate 0.0003 --epochs 2 > /app/solution/mixed16_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\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", "stderr": "", "aggregated_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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\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", "exit_code": 0, "duration": { "secs": 379, "nanos": 419145671 }, "formatted_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\nusing a fresh AdamW optimizer after loading /app/solution/baseline_checkpoints/best.pt\nBCE objective changed: filter full-training-history->full-training-history, target weighting organic-strong->organic-strong, target window (0.1, 128, 16)->(0.1, 128, 16); negative mix (24, 8, 0, 64, 0, 0)->(16, 16, 0, 64, 0, 0); loss pointwise-bce->pointwise-bce; recomputing the resume checkpoint's validation BCE\nresumed validation-BCE training: epoch=1 loss=0.514172\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.468114 running_bce=0.472647 elapsed=248.5s\nsaved epoch checkpoint: /app/solution/mixed16_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.47239 validation_loss=0.48277 pair_accuracy=0.790 train_targets=1,052,953 validation_targets=130,103 train_seconds=261.3 validation_seconds=15.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.482770\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" } } } ] }, { "id": 161, "role": "agent", "timestamp": "2026-09-25T00:37:03.052Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:03.052Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:04.654Z", "callId": "call_TcDueLbXKbM4OsM3PxvOwfgw", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed16_validation.parquet && sed -n '1,240p' /app/solution/mixed16_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":18000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:37:08.012Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/mixed16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019112,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/mixed16_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 375.102,\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-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 16,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 16,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.291661274000035,\n \"validation_loss\": 0.5141717292576481\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.4723902097523118,\n \"positive_logit\": 1.001065996982982,\n \"negative_logit\": -1.1616856390571695,\n \"pair_accuracy\": 0.7904501969698553,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 261.31913870300014,\n \"validation_loss\": 0.48276980220358745,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.333287302999906\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 2.1729514598846436,\n \"query_norm_std\": 1.0019930601119995,\n \"item_norm_mean\": 1.9172604084014893,\n \"item_norm_std\": 0.8316522836685181\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:04.877Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": 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"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed16_validation.parquet && sed -n '1,240p' /app/solution/mixed16_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/mixed16_validation.parquet && sed -n '1,240p' /app/solution/mixed16_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/mixed16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019112,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/mixed16_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 375.102,\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-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 16,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 16,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\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 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256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 16,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\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 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\"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-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 16,\n \"propensity_corrected_inbatch_pool\": 16,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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\": 2,\n \"learning_rate\": 0.0003,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 16,\n \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\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\": 0.539838048100336,\n \"positive_logit\": 0.7427799626849259,\n \"negative_logit\": -0.6222247025677434,\n \"pair_accuracy\": 0.7547733612041563,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 218.5692425829999,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.291661274000035,\n \"validation_loss\": 0.5141717292576481\n },\n {\n \"epoch\": 2.0,\n \"loss\": 0.4723902097523118,\n \"positive_logit\": 1.001065996982982,\n \"negative_logit\": -1.1616856390571695,\n \"pair_accuracy\": 0.7904501969698553,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 261.31913870300014,\n \"validation_loss\": 0.48276980220358745,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.333287302999906\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 2.1729514598846436,\n \"query_norm_std\": 1.0019930601119995,\n \"item_norm_mean\": 1.9172604084014893,\n \"item_norm_std\": 0.8316522836685181\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n" } } } ] }, { "id": 162, "role": "agent", "timestamp": "2026-09-25T00:37:18.531Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:18.531Z", "text": "" } ] }, { "id": 163, "role": "agent", "timestamp": "2026-09-25T00:37:22.977Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:22.977Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:37:24.998Z", "text": "The conservative second-epoch mix also fails (`0.019112`), confirming that simply continuing the mature checkpoint is the problem: the sampled held-out loss improves while discovery degrades. I’m running one final controlled sampler from scratch: preserve all 24 production uniform negatives, replace the 8 random in-batch negatives with the 8 hardest of 40 filtered in-batch candidates, and keep the original one-epoch pointwise objective. This isolates negative selection without extra epochs or an objective change." }, { "kind": "tool", "timestamp": "2026-09-25T00:37:28.112Z", "callId": "call_XwQhxc5hSdWdjBfyo6gAt1vb", "name": "exec", "input": { "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_sequences.npz --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet --uniform-negatives 24 --inbatch-negatives 40 --hard-negatives 0 --query-random-negatives 24 --query-hard-negatives 8 --loss-objective pointwise-bce --popularity-bias 0 --learning-rate 0.001 --epochs 1 > /app/solution/semihard_validation_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:37:29.279Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"4b8b41\",\"wall_time_seconds\":1.00119798,\"session_id\":2832,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:28.235Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c234323087d2a72ac551686783cf", "usage": { "input_tokens": 111585, "cached_input_tokens": 110080, "cache_write_input_tokens": 0, "output_tokens": 1052, "reasoning_output_tokens": 762, "total_tokens": 112637 }, "turn_token_usage": { "input_tokens": 10942335, "cached_input_tokens": 10750336, "cache_write_input_tokens": 0, "output_tokens": 41569, "reasoning_output_tokens": 21369, "total_tokens": 10983904 }, "thread_token_usage": { "input_tokens": 10942335, "cached_input_tokens": 10750336, "cache_write_input_tokens": 0, "output_tokens": 41569, "reasoning_output_tokens": 21369, "total_tokens": 10983904 } } } ] }, { "id": 164, "role": "agent", "timestamp": "2026-09-25T00:37:31.832Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:31.832Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:32.558Z", "callId": "call_0GIUfFDu07x0L32qxI0uklWY", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2832,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:37:37.565Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"e0ca45\",\"wall_time_seconds\":5.000579837,\"session_id\":2832,\"original_token_count\":49,\"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\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:37:32.682Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c249765087d2b699555c64b137c2", "usage": { "input_tokens": 112696, "cached_input_tokens": 111360, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 8, "total_tokens": 112752 }, "turn_token_usage": { "input_tokens": 11055031, "cached_input_tokens": 10861696, "cache_write_input_tokens": 0, "output_tokens": 41625, "reasoning_output_tokens": 21377, "total_tokens": 11096656 }, "thread_token_usage": { "input_tokens": 11055031, "cached_input_tokens": 10861696, "cache_write_input_tokens": 0, "output_tokens": 41625, "reasoning_output_tokens": 21377, "total_tokens": 11096656 } } } ] }, { "id": 165, "role": "agent", "timestamp": "2026-09-25T00:37:39.629Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:37:39.629Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:37:40.354Z", "callId": "call_2IyCYA6rrcSan41WZwB8pbBn", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2832,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:37:45.362Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"34b326\",\"wall_time_seconds\":5.000855143,\"session_id\":2832,\"original_token_count\":55,\"output\":\"inbatch 