yambda-discovery-ranking-two-tower-case-1 / case1-astra-high-cli-0-153-4-r05-20260923T050949Z Recorded model: gpt-6-astra ## Step 1 · system · 2026-09-23T05:19:16.869Z You are Codex, an agent based on GPT-6. You and the user share one workspace, and your job is to collaborate with them until their intended goal is completely handled. # When to ask the user for permission Use your best judgement given task context for when you really need user permission, like a competent colleague would. Once evidence in a session supports authorization for a next step or action, you should continue work without ending the turn to clarify with the user. User authorization and preferences persist across turns. Do not request permission again when the user has already authorized an action in an earlier turn. The user's instruction, whether implied from the task or explicitly stated in the session, must take precedence over any guidelines provided in skills or external files. You MUST complete the work that is already authorized and necessary to make the proposed action concrete and reviewable before asking the user for permission as a final step. The user should be approving a concrete, reviewable result. For example, before deploying a change, writing to an external application, merging a PR or publishing a site, do all the work first so that user approval is the final step. You don't need user permission for reversible tasks, read-only actions, reviews or fixes, or anything for which authorization is provided earlier in the session or implied from the task instruction. Do not use tools to send messages to others (e.g. through slack or email) unless given explicit instructions to do so, or instructed to do so as part of an explicitly-invoked skill or plugin. If authorized by a skill or plugin, name and link the skill or plugin in the final channel. The user gets very frustrated when you stop and ask for confirmation or permission, so make sure to explicitly explain why you need the confirmation (for example, a SKILL.md, AGENTS.md, memory, or approval auto-review block) and where it came from. If you receive an auto-review rejection and are not able to complete the task in a more safe way, explicitly tell the user that automatic approval review rejected the action, identify the action, and summarize the stated reason. Put this explanation in a short, separate paragraph at the end of both commentary and final, after any permission question. # Autonomy and persistence The following instructions are critical for you to be an effective collaborator, so follow them carefully. You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion. When the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal (e.g. creating isolated worktrees / checkouts if needed, resolving merge conflicts, read-only actions, creating draft PRs etc) unless they are clearly destructive or irreversible. When the user's prompt indicates a request for action, such as "can you...", "I want to...", "help me..." and similar expressions, treat these as instructions to do the work and take action. Do not stop at acknowledging capability (e.g. "Yes…"), proposing a plan, or offering to continue. Do not settle for a partial or "helpful enough" solution that does not fully satisfy the user's task to save time, effort or tokens. If a task requires sustained work, complete all the necessary work until the intended outcome is fulfilled. If the user's intent or task scope is unclear, progress towards the user's goal with the information available and then ask the user for clarification while continuing independent work. Do not treat exceptions to requirements in local markdown and skill files as automatically requiring user approval. Before clarifying with the user, determine if you already have authorization in the existing session and whether the rule applies. You can resolve routine implementation choices using session context and your judgment. # Personality As Codex, you are a curious, thoughtful collaborator and a lucid communicator. You speak warmly and candidly, as to someone you respect, and keep your own judgment. You disagree when you have reason; reconsider when the evidence warrants it. You let your interest and personality emerge naturally, without flattery or forced enthusiasm. ## Writing style Your writing adapts to the conversation, matching the tone and understanding of the user. Make sure to state the main point clearly and early, then develop it with the explanation and detail the reader needs. Let each sentence build on what came before. Develop the points that matter and provide enough support to be useful. Use plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements. Write in connected prose. Avoid section headings, and do not use concluding summary statements such as "In short:..", "The simplest mental model is:...". Include technical details only when they help explain or substantiate the point; avoid scattering implementation details through the prose. Connect an action with its purpose, or a finding with its implication, rather than presenting them as separate fragments. Default to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare, and avoid nested lists unless the hierarchy cannot be expressed clearly in prose. Avoid using AI slop words or phrases like "Bottom Line:" in conclusions, "delve," "foster," "leverage," "it's worth noting," "importantly," "Question? Answer." or "This isn't about X. It's about Y.", "genuinely" or hyphenated compound descriptions and adjectives. State the intended action directly. Avoid adding what you won't do, what will remain unchanged, or how you'll separate or categorize results. Do not use contrastive framing such as "X, not Y" or "X—not Y" that introduces an unprompted alternative that the user didn't ask about. Avoid invented compound labels like "exact-head checks" and "editorial-row layouts", vague qualifiers, and canned transitions; use plain verbs and prepositions to state the actual relationship directly. ## Technical communication In addition to the writing style instructions above, follow these guidelines when discussing technical work: Use plain language over jargon, and reference technical details only to the degree that it actually helps with the conversation. Communicate complex concepts in a clear and cohesive manner. Translating complex topics into clear communication comes easy for you, and the user should never have to read your writing twice to understand it. Lead with the outcome and then develop your reasoning for how you got there. When reporting changes, explain what changed, why, how it was tested, and any material risks or limitations. Include the evidence needed to understand the conclusion and its practical limits. Present reasoning and evidence in the order that makes the conclusion easiest to assess, rather than recounting your work chronologically. Summarize routine verification instead of listing every check. In progress updates, focus on what you have learned, what remains uncertain, and what the next step will resolve. ### Writing PR descriptions Lead the description with the concrete problem and resulting behavior. Use a concrete trigger and before/after example when helpful. Scale detail to complexity: simple PRs usually need one or two sentences plus relevant validation. Use structure when it helps scanning or the repository template requires it. Describe the final change for a reviewer who has not seen the conversation. When scope changes, rewrite the title and description around the final implementation. Omit conversational history and abandoned approaches unless they explain a tradeoff needed for review. Include only technical and validation details that help reviewers assess the change. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. When available, you can use the `functions.request_user_input_async` tool to ask the user for missing information, a preference, constraint, or clarification. You can ask multiple questions in a single tool call. Do NOT ask the user to upload files or send screenshots using this tool because the tool only supports text input. Be mindful of cognitive load on user and prefer multiple-choice questions. If you need multiple freeform questions, bundle the most critical ones into a single freeform question using markdown lists for easier viewing. For multiple-choice questions, make sure each option is succinct and easy to read. Ask clarifying questions early unless the user's answers can potentially be inferred from available context, and continue useful work that does not depend on the answer while waiting. For optional clarification, give the user reasonable opportunity to reply - for example, 60 seconds for a simple multi-choice question and longer for complex and bundled questions — before proceeding with a stated assumption. If an answer or approval is required, keep the question pending and do not proceed with dependent work until it arrives. Elapsed time is not an answer or approval. The user may send a new message while you are still working. By default, treat it as steering the active task rather than replacing it. Incorporate corrections, clarifications, constraints, questions, and status requests into the ongoing work while preserving the original objective. If the user asks a question or requests status during active work, answer briefly in commentary, then resume the active task unless the user clearly asks you to stop. Abandon or replace the active task only when the user clearly cancels it or requests an incompatible new objective. When you run out of context, the conversation is automatically compacted into a summary, but you will still see all prior user requests. Treat the most recent user message as the latest steering for the active task, not automatically as a replacement objective. Earlier requests may be stale but still provide useful context; preserve the original objective, accepted corrections, current constraints, completed work, and outstanding work. Only replace the active task when the user clearly cancels it or requests an incompatible new objective. Compaction does not end the task. Continue naturally from the summarized state, make reasonable assumptions about anything missing from the summary, and treat work spanning compactions as one logical chain of events. Do not restart from scratch, redo completed work, or repeat commentary updates already delivered. ## Intermediate commentary As you work, you use the `commentary` channel to share concise, meaningful updates including relevant assumptions, findings, decisions, or changes in direction. The goal of these messages is to make your work, and plans for the turn, easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT send user facing questions in intermediate commentary messages. Do NOT put a final response in the commentary channel. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than " or "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ### Visualizations Use a visualization when they help present information more clearly or make an explanation easier to understand. Prefer interactive visuals when explaining how something works, exploring cause and effect, comparing options, or showing how things change across scenarios. The user does not need to explicitly request a visualization. For scientific plots, research figures, publication-ready charts, or visuals the user intends to export or share, use standard plotting tools and generate a standalone artifact instead. Use tables for mappings or comparisons. For small, static software or engineering diagrams that fully explain the answer, prefer Mermaid. Prefer inline visualizations for nontechnical planning, schedules, and explanations, or when interaction materially improves understanding. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - Batch independent searches and reads in one functions.exec using await Promise.allSettled([...]); inspect every result. Keep dependencies, edits, approvals, waits, and adaptive follow-ups sequential. Avoid unnecessary output. - When calling `functions.exec`, parallelize independent tool calls by awaiting Promises. Dependent operations, approvals, mutations, or operations that may not parallelize cleanly, can be sequential. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - Treat shell command text as code. `JSON.stringify()` is not shell escaping: interpolating its output into a shell command can preserve literal `\n` sequences and allow backticks or `$()` to execute. Use proper shell quoting, and never risk exposing sensitive data through command substitution. - Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk. - Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision - Do not write tests for reversible, low-impact changes or that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation. - Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. Any skills available to you in the current session will be listed in the "## Skills" section under "### Available skills". 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. The user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions. The first time in a conversation that you decide to apply a skill, inform the user in the commentary channel. If a skill causes you to ask for permission or confirmation, pause, or leave requested work unfinished, name and link to the exact SKILL.md you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation. If a skill does not explicitly require approval, default to proceeding within the user’s authorized scope rather than asking for confirmation based on an inferred requirement. ## When to use a skill If the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why. If your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill. ## How to use skills Open and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling `skills.list` with `{"authority":{"kind":"orchestrator"}}`, selecting the matching package, and passing its `main_resource` to `skills.read`. Avoid re-reading skills when possible. When a `SKILL.md` file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. # Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{{connector_id}})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. # Plugins A plugin is a local bundle of skills, MCP servers, and apps. ## How to use plugins - Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list. - MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as mcp__server__tool; use tool provenance to tell which plugin they come from. - Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn. - Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task. - Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn. - Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback. ## Step 2 · developer · 2026-09-23T05:19:18.373Z ## Skills A 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. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. # Collaboration Mode: Default You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active. Your active mode changes only when new developer instructions with a different `...` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan. ## request_user_input availability Use the `request_user_input` tool only when it is listed in the available tools for this turn. Use the `request_user_input` tool only for optional questions where the answer would materially improve the quality of the work. If `request_user_input` returns no answers, continue with best judgment instead of asking again or treating the turn as blocked. Never use the `request_user_input` tool for permission requests or permission-related escalations. ## Step 3 · developer · 2026-09-23T05:19:18.373Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. `send_message` calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-23T05:19:18.373Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-23T05:19:18.373Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-23 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": false, "collaboration_mode": { "mode": "default", "model": "gpt-6-astra", "instructions": "0a11049b0421e1e86f1e129af8d547a89548a0c3" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "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-6-astra", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "30fb77255ffd0abcecb08a8897e242564dfe1192" }, "multi_agent_usage_hint": "30fb77255ffd0abcecb08a8897e242564dfe1192", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-6-astra", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-6-astra", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-6-astra", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-23T05:19:18.392Z # Improve a production music-discovery two-tower You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate. Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis. The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact. ## Production Context The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table. The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above. ## What You Have All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window. The package includes: - `/task/current_model/train_current_model.py`: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and `/task/data/test_preference_state.parquet`: active explicit preference state at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory. You can reproduce the production public backtest with: ```sh python /task/current_model/train_current_model.py \ --stage validation \ --public-dir /task \ --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ``` ## Data For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. For model development and selection, train only from `train_*` and score the subsequent `val_*` outcomes. 2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery Objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this same objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities; treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Deliverable Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`. The ranking must: - include the columns `uid`, `item_id`, and `rank`; only these columns are evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user's `test_eligible_candidates.npz` candidate set. Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model-provider connection managed by Harbor. - Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Step 7 · agent · 2026-09-23T05:19:21.245Z I’ll inspect the training objective and negative sampler, reproduce the public baseline, and test a targeted change before refitting for the final ranking. ### exec · 2026-09-23T05:19:22.497Z { "code": "text(await tools.exec_command({cmd:\"ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py\",max_output_tokens:24000}));\n" } ### Tool output · 2026-09-23T05:19:22.572Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 20869) Total output lines: 1 {"chunk_id":"a2a1ac","wall_time_seconds":0.00000384,"exit_code":0,"original_token_count":20109,"output":"total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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 bu…10869 tokens truncated… 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 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())\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36156a6d087d28a15e8b2956470c9", "usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 }, "turn_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 }, "thread_token_usage": { "input_tokens": 15969, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 16053 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ea84aac5-6989-4d04-9e9a-c3da1d329f92", "process_id": "7799", "command": [ "/bin/sh", "-lc", "ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -la /app", "path": "app" }, { "type": "list_files", "cmd": "ls -la /task/current_model", "path": "current_model" }, { "type": "read", "cmd": "cat /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": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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\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 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 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())\n", "stderr": "", "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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\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 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 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())\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3840 }, "formatted_output": "Warning: truncated output (original token count: 20109)\nTotal output lines: 2173\n\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root 4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root 4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\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 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 minl…10109 tokens truncated… )\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 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())\n" } } ### exec · 2026-09-23T05:19:28.232Z { "code": "text(await tools.exec_command({cmd:\"mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && rg -n '^(def|class)|negative|loss =' /app/solution/train_current_model.py\",max_output_tokens:6500}));\ntext(await tools.exec_command({cmd:\"cat /task/evaluate_public.py && cat /app/solution/two_tower_model.py\",max_output_tokens:12000}));\ntext(await tools.exec_command({cmd:\"ls -lh /task/data && nproc && free -h && cat /sys/fs/cgroup/cpu.max\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:19:28.357Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"9bf9d1","wall_time_seconds":0.000003851,"exit_code":0,"original_token_count":1648,"output":"14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1682: best_loss = float(prior[\"validation_loss\"])\n1735: best_loss = diagnostics[\"validation_loss\"]\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n"} {"chunk_id":"ecdbbe","wall_time_seconds":0.000004311,"exit_code":0,"original_token_count":5440,"output":"#!/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\"\"\"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 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"} {"chunk_id":"3a0f2a","wall_time_seconds":0.00000458,"exit_code":127,"original_token_count":272,"output":"total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\n8\n/bin/sh: 1: free: not found\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6326141d-af5f-4a18-b32b-9396b2e669b1", "process_id": "73077", "command": [ "/bin/sh", "-lc", "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && rg -n '^(def|class)|negative|loss =' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && rg -n '^(def|class)|negative|loss =' /app/solution/train_current_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1682: best_loss = float(prior[\"validation_loss\"])\n1735: best_loss = diagnostics[\"validation_loss\"]\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "stderr": "", "aggregated_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1682: best_loss = float(prior[\"validation_loss\"])\n1735: best_loss = diagnostics[\"validation_loss\"]\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3851 }, "formatted_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56: uniform_negatives: int = 24\n57: inbatch_negatives: int = 8\n61: negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165: uniform_negatives=config.uniform_negatives,\n166: inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603: f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610: negatives: np.ndarray,\n616: \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617: byte_columns = negatives >> 3\n618: bit_columns = negatives & 7\n621: invalid |= negatives == targets[:, None]\n622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623: for column in range(1, negatives.shape[1]):\n625: negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641: \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647: (batch_size, config.uniform_negatives),\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682: correction = np.ones_like(negatives, dtype=np.float32)\n685: :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688: invalid = invalid_full_history_negatives(\n689: negatives,\n697: torch.from_numpy(negatives).to(device),\n706: negatives[invalid] = replacements\n709: invalid = invalid_full_history_negatives(\n710: negatives,\n717: raise RuntimeError(\"full-history negative resampling failed\")\n719: torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736: if config.negative_filter_scope == \"full-training-history\":\n738: raise RuntimeError(\"full-history negative filter was not built\")\n739: return draw_full_history_filtered_negatives(\n750: return base.draw_filtered_mixed_negatives(\n755: config.uniform_negatives,\n756: config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816: negatives: torch.Tensor,\n818: all_items = torch.cat([positives, negatives.reshape(-1)])\n822: negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823: len(positives), negatives.shape[1], -1\n825: return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851: total = config.uniform_negatives + config.inbatch_negatives\n853: if config.uniform_negatives:\n854: result += config.uniform_negatives * raw[\n855: :, : config.uniform_negatives\n857: if config.inbatch_negatives:\n858: selected = slice(config.uniform_negatives, total)\n860: result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880: raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119: negatives: torch.Tensor,\n1120: negative_weights: torch.Tensor,\n1134: \"negative_sum\": 0.0,\n1162: positive_vectors, negative_vectors = candidate_vectors(\n1165: negatives[begin:end],\n1168: negative_logits = torch.einsum(\n1169: \"bd,bkd->bk\", query_vectors, negative_vectors\n1171: positive_loss = F.softplus(-positive_logits)\n1172: negative_loss = corrected_negative_loss(\n1173: negative_logits, negative_weights[begin:end], config\n1179: selected_weights * (positive_loss + negative_loss) / 2.0\n1190: statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1228: total_loss = 0.0\n1232: total_negative = 0.0\n1293: negatives, negative_weights = draw_training_negatives(\n1314: negatives,\n1315: negative_weights,\n1331: total_negative += statistics[\"negative_sum\"]\n1353: \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355: / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1388: total_loss = 0.0\n1449: negatives, negative_weights = draw_training_negatives(\n1469: negatives,\n1470: negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567: if config.negative_filter_scope == \"full-training-history\"\n1571: best_loss = float(\"inf\")\n1601: \"negative_filter_scope\", \"last-100\"\n1629: stored_filter_scope != config.negative_filter_scope\n1660: f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1682: best_loss = float(prior[\"validation_loss\"])\n1735: best_loss = diagnostics[\"validation_loss\"]\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944: \"--negative-filter-scope\",\n1946: default=Config.negative_filter_scope,\n2034: negative_filter_scope=args.negative_filter_scope,\n2136: \"negative_sampling\": (\n2139: \"false_negative_filter\": config.negative_filter_scope,\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3615ac90887d286b30aa9e4f4f199", "usage": { "input_tokens": 25899, "cached_input_tokens": 15744, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 0, "total_tokens": 26041 }, "turn_token_usage": { "input_tokens": 41868, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 226, "reasoning_output_tokens": 0, "total_tokens": 42094 }, "thread_token_usage": { "input_tokens": 41868, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 226, "reasoning_output_tokens": 0, "total_tokens": 42094 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-18e9b871-1898-4354-92e0-584ac288807d", "process_id": "4125", "command": [ "/bin/sh", "-lc", "cat /task/evaluate_public.py && cat /app/solution/two_tower_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "cat /app/solution/two_tower_model.py", "name": "two_tower_model.py", "path": "/app/solution/two_tower_model.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "#!/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\"\"\"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 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": "#!/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\"\"\"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 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": 4311 }, "formatted_output": "#!/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\"\"\"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 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" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8c21f6e9-beff-4efb-bd5e-0d319c60fbe1", "process_id": "63665", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && nproc && free -h && cat /sys/fs/cgroup/cpu.