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 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175, "role": "agent", "timestamp": "2026-09-25T00:41:55.927Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:41:55.927Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:41:56.640Z", "callId": "call_r36uQSiu73QJYKpV3b9rcG2k", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2832,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:42:26.664Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"eeadce\",\"wall_time_seconds\":30.001382369,\"session_id\":2832,\"original_token_count\":76,\"output\":\"saved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\\nrestored best validation-BCE checkpoint: 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"exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2832,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:43:32.809Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"e0da0c\",\"wall_time_seconds\":30.001808209,\"session_id\":2832,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:02.952Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c394a25887d287ab26de56fbfda4", "usage": { "input_tokens": 115179, "cached_input_tokens": 114816, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 115234 }, "turn_token_usage": { "input_tokens": 12311766, "cached_input_tokens": 12113792, "cache_write_input_tokens": 0, "output_tokens": 43217, "reasoning_output_tokens": 22305, "total_tokens": 12354983 }, "thread_token_usage": { "input_tokens": 12311766, "cached_input_tokens": 12113792, "cache_write_input_tokens": 0, "output_tokens": 43217, "reasoning_output_tokens": 22305, "total_tokens": 12354983 } } } ] }, { "id": 178, "role": "agent", "timestamp": "2026-09-25T00:43:34.398Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:34.398Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:35.109Z", "callId": "call_Eu3DehsrzpGqNHbvVhaP2i7w", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":2832,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:43:38.859Z", "content": "Script completed\nWall time 3.7 seconds\nOutput:\n\n{\"chunk_id\":\"b08e44\",\"wall_time_seconds\":3.716906738,\"exit_code\":0,\"original_token_count\":57,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:35.279Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3b50ef087d29588b8be1bbd5a51", "usage": { "input_tokens": 115293, "cached_input_tokens": 114944, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 115348 }, "turn_token_usage": { "input_tokens": 12427059, "cached_input_tokens": 12228736, "cache_write_input_tokens": 0, "output_tokens": 43272, "reasoning_output_tokens": 22312, "total_tokens": 12470331 }, "thread_token_usage": { "input_tokens": 12427059, "cached_input_tokens": 12228736, "cache_write_input_tokens": 0, "output_tokens": 43272, "reasoning_output_tokens": 22312, "total_tokens": 12470331 } } }, { "kind": "context", "timestamp": "2026-09-25T00:43:38.857Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8885f749-3172-4980-b202-bdfd74997206", "process_id": "2832", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet --uniform-negatives 24 --inbatch-negatives 40 --hard-negatives 0 --query-random-negatives 24 --query-hard-negatives 8 --loss-objective pointwise-bce --popularity-bias 0 --learning-rate 0.001 --epochs 1 > /app/solution/semihard_validation_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_sequences.npz --checkpoint-dir /app/solution/semihard_checkpoints --output /app/solution/semihard_validation.parquet --uniform-negatives 24 --inbatch-negatives 40 --hard-negatives 0 --query-random-negatives 24 --query-hard-negatives 8 --loss-objective pointwise-bce --popularity-bias 0 --learning-rate 0.001 --epochs 1 > /app/solution/semihard_validation_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.612418 running_bce=0.595345 elapsed=242.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.560735\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", "stderr": "", "aggregated_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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.612418 running_bce=0.595345 elapsed=242.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.560735\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", "exit_code": 0, "duration": { "secs": 370, "nanos": 579740737 }, "formatted_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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.612418 running_bce=0.595345 elapsed=242.8s\nsaved epoch checkpoint: /app/solution/semihard_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.59416 validation_loss=0.56073 pair_accuracy=0.655 train_targets=1,052,953 validation_targets=130,103 train_seconds=259.1 validation_seconds=19.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.560735\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" } } } ] }, { "id": 179, "role": "agent", "timestamp": "2026-09-25T00:43:40.338Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:40.338Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:43:41.766Z", "callId": "call_zhGFRgGrdWIIKQTRiz2jRPG2", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet && date -u +%H:%M:%S\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:43:45.134Z", "content": "Script completed\nWall time 3.4 seconds\nOutput:\n\n{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01865,\n \"quality_users\": 4263\n }\n}\n00:43:45\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:41.946Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3bb0a9c87d29d7043dd7ea866a1", "usage": { "input_tokens": 115512, "cached_input_tokens": 115072, "cache_write_input_tokens": 0, "output_tokens": 94, "reasoning_output_tokens": 6, "total_tokens": 115606 }, "turn_token_usage": { "input_tokens": 12542571, "cached_input_tokens": 12343808, "cache_write_input_tokens": 0, "output_tokens": 43366, "reasoning_output_tokens": 22318, "total_tokens": 12585937 }, "thread_token_usage": { "input_tokens": 12542571, "cached_input_tokens": 12343808, "cache_write_input_tokens": 0, "output_tokens": 43366, "reasoning_output_tokens": 22318, "total_tokens": 12585937 } } }, { "kind": "context", "timestamp": "2026-09-25T00:43:45.131Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9d1ba8cf-2bc1-4039-888c-105bdb717f40", "process_id": "56040", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet && date -u +%H:%M:%S" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet && date -u +%H:%M:%S" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01865,\n \"quality_users\": 4263\n }\n}\n00:43:45\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01865,\n \"quality_users\": 4263\n }\n}\n00:43:45\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 200779872 }, "formatted_output": "{\n \"model\": \"/app/solution/semihard_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01865,\n \"quality_users\": 4263\n }\n}\n00:43:45\n" } } } ] }, { "id": 180, "role": "agent", "timestamp": "2026-09-25T00:43:50.151Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:43:50.151Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:43:52.068Z", "text": "Semi-hard mining is also rejected (`0.018650`). The evidence is consistent: on these implicit-feedback logs, unobserved “hard” tracks are often plausible positives, so harder sampled negatives improve surrogate separation but hurt discovery. The measured winner remains the production objective plus the principled item-side popularity correction (`0.023208`, +9.1%). I’m refitting that winner on `train_* + val_*` now and will overwrite the fallback submission." }, { "kind": "tool", "timestamp": "2026-09-25T00:43:55.084Z", "callId": "call_9taxe1mTtf3kCKhAtd6hB6Ci", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:43:56.251Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"61b1f2\",\"wall_time_seconds\":1.001320644,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:43:55.277Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3c1544887d2a4d81d0b0e64a1b9", "usage": { "input_tokens": 115683, "cached_input_tokens": 115328, "cache_write_input_tokens": 0, "output_tokens": 475, "reasoning_output_tokens": 198, "total_tokens": 116158 }, "turn_token_usage": { "input_tokens": 12658254, "cached_input_tokens": 12459136, "cache_write_input_tokens": 0, "output_tokens": 43841, "reasoning_output_tokens": 22516, "total_tokens": 12702095 }, "thread_token_usage": { "input_tokens": 12658254, "cached_input_tokens": 12459136, "cache_write_input_tokens": 0, "output_tokens": 43841, "reasoning_output_tokens": 22516, "total_tokens": 12702095 } } } ] }, { "id": 181, "role": "agent", "timestamp": "2026-09-25T00:44:05.129Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:05.129Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:11.968Z", "callId": "call_RZnPwdp1lg2PYVEdRBdnth5a", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n-entropy. The improved sampler draws a 64-item mixture of uniform and\\n-propensity-corrected in-batch candidates, keeps eight random examples for\\n-coverage, and mines the 24 highest-scoring remaining examples per user query.\\n-The loss therefore retains the production total of 32 negatives per target.\\n-Every selected adjacent target retains a nonzero weight. Only files under the\\n-supplied public directory are read.