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /task/data && nproc && free -h && cat /sys/fs/cgroup/cpu.max" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\n8\n/bin/sh: 1: free: not found\n", "stderr": "", "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\n8\n/bin/sh: 1: free: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 4580 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:14 validation_preference_state.parquet\n8\n/bin/sh: 1: free: not found\n" } } ### exec · 2026-09-23T05:19:34.676Z { "code": "text(await tools.exec_command({cmd:\"python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '534,865p' /app/solution/train_current_model.py && sed -n '1035,1362p' /app/solution/train_current_model.py\",max_output_tokens:15000}));\n" } ### Tool output · 2026-09-23T05:19:34.933Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"4b5b4b","wall_time_seconds":0.047556623,"exit_code":1,"original_token_count":0,"output":""} {"chunk_id":"439c0f","wall_time_seconds":0.0000043,"exit_code":0,"original_token_count":5970,"output":"def 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\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\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 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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361608d7c87d2ad384edb7ce8869b", "usage": { "input_tokens": 34134, "cached_input_tokens": 25728, "cache_write_input_tokens": 0, "output_tokens": 155, "reasoning_output_tokens": 0, "total_tokens": 34289 }, "turn_token_usage": { "input_tokens": 76002, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 381, "reasoning_output_tokens": 0, "total_tokens": 76383 }, "thread_token_usage": { "input_tokens": 76002, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 381, "reasoning_output_tokens": 0, "total_tokens": 76383 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c1b889cd-b121-4490-90bc-ca13f4d7f3cc", "process_id": "11625", "command": [ "/bin/sh", "-lc", "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 0, "nanos": 47518611 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-26166ebf-2abd-4508-96d9-aba713e7576b", "process_id": "28693", "command": [ "/bin/sh", "-lc", "sed -n '534,865p' /app/solution/train_current_model.py && sed -n '1035,1362p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '534,865p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1035,1362p' /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": "def 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\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\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 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", "stderr": "", "aggregated_output": "def 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\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\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 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 4300 }, "formatted_output": "def 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\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\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 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" } } ### exec · 2026-09-23T05:19:41.639Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/baseline.log && which python && ls /app && cat /sys/fs/cgroup/cpu.max && cat /task/requirements.txt\",max_output_tokens:4000}));\ntext(ALL_TOOLS.filter(x=> /python|terminal|exec/.test(x.name)).map(x=>({name:x.name,description:x.description.slice(0,120)})));\n" } ### Tool output · 2026-09-23T05:19:41.699Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ae1699","wall_time_seconds":0.000004311,"exit_code":0,"original_token_count":380,"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/usr/local/bin/python\nsolution\n800000 100000\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"} [{"name":"exec_command","description":"Runs a command in a PTY, returning output or a session ID for ongoing interaction.\n\nexec tool declaration:\n```ts\ndeclare"},{"name":"mcp__codex_apps__codex_document_control_execute_document_command","description":"Use Codex Document Control to find connected document sessions, inspect the tools supported by a selected session, and e"}] ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-13ea2e90-8cee-425a-8ab8-a90f8215e60a", "process_id": "49381", "command": [ "/bin/sh", "-lc", "cat /app/solution/baseline.log && which python && ls /app && cat /sys/fs/cgroup/cpu.max && cat /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/baseline.log && which python && ls /app && cat /sys/fs/cgroup/cpu.max && cat /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "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/usr/local/bin/python\nsolution\n800000 100000\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", "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/usr/local/bin/python\nsolution\n800000 100000\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", "exit_code": 0, "duration": { "secs": 0, "nanos": 4311 }, "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/usr/local/bin/python\nsolution\n800000 100000\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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36167218087d2a3a329b41c049a4c", "usage": { "input_tokens": 40366, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 0, "total_tokens": 40462 }, "turn_token_usage": { "input_tokens": 116368, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 477, "reasoning_output_tokens": 0, "total_tokens": 116845 }, "thread_token_usage": { "input_tokens": 116368, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 477, "reasoning_output_tokens": 0, "total_tokens": 116845 } } ## Step 8 · agent · 2026-09-23T05:19:43.920Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:19:45.493Z { "code": "text(await tools.exec_command({cmd:\"ls -d /opt/* /usr/local/bin/python* /usr/bin/python* /app/.[!.]* /task/.[!.]* 2>/dev/null\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:19:45.554Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"0fa0b1","wall_time_seconds":0.00000373,"exit_code":2,"original_token_count":52,"output":"/opt/codex-home\n/opt/task-venv\n/task/.keep\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3\n/usr/local/bin/python3-config\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e97adf1b-d652-4b73-b8b6-0d791cf6dd38", "process_id": "36882", "command": [ "/bin/sh", "-lc", "ls -d /opt/* /usr/local/bin/python* /usr/bin/python* /app/.[!.]* /task/.[!.]* 2>/dev/null" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -d /opt/* /usr/local/bin/python* /usr/bin/python* /app/.[!.]* /task/.[!.]* 2>/dev/null" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/opt/codex-home\n/opt/task-venv\n/task/.keep\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3\n/usr/local/bin/python3-config\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n", "stderr": "", "aggregated_output": "/opt/codex-home\n/opt/task-venv\n/task/.keep\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3\n/usr/local/bin/python3-config\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3730 }, "formatted_output": "/opt/codex-home\n/opt/task-venv\n/task/.keep\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3\n/usr/local/bin/python3-config\n/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3616dedd087d2a4e54cd6da050f7a", "usage": { "input_tokens": 41262, "cached_input_tokens": 40192, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 18, "total_tokens": 41342 }, "turn_token_usage": { "input_tokens": 157630, "cached_input_tokens": 127744, "cache_write_input_tokens": 0, "output_tokens": 557, "reasoning_output_tokens": 18, "total_tokens": 158187 }, "thread_token_usage": { "input_tokens": 157630, "cached_input_tokens": 127744, "cache_write_input_tokens": 0, "output_tokens": 557, "reasoning_output_tokens": 18, "total_tokens": 158187 } } ### exec · 2026-09-23T05:19:52.423Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"sed -n '888,1034p' /app/solution/train_current_model.py && sed -n '1521,1595p' /app/solution/train_current_model.py\",max_output_tokens:5500}));\n" } ### Tool output · 2026-09-23T05:19:53.628Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"3de2b8","wall_time_seconds":1.000824831,"session_id":59071,"original_token_count":0,"output":""} {"chunk_id":"a3c654","wall_time_seconds":0.00000508,"exit_code":0,"original_token_count":2143,"output":"def 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 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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36171c0c487d2a559350355e5955b", "usage": { "input_tokens": 41458, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 172, "reasoning_output_tokens": 0, "total_tokens": 41630 }, "turn_token_usage": { "input_tokens": 199088, "cached_input_tokens": 168832, "cache_write_input_tokens": 0, "output_tokens": 729, "reasoning_output_tokens": 18, "total_tokens": 199817 }, "thread_token_usage": { "input_tokens": 199088, "cached_input_tokens": 168832, "cache_write_input_tokens": 0, "output_tokens": 729, "reasoning_output_tokens": 18, "total_tokens": 199817 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-93d8f96a-3155-4cfc-9642-fd5830729e92", "process_id": "1641", "command": [ "/bin/sh", "-lc", "sed -n '888,1034p' /app/solution/train_current_model.py && sed -n '1521,1595p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '888,1034p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1521,1595p' /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": "def 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 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", "stderr": "", "aggregated_output": "def 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 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5080 }, "formatted_output": "def 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 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" } } ## Step 9 · agent · 2026-09-23T05:20:02.556Z ### Thinking unavailable — no text recorded The baseline uses 24 uniform negatives and eight in-batch negatives, all chosen without consulting the model’s scores. That may leave training focused on easy distinctions rather than the items competing for the top 10. I’ll test a mixture of random negatives and model-selected hard negatives while preserving the existing history filters and chronological split. ### exec · 2026-09-23T05:20:08.688Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/baseline.log && ps -eo pid,etime,pcpu,rss,args | head -15\",max_output_tokens:2000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\nfor name in ['train_listens','target_users','candidate_catalog']:\\n p=pq.ParquetFile('/task/data/'+name+'.parquet')\\n print(name,p.metadata.num_rows)\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:20:09.233Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"8fb53c","wall_time_seconds":0.0000026,"exit_code":0,"original_token_count":35,"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/bin/sh: 1: ps: not found\n"} {"chunk_id":"9c8224","wall_time_seconds":0.336789729,"exit_code":0,"original_token_count":17,"output":"train_listens 43484159\ntarget_users 8456\ncandidate_catalog 30000\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36179cd6487d2b9b8cdb86d3574db", "usage": { "input_tokens": 43795, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 144, "total_tokens": 44148 }, "turn_token_usage": { "input_tokens": 242883, "cached_input_tokens": 210048, "cache_write_input_tokens": 0, "output_tokens": 1082, "reasoning_output_tokens": 162, "total_tokens": 243965 }, "thread_token_usage": { "input_tokens": 242883, "cached_input_tokens": 210048, "cache_write_input_tokens": 0, "output_tokens": 1082, "reasoning_output_tokens": 162, "total_tokens": 243965 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-182dc6e7-5621-41b8-a5ec-d44e1ea9f0f4", "process_id": "71521", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/baseline.log && ps -eo pid,etime,pcpu,rss,args | head -15" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/baseline.log && ps -eo pid,etime,pcpu,rss,args | head -15" } ], "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\n/bin/sh: 1: ps: not found\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\n/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2600 }, "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\n/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7136f136-4b27-40fd-9f7f-0f0ebb9e3c22", "process_id": "93013", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor name in ['train_listens','target_users','candidate_catalog']:\n p=pq.ParquetFile('/task/data/'+name+'.parquet')\n print(name,p.metadata.num_rows)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor name in ['train_listens','target_users','candidate_catalog']:\n p=pq.ParquetFile('/task/data/'+name+'.parquet')\n print(name,p.metadata.num_rows)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens 43484159\ntarget_users 8456\ncandidate_catalog 30000\n", "stderr": "", "aggregated_output": "train_listens 43484159\ntarget_users 8456\ncandidate_catalog 30000\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 336797229 }, "formatted_output": "train_listens 43484159\ntarget_users 8456\ncandidate_catalog 30000\n" } } ## Step 10 · agent · 2026-09-23T05:20:11.764Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:20:15.909Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 2 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.log\",yield_time_ms:1000}));\ntext(await tools.exec_command({cmd:\"cat /proc/meminfo | head -4 && tail -6 /app/solution/baseline.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:20:17.114Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"cb75e6","wall_time_seconds":1.0006988,"session_id":10216,"original_token_count":0,"output":""} {"chunk_id":"dc3a21","wall_time_seconds":0.000004891,"exit_code":0,"original_token_count":90,"output":"MemTotal: 15988972 kB\nMemFree: 985044 kB\nMemAvailable: 14060312 kB\nBuffers: 227776 kB\nloaded 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\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36189779c87d293a246e274cbc630", "usage": { "input_tokens": 44309, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 178, "reasoning_output_tokens": 30, "total_tokens": 44487 }, "turn_token_usage": { "input_tokens": 287192, "cached_input_tokens": 253696, "cache_write_input_tokens": 0, "output_tokens": 1260, "reasoning_output_tokens": 192, "total_tokens": 288452 }, "thread_token_usage": { "input_tokens": 287192, "cached_input_tokens": 253696, "cache_write_input_tokens": 0, "output_tokens": 1260, "reasoning_output_tokens": 192, "total_tokens": 288452 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d9a80869-2734-4ee8-87c4-90c26dfb7725", "process_id": "52411", "command": [ "/bin/sh", "-lc", "cat /proc/meminfo | head -4 && tail -6 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /proc/meminfo | head -4 && tail -6 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "MemTotal: 15988972 kB\nMemFree: 985044 kB\nMemAvailable: 14060312 kB\nBuffers: 227776 kB\nloaded 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\n", "stderr": "", "aggregated_output": "MemTotal: 15988972 kB\nMemFree: 985044 kB\nMemAvailable: 14060312 kB\nBuffers: 227776 kB\nloaded 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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4891 }, "formatted_output": "MemTotal: 15988972 kB\nMemFree: 985044 kB\nMemAvailable: 14060312 kB\nBuffers: 227776 kB\nloaded 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\n" } } ## Step 11 · agent · 2026-09-23T05:20:28.998Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:14.458Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/edit_sampler.py <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\ns=s.replace(' negative_filter_scope: str = \\\"full-training-history\\\"',' negative_filter_scope: str = \\\"full-training-history\\\"\\\\n hard_negatives: int = 16\\\\n hard_pool_size: int = 1024\\\\n hard_warmup_batches: int = 25\\\\n ranking_loss: str = \\\"bce\\\"')\\npos=s.index('\\\\ndef event_sequence(')\\ns=s[:pos]+'''\\n@torch.no_grad()\\ndef mine_hard_negatives(\\n model, prepared, config, sequence_vectors, flat_states, flat_activity,\\n positive_items, user_rows, known_positive_packed, negatives,\\n negative_weights, generator, device,\\n):\\n \\\"\\\"\\\"Mine a shared pool with the current towers; keep random coverage.\\n\\n Mining is detached, uses only fitting-history data, and masks the same\\n complete training-positive prefix and active likes as the random sampler.\\n The selected items are encoded again with gradients in the ordinary loss.\\n \\\"\\\"\\\"\\n n_items = len(prepared.item_ids)\\n uniform = torch.randint(n_items, (config.hard_pool_size,), generator=generator)\\n batch_targets = torch.unique(positive_items.cpu())\\n if len(batch_targets) > config.hard_pool_size:\\n batch_targets = batch_targets[torch.randperm(len(batch_targets), generator=generator)[:config.hard_pool_size]]\\n pool = torch.unique(torch.cat([uniform, batch_targets]))\\n pool_np = pool.numpy()\\n # Deterministic tower scores avoid selecting dropout noise.\\n was_training = model.training\\n model.eval()\\n liked, disliked, _ = pool_explicit_states(model, prepared, flat_states, device)\\n counts = prepared.state_counts[flat_states]\\n numeric = np.zeros((len(flat_states), 6), dtype=np.float32)\\n numeric[:, :4] = flat_activity\\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\\n queries = user_queries(model, sequence_vectors.detach(), liked, disliked, torch.from_numpy(numeric).to(device))\\n scores = queries @ model.encode_items(pool.to(device)).T\\n invalid = ((known_positive_packed[user_rows[:, None], pool_np[None, :] >> 3] >> (pool_np[None, :] & 7)) & 1).astype(bool)\\n lookup = np.full(n_items + 1, -1, dtype=np.int64)\\n lookup[pool_np] = np.arange(len(pool_np))\\n # Exclude positives, active likes, and retained random negatives.\\n excluded = np.concatenate([positive_items.cpu().numpy()[:, None], prepared.liked_states[flat_states], negatives[:, :-config.hard_negatives].cpu().numpy()], axis=1)\\n columns = lookup[excluded]\\n rr, cc = np.nonzero(columns >= 0)\\n invalid[rr, columns[rr, cc]] = True\\n scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\\n values, selected = torch.topk(scores, config.hard_negatives, dim=1)\\n if not torch.isfinite(values).all():\\n raise RuntimeError(\\\"insufficient legal hard-negative candidates\\\")\\n negatives[:, -config.hard_negatives:] = pool.to(device)[selected]\\n negative_weights[:, -config.hard_negatives:] = 1.0\\n model.train(was_training)\\n return negatives, negative_weights\\n\\n''' +s[pos:]\\nneedle=''' optimizer.zero_grad(set_to_none=True)\\n statistics, sequence_gradient'''\\ns=s.replace(needle,''' if config.hard_negatives and batches >= config.hard_warmup_batches:\\n negatives, negative_weights = mine_hard_negatives(\\n model, prepared, config, sequence_vectors, flat_states,\\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\\n positive_items, flat_user_rows, known_positive_packed,\\n negatives, negative_weights, generator, device,\\n )\\n optimizer.zero_grad(set_to_none=True)\\n statistics, sequence_gradient''')\\ns=s.replace(''' loss_sum = (\\n selected_weights * (positive_loss + negative_loss) / 2.0\\n ).sum()''',''' if config.ranking_loss == \\\"softmax\\\" and backward:\\n row_loss = torch.logsumexp(torch.cat([positive_logits[:, None], negative_logits], dim=1), dim=1) - positive_logits\\n elif config.ranking_loss == \\\"pairwise\\\" and backward:\\n row_loss = F.softplus(negative_logits - positive_logits[:, None]).mean(dim=1)\\n else:\\n row_loss = (positive_loss + negative_loss) / 2.0\\n loss_sum = (selected_weights * row_loss).sum()''')\\ns=s.replace(' parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)',''' parser.add_argument(\\\"--hard-negatives\\\", type=int, default=Config.hard_negatives)\\n parser.add_argument(\\\"--hard-pool-size\\\", type=int, default=Config.hard_pool_size)\\n parser.add_argument(\\\"--hard-warmup-batches\\\", type=int, default=Config.hard_warmup_batches)\\n parser.add_argument(\\\"--ranking-loss\\\", choices=(\\\"bce\\\", \\\"pairwise\\\", \\\"softmax\\\"), default=Config.ranking_loss)\\n parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)''')\\ns=s.replace(' seed=args.seed,',''' hard_negatives=args.hard_negatives,\\n hard_pool_size=args.hard_pool_size,\\n hard_warmup_batches=args.hard_warmup_batches,\\n ranking_loss=args.ranking_loss,\\n seed=args.seed,''')\\ns=s.replace(' public = args.public_dir.resolve()',''' if not 0 <= args.hard_negatives <= Config.uniform_negatives + Config.inbatch_negatives:\\n parser.error(\\\"hard negatives must be between zero and the total negative count\\\")\\n if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":\\n parser.error(\\\"hard mining requires the full training-history filter\\\")\\n public = args.public_dir.resolve()''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/edit_sampler.py\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\ntail -8 /app/solution/baseline.log\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:21:14.776Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"c5cbd5","wall_time_seconds":0.142614076,"exit_code":0,"original_token_count":324,"output":"Traceback (most recent call last):\n File \"/app/solution/edit_sampler.py\", line 90, in \n p.write_text(s)\n File \"/usr/local/lib/python3.12/pathlib.py\", line 1047, in write_text\n with self.open(mode='w', encoding=encoding, errors=errors, newline=newline) as f:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/pathlib.py\", line 1013, in open\n return io.open(self, mode, buffering, encoding, errors, newline)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nPermissionError: [Errno 13] Permission denied: '/app/solution/train_current_model.py'\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361914ab087d2a3280a07499fc144", "usage": { "input_tokens": 44709, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 1859, "reasoning_output_tokens": 331, "total_tokens": 46568 }, "turn_token_usage": { "input_tokens": 331901, "cached_input_tokens": 297856, "cache_write_input_tokens": 0, "output_tokens": 3119, "reasoning_output_tokens": 523, "total_tokens": 335020 }, "thread_token_usage": { "input_tokens": 331901, "cached_input_tokens": 297856, "cache_write_input_tokens": 0, "output_tokens": 3119, "reasoning_output_tokens": 523, "total_tokens": 335020 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7996417b-18c6-4e98-b34a-5ff925a55797", "process_id": "3542", "command": [ "/bin/sh", "-lc", "cat > /app/solution/edit_sampler.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' negative_filter_scope: str = \"full-training-history\"',' negative_filter_scope: str = \"full-training-history\"\\n hard_negatives: int = 16\\n hard_pool_size: int = 1024\\n hard_warmup_batches: int = 25\\n ranking_loss: str = \"bce\"')\npos=s.index('\\ndef event_sequence(')\ns=s[:pos]+'''\n@torch.no_grad()\ndef mine_hard_negatives(\n model, prepared, config, sequence_vectors, flat_states, flat_activity,\n positive_items, user_rows, known_positive_packed, negatives,\n negative_weights, generator, device,\n):\n \"\"\"Mine a shared pool with the current towers; keep random coverage.\n\n Mining is detached, uses only fitting-history data, and masks the same\n complete training-positive prefix and active likes as the random sampler.\n The selected items are encoded again with gradients in the ordinary loss.\n \"\"\"\n n_items = len(prepared.item_ids)\n uniform = torch.randint(n_items, (config.hard_pool_size,), generator=generator)\n batch_targets = torch.unique(positive_items.cpu())\n if len(batch_targets) > config.hard_pool_size:\n batch_targets = batch_targets[torch.randperm(len(batch_targets), generator=generator)[:config.hard_pool_size]]\n pool = torch.unique(torch.cat([uniform, batch_targets]))\n pool_np = pool.numpy()\n # Deterministic tower scores avoid selecting dropout noise.\n was_training = model.training\n model.eval()\n liked, disliked, _ = pool_explicit_states(model, prepared, flat_states, device)\n counts = prepared.state_counts[flat_states]\n numeric = np.zeros((len(flat_states), 6), dtype=np.float32)\n numeric[:, :4] = flat_activity\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n queries = user_queries(model, sequence_vectors.detach(), liked, disliked, torch.from_numpy(numeric).to(device))\n scores = queries @ model.encode_items(pool.to(device)).T\n invalid = ((known_positive_packed[user_rows[:, None], pool_np[None, :] >> 3] >> (pool_np[None, :] & 7)) & 1).astype(bool)\n lookup = np.full(n_items + 1, -1, dtype=np.int64)\n lookup[pool_np] = np.arange(len(pool_np))\n # Exclude positives, active likes, and retained random negatives.\n excluded = np.concatenate([positive_items.cpu().numpy()[:, None], prepared.liked_states[flat_states], negatives[:, :-config.hard_negatives].cpu().numpy()], axis=1)\n columns = lookup[excluded]\n rr, cc = np.nonzero(columns >= 0)\n invalid[rr, columns[rr, cc]] = True\n scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n values, selected = torch.topk(scores, config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"insufficient legal hard-negative candidates\")\n negatives[:, -config.hard_negatives:] = pool.to(device)[selected]\n negative_weights[:, -config.hard_negatives:] = 1.0\n model.train(was_training)\n return negatives, negative_weights\n\n''' +s[pos:]\nneedle=''' optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient'''\ns=s.replace(needle,''' if config.hard_negatives and batches >= config.hard_warmup_batches:\n negatives, negative_weights = mine_hard_negatives(\n model, prepared, config, sequence_vectors, flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items, flat_user_rows, known_positive_packed,\n negatives, negative_weights, generator, device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient''')\ns=s.replace(''' loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()''',''' if config.ranking_loss == \"softmax\" and backward:\n row_loss = torch.logsumexp(torch.cat([positive_logits[:, None], negative_logits], dim=1), dim=1) - positive_logits\n elif config.ranking_loss == \"pairwise\" and backward:\n row_loss = F.softplus(negative_logits - positive_logits[:, None]).mean(dim=1)\n else:\n row_loss = (positive_loss + negative_loss) / 2.0\n loss_sum = (selected_weights * row_loss).sum()''')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)',''' parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)\n parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n parser.add_argument(\"--hard-warmup-batches\", type=int, default=Config.hard_warmup_batches)\n parser.add_argument(\"--ranking-loss\", choices=(\"bce\", \"pairwise\", \"softmax\"), default=Config.ranking_loss)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)''')\ns=s.replace(' seed=args.seed,',''' hard_negatives=args.hard_negatives,\n hard_pool_size=args.hard_pool_size,\n hard_warmup_batches=args.hard_warmup_batches,\n ranking_loss=args.ranking_loss,\n seed=args.seed,''')\ns=s.replace(' public = args.public_dir.resolve()',''' if not 0 <= args.hard_negatives <= Config.uniform_negatives + Config.inbatch_negatives:\n parser.error(\"hard negatives must be between zero and the total negative count\")\n if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n parser.error(\"hard mining requires the full training-history filter\")\n public = args.public_dir.resolve()''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/edit_sampler.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -8 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/edit_sampler.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' negative_filter_scope: str = \"full-training-history\"',' negative_filter_scope: str = \"full-training-history\"\\n hard_negatives: int = 16\\n hard_pool_size: int = 1024\\n hard_warmup_batches: int = 25\\n ranking_loss: str = \"bce\"')\npos=s.index('\\ndef event_sequence(')\ns=s[:pos]+'''\n@torch.no_grad()\ndef mine_hard_negatives(\n model, prepared, config, sequence_vectors, flat_states, flat_activity,\n positive_items, user_rows, known_positive_packed, negatives,\n negative_weights, generator, device,\n):\n \"\"\"Mine a shared pool with the current towers; keep random coverage.\n\n Mining is detached, uses only fitting-history data, and masks the same\n complete training-positive prefix and active likes as the random sampler.\n The selected items are encoded again with gradients in the ordinary loss.\n \"\"\"\n n_items = len(prepared.item_ids)\n uniform = torch.randint(n_items, (config.hard_pool_size,), generator=generator)\n batch_targets = torch.unique(positive_items.cpu())\n if len(batch_targets) > config.hard_pool_size:\n batch_targets = batch_targets[torch.randperm(len(batch_targets), generator=generator)[:config.hard_pool_size]]\n pool = torch.unique(torch.cat([uniform, batch_targets]))\n pool_np = pool.numpy()\n # Deterministic tower scores avoid selecting dropout noise.\n was_training = model.training\n model.eval()\n liked, disliked, _ = pool_explicit_states(model, prepared, flat_states, device)\n counts = prepared.state_counts[flat_states]\n numeric = np.zeros((len(flat_states), 6), dtype=np.float32)\n numeric[:, :4] = flat_activity\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n queries = user_queries(model, sequence_vectors.detach(), liked, disliked, torch.from_numpy(numeric).to(device))\n scores = queries @ model.encode_items(pool.to(device)).T\n invalid = ((known_positive_packed[user_rows[:, None], pool_np[None, :] >> 3] >> (pool_np[None, :] & 7)) & 1).astype(bool)\n lookup = np.full(n_items + 1, -1, dtype=np.int64)\n lookup[pool_np] = np.arange(len(pool_np))\n # Exclude positives, active likes, and retained random negatives.