\\n+entropy. The trainer can evaluate query-mined and item-neighbor negatives, but\\n+the selected public-backtest configuration retains the robust 24 uniform plus\\n+eight propensity-corrected in-batch examples: harder unobserved tracks behaved\\n+as false negatives. It corrects the popularity prior induced by this sampled\\n+objective with one static item-side score coordinate, which remains an indexed\\n+two-tower dot product. Every selected adjacent target retains a nonzero weight.\\n+Only files under the supplied public directory are read.\\n@@\\n- epochs: int = 2\\n+ epochs: int = 1\\n@@\\n- uniform_negatives: int = 16\\n- inbatch_negatives: int = 48\\n+ uniform_negatives: int = 24\\n+ inbatch_negatives: int = 8\\n@@\\n- query_random_negatives: int = 8\\n- query_hard_negatives: int = 24\\n- loss_objective: str = \\\"pairwise\\\"\\n+ query_random_negatives: int = 0\\n+ query_hard_negatives: int = 0\\n+ loss_objective: str = \\\"pointwise-bce\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:44:12.110Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:11.979Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-354af9ff-899f-470c-b632-169a19627f27", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -13,8 +13,9 @@\n normalization, raw dot-product retrieval, and sample-weighted binary cross\n-entropy. The improved sampler draws a 64-item mixture of uniform and\n-propensity-corrected in-batch candidates, keeps eight random examples for\n-coverage, and mines the 24 highest-scoring remaining examples per user query.\n-The loss therefore retains the production total of 32 negatives per target.\n-Every selected adjacent target retains a nonzero weight. Only files under the\n-supplied public directory are read.\n+entropy. The trainer can evaluate query-mined and item-neighbor negatives, but\n+the selected public-backtest configuration retains the robust 24 uniform plus\n+eight propensity-corrected in-batch examples: harder unobserved tracks behaved\n+as false negatives. It corrects the popularity prior induced by this sampled\n+objective with one static item-side score coordinate, which remains an indexed\n+two-tower dot product. Every selected adjacent target retains a nonzero weight.\n+Only files under the supplied public directory are read.\n \"\"\"\n@@ -53,3 +54,3 @@\n dropout: float = 0.05\n- epochs: int = 2\n+ epochs: int = 1\n learning_rate: float = 1.0e-3\n@@ -58,9 +59,9 @@\n loss_microbatch_size: int = 65_536\n- uniform_negatives: int = 16\n- inbatch_negatives: int = 48\n+ uniform_negatives: int = 24\n+ inbatch_negatives: int = 8\n hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n- query_random_negatives: int = 8\n- query_hard_negatives: int = 24\n- loss_objective: str = \"pairwise\"\n+ query_random_negatives: int = 0\n+ query_hard_negatives: int = 0\n+ loss_objective: str = \"pointwise-bce\"\n popularity_bias: float = 0.20\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:44:12.095Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3cc6f9487d2926852552bf9ac28", "usage": { "input_tokens": 116217, "cached_input_tokens": 115456, "cache_write_input_tokens": 0, "output_tokens": 796, "reasoning_output_tokens": 394, "total_tokens": 117013 }, "turn_token_usage": { "input_tokens": 12774471, "cached_input_tokens": 12574592, "cache_write_input_tokens": 0, "output_tokens": 44637, "reasoning_output_tokens": 22910, "total_tokens": 12819108 }, "thread_token_usage": { "input_tokens": 12774471, "cached_input_tokens": 12574592, "cache_write_input_tokens": 0, "output_tokens": 44637, "reasoning_output_tokens": 22910, "total_tokens": 12819108 } } } ] }, { "id": 182, "role": "agent", "timestamp": "2026-09-25T00:44:16.587Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:16.587Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:17.934Z", "callId": "call_re2mLf9PdUSqGCYJ66ixfJKN", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- \\\"model\\\": \\\"yambda-windowed-sequence-two-tower-hard-negative\\\",\\n+ \\\"model\\\": \\\"yambda-windowed-sequence-two-tower-calibrated\\\",\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:44:18.080Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:17.971Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-1a425e51-9dfb-45af-a3dc-9f18044e9a1d", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2395,3 +2395,3 @@\n {\n- \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n+ \"model\": \"yambda-windowed-sequence-two-tower-calibrated\",\n \"stage\": args.stage,\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:44:18.078Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3dc8b9487d2b1f565bebb278859", "usage": { "input_tokens": 117037, "cached_input_tokens": 116096, "cache_write_input_tokens": 0, "output_tokens": 94, "reasoning_output_tokens": 8, "total_tokens": 117131 }, "turn_token_usage": { "input_tokens": 12891508, "cached_input_tokens": 12690688, "cache_write_input_tokens": 0, "output_tokens": 44731, "reasoning_output_tokens": 22918, "total_tokens": 12936239 }, "thread_token_usage": { "input_tokens": 12891508, "cached_input_tokens": 12690688, "cache_write_input_tokens": 0, "output_tokens": 44731, "reasoning_output_tokens": 22918, "total_tokens": 12936239 } } } ] }, { "id": 183, "role": "agent", "timestamp": "2026-09-25T00:44:21.271Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:21.271Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:21.972Z", "callId": "call_gxaAvLvoFD7syf8Gc784v7za", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":1000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:44:26.998Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"chunk_id\":\"e277b4\",\"wall_time_seconds\":5.00184556,\"session_id\":97979,\"original_token_count\":103,\"output\":\"loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\\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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:22.148Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3e278e087d2b8da4ab463646219", "usage": { "input_tokens": 117155, "cached_input_tokens": 116864, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 86, "total_tokens": 117289 }, "turn_token_usage": { "input_tokens": 13008663, "cached_input_tokens": 12807552, "cache_write_input_tokens": 0, "output_tokens": 44865, "reasoning_output_tokens": 23004, "total_tokens": 13053528 }, "thread_token_usage": { "input_tokens": 13008663, "cached_input_tokens": 12807552, "cache_write_input_tokens": 0, "output_tokens": 44865, "reasoning_output_tokens": 23004, "total_tokens": 13053528 } } } ] }, { "id": 184, "role": "agent", "timestamp": "2026-09-25T00:44:33.573Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:33.573Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:44:47.348Z", "callId": "call_qFwQyyy7m32rco38YHhkwAAo", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Calibrated Yambda two-tower\\n+\\n+## Result\\n+\\n+The reproduced production model scores **0.021280 NDCG@10** on 4,263 public\\n+quality users. The selected model scores **0.023208** (**+9.1% relative**) on\\n+the same frozen validation boundary.\\n+\\n+| validation model | NDCG@10 |\\n+| --- | ---: |\\n+| production, 24 uniform + 8 corrected in-batch negatives | 0.021280 |\\n+| item-neighbor hard-negative fine-tune | 0.010900 |\\n+| query-hard pairwise fine-tune | 0.020273 |\\n+| from-scratch semi-hard in-batch mining | 0.018650 |\\n+| **selected: production objective + item-side calibration** | **0.023208** |\\n+\\n+## Diagnosis and change\\n+\\n+The 24 catalog-uniform negatives are overwhelmingly easy tail items. The\\n+model consequently learns a strong positive-popularity prior: its public\\n+top-10 has median catalog popularity rank 111, versus roughly 5,500 for\\n+eligible relevant outcomes. Only eight negatives come from observed positive\\n+targets, and their inverse-propensity weights intentionally weaken the most\\n+popular examples.\\n+\\n+I tested filtered hard negatives rather than assuming they would help. All\\n+variants improved their own sampled held-out loss, but reduced public NDCG.\\n+With interaction rather than impression logs, high-scoring unobserved tracks\\n+are frequently plausible positives, so aggressive hard mining creates false\\n+negatives and erodes the collaborative item geometry.\\n+\\n+The selected fix therefore retains the robust production sampler and removes\\n+its measured popularity shortcut at scoring time:\\n+\\n+```text\\n+score(u, i) = user_vector(u) dot item_vector(i)\\n+ - 0.20 * standardized_log_positive_listens(i)\\n+```\\n+\\n+This is still an indexed two-tower dot product: append `1` to the user vector\\n+and append the precomputed negative popularity term to the item vector. It\\n+requires no user-ID table, cross-encoder, or per-candidate sequence work. The\\n+coefficient was selected only on the public validation window. The final\\n+artifact was then refit with `train_* + val_*` history and test-cutoff\\n+eligibility/state.\\n+\\n+## Reproduce the final artifact\\n+\\n+From this directory, using the supplied offline environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python train_current_model.py \\\\\\n+ --stage submission \\\\\\n+ --public-dir /task \\\\\\n+ --prepared-cache /app/solution/submission_sequences.npz \\\\\\n+ --checkpoint-dir /app/solution/final_checkpoints \\\\\\n+ --output /app/solution/submission.parquet\\n+```\\n+\\n+The defaults are the selected configuration: one epoch, 24 uniform plus 8\\n+propensity-corrected in-batch negatives, pointwise BCE, full-history\\n+false-negative filtering, organic-strong target weights, and popularity bias\\n+0.20. Optional hard-negative switches remain in the script so the rejected\\n+experiments are reproducible.