\n excluded = np.concatenate([positive_items.cpu().numpy()[:, None], prepared.liked_states[flat_states], negatives[:, :-config.hard_negatives].cpu().numpy()], axis=1)\n columns = lookup[excluded]\n rr, cc = np.nonzero(columns >= 0)\n invalid[rr, columns[rr, cc]] = True\n scores.masked_fill_(torch.from_numpy(invalid).to(device), -torch.inf)\n values, selected = torch.topk(scores, config.hard_negatives, dim=1)\n if not torch.isfinite(values).all():\n raise RuntimeError(\"insufficient legal hard-negative candidates\")\n negatives[:, -config.hard_negatives:] = pool.to(device)[selected]\n negative_weights[:, -config.hard_negatives:] = 1.0\n model.train(was_training)\n return negatives, negative_weights\n\n''' +s[pos:]\nneedle=''' optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient'''\ns=s.replace(needle,''' if config.hard_negatives and batches >= config.hard_warmup_batches:\n negatives, negative_weights = mine_hard_negatives(\n model, prepared, config, sequence_vectors, flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items, flat_user_rows, known_positive_packed,\n negatives, negative_weights, generator, device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient''')\ns=s.replace(''' loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()''',''' if config.ranking_loss == \"softmax\" and backward:\n row_loss = torch.logsumexp(torch.cat([positive_logits[:, None], negative_logits], dim=1), dim=1) - positive_logits\n elif config.ranking_loss == \"pairwise\" and backward:\n row_loss = F.softplus(negative_logits - positive_logits[:, None]).mean(dim=1)\n else:\n row_loss = (positive_loss + negative_loss) / 2.0\n loss_sum = (selected_weights * row_loss).sum()''')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)',''' parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)\n parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n parser.add_argument(\"--hard-warmup-batches\", type=int, default=Config.hard_warmup_batches)\n parser.add_argument(\"--ranking-loss\", choices=(\"bce\", \"pairwise\", \"softmax\"), default=Config.ranking_loss)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)''')\ns=s.replace(' seed=args.seed,',''' hard_negatives=args.hard_negatives,\n hard_pool_size=args.hard_pool_size,\n hard_warmup_batches=args.hard_warmup_batches,\n ranking_loss=args.ranking_loss,\n seed=args.seed,''')\ns=s.replace(' public = args.public_dir.resolve()',''' if not 0 <= args.hard_negatives <= Config.uniform_negatives + Config.inbatch_negatives:\n parser.error(\"hard negatives must be between zero and the total negative count\")\n if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":\n parser.error(\"hard mining requires the full training-history filter\")\n public = args.public_dir.resolve()''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/edit_sampler.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -8 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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", "stderr": "", "aggregated_output": "prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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", "exit_code": 0, "duration": { "secs": 0, "nanos": 142618827 }, "formatted_output": "prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fbce95d9-5d28-49b8-85d8-a1514215657f", "process_id": "10216", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 2 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_cache.npz --prepare-only --threads 2 > /app/solution/prepare_submission.json 2> /app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 60, "nanos": 135112735 }, "formatted_output": "" } } ## Step 12 · agent · 2026-09-23T05:21:16.916Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:22.978Z { "code": "text(await tools.exec_command({cmd:\"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_sampler.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && tail -4 /app/solution/prepare_submission.log\",max_output_tokens:2000}));\ntext(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:21:24.480Z Script completed Wall time 1.5 seconds Output: {"chunk_id":"ae7fa3","wall_time_seconds":0.156055988,"exit_code":0,"original_token_count":77,"output":"prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=49.8s\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_cache.npz\n"} {"chunk_id":"3a67e2","wall_time_seconds":1.000908029,"session_id":90104,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361cb130c87d2801b33f03844ef1e", "usage": { "input_tokens": 46995, "cached_input_tokens": 44544, "cache_write_input_tokens": 0, "output_tokens": 223, "reasoning_output_tokens": 12, "total_tokens": 47218 }, "turn_token_usage": { "input_tokens": 378896, "cached_input_tokens": 342400, "cache_write_input_tokens": 0, "output_tokens": 3342, "reasoning_output_tokens": 535, "total_tokens": 382238 }, "thread_token_usage": { "input_tokens": 378896, "cached_input_tokens": 342400, "cache_write_input_tokens": 0, "output_tokens": 3342, "reasoning_output_tokens": 535, "total_tokens": 382238 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1e691e2d-26ca-4865-9715-d535bfc01a2f", "process_id": "72999", "command": [ "/bin/sh", "-lc", "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_sampler.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && tail -4 /app/solution/prepare_submission.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py && /opt/task-venv/bin/python /app/solution/edit_sampler.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && tail -4 /app/solution/prepare_submission.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=49.8s\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_cache.npz\n", "stderr": "", "aggregated_output": "prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=49.8s\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_cache.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 156041377 }, "formatted_output": "prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=49.8s\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_cache.npz\n" } } ## Step 13 · agent · 2026-09-23T05:21:31.847Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:21:53.815Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/rank_checkpoint.py <<'PY'\\n\\\"\\\"\\\"Rank with a saved two-tower checkpoint and stage-correct observed history.\\\"\\\"\\\"\\nimport argparse\\nfrom pathlib import Path\\nimport torch\\nimport train_current_model as m\\np=argparse.ArgumentParser()\\np.add_argument('--checkpoint',type=Path,required=True)\\np.add_argument('--stage',choices=['validation','submission'],required=True)\\np.add_argument('--output',type=Path,required=True)\\np.add_argument('--threads',type=int,default=4)\\na=p.parse_args()\\ntorch.set_num_threads(a.threads)\\ntorch.set_num_interop_threads(1)\\npayload=torch.load(a.checkpoint,map_location='cpu',weights_only=False)\\nc=m.Config(**payload['config'])\\ndata,eligible=m.load_or_prepare(Path('/task'),a.stage,c,Path('/app/solution')/(a.stage+'_cache.npz'))\\nmodel=m.base.SequentialTwoTower(data.item_features,m.model_config(c))\\nmodel.load_state_dict(payload['model_state'])\\nmodel.eval()\\nranking,diagnostics=m.rank_candidates(model,data,eligible,c,torch.device('cpu'),64)\\nm.write_ranking(ranking,a.output)\\nprint(diagnostics)\\nPY\\ncat > /app/solution/test_hard_sampler.py <<'PY'\\n\\\"\\\"\\\"Check mining filters, uniqueness, detached selection, and deterministic output.\\\"\\\"\\\"\\nfrom types import SimpleNamespace\\nimport numpy as np\\nimport torch\\nimport train_current_model as m\\n\\ntorch.set_num_threads(1)\\nm.seed_everything(10)\\nn=200\\nc=m.Config(hard_negatives=8,hard_pool_size=128)\\nmodel=m.base.SequentialTwoTower(np.zeros((n,4),np.float32),m.model_config(c))\\ndata=SimpleNamespace(item_ids=np.arange(n),liked_states=np.array([[12,13,n],[14,15,n]]),disliked_states=np.full((2,3),n),state_counts=np.ones((2,2)))\\nknown=np.zeros((2,(n+7)//8),np.uint8)\\nfor r in range(2):\\n for i in range(r*10,r*10+10): known[r,i>>3] |= 1 << (i&7)\\npos=torch.tensor([11,16])\\nneg=torch.tensor([list(range(30,62)),list(range(70,102))])\\nargs=(model,data,c,torch.randn(2,192),np.arange(2),np.zeros((2,4),np.float32),pos,np.arange(2),known)\\nresults=[]\\nfor _ in range(2):\\n result,weights=m.mine_hard_negatives(*args,neg.clone(),torch.ones((2,32)),torch.Generator().manual_seed(19),torch.device('cpu'))\\n assert not m.invalid_full_history_negatives(result.numpy(),pos.numpy(),np.arange(2),known,data.liked_states).any()\\n assert torch.equal(result[:,:24],neg[:,:24])\\n assert all(p.grad is None for p in model.parameters())\\n assert model.training\\n results.append(result)\\nassert torch.equal(*results)\\nprint('Hard sampler invariants passed')\\nPY\\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\\ntail -3 /app/solution/baseline.log\\ntail -3 /app/solution/hard_bce.log\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:21:59.736Z Script completed Wall time 5.9 seconds Output: {"chunk_id":"ba9ba2","wall_time_seconds":5.737426049,"exit_code":0,"original_token_count":117,"output":"Hard sampler invariants passed\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\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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361d4a52887d2adc6d49c9a1da35f", "usage": { "input_tokens": 47395, "cached_input_tokens": 46848, "cache_write_input_tokens": 0, "output_tokens": 928, "reasoning_output_tokens": 169, "total_tokens": 48323 }, "turn_token_usage": { "input_tokens": 426291, "cached_input_tokens": 389248, "cache_write_input_tokens": 0, "output_tokens": 4270, "reasoning_output_tokens": 704, "total_tokens": 430561 }, "thread_token_usage": { "input_tokens": 426291, "cached_input_tokens": 389248, "cache_write_input_tokens": 0, "output_tokens": 4270, "reasoning_output_tokens": 704, "total_tokens": 430561 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ff477d03-5b07-47d0-9f5d-7ade68fb342d", "process_id": "41548", "command": [ "/bin/sh", "-lc", "cat > /app/solution/rank_checkpoint.py <<'PY'\n\"\"\"Rank with a saved two-tower checkpoint and stage-correct observed history.\"\"\"\nimport argparse\nfrom pathlib import Path\nimport torch\nimport train_current_model as m\np=argparse.ArgumentParser()\np.add_argument('--checkpoint',type=Path,required=True)\np.add_argument('--stage',choices=['validation','submission'],required=True)\np.add_argument('--output',type=Path,required=True)\np.add_argument('--threads',type=int,default=4)\na=p.parse_args()\ntorch.set_num_threads(a.threads)\ntorch.set_num_interop_threads(1)\npayload=torch.load(a.checkpoint,map_location='cpu',weights_only=False)\nc=m.Config(**payload['config'])\ndata,eligible=m.load_or_prepare(Path('/task'),a.stage,c,Path('/app/solution')/(a.stage+'_cache.npz'))\nmodel=m.base.SequentialTwoTower(data.item_features,m.model_config(c))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nranking,diagnostics=m.rank_candidates(model,data,eligible,c,torch.device('cpu'),64)\nm.write_ranking(ranking,a.output)\nprint(diagnostics)\nPY\ncat > /app/solution/test_hard_sampler.py <<'PY'\n\"\"\"Check mining filters, uniqueness, detached selection, and deterministic output.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as m\n\ntorch.set_num_threads(1)\nm.seed_everything(10)\nn=200\nc=m.Config(hard_negatives=8,hard_pool_size=128)\nmodel=m.base.SequentialTwoTower(np.zeros((n,4),np.float32),m.model_config(c))\ndata=SimpleNamespace(item_ids=np.arange(n),liked_states=np.array([[12,13,n],[14,15,n]]),disliked_states=np.full((2,3),n),state_counts=np.ones((2,2)))\nknown=np.zeros((2,(n+7)//8),np.uint8)\nfor r in range(2):\n for i in range(r*10,r*10+10): known[r,i>>3] |= 1 << (i&7)\npos=torch.tensor([11,16])\nneg=torch.tensor([list(range(30,62)),list(range(70,102))])\nargs=(model,data,c,torch.randn(2,192),np.arange(2),np.zeros((2,4),np.float32),pos,np.arange(2),known)\nresults=[]\nfor _ in range(2):\n result,weights=m.mine_hard_negatives(*args,neg.clone(),torch.ones((2,32)),torch.Generator().manual_seed(19),torch.device('cpu'))\n assert not m.invalid_full_history_negatives(result.numpy(),pos.numpy(),np.arange(2),known,data.liked_states).any()\n assert torch.equal(result[:,:24],neg[:,:24])\n assert all(p.grad is None for p in model.parameters())\n assert model.training\n results.append(result)\nassert torch.equal(*results)\nprint('Hard sampler invariants passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -3 /app/solution/baseline.log\ntail -3 /app/solution/hard_bce.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/rank_checkpoint.py <<'PY'\n\"\"\"Rank with a saved two-tower checkpoint and stage-correct observed history.\"\"\"\nimport argparse\nfrom pathlib import Path\nimport torch\nimport train_current_model as m\np=argparse.ArgumentParser()\np.add_argument('--checkpoint',type=Path,required=True)\np.add_argument('--stage',choices=['validation','submission'],required=True)\np.add_argument('--output',type=Path,required=True)\np.add_argument('--threads',type=int,default=4)\na=p.parse_args()\ntorch.set_num_threads(a.threads)\ntorch.set_num_interop_threads(1)\npayload=torch.load(a.checkpoint,map_location='cpu',weights_only=False)\nc=m.Config(**payload['config'])\ndata,eligible=m.load_or_prepare(Path('/task'),a.stage,c,Path('/app/solution')/(a.stage+'_cache.npz'))\nmodel=m.base.SequentialTwoTower(data.item_features,m.model_config(c))\nmodel.load_state_dict(payload['model_state'])\nmodel.eval()\nranking,diagnostics=m.rank_candidates(model,data,eligible,c,torch.device('cpu'),64)\nm.write_ranking(ranking,a.output)\nprint(diagnostics)\nPY\ncat > /app/solution/test_hard_sampler.py <<'PY'\n\"\"\"Check mining filters, uniqueness, detached selection, and deterministic output.\"\"\"\nfrom types import SimpleNamespace\nimport numpy as np\nimport torch\nimport train_current_model as m\n\ntorch.set_num_threads(1)\nm.seed_everything(10)\nn=200\nc=m.Config(hard_negatives=8,hard_pool_size=128)\nmodel=m.base.SequentialTwoTower(np.zeros((n,4),np.float32),m.model_config(c))\ndata=SimpleNamespace(item_ids=np.arange(n),liked_states=np.array([[12,13,n],[14,15,n]]),disliked_states=np.full((2,3),n),state_counts=np.ones((2,2)))\nknown=np.zeros((2,(n+7)//8),np.uint8)\nfor r in range(2):\n for i in range(r*10,r*10+10): known[r,i>>3] |= 1 << (i&7)\npos=torch.tensor([11,16])\nneg=torch.tensor([list(range(30,62)),list(range(70,102))])\nargs=(model,data,c,torch.randn(2,192),np.arange(2),np.zeros((2,4),np.float32),pos,np.arange(2),known)\nresults=[]\nfor _ in range(2):\n result,weights=m.mine_hard_negatives(*args,neg.clone(),torch.ones((2,32)),torch.Generator().manual_seed(19),torch.device('cpu'))\n assert not m.invalid_full_history_negatives(result.numpy(),pos.numpy(),np.arange(2),known,data.liked_states).any()\n assert torch.equal(result[:,:24],neg[:,:24])\n assert all(p.grad is None for p in model.parameters())\n assert model.training\n results.append(result)\nassert torch.equal(*results)\nprint('Hard sampler invariants passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -3 /app/solution/baseline.log\ntail -3 /app/solution/hard_bce.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard sampler invariants passed\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\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", "stderr": "", "aggregated_output": "Hard sampler invariants passed\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\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", "exit_code": 0, "duration": { "secs": 5, "nanos": 737485553 }, "formatted_output": "Hard sampler invariants passed\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\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n" } } ## Step 14 · agent · 2026-09-23T05:22:02.818Z ### Thinking unavailable — no text recorded The hard-negative sampler now scores a shared candidate pool and replaces half of each row’s negatives with high-scoring items. It retains random coverage and excludes known positives, active likes, the target, and duplicates. Those checks passed in a focused test; the baseline and first changed model are training now. ### exec · 2026-09-23T05:22:06.692Z { "code": "text(await tools.exec_command({cmd:\"date -u && ls -lh /app/solution/*.npz && tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard_bce.log\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:22:06.772Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"aef270","wall_time_seconds":0.00000589,"exit_code":0,"original_token_count":255,"output":"Wed Sep 23 05:22:06 UTC 2026\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 /app/solution/submission_cache.npz\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 /app/solution/validation_cache.npz\nsaved public-derived full-sequence cache: /app/solution/validation_cache.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\nloaded 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"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361f7e49487d2a07baeb39c884b71", "usage": { "input_tokens": 48531, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 172, "reasoning_output_tokens": 38, "total_tokens": 48703 }, "turn_token_usage": { "input_tokens": 474822, "cached_input_tokens": 436480, "cache_write_input_tokens": 0, "output_tokens": 4442, "reasoning_output_tokens": 742, "total_tokens": 479264 }, "thread_token_usage": { "input_tokens": 474822, "cached_input_tokens": 436480, "cache_write_input_tokens": 0, "output_tokens": 4442, "reasoning_output_tokens": 742, "total_tokens": 479264 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-08ad0713-ffaf-41e2-885a-1eb4d6ad2061", "process_id": "38409", "command": [ "/bin/sh", "-lc", "date -u && ls -lh /app/solution/*.npz && tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard_bce.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "date -u && ls -lh /app/solution/*.npz && tail -5 /app/solution/baseline.log && tail -5 /app/solution/hard_bce.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Wed Sep 23 05:22:06 UTC 2026\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 /app/solution/submission_cache.npz\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 /app/solution/validation_cache.npz\nsaved public-derived full-sequence cache: /app/solution/validation_cache.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\nloaded 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", "stderr": "", "aggregated_output": "Wed Sep 23 05:22:06 UTC 2026\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 /app/solution/submission_cache.npz\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 /app/solution/validation_cache.npz\nsaved public-derived full-sequence cache: /app/solution/validation_cache.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\nloaded 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5890 }, "formatted_output": "Wed Sep 23 05:22:06 UTC 2026\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 /app/solution/submission_cache.npz\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 /app/solution/validation_cache.npz\nsaved public-derived full-sequence cache: /app/solution/validation_cache.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\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n" } } ## Step 15 · agent · 2026-09-23T05:22:10.064Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:13.152Z { "code": "text(await tools.write_stdin({session_id:59071,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:90104,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"du -sh /app/solution && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.max\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:22:23.213Z Script completed Wall time 10.1 seconds Output: {"chunk_id":"0bfa0e","wall_time_seconds":5.000817752,"session_id":59071,"original_token_count":0,"output":""} {"chunk_id":"17a767","wall_time_seconds":5.001605547,"session_id":90104,"original_token_count":0,"output":""} {"chunk_id":"b4b054","wall_time_seconds":0.00000525,"exit_code":0,"original_token_count":11,"output":"1.1G\t/app/solution\n7157739520\n8589934592\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab361fefd5c87d2803fdbf45e375fdd", "usage": { "input_tokens": 49100, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 166, "reasoning_output_tokens": 53, "total_tokens": 49266 }, "turn_token_usage": { "input_tokens": 523922, "cached_input_tokens": 484864, "cache_write_input_tokens": 0, "output_tokens": 4608, "reasoning_output_tokens": 795, "total_tokens": 528530 }, "thread_token_usage": { "input_tokens": 523922, "cached_input_tokens": 484864, "cache_write_input_tokens": 0, "output_tokens": 4608, "reasoning_output_tokens": 795, "total_tokens": 528530 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a010e0c0-b470-4340-84aa-24f7bd45877d", "process_id": "59399", "command": [ "/bin/sh", "-lc", "du -sh /app/solution && cat /sys/fs/cgroup/memory.current && cat /sys/fs/cgroup/memory.max" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "du -sh /app/solution", "path": "solution" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.current", "name": "memory.current", "path": "/sys/fs/cgroup/memory.current" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.max", "name": "memory.max", "path": "/sys/fs/cgroup/memory.max" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1.1G\t/app/solution\n7157739520\n8589934592\n", "stderr": "", "aggregated_output": "1.1G\t/app/solution\n7157739520\n8589934592\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5250 }, "formatted_output": "1.1G\t/app/solution\n7157739520\n8589934592\n" } } ## Step 16 · agent · 2026-09-23T05:22:35.019Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:22:39.807Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().replace(b'\\\\x00',b' ').decode()\\n if 'train_current_model.py --stage' in cmd and 'python' in cmd and not '/bin/sh' in cmd:\\n status=(p/'status').read_text().splitlines()\\n print(p.name,cmd[:160],*[l for l in status if l.startswith(('VmRSS:','Threads:'))])\\n except (OSError,UnicodeError): pass\\nPY\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:22:40.018Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"637384","wall_time_seconds":0.00000377,"exit_code":0,"original_token_count":98,"output":"282 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet VmRSS:\t 4306128 kB Threads:\t21\n389 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepar VmRSS:\t 2720428 kB Threads:\t15\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3620f613887d2a0e837c645b590b3", "usage": { "input_tokens": 49411, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 515, "reasoning_output_tokens": 343, "total_tokens": 49926 }, "turn_token_usage": { "input_tokens": 573333, "cached_input_tokens": 533760, "cache_write_input_tokens": 0, "output_tokens": 5123, "reasoning_output_tokens": 1138, "total_tokens": 578456 }, "thread_token_usage": { "input_tokens": 573333, "cached_input_tokens": 533760, "cache_write_input_tokens": 0, "output_tokens": 5123, "reasoning_output_tokens": 1138, "total_tokens": 578456 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cc55cf66-aa74-4949-8f94-627c1beb9feb", "process_id": "66713", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\x00',b' ').decode()\n if 'train_current_model.py --stage' in cmd and 'python' in cmd and not '/bin/sh' in cmd:\n status=(p/'status').read_text().splitlines()\n print(p.name,cmd[:160],*[l for l in status if l.startswith(('VmRSS:','Threads:'))])\n except (OSError,UnicodeError): pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\x00',b' ').decode()\n if 'train_current_model.py --stage' in cmd and 'python' in cmd and not '/bin/sh' in cmd:\n status=(p/'status').read_text().splitlines()\n print(p.name,cmd[:160],*[l for l in status if l.startswith(('VmRSS:','Threads:'))])\n except (OSError,UnicodeError): pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "282 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet VmRSS:\t 4306128 kB Threads:\t21\n389 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepar VmRSS:\t 2720428 kB Threads:\t15\n", "stderr": "", "aggregated_output": "282 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet VmRSS:\t 4306128 kB Threads:\t21\n389 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepar VmRSS:\t 2720428 kB Threads:\t15\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3770 }, "formatted_output": "282 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet VmRSS:\t 4306128 kB Threads:\t21\n389 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepar VmRSS:\t 2720428 kB Threads:\t15\n" } } ## Step 17 · agent · 2026-09-23T05:22:47.716Z ### Thinking unavailable — no text recorded Both runs use the same 1.05 million training targets and 130,103 chronological holdout targets. The first experiment changes only negative selection, so its public score will give a direct comparison with the baseline. The final-stage history cache is also ready for refitting. ### exec · 2026-09-23T05:22:51.127Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:22:51.197Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"f8d98f","wall_time_seconds":0.00000535,"exit_code":0,"original_token_count":181,"output":"chronological 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\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\nWed Sep 23 05:22:51 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e73b8580-fbe2-404c-83de-8543fe348242", "process_id": "82024", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological 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\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\nWed Sep 23 05:22:51 UTC 2026\n", "stderr": "", "aggregated_output": "chronological 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\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\nWed Sep 23 05:22:51 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5350 }, "formatted_output": "chronological 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\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\nWed Sep 23 05:22:51 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3622029e887d2b04f4d0a1c04fff3", "usage": { "input_tokens": 50088, "cached_input_tokens": 49280, "cache_write_input_tokens": 0, "output_tokens": 274, "reasoning_output_tokens": 156, "total_tokens": 50362 }, "turn_token_usage": { "input_tokens": 623421, "cached_input_tokens": 583040, "cache_write_input_tokens": 0, "output_tokens": 5397, "reasoning_output_tokens": 1294, "total_tokens": 628818 }, "thread_token_usage": { "input_tokens": 623421, "cached_input_tokens": 583040, "cache_write_input_tokens": 0, "output_tokens": 5397, "reasoning_output_tokens": 1294, "total_tokens": 628818 } } ## Step 18 · agent · 2026-09-23T05:22:54.621Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:22:54.905Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:23:39.914Z Wall time: 45.0043 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3622b69a087d2a73c66ffd74d2c15", "usage": { "input_tokens": 50662, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 74, "reasoning_output_tokens": 53, "total_tokens": 50736 }, "turn_token_usage": { "input_tokens": 674083, "cached_input_tokens": 632960, "cache_write_input_tokens": 0, "output_tokens": 5471, "reasoning_output_tokens": 1347, "total_tokens": 679554 }, "thread_token_usage": { "input_tokens": 674083, "cached_input_tokens": 632960, "cache_write_input_tokens": 0, "output_tokens": 5471, "reasoning_output_tokens": 1347, "total_tokens": 679554 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Mycrj25W6UwTVQiIMganb71n", "durationMs": 45000 } } ### exec · 2026-09-23T05:23:43.939Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:23:44.020Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"e3d086","wall_time_seconds":0.00000616,"exit_code":0,"original_token_count":181,"output":"chronological 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\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\nWed Sep 23 05:23:43 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-65b19903-e2df-4455-8812-bea3d54ba4bd", "process_id": "79869", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/baseline.log && tail -4 /app/solution/hard_bce.