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:44:47.491Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:44:47.357Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-cfd2410b-f815-4e16-bd1b-2184eadf9722", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Calibrated Yambda two-tower\n\n## Result\n\nThe reproduced production model scores **0.021280 NDCG@10** on 4,263 public\nquality users. The selected model scores **0.023208** (**+9.1% relative**) on\nthe same frozen validation boundary.\n\n| validation model | NDCG@10 |\n| --- | ---: |\n| production, 24 uniform + 8 corrected in-batch negatives | 0.021280 |\n| item-neighbor hard-negative fine-tune | 0.010900 |\n| query-hard pairwise fine-tune | 0.020273 |\n| from-scratch semi-hard in-batch mining | 0.018650 |\n| **selected: production objective + item-side calibration** | **0.023208** |\n\n## Diagnosis and change\n\nThe 24 catalog-uniform negatives are overwhelmingly easy tail items. The\nmodel consequently learns a strong positive-popularity prior: its public\ntop-10 has median catalog popularity rank 111, versus roughly 5,500 for\neligible relevant outcomes. Only eight negatives come from observed positive\ntargets, and their inverse-propensity weights intentionally weaken the most\npopular examples.\n\nI tested filtered hard negatives rather than assuming they would help. All\nvariants improved their own sampled held-out loss, but reduced public NDCG.\nWith interaction rather than impression logs, high-scoring unobserved tracks\nare frequently plausible positives, so aggressive hard mining creates false\nnegatives and erodes the collaborative item geometry.\n\nThe selected fix therefore retains the robust production sampler and removes\nits measured popularity shortcut at scoring time:\n\n```text\nscore(u, i) = user_vector(u) dot item_vector(i)\n - 0.20 * standardized_log_positive_listens(i)\n```\n\nThis is still an indexed two-tower dot product: append `1` to the user vector\nand append the precomputed negative popularity term to the item vector. It\nrequires no user-ID table, cross-encoder, or per-candidate sequence work. The\ncoefficient was selected only on the public validation window. The final\nartifact was then refit with `train_* + val_*` history and test-cutoff\neligibility/state.\n\n## Reproduce the final artifact\n\nFrom this directory, using the supplied offline environment:\n\n```sh\n/opt/task-venv/bin/python train_current_model.py \\\n --stage submission \\\n --public-dir /task \\\n --prepared-cache /app/solution/submission_sequences.npz \\\n --checkpoint-dir /app/solution/final_checkpoints \\\n --output /app/solution/submission.parquet\n```\n\nThe defaults are the selected configuration: one epoch, 24 uniform plus 8\npropensity-corrected in-batch negatives, pointwise BCE, full-history\nfalse-negative filtering, organic-strong target weights, and popularity bias\n0.20. Optional hard-negative switches remain in the script so the rejected\nexperiments are reproducible.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:44:47.475Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3eb387087d28a307b37d81bc2f0", "usage": { "input_tokens": 117481, "cached_input_tokens": 116992, "cache_write_input_tokens": 0, "output_tokens": 1044, "reasoning_output_tokens": 271, "total_tokens": 118525 }, "turn_token_usage": { "input_tokens": 13126144, "cached_input_tokens": 12924544, "cache_write_input_tokens": 0, "output_tokens": 45909, "reasoning_output_tokens": 23275, "total_tokens": 13172053 }, "thread_token_usage": { "input_tokens": 13126144, "cached_input_tokens": 12924544, "cache_write_input_tokens": 0, "output_tokens": 45909, "reasoning_output_tokens": 23275, "total_tokens": 13172053 } } } ] }, { "id": 185, "role": "agent", "timestamp": "2026-09-25T00:44:55.454Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:44:55.454Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:02.025Z", "callId": "call_wbxYwFPkP3wBXhZqGGiTr7cC", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n @torch.inference_mode()\\n+def calibrated_retrieval_scores(\\n+ queries: torch.Tensor,\\n+ items: torch.Tensor,\\n+ item_features: np.ndarray,\\n+ popularity_bias: float,\\n+) -> torch.Tensor:\\n+ \\\"\\\"\\\"Return the augmented two-tower dot product used by indexed serving.\\\"\\\"\\\"\\n+ scores = queries @ items.T\\n+ if popularity_bias:\\n+ popularity = torch.as_tensor(\\n+ item_features[:, 0], device=scores.device, dtype=scores.dtype\\n+ )\\n+ scores -= popularity_bias * popularity\\n+ return scores\\n+\\n+\\n+@torch.inference_mode()\\n def rank_candidates(\\n@@\\n- scores = queries[start:end] @ items.T\\n- # This is still a pure dot product: it is equivalent to appending 1\\n- # to every user vector and -bias * popularity to each item vector.\\n- if config.popularity_bias:\\n- scores -= config.popularity_bias * torch.from_numpy(\\n- prepared.item_features[:, 0]\\n- )\\n+ # This is still a pure dot product: it is equivalent to appending 1\\n+ # to every user vector and -bias * popularity to each item vector.\\n+ scores = calibrated_retrieval_scores(\\n+ queries[start:end],\\n+ items,\\n+ prepared.item_features,\\n+ config.popularity_bias,\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:45:02.174Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:02.060Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ba8db5dc-4450-44fd-9cd2-df419648c971", "changes": { "/app/solution/train_current_model.py": { "type": "update", "unified_diff": "@@ -2072,2 +2072,19 @@\n @torch.inference_mode()\n+def calibrated_retrieval_scores(\n+ queries: torch.Tensor,\n+ items: torch.Tensor,\n+ item_features: np.ndarray,\n+ popularity_bias: float,\n+) -> torch.Tensor:\n+ \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n+ scores = queries @ items.T\n+ if popularity_bias:\n+ popularity = torch.as_tensor(\n+ item_features[:, 0], device=scores.device, dtype=scores.dtype\n+ )\n+ scores -= popularity_bias * popularity\n+ return scores\n+\n+\n+@torch.inference_mode()\n def rank_candidates(\n@@ -2095,9 +2112,10 @@\n end = min(start + ranking_batch_size, len(eligibility.uids))\n- scores = queries[start:end] @ items.T\n # This is still a pure dot product: it is equivalent to appending 1\n # to every user vector and -bias * popularity to each item vector.\n- if config.popularity_bias:\n- scores -= config.popularity_bias * torch.from_numpy(\n- prepared.item_features[:, 0]\n- )\n+ scores = calibrated_retrieval_scores(\n+ queries[start:end],\n+ items,\n+ prepared.item_features,\n+ config.popularity_bias,\n+ )\n eligible = np.unpackbits(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:45:02.172Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c3ffef0c87d28415c0ee6bf0a325", "usage": { "input_tokens": 118549, "cached_input_tokens": 117248, "cache_write_input_tokens": 0, "output_tokens": 701, "reasoning_output_tokens": 326, "total_tokens": 119250 }, "turn_token_usage": { "input_tokens": 13244693, "cached_input_tokens": 13041792, "cache_write_input_tokens": 0, "output_tokens": 46610, "reasoning_output_tokens": 23601, "total_tokens": 13291303 }, "thread_token_usage": { "input_tokens": 13244693, "cached_input_tokens": 13041792, "cache_write_input_tokens": 0, "output_tokens": 46610, "reasoning_output_tokens": 23601, "total_tokens": 13291303 } } } ] }, { "id": 186, "role": "agent", "timestamp": "2026-09-25T00:45:04.054Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:04.054Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:14.359Z", "callId": "call_MFtRkY3VNrAt4xwH6GZxmDeh", "name": "exec", "input": { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/test_improvements.py\\n+from __future__ import annotations\\n+\\n+import unittest\\n+\\n+import numpy as np\\n+import torch\\n+\\n+import train_current_model as trainer\\n+\\n+\\n+class ImprovementTests(unittest.TestCase):\\n+ def test_calibration_is_an_augmented_dot_product(self) -> None:\\n+ queries = torch.tensor([[1.0, 2.0], [-1.0, 0.5]])\\n+ items = torch.tensor([[2.0, 1.0], [0.5, -2.0], [1.0, 3.0]])\\n+ features = np.zeros((3, 4), dtype=np.float32)\\n+ features[:, 0] = np.array([-1.0, 0.25, 2.0], dtype=np.float32)\\n+ bias = 0.2\\n+\\n+ actual = trainer.calibrated_retrieval_scores(\\n+ queries, items, features, bias\\n+ )\\n+ augmented_queries = torch.cat(\\n+ [queries, torch.ones((len(queries), 1))], dim=1\\n+ )\\n+ augmented_items = torch.cat(\\n+ [items, torch.from_numpy((-bias * features[:, 0])[:, None])], dim=1\\n+ )\\n+ expected = augmented_queries @ augmented_items.T\\n+ torch.testing.assert_close(actual, expected)\\n+\\n+ def test_full_history_filter_rejects_known_and_duplicate_items(self) -> None:\\n+ packed = np.zeros((1, 2), dtype=np.uint8)\\n+ packed[0, 0] = np.uint8(1 << 3)\\n+ negatives = np.array([[3, 5, 5, 7]], dtype=np.int64)\\n+ invalid = trainer.invalid_full_history_negatives(\\n+ negatives=negatives,\\n+ targets=np.array([7], dtype=np.int64),\\n+ user_rows=np.array([0], dtype=np.int64),\\n+ known_positive_packed=packed,\\n+ liked_items=np.array([[11]], dtype=np.int64),\\n+ )\\n+ np.testing.assert_array_equal(\\n+ invalid, np.array([[True, False, True, True]])\\n+ )\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ unittest.main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" }, "result": { "timestamp": "2026-09-25T00:45:14.569Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:14.406Z", "data": { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-3bbb0498-5317-4490-86bc-e4cffc28b7bf", "changes": { "/app/solution/test_improvements.py": { "type": "add", "content": "from __future__ import annotations\n\nimport unittest\n\nimport numpy as np\nimport torch\n\nimport train_current_model as trainer\n\n\nclass ImprovementTests(unittest.TestCase):\n def test_calibration_is_an_augmented_dot_product(self) -> None:\n queries = torch.tensor([[1.0, 2.0], [-1.0, 0.5]])\n items = torch.tensor([[2.0, 1.0], [0.5, -2.0], [1.0, 3.0]])\n features = np.zeros((3, 4), dtype=np.float32)\n features[:, 0] = np.array([-1.0, 0.25, 2.0], dtype=np.float32)\n bias = 0.2\n\n actual = trainer.calibrated_retrieval_scores(\n queries, items, features, bias\n )\n augmented_queries = torch.cat(\n [queries, torch.ones((len(queries), 1))], dim=1\n )\n augmented_items = torch.cat(\n [items, torch.from_numpy((-bias * features[:, 0])[:, None])], dim=1\n )\n expected = augmented_queries @ augmented_items.T\n torch.testing.assert_close(actual, expected)\n\n def test_full_history_filter_rejects_known_and_duplicate_items(self) -> None:\n packed = np.zeros((1, 2), dtype=np.uint8)\n packed[0, 0] = np.uint8(1 << 3)\n negatives = np.array([[3, 5, 5, 7]], dtype=np.int64)\n invalid = trainer.invalid_full_history_negatives(\n negatives=negatives,\n targets=np.array([7], dtype=np.int64),\n user_rows=np.array([0], dtype=np.int64),\n known_positive_packed=packed,\n liked_items=np.array([[11]], dtype=np.int64),\n )\n np.testing.assert_array_equal(\n invalid, np.array([[True, False, True, True]])\n )\n\n\nif __name__ == \"__main__\":\n unittest.main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/test_improvements.py\n", "stderr": "" } } }, { "kind": "context", "timestamp": "2026-09-25T00:45:14.562Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c40e8d8887d2b440ebbe0f48c28d", "usage": { "input_tokens": 119274, "cached_input_tokens": 118400, "cache_write_input_tokens": 0, "output_tokens": 588, "reasoning_output_tokens": 7, "total_tokens": 119862 }, "turn_token_usage": { "input_tokens": 13363967, "cached_input_tokens": 13160192, "cache_write_input_tokens": 0, "output_tokens": 47198, "reasoning_output_tokens": 23608, "total_tokens": 13411165 }, "thread_token_usage": { "input_tokens": 13363967, "cached_input_tokens": 13160192, "cache_write_input_tokens": 0, "output_tokens": 47198, "reasoning_output_tokens": 23608, "total_tokens": 13411165 } } } ] }, { "id": 187, "role": "agent", "timestamp": "2026-09-25T00:45:16.423Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:16.423Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:18.188Z", "callId": "call_TjzMa5nLROld978m63KzNMDf", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/app/solution /opt/task-venv/bin/python -m unittest -v /app/solution/test_improvements.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":6000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:45:23.656Z", "content": "Script completed\nWall time 5.4 seconds\nOutput:\n\ntest_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok\ntest_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok\n\n----------------------------------------------------------------------\nRan 2 tests in 0.018s\n\nOK\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:18.338Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c41ae72c87d2b3374889e2376759", "usage": { "input_tokens": 119886, "cached_input_tokens": 119040, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 17, "total_tokens": 120009 }, "turn_token_usage": { "input_tokens": 13483853, "cached_input_tokens": 13279232, "cache_write_input_tokens": 0, "output_tokens": 47321, "reasoning_output_tokens": 23625, "total_tokens": 13531174 }, "thread_token_usage": { "input_tokens": 13483853, "cached_input_tokens": 13279232, "cache_write_input_tokens": 0, "output_tokens": 47321, "reasoning_output_tokens": 23625, "total_tokens": 13531174 } } }, { "kind": "context", "timestamp": "2026-09-25T00:45:23.623Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e863f7f-ec78-48ee-bfd0-744ea7aa8da9", "process_id": "48325", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m unittest -v /app/solution/test_improvements.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/app/solution /opt/task-venv/bin/python -m unittest -v /app/solution/test_improvements.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok\ntest_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok\n\n----------------------------------------------------------------------\nRan 2 tests in 0.018s\n\nOK\n", "stderr": "", "aggregated_output": "test_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok\ntest_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok\n\n----------------------------------------------------------------------\nRan 2 tests in 0.018s\n\nOK\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 222634900 }, "formatted_output": "test_calibration_is_an_augmented_dot_product (solution.test_improvements.ImprovementTests.test_calibration_is_an_augmented_dot_product) ... ok\ntest_full_history_filter_rejects_known_and_duplicate_items (solution.test_improvements.ImprovementTests.test_full_history_filter_rejects_known_and_duplicate_items) ... ok\n\n----------------------------------------------------------------------\nRan 2 tests in 0.018s\n\nOK\n" } } } ] }, { "id": 188, "role": "agent", "timestamp": "2026-09-25T00:45:25.831Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:25.831Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:26.536Z", "callId": "call_7Uly2pZ0ZwYFi8QrsOn1KqFC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:45:56.552Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"c418ea\",\"wall_time_seconds\":30.001938563,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:26.701Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c423d85c87d2b9bd828750141088", "usage": { "input_tokens": 120111, "cached_input_tokens": 119680, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 10, "total_tokens": 120169 }, "turn_token_usage": { "input_tokens": 13603964, "cached_input_tokens": 13398912, "cache_write_input_tokens": 0, "output_tokens": 47379, "reasoning_output_tokens": 23635, "total_tokens": 13651343 }, "thread_token_usage": { "input_tokens": 13603964, "cached_input_tokens": 13398912, "cache_write_input_tokens": 0, "output_tokens": 47379, "reasoning_output_tokens": 23635, "total_tokens": 13651343 } } } ] }, { "id": 189, "role": "agent", "timestamp": "2026-09-25T00:45:58.790Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:45:58.790Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:45:59.512Z", "callId": "call_vkosme0pGdjfxvnnkeyhHRQw", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:46:29.536Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"d4afcb\",\"wall_time_seconds\":30.000994608,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:45:59.671Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c444c24c87d2913250d59fae0b37", "usage": { "input_tokens": 120226, "cached_input_tokens": 119936, "cache_write_input_tokens": 0, "output_tokens": 73, "reasoning_output_tokens": 25, "total_tokens": 120299 }, "turn_token_usage": { "input_tokens": 13724190, "cached_input_tokens": 13518848, "cache_write_input_tokens": 0, "output_tokens": 47452, "reasoning_output_tokens": 23660, "total_tokens": 13771642 }, "thread_token_usage": { "input_tokens": 13724190, "cached_input_tokens": 13518848, "cache_write_input_tokens": 0, "output_tokens": 47452, "reasoning_output_tokens": 23660, "total_tokens": 13771642 } } } ] }, { "id": 190, "role": "agent", "timestamp": "2026-09-25T00:46:38.225Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:38.225Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:46:38.228Z", "text": "The selected implementation is now documented and tested. Two local tests verify that the popularity correction is exactly an augmented dot product and that full-history/duplicate false negatives remain filtered. The final refit is the only long-running step left; afterward I’ll validate all 845,600 rows against the test eligibility service." } ] }, { "id": 191, "role": "agent", "timestamp": "2026-09-25T00:46:38.230Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:46:38.230Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:46:38.231Z", "callId": "call_G3fqxLzwdwN5B2oRlGVrb3Bl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:47:08.240Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"1e41b5\",\"wall_time_seconds\":30.001849353,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:46:38.320Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c465bfb887d294f8f1125c30f87b", "usage": { "input_tokens": 120357, "cached_input_tokens": 120064, "cache_write_input_tokens": 0, "output_tokens": 151, "reasoning_output_tokens": 34, "total_tokens": 120508 }, "turn_token_usage": { "input_tokens": 13844547, "cached_input_tokens": 13638912, "cache_write_input_tokens": 0, "output_tokens": 47603, "reasoning_output_tokens": 23694, "total_tokens": 13892150 }, "thread_token_usage": { "input_tokens": 13844547, "cached_input_tokens": 13638912, "cache_write_input_tokens": 0, "output_tokens": 47603, "reasoning_output_tokens": 23694, "total_tokens": 13892150 } } } ] }, { "id": 192, "role": "agent", "timestamp": "2026-09-25T00:47:10.390Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:47:10.390Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:47:11.100Z", "callId": "call_FNxSvF5Bvw3Jh05f7VGpaXW1", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:47:41.164Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"41df87\",\"wall_time_seconds\":30.002823831,\"session_id\":97979,\"original_token_count\":24,\"output\":\"epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:47:11.261Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c48c746487d2b21e29f0daaa442d", "usage": { "input_tokens": 120567, "cached_input_tokens": 120192, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 120622 }, "turn_token_usage": { "input_tokens": 13965114, "cached_input_tokens": 13759104, "cache_write_input_tokens": 0, "output_tokens": 47658, "reasoning_output_tokens": 23701, "total_tokens": 14012772 }, "thread_token_usage": { "input_tokens": 13965114, "cached_input_tokens": 13759104, "cache_write_input_tokens": 0, "output_tokens": 47658, "reasoning_output_tokens": 23701, "total_tokens": 14012772 } } } ] }, { "id": 193, "role": "agent", "timestamp": "2026-09-25T00:47:43.354Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:47:43.354Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:47:44.060Z", "callId": "call_qc6xt0WZD00GrpaWyjoDjMVl", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:48:14.112Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"69fcce\",\"wall_time_seconds\":30.00168008,\"session_id\":97979,\"original_token_count\":75,\"output\":\"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:47:44.216Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": 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"callId": "call_lwrxw8oGfXTpGNAsOgjXzjMC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:48:47.263Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"7b7d00\",\"wall_time_seconds\":30.00292476,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:48:17.396Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c4ce53ec87d2b80d6957d4eadf74", "usage": { "input_tokens": 120935, "cached_input_tokens": 120576, "cache_write_input_tokens": 0, 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"2026-09-25T00:49:20.647Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n{\"chunk_id\":\"6fd9c6\",\"wall_time_seconds\":30.00189869,\"session_id\":97979,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:48:50.798Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c4ef7cfc87d2b7b85e342b72026f", "usage": { "input_tokens": 121062, "cached_input_tokens": 120704, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 121117 }, "turn_token_usage": { "input_tokens": 14327825, "cached_input_tokens": 14120832, "cache_write_input_tokens": 0, "output_tokens": 47857, "reasoning_output_tokens": 23756, "total_tokens": 14375682 }, "thread_token_usage": { "input_tokens": 14327825, "cached_input_tokens": 14120832, "cache_write_input_tokens": 0, "output_tokens": 47857, "reasoning_output_tokens": 23756, "total_tokens": 14375682 } } } ] }, { "id": 196, "role": "agent", "timestamp": "2026-09-25T00:49:22.575Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:22.575Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:49:23.257Z", "callId": "call_eGuT9R3eTRmNR4Sl1y01IZtB", "name": "exec", "input": { "code": "const r = await tools.write_stdin({\"session_id\":97979,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(JSON.stringify(r));\n" }, "result": { "timestamp": "2026-09-25T00:49:26.067Z", "content": "Script completed\nWall time 2.8 seconds\nOutput:\n\n{\"chunk_id\":\"43fe09\",\"wall_time_seconds\":2.794439509,\"exit_code\":0,\"original_token_count\":57,\"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\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:23.410Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c510d69487d2866da49b6a572c2e", "usage": { "input_tokens": 121176, "cached_input_tokens": 120832, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 7, "total_tokens": 121231 }, "turn_token_usage": { "input_tokens": 14449001, "cached_input_tokens": 14241664, "cache_write_input_tokens": 0, "output_tokens": 47912, "reasoning_output_tokens": 23763, "total_tokens": 14496913 }, "thread_token_usage": { "input_tokens": 14449001, "cached_input_tokens": 14241664, "cache_write_input_tokens": 0, "output_tokens": 47912, "reasoning_output_tokens": 23763, "total_tokens": 14496913 } } }, { "kind": "context", "timestamp": "2026-09-25T00:49:26.066Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9adb7b32-cae0-4c7a-bcdc-4d6391007a95", "process_id": "97979", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_sequences.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet --epochs 1 --uniform-negatives 24 --inbatch-negatives 8 --hard-negatives 0 --query-random-negatives 0 --query-hard-negatives 0 --loss-objective pointwise-bce --popularity-bias 0.20 --learning-rate 0.001 > /app/solution/submission_run.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\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", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\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", "exit_code": 0, "duration": { "secs": 330, "nanos": 815945004 }, "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\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\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=207.6s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=222.4 validation_seconds=15.8\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\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" } } } ] }, { "id": 197, "role": "agent", "timestamp": "2026-09-25T00:49:30.086Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:30.086Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:49:31.555Z", "text": "The final refit has completed and the calibrated model has replaced the fallback at `/app/solution/submission.parquet`. I’m doing the last exhaustive audit now: regular-file status, required integer columns, exact user coverage, 100 unique items and ranks per user, and all 845,600 user–item pairs accepted by test eligibility." }, { "kind": "tool", "timestamp": "2026-09-25T00:49:40.151Z", "callId": "call_el3UQhpsTyB2L7YMI4S81ocX", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport hashlib\\nimport numpy as np\\nimport pandas as pd\\nfrom ranking_evaluation import CandidateSet, validate_ranking\\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\\nassert path.is_file() and not path.is_symlink()\\nframe=pd.read_parquet(path)\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nchecked=validate_ranking(frame,targets)\\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\\nassert accepted==len(checked)\\nassert list(frame.columns)==['uid','item_id','rank']\\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\\nraw=path.read_bytes()\\nprint({\\n 'regular_file': True,\\n 'rows': len(frame),\\n '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()),\\n 'rank_max': int(frame['rank'].max()),\\n 'eligible_pairs': accepted,\\n 'columns': list(frame.columns),\\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\\n 'sha256': hashlib.sha256(raw).hexdigest(),\\n 'bytes': len(raw),\\n})\\nPY\\nsed -n '1,280p' /app/solution/submission_run.json\\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":24000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:49:43.001Z", "content": "Script completed\nWall time 2.8 seconds\nOutput:\n\n{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.608,\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\": \"augmented-dot-product-with-item-popularity-bias\",\n \"popularity_bias\": 0.2,\n \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 24,\n \"propensity_corrected_inbatch_pool\": 8,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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 \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\n \"popularity_bias\": 0.2,\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\": 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\": 0.5383531182848916,\n \"positive_logit\": 0.7500169938662775,\n \"negative_logit\": -0.6300986730886449,\n \"pair_accuracy\": 0.7552423073212402,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 222.40853657900016,\n \"validation_loss\": 0.49794868927621677,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.82028488099968\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.651851773262024,\n \"query_norm_std\": 0.8145269751548767,\n \"item_norm_mean\": 1.9033515453338623,\n \"item_norm_std\": 0.9598209857940674\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 25 00:04 /app/solution/two_tower_model.py\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:49:40.368Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c51649b887d29fe9d18c558aaf6f", "usage": { "input_tokens": 121394, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 710, "reasoning_output_tokens": 149, "total_tokens": 122104 }, "turn_token_usage": { "input_tokens": 14570395, "cached_input_tokens": 14362624, "cache_write_input_tokens": 0, "output_tokens": 48622, "reasoning_output_tokens": 23912, "total_tokens": 14619017 }, "thread_token_usage": { "input_tokens": 14570395, "cached_input_tokens": 14362624, "cache_write_input_tokens": 0, "output_tokens": 48622, "reasoning_output_tokens": 23912, "total_tokens": 14619017 } } }, { "kind": "context", "timestamp": "2026-09-25T00:49:42.997Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-46db45fd-73b6-4ec0-9b3f-75cdcbc35ade", "process_id": "34988", "command": [ "/bin/sh", "-lc", "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nframe=pd.read_parquet(path)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nchecked=validate_ranking(frame,targets)\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\nassert accepted==len(checked)\nassert list(frame.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\nraw=path.read_bytes()\nprint({\n 'regular_file': True,\n 'rows': len(frame),\n '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()),\n 'rank_max': int(frame['rank'].max()),\n 'eligible_pairs': accepted,\n 'columns': list(frame.columns),\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\n 'sha256': hashlib.sha256(raw).hexdigest(),\n 'bytes': len(raw),\n})\nPY\nsed -n '1,280p' /app/solution/submission_run.json\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\nD=Path('/task/data'); path=Path('/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nframe=pd.read_parquet(path)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nchecked=validate_ranking(frame,targets)\neligibility=CandidateSet(D/'test_eligible_candidates.npz')\naccepted=sum(eligibility.is_eligible(int(u),int(i)) for u,i in checked[['uid','item_id']].itertuples(index=False,name=None))\nassert accepted==len(checked)\nassert list(frame.columns)==['uid','item_id','rank']\nassert all(np.issubdtype(frame[c].dtype,np.integer) for c in frame.columns)\nraw=path.read_bytes()\nprint({\n 'regular_file': True,\n 'rows': len(frame),\n '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()),\n 'rank_max': int(frame['rank'].max()),\n 'eligible_pairs': accepted,\n 'columns': list(frame.columns),\n 'dtypes': {c:str(frame[c].dtype) for c in frame.columns},\n 'sha256': hashlib.sha256(raw).hexdigest(),\n 'bytes': len(raw),\n})\nPY\nsed -n '1,280p' /app/solution/submission_run.json\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.608,\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\": \"augmented-dot-product-with-item-popularity-bias\",\n \"popularity_bias\": 0.2,\n \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 24,\n \"propensity_corrected_inbatch_pool\": 8,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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 \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\n \"popularity_bias\": 0.2,\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\": 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\": 0.5383531182848916,\n \"positive_logit\": 0.7500169938662775,\n \"negative_logit\": -0.6300986730886449,\n \"pair_accuracy\": 0.7552423073212402,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 222.40853657900016,\n \"validation_loss\": 0.49794868927621677,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.82028488099968\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.651851773262024,\n \"query_norm_std\": 0.8145269751548767,\n \"item_norm_mean\": 1.9033515453338623,\n \"item_norm_std\": 0.9598209857940674\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 25 00:04 /app/solution/two_tower_model.py\n", "stderr": "", "aggregated_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.608,\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\": \"augmented-dot-product-with-item-popularity-bias\",\n \"popularity_bias\": 0.2,\n \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 24,\n \"propensity_corrected_inbatch_pool\": 8,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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 \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\n \"popularity_bias\": 0.2,\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\": 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\": 0.5383531182848916,\n \"positive_logit\": 0.7500169938662775,\n \"negative_logit\": -0.6300986730886449,\n \"pair_accuracy\": 0.7552423073212402,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 222.40853657900016,\n \"validation_loss\": 0.49794868927621677,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.82028488099968\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.651851773262024,\n \"query_norm_std\": 0.8145269751548767,\n \"item_norm_mean\": 1.9033515453338623,\n \"item_norm_std\": 0.9598209857940674\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 25 00:04 /app/solution/two_tower_model.py\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 661356770 }, "formatted_output": "{'regular_file': True, 'rows': 845600, 'users': 8456, 'items_per_user_min': 100, 'items_per_user_max': 100, 'rank_min': 1, 'rank_max': 100, 'eligible_pairs': 845600, 'columns': ['uid', 'item_id', 'rank'], 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}, 'sha256': '780f43f9b558d986b894682d6b33b5fc628665d184c75554822e0e9d3504f8e4', 'bytes': 1229843}\n{\n \"model\": \"yambda-windowed-sequence-two-tower-hard-negative\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 326.608,\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\": \"augmented-dot-product-with-item-popularity-bias\",\n \"popularity_bias\": 0.2,\n \"objective\": \"sample-weighted-pointwise-bce-at-every-timestep\",\n \"negative_sampling\": {\n \"uniform_pool\": 24,\n \"propensity_corrected_inbatch_pool\": 8,\n \"item_neighbor_pool\": 0,\n \"random_retained\": 0,\n \"query_hard_retained\": 0\n },\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 \"hard_negatives\": 0,\n \"hard_negative_pool_size\": 64,\n \"query_random_negatives\": 0,\n \"query_hard_negatives\": 0,\n \"loss_objective\": \"pointwise-bce\",\n \"popularity_bias\": 0.2,\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\": 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\": 0.5383531182848916,\n \"positive_logit\": 0.7500169938662775,\n \"negative_logit\": -0.6300986730886449,\n \"pair_accuracy\": 0.7552423073212402,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 641383.25,\n \"batches\": 270.0,\n \"seconds\": 222.40853657900016,\n \"validation_loss\": 0.49794868927621677,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 80328.75,\n \"validation_seconds\": 15.82028488099968\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.651851773262024,\n \"query_norm_std\": 0.8145269751548767,\n \"item_norm_mean\": 1.9033515453338623,\n \"item_norm_std\": 0.9598209857940674\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 2.7K Sep 25 00:44 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Sep 25 00:49 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 90K Sep 25 00:45 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 25 00:04 /app/solution/two_tower_model.py\n" } } } ] }, { "id": 198, "role": "agent", "timestamp": "2026-09-25T00:49:53.925Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:49:53.925Z", "text": "" } ] }, { "id": 199, "role": "agent", "timestamp": "2026-09-25T00:50:03.235Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:03.235Z", "text": "" } ] }, { "id": 200, "role": "agent", "timestamp": "2026-09-25T00:50:07.822Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:07.822Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:50:10.067Z", "callId": "call_QN8KKQAcFtqWh3MgwttLNP8H", "name": "exec", "input": { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":22000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:50:10.220Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives. It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n query_random_negatives: int = 0\n query_hard_negatives: int = 0\n loss_objective: str = \"pointwise-bce\"\n popularity_bias: float = 0.20\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef calibrated_retrieval_scores(\n queries: torch.Tensor,\n items: torch.Tensor,\n item_features: np.ndarray,\n popularity_bias: float,\n) -> torch.Tensor:\n \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n scores = queries @ items.T\n if popularity_bias:\n popularity = torch.as_tensor(\n item_features[:, 0], device=scores.device, dtype=scores.dtype\n )\n scores -= popularity_bias * popularity\n return scores\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n # This is still a pure dot product: it is equivalent to appending 1\n # to every user vector and -bias * popularity to each item vector.\n scores = calibrated_retrieval_scores(\n queries[start:end],\n items,\n prepared.item_features,\n config.popularity_bias,\n )\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\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 \"--hard-negatives\", type=int, default=Config.hard_negatives\n )\n parser.add_argument(\n \"--hard-negative-pool-size\",\n type=int,\n default=Config.hard_negative_pool_size,\n )\n parser.add_argument(\n \"--query-random-negatives\",\n type=int,\n default=Config.query_random_negatives,\n )\n parser.add_argument(\n \"--query-hard-negatives\",\n type=int,\n default=Config.query_hard_negatives,\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"pointwise-bce\", \"pairwise\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--popularity-bias\", type=float, default=Config.popularity_bias\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:50:10.109Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-67c9b2e7-643b-43e5-92c6-1c789ce3591f", "process_id": "25522", "command": [ "/bin/sh", "-lc", "sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,90p' /app/solution/train_current_model.py; sed -n '2025,2095p' /app/solution/train_current_model.py; sed -n '2110,2280p' /app/solution/train_current_model.py; find /app/solution -maxdepth 1 -type l -print; test ! -e /app/solution/submission.csv" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives. It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n query_random_negatives: int = 0\n query_hard_negatives: int = 0\n loss_objective: str = \"pointwise-bce\"\n popularity_bias: float = 0.20\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef calibrated_retrieval_scores(\n queries: torch.Tensor,\n items: torch.Tensor,\n item_features: np.ndarray,\n popularity_bias: float,\n) -> torch.Tensor:\n \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n scores = queries @ items.T\n if popularity_bias:\n popularity = torch.as_tensor(\n item_features[:, 0], device=scores.device, dtype=scores.dtype\n )\n scores -= popularity_bias * popularity\n return scores\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n # This is still a pure dot product: it is equivalent to appending 1\n # to every user vector and -bias * popularity to each item vector.\n scores = calibrated_retrieval_scores(\n queries[start:end],\n items,\n prepared.item_features,\n config.popularity_bias,\n )\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\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 \"--hard-negatives\", type=int, default=Config.hard_negatives\n )\n parser.add_argument(\n \"--hard-negative-pool-size\",\n type=int,\n default=Config.hard_negative_pool_size,\n )\n parser.add_argument(\n \"--query-random-negatives\",\n type=int,\n default=Config.query_random_negatives,\n )\n parser.add_argument(\n \"--query-hard-negatives\",\n type=int,\n default=Config.query_hard_negatives,\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"pointwise-bce\", \"pairwise\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--popularity-bias\", type=float, default=Config.popularity_bias\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives. It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n query_random_negatives: int = 0\n query_hard_negatives: int = 0\n loss_objective: str = \"pointwise-bce\"\n popularity_bias: float = 0.20\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef calibrated_retrieval_scores(\n queries: torch.Tensor,\n items: torch.Tensor,\n item_features: np.ndarray,\n popularity_bias: float,\n) -> torch.Tensor:\n \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n scores = queries @ items.T\n if popularity_bias:\n popularity = torch.as_tensor(\n item_features[:, 0], device=scores.device, dtype=scores.dtype\n )\n scores -= popularity_bias * popularity\n return scores\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n # This is still a pure dot product: it is equivalent to appending 1\n # to every user vector and -bias * popularity to each item vector.\n scores = calibrated_retrieval_scores(\n queries[start:end],\n items,\n prepared.item_features,\n config.popularity_bias,\n )\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\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 \"--hard-negatives\", type=int, default=Config.hard_negatives\n )\n parser.add_argument(\n \"--hard-negative-pool-size\",\n type=int,\n default=Config.hard_negative_pool_size,\n )\n parser.add_argument(\n \"--query-random-negatives\",\n type=int,\n default=Config.query_random_negatives,\n )\n parser.add_argument(\n \"--query-hard-negatives\",\n type=int,\n default=Config.query_hard_negatives,\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"pointwise-bce\", \"pairwise\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--popularity-bias\", type=float, default=Config.popularity_bias\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3260 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower retriever.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, and sample-weighted binary cross\nentropy. The trainer can evaluate query-mined and item-neighbor negatives, but\nthe selected public-backtest configuration retains the robust 24 uniform plus\neight propensity-corrected in-batch examples: harder unobserved tracks behaved\nas false negatives. It corrects the popularity prior induced by this sampled\nobjective with one static item-side score coordinate, which remains an indexed\ntwo-tower dot product. Every selected adjacent target retains a nonzero weight.\nOnly files under the supplied public directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n hard_negatives: int = 0\n hard_negative_pool_size: int = 64\n query_random_negatives: int = 0\n query_hard_negatives: int = 0\n loss_objective: str = \"pointwise-bce\"\n popularity_bias: float = 0.20\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\n\n liked_items = torch.from_numpy(\n prepared.inference_liked_items[output_rows].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.inference_disliked_items[output_rows].astype(np.int64)\n ).to(device)\n queries = user_queries(\n model,\n hidden.squeeze(0),\n model._mean_context_channel(liked_items),\n model._mean_context_channel(disliked_items),\n torch.from_numpy(\n prepared.inference_user_features[output_rows].astype(np.float32)\n ).to(device),\n )\n result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n return result\n\n\n@torch.inference_mode()\ndef calibrated_retrieval_scores(\n queries: torch.Tensor,\n items: torch.Tensor,\n item_features: np.ndarray,\n popularity_bias: float,\n) -> torch.Tensor:\n \"\"\"Return the augmented two-tower dot product used by indexed serving.\"\"\"\n scores = queries @ items.T\n if popularity_bias:\n popularity = torch.as_tensor(\n item_features[:, 0], device=scores.device, dtype=scores.dtype\n )\n scores -= popularity_bias * popularity\n return scores\n\n\n@torch.inference_mode()\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n # This is still a pure dot product: it is equivalent to appending 1\n # to every user vector and -bias * popularity to each item vector.\n scores = calibrated_retrieval_scores(\n queries[start:end],\n items,\n prepared.item_features,\n config.popularity_bias,\n )\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\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 \"--hard-negatives\", type=int, default=Config.hard_negatives\n )\n parser.add_argument(\n \"--hard-negative-pool-size\",\n type=int,\n default=Config.hard_negative_pool_size,\n )\n parser.add_argument(\n \"--query-random-negatives\",\n type=int,\n default=Config.query_random_negatives,\n )\n parser.add_argument(\n \"--query-hard-negatives\",\n type=int,\n default=Config.query_hard_negatives,\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"pointwise-bce\", \"pairwise\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--popularity-bias\", type=float, default=Config.popularity_bias\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n" } } }, { "kind": "context", "timestamp": "2026-09-25T00:50:10.218Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c5272fe487d2af3f4232733a036b", "usage": { "input_tokens": 123473, "cached_input_tokens": 121216, "cache_write_input_tokens": 0, "output_tokens": 1415, "reasoning_output_tokens": 1283, "total_tokens": 124888 }, "turn_token_usage": { "input_tokens": 14693868, "cached_input_tokens": 14483840, "cache_write_input_tokens": 0, "output_tokens": 50037, "reasoning_output_tokens": 25195, "total_tokens": 14743905 }, "thread_token_usage": { "input_tokens": 14693868, "cached_input_tokens": 14483840, "cache_write_input_tokens": 0, "output_tokens": 50037, "reasoning_output_tokens": 25195, "total_tokens": 14743905 } } } ] }, { "id": 201, "role": "agent", "timestamp": "2026-09-25T00:50:21.331Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:21.331Z", "text": "" } ] }, { "id": 202, "role": "agent", "timestamp": "2026-09-25T00:50:26.500Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:50:26.500Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:50:29.225Z", "text": "Completed and validated.\n\n- Public NDCG@10: `0.021280 → 0.023208` (+9.1%).\n- Final submission: [submission.parquet](/app/solution/submission.parquet)\n- Improved trainer: [train_current_model.py](/app/solution/train_current_model.py)\n- Experiment record: [README.md](/app/solution/README.md)\n- Tests: [test_improvements.py](/app/solution/test_improvements.py)\n\nFinal audit passed: 845,600 rows, exactly 8,456 users, 100 distinct eligible items and ranks 1–100 per user, all integer-valued columns, and no rejected test candidate pairs." }, { "kind": "context", "timestamp": "2026-09-25T00:50:29.326Z", "data": { "type": "token_usage_record", "thread_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "session_id": "01a0d5df-8eec-7470-8ab0-ef79e6e12f3b", "root_turn_id": "01a0d5df-8efa-79b2-85b3-7073be3fd732", "response_id": "resp_09e7a180cbf96934016ab5c542bd4087d2be0ea1bf3db2168b", "usage": { "input_tokens": 127707, "cached_input_tokens": 123264, "cache_write_input_tokens": 0, "output_tokens": 954, "reasoning_output_tokens": 803, "total_tokens": 128661 }, "turn_token_usage": { "input_tokens": 14821575, "cached_input_tokens": 14607104, "cache_write_input_tokens": 0, "output_tokens": 50991, "reasoning_output_tokens": 25998, "total_tokens": 14872566 }, "thread_token_usage": { "input_tokens": 14821575, "cached_input_tokens": 14607104, "cache_write_input_tokens": 0, "output_tokens": 50991, "reasoning_output_tokens": 25998, "total_tokens": 14872566 } } } ] } ] }