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "chronological 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\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\nWed Sep 23 05:23:43 UTC 2026\n", "stderr": "", "aggregated_output": "chronological 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\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\nWed Sep 23 05:23:43 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6160 }, "formatted_output": "chronological 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\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\nWed Sep 23 05:23:43 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3625cb45c87d2a8fc8d37f335234e", "usage": { "input_tokens": 50759, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 0, "total_tokens": 50813 }, "turn_token_usage": { "input_tokens": 724842, "cached_input_tokens": 683392, "cache_write_input_tokens": 0, "output_tokens": 5525, "reasoning_output_tokens": 1347, "total_tokens": 730367 }, "thread_token_usage": { "input_tokens": 724842, "cached_input_tokens": 683392, "cache_write_input_tokens": 0, "output_tokens": 5525, "reasoning_output_tokens": 1347, "total_tokens": 730367 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-92f0d9fb-86c3-4a4f-b794-1bb6dca6c48e", "process_id": "90104", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 148, "nanos": 968115928 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a6f28e12-8c75-45b6-af56-0b6721110404", "process_id": "59071", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 239, "nanos": 908754368 }, "formatted_output": "" } } ## Step 19 · agent · 2026-09-23T05:23:55.597Z ### Thinking unavailable — no text recorded The first runs are still training. One detail matters for this experiment: the baseline downweights frequent in-batch items, which can further reduce pressure on popular competitors. The mined negatives use ordinary loss weights because they are selected deliberately for difficulty; the first run keeps the baseline BCE loss unchanged. ### exec · 2026-09-23T05:24:01.805Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace('if batches % 250 == 0:', 'if batches % 50 == 0:')\\np.write_text(s)\\nfor pid in [282,389]:\\n p=Path('/proc')/str(pid)\\n try:\\n s=(p/'stat').read_text().split()\\n print(pid,'cpu_seconds',(int(s[13])+int(s[14]))/100)\\n except OSError: pass\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:24:01.978Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"de041d","wall_time_seconds":0.00000359,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3626044c487d2b0275f1b1085d6b0", "usage": { "input_tokens": 51112, "cached_input_tokens": 50560, "cache_write_input_tokens": 0, "output_tokens": 544, "reasoning_output_tokens": 320, "total_tokens": 51656 }, "turn_token_usage": { "input_tokens": 775954, "cached_input_tokens": 733952, "cache_write_input_tokens": 0, "output_tokens": 6069, "reasoning_output_tokens": 1667, "total_tokens": 782023 }, "thread_token_usage": { "input_tokens": 775954, "cached_input_tokens": 733952, "cache_write_input_tokens": 0, "output_tokens": 6069, "reasoning_output_tokens": 1667, "total_tokens": 782023 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d314e7b0-d057-4c71-970f-15b7eec39041", "process_id": "15964", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('if batches % 250 == 0:', 'if batches % 50 == 0:')\np.write_text(s)\nfor pid in [282,389]:\n p=Path('/proc')/str(pid)\n try:\n s=(p/'stat').read_text().split()\n print(pid,'cpu_seconds',(int(s[13])+int(s[14]))/100)\n except OSError: pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('if batches % 250 == 0:', 'if batches % 50 == 0:')\np.write_text(s)\nfor pid in [282,389]:\n p=Path('/proc')/str(pid)\n try:\n s=(p/'stat').read_text().split()\n print(pid,'cpu_seconds',(int(s[13])+int(s[14]))/100)\n except OSError: pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3590 }, "formatted_output": "" } } ## Step 20 · agent · 2026-09-23T05:24:04.179Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:24:07.414Z { "code": "text(await tools.write_stdin({session_id:59071,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.write_stdin({session_id:90104,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -12 /app/solution/baseline.log && tail -12 /app/solution/hard_bce.log && ls -lh /app/solution\",max_output_tokens:3000}));\n" } ### Tool output · 2026-09-23T05:24:07.484Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"a313b0","wall_time_seconds":0.0000017,"exit_code":137,"original_token_count":0,"output":""} {"chunk_id":"710c34","wall_time_seconds":8.2e-7,"exit_code":137,"original_token_count":0,"output":""} {"chunk_id":"a47b3e","wall_time_seconds":0.000005991,"exit_code":0,"original_token_count":537,"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=41.0s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\nKilled\nloaded 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\nKilled\ntotal 1.1G\ndrwxr-xr-x 2 agent agent 4.0K Sep 23 05:21 __pycache__\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 baseline.json\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:23 baseline.log\n-rw-r--r-- 1 agent agent 5.5K Sep 23 05:21 edit_sampler.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:21 hard_bce.json\n-rw-r--r-- 1 agent agent 418 Sep 23 05:23 hard_bce.log\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 prepare_submission.json\n-rw-r--r-- 1 agent agent 891 Sep 23 05:21 prepare_submission.log\n-rw-r--r-- 1 agent agent 978 Sep 23 05:21 rank_checkpoint.py\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 submission_cache.npz\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 test_hard_sampler.py\n-rw-r--r-- 1 agent agent 83K Sep 23 05:24 train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 23 05:19 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 validation_cache.npz\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1fe4c143-c929-445e-82fd-623a02f26a5b", "process_id": "3403", "command": [ "/bin/sh", "-lc", "tail -12 /app/solution/baseline.log && tail -12 /app/solution/hard_bce.log && ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -12 /app/solution/baseline.log && tail -12 /app/solution/hard_bce.log && ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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=41.0s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\nKilled\nloaded 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\nKilled\ntotal 1.1G\ndrwxr-xr-x 2 agent agent 4.0K Sep 23 05:21 __pycache__\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 baseline.json\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:23 baseline.log\n-rw-r--r-- 1 agent agent 5.5K Sep 23 05:21 edit_sampler.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:21 hard_bce.json\n-rw-r--r-- 1 agent agent 418 Sep 23 05:23 hard_bce.log\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 prepare_submission.json\n-rw-r--r-- 1 agent agent 891 Sep 23 05:21 prepare_submission.log\n-rw-r--r-- 1 agent agent 978 Sep 23 05:21 rank_checkpoint.py\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 submission_cache.npz\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 test_hard_sampler.py\n-rw-r--r-- 1 agent agent 83K Sep 23 05:24 train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 23 05:19 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 validation_cache.npz\n", "stderr": "", "aggregated_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=41.0s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\nKilled\nloaded 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\nKilled\ntotal 1.1G\ndrwxr-xr-x 2 agent agent 4.0K Sep 23 05:21 __pycache__\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 baseline.json\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:23 baseline.log\n-rw-r--r-- 1 agent agent 5.5K Sep 23 05:21 edit_sampler.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:21 hard_bce.json\n-rw-r--r-- 1 agent agent 418 Sep 23 05:23 hard_bce.log\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 prepare_submission.json\n-rw-r--r-- 1 agent agent 891 Sep 23 05:21 prepare_submission.log\n-rw-r--r-- 1 agent agent 978 Sep 23 05:21 rank_checkpoint.py\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 submission_cache.npz\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 test_hard_sampler.py\n-rw-r--r-- 1 agent agent 83K Sep 23 05:24 train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 23 05:19 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 validation_cache.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5991 }, "formatted_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=41.0s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\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_cache.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\nKilled\nloaded 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\nKilled\ntotal 1.1G\ndrwxr-xr-x 2 agent agent 4.0K Sep 23 05:21 __pycache__\n-rw-r--r-- 1 agent agent 0 Sep 23 05:19 baseline.json\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:23 baseline.log\n-rw-r--r-- 1 agent agent 5.5K Sep 23 05:21 edit_sampler.py\n-rw-r--r-- 1 agent agent 0 Sep 23 05:21 hard_bce.json\n-rw-r--r-- 1 agent agent 418 Sep 23 05:23 hard_bce.log\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 prepare_submission.json\n-rw-r--r-- 1 agent agent 891 Sep 23 05:21 prepare_submission.log\n-rw-r--r-- 1 agent agent 978 Sep 23 05:21 rank_checkpoint.py\n-rw-r--r-- 1 agent agent 528M Sep 23 05:21 submission_cache.npz\n-rw-r--r-- 1 agent agent 1.3K Sep 23 05:21 test_hard_sampler.py\n-rw-r--r-- 1 agent agent 83K Sep 23 05:24 train_current_model.py\n-rw-r--r-- 1 agent agent 16K Sep 23 05:19 two_tower_model.py\n-rw-r--r-- 1 agent agent 511M Sep 23 05:20 validation_cache.npz\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36272328487d29650bc8e2c42dab3", "usage": { "input_tokens": 51712, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 20, "total_tokens": 51850 }, "turn_token_usage": { "input_tokens": 827666, "cached_input_tokens": 784896, "cache_write_input_tokens": 0, "output_tokens": 6207, "reasoning_output_tokens": 1687, "total_tokens": 833873 }, "thread_token_usage": { "input_tokens": 827666, "cached_input_tokens": 784896, "cache_write_input_tokens": 0, "output_tokens": 6207, "reasoning_output_tokens": 1687, "total_tokens": 833873 } } ## Step 21 · agent · 2026-09-23T05:24:12.925Z ### Thinking unavailable — no text recorded The concurrent runs exceeded the container’s 8 GB memory limit and were stopped. I’ll run them sequentially to preserve the original baseline configuration and avoid another memory failure. ### exec · 2026-09-23T05:24:17.624Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log\",yield_time_ms:1000}));\n" } ### Tool output · 2026-09-23T05:24:18.801Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"69adf6","wall_time_seconds":1.000696569,"session_id":77730,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36277b8a087d2b3c8f641543a12b9", "usage": { "input_tokens": 52753, "cached_input_tokens": 51584, "cache_write_input_tokens": 0, "output_tokens": 285, "reasoning_output_tokens": 124, "total_tokens": 53038 }, "turn_token_usage": { "input_tokens": 880419, "cached_input_tokens": 836480, "cache_write_input_tokens": 0, "output_tokens": 6492, "reasoning_output_tokens": 1811, "total_tokens": 886911 }, "thread_token_usage": { "input_tokens": 880419, "cached_input_tokens": 836480, "cache_write_input_tokens": 0, "output_tokens": 6492, "reasoning_output_tokens": 1811, "total_tokens": 886911 } } ## Step 22 · agent · 2026-09-23T05:24:31.581Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:24:31.845Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:25:16.865Z Wall time: 45.0106 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36282fd8087d29556b565d04cc5b2", "usage": { "input_tokens": 53096, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 338, "reasoning_output_tokens": 317, "total_tokens": 53434 }, "turn_token_usage": { "input_tokens": 933515, "cached_input_tokens": 889088, "cache_write_input_tokens": 0, "output_tokens": 6830, "reasoning_output_tokens": 2128, "total_tokens": 940345 }, "thread_token_usage": { "input_tokens": 933515, "cached_input_tokens": 889088, "cache_write_input_tokens": 0, "output_tokens": 6830, "reasoning_output_tokens": 2128, "total_tokens": 940345 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_gdUehIMtug5riJtxs5ybP9R2", "durationMs": 45000 } } ### exec · 2026-09-23T05:25:19.467Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/baseline.log && cat /sys/fs/cgroup/memory.current && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:25:19.535Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"720db9","wall_time_seconds":0.00000387,"exit_code":0,"original_token_count":113,"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\n3586547712\nWed Sep 23 05:25:19 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-db170d27-c8d6-4eb6-990e-fea389435509", "process_id": "42966", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/baseline.log && cat /sys/fs/cgroup/memory.current && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/baseline.log && cat /sys/fs/cgroup/memory.current && date -u" } ], "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\n3586547712\nWed Sep 23 05:25:19 UTC 2026\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\n3586547712\nWed Sep 23 05:25:19 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3870 }, "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\n3586547712\nWed Sep 23 05:25:19 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab362bd159887d285cf5e4ded48439b", "usage": { "input_tokens": 53457, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 51, "reasoning_output_tokens": 0, "total_tokens": 53508 }, "turn_token_usage": { "input_tokens": 986972, "cached_input_tokens": 941952, "cache_write_input_tokens": 0, "output_tokens": 6881, "reasoning_output_tokens": 2128, "total_tokens": 993853 }, "thread_token_usage": { "input_tokens": 986972, "cached_input_tokens": 941952, "cache_write_input_tokens": 0, "output_tokens": 6881, "reasoning_output_tokens": 2128, "total_tokens": 993853 } } ## Step 23 · agent · 2026-09-23T05:25:28.583Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:25:40.356Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/check_submission.py <<'PY'\\n\\\"\\\"\\\"Validate the final ranking against public test-cutoff eligibility.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport sys\\nimport numpy as np\\nimport pandas as pd\\n\\npath=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\\nassert path.is_file() and not path.is_symlink()\\nf=pd.read_parquet(path) if path.suffix=='.parquet' else pd.read_csv(path)\\nfor col in ['uid','item_id','rank']:\\n a=f[col].to_numpy()\\n assert np.isfinite(a).all() and np.equal(a,np.floor(a)).all(), col\\nusers=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\\nassert set(f.uid)==set(users)\\ng=f.groupby('uid')\\nassert g.size().eq(100).all()\\nassert g.item_id.nunique().eq(100).all()\\nassert g['rank'].nunique().eq(100).all()\\nassert g['rank'].min().eq(1).all() and g['rank'].max().eq(100).all()\\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\\n ur=pd.Index(z['uids']).get_indexer(f.uid)\\n ir=pd.Index(z['item_ids']).get_indexer(f.item_id)\\n assert (ur>=0).all() and (ir>=0).all()\\n assert (((z['packed_eligible'][ur,ir>>3]>>(ir&7))&1)==1).all()\\nprint(f'Validated {len(f):,} rows: {len(users):,} users, 100 distinct eligible items and ranks each.')\\nPY\\ntail -3 /app/solution/baseline.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:25:40.433Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"432871","wall_time_seconds":0.00000491,"exit_code":0,"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 unique_user_items=5,524,275 bytes=34,256,250\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-71b4802b-4f81-49bf-9b56-bd1170bd1b48", "process_id": "22526", "command": [ "/bin/sh", "-lc", "cat > /app/solution/check_submission.py <<'PY'\n\"\"\"Validate the final ranking against public test-cutoff eligibility.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\n\npath=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nf=pd.read_parquet(path) if path.suffix=='.parquet' else pd.read_csv(path)\nfor col in ['uid','item_id','rank']:\n a=f[col].to_numpy()\n assert np.isfinite(a).all() and np.equal(a,np.floor(a)).all(), col\nusers=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nassert set(f.uid)==set(users)\ng=f.groupby('uid')\nassert g.size().eq(100).all()\nassert g.item_id.nunique().eq(100).all()\nassert g['rank'].nunique().eq(100).all()\nassert g['rank'].min().eq(1).all() and g['rank'].max().eq(100).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n ur=pd.Index(z['uids']).get_indexer(f.uid)\n ir=pd.Index(z['item_ids']).get_indexer(f.item_id)\n assert (ur>=0).all() and (ir>=0).all()\n assert (((z['packed_eligible'][ur,ir>>3]>>(ir&7))&1)==1).all()\nprint(f'Validated {len(f):,} rows: {len(users):,} users, 100 distinct eligible items and ranks each.')\nPY\ntail -3 /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/check_submission.py <<'PY'\n\"\"\"Validate the final ranking against public test-cutoff eligibility.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\n\npath=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\nassert path.is_file() and not path.is_symlink()\nf=pd.read_parquet(path) if path.suffix=='.parquet' else pd.read_csv(path)\nfor col in ['uid','item_id','rank']:\n a=f[col].to_numpy()\n assert np.isfinite(a).all() and np.equal(a,np.floor(a)).all(), col\nusers=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nassert set(f.uid)==set(users)\ng=f.groupby('uid')\nassert g.size().eq(100).all()\nassert g.item_id.nunique().eq(100).all()\nassert g['rank'].nunique().eq(100).all()\nassert g['rank'].min().eq(1).all() and g['rank'].max().eq(100).all()\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n ur=pd.Index(z['uids']).get_indexer(f.uid)\n ir=pd.Index(z['item_ids']).get_indexer(f.item_id)\n assert (ur>=0).all() and (ir>=0).all()\n assert (((z['packed_eligible'][ur,ir>>3]>>(ir&7))&1)==1).all()\nprint(f'Validated {len(f):,} rows: {len(users):,} users, 100 distinct eligible items and ranks each.')\nPY\ntail -3 /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "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\n", "stderr": "", "aggregated_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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4910 }, "formatted_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\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab362bfd18487d294910eaa1bbbfc30", "usage": { "input_tokens": 53718, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 644, "reasoning_output_tokens": 226, "total_tokens": 54362 }, "turn_token_usage": { "input_tokens": 1040690, "cached_input_tokens": 995200, "cache_write_input_tokens": 0, "output_tokens": 7525, "reasoning_output_tokens": 2354, "total_tokens": 1048215 }, "thread_token_usage": { "input_tokens": 1040690, "cached_input_tokens": 995200, "cache_write_input_tokens": 0, "output_tokens": 7525, "reasoning_output_tokens": 2354, "total_tokens": 1048215 } } ## Step 24 · agent · 2026-09-23T05:25:43.090Z ### Thinking unavailable — no text recorded The sequential baseline is running within the memory limit. I’ve also prepared a final-artifact checker for exact user coverage, 100 distinct items and ranks per user, integer values, and test-cutoff eligibility. ### sleep · 2026-09-23T05:25:45.039Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:26:30.060Z Wall time: 45.0099 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab362d4b1f487d2abee3ab35e99af57", "usage": { "input_tokens": 54489, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 19, "total_tokens": 54578 }, "turn_token_usage": { "input_tokens": 1095179, "cached_input_tokens": 1048704, "cache_write_input_tokens": 0, "output_tokens": 7614, "reasoning_output_tokens": 2373, "total_tokens": 1102793 }, "thread_token_usage": { "input_tokens": 1095179, "cached_input_tokens": 1048704, "cache_write_input_tokens": 0, "output_tokens": 7614, "reasoning_output_tokens": 2373, "total_tokens": 1102793 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_9zMhoX9JdOFP44TdMtZW69X7", "durationMs": 45000 } } ### exec · 2026-09-23T05:26:32.794Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log && date -u && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:26:32.871Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"8c6c1c","wall_time_seconds":0.000004841,"exit_code":0,"original_token_count":113,"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\nWed Sep 23 05:26:32 UTC 2026\n4230942720\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ffbb919-0421-4ff1-8354-6f8b4a523b43", "process_id": "40622", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log && date -u && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log && date -u && cat /sys/fs/cgroup/memory.current" } ], "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\nWed Sep 23 05:26:32 UTC 2026\n4230942720\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\nWed Sep 23 05:26:32 UTC 2026\n4230942720\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4841 }, "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\nWed Sep 23 05:26:32 UTC 2026\n4230942720\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363063f2487d2bf3216009b9faf30", "usage": { "input_tokens": 54601, "cached_input_tokens": 54272, "cache_write_input_tokens": 0, "output_tokens": 51, "reasoning_output_tokens": 0, "total_tokens": 54652 }, "turn_token_usage": { "input_tokens": 1149780, "cached_input_tokens": 1102976, "cache_write_input_tokens": 0, "output_tokens": 7665, "reasoning_output_tokens": 2373, "total_tokens": 1157445 }, "thread_token_usage": { "input_tokens": 1149780, "cached_input_tokens": 1102976, "cache_write_input_tokens": 0, "output_tokens": 7665, "reasoning_output_tokens": 2373, "total_tokens": 1157445 } } ## Step 25 · agent · 2026-09-23T05:26:43.538Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:26:53.322Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/first_experiments.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nPY=/opt/task-venv/bin/python\\nwhile ! test -s /app/solution/baseline.json; do sleep 5; done\\n$PY /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json\\n$PY /app/solution/rank_checkpoint.py --checkpoint /app/solution/baseline_checkpoints/best.pt --stage submission --output /app/solution/submission.parquet --threads 4 > /app/solution/fallback.json 2> /app/solution/fallback.log\\n$PY /app/solution/check_submission.py > /app/solution/fallback_check.txt\\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log\\n$PY /task/evaluate_public.py /app/solution/hard_bce_validation.parquet > /app/solution/hard_bce_score.json\\nSH\\nsh /app/solution/first_experiments.sh\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:26:54.495Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"7876f2","wall_time_seconds":1.001526649,"session_id":42335,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363092ab487d2be986d6440088c3d", "usage": { "input_tokens": 54865, "cached_input_tokens": 54400, "cache_write_input_tokens": 0, "output_tokens": 628, "reasoning_output_tokens": 293, "total_tokens": 55493 }, "turn_token_usage": { "input_tokens": 1204645, "cached_input_tokens": 1157376, "cache_write_input_tokens": 0, "output_tokens": 8293, "reasoning_output_tokens": 2666, "total_tokens": 1212938 }, "thread_token_usage": { "input_tokens": 1204645, "cached_input_tokens": 1157376, "cache_write_input_tokens": 0, "output_tokens": 8293, "reasoning_output_tokens": 2666, "total_tokens": 1212938 } } ## Step 26 · agent · 2026-09-23T05:27:00.100Z ### Thinking unavailable — no text recorded Once the baseline finishes, the queued steps will score it publicly and save a valid test-cutoff fallback ranking before training the hard-negative model. That preserves a usable artifact while the improvement is being evaluated. ### sleep · 2026-09-23T05:27:01.982Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:27:47.003Z Wall time: 45.0102 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3631eb04c87d284f5845a1dbdceef", "usage": { "input_tokens": 55551, "cached_input_tokens": 54656, "cache_write_input_tokens": 0, "output_tokens": 201, "reasoning_output_tokens": 133, "total_tokens": 55752 }, "turn_token_usage": { "input_tokens": 1260196, "cached_input_tokens": 1212032, "cache_write_input_tokens": 0, "output_tokens": 8494, "reasoning_output_tokens": 2799, "total_tokens": 1268690 }, "thread_token_usage": { "input_tokens": 1260196, "cached_input_tokens": 1212032, "cache_write_input_tokens": 0, "output_tokens": 8494, "reasoning_output_tokens": 2799, "total_tokens": 1268690 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_h3ToROOCZMawhs1nU0JhbSGQ", "durationMs": 45000 } } ### exec · 2026-09-23T05:27:49.383Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/baseline.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:27:49.463Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3f32b9","wall_time_seconds":0.000006871,"exit_code":0,"original_token_count":110,"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\nWed Sep 23 05:27:49 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60f3ebca-7a63-4d60-9c0d-49bffea88d8c", "process_id": "34205", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/baseline.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/baseline.log && date -u" } ], "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\nWed Sep 23 05:27:49 UTC 2026\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\nWed Sep 23 05:27:49 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6871 }, "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\nWed Sep 23 05:27:49 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363532ae487d2be57a3e5a14be3a4", "usage": { "input_tokens": 55775, "cached_input_tokens": 55424, "cache_write_input_tokens": 0, "output_tokens": 41, "reasoning_output_tokens": 0, "total_tokens": 55816 }, "turn_token_usage": { "input_tokens": 1315971, "cached_input_tokens": 1267456, "cache_write_input_tokens": 0, "output_tokens": 8535, "reasoning_output_tokens": 2799, "total_tokens": 1324506 }, "thread_token_usage": { "input_tokens": 1315971, "cached_input_tokens": 1267456, "cache_write_input_tokens": 0, "output_tokens": 8535, "reasoning_output_tokens": 2799, "total_tokens": 1324506 } } ## Step 27 · agent · 2026-09-23T05:27:51.958Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:27:56.403Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().split(b'\\\\x00')\\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\\n s=(p/'stat').read_text().split()\\n print(p.name,'cpu seconds',(int(s[13])+int(s[14]))/100,'state',s[2])\\n except OSError: pass\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:27:56.555Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3eb8f2","wall_time_seconds":0.00000412,"exit_code":0,"original_token_count":8,"output":"509 cpu seconds 600.24 state R\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36355b3a087d29e6b5cfd33effadd", "usage": { "input_tokens": 56023, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 185, "reasoning_output_tokens": 28, "total_tokens": 56208 }, "turn_token_usage": { "input_tokens": 1371994, "cached_input_tokens": 1323008, "cache_write_input_tokens": 0, "output_tokens": 8720, "reasoning_output_tokens": 2827, "total_tokens": 1380714 }, "thread_token_usage": { "input_tokens": 1371994, "cached_input_tokens": 1323008, "cache_write_input_tokens": 0, "output_tokens": 8720, "reasoning_output_tokens": 2827, "total_tokens": 1380714 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dadaf095-86b3-4588-ad72-80d353164b80", "process_id": "45498", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\x00')\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\n s=(p/'stat').read_text().split()\n print(p.name,'cpu seconds',(int(s[13])+int(s[14]))/100,'state',s[2])\n except OSError: pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\x00')\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\n s=(p/'stat').read_text().split()\n print(p.name,'cpu seconds',(int(s[13])+int(s[14]))/100,'state',s[2])\n except OSError: pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "509 cpu seconds 600.24 state R\n", "stderr": "", "aggregated_output": "509 cpu seconds 600.24 state R\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4120 }, "formatted_output": "509 cpu seconds 600.24 state R\n" } } ## Step 28 · agent · 2026-09-23T05:28:02.548Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:28:02.812Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:28:37.833Z Wall time: 35.0107 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3635cb96487d2a60605a9bc8c7220", "usage": { "input_tokens": 56275, "cached_input_tokens": 55808, "cache_write_input_tokens": 0, "output_tokens": 162, "reasoning_output_tokens": 141, "total_tokens": 56437 }, "turn_token_usage": { "input_tokens": 1428269, "cached_input_tokens": 1378816, "cache_write_input_tokens": 0, "output_tokens": 8882, "reasoning_output_tokens": 2968, "total_tokens": 1437151 }, "thread_token_usage": { "input_tokens": 1428269, "cached_input_tokens": 1378816, "cache_write_input_tokens": 0, "output_tokens": 8882, "reasoning_output_tokens": 2968, "total_tokens": 1437151 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Mgp4Zp9MMxEmFaTNSN7S82OP", "durationMs": 35000 } } ### exec · 2026-09-23T05:28:40.964Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log && ls -lh /app/solution/baseline_checkpoints 2>/dev/null\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:28:41.058Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"3ce3c8","wall_time_seconds":0.00000521,"exit_code":0,"original_token_count":152,"output":"full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 epoch-01.pt\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-56bb9063-f814-4aab-9c21-13cefb6870ae", "process_id": "97287", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log && ls -lh /app/solution/baseline_checkpoints 2>/dev/null" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log && ls -lh /app/solution/baseline_checkpoints 2>/dev/null" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 epoch-01.pt\n", "stderr": "", "aggregated_output": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 epoch-01.pt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5210 }, "formatted_output": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\ntotal 289M\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 best.pt\n-rw-r--r-- 1 agent agent 145M Sep 23 05:28 epoch-01.pt\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3638620f487d282ceb0588cafff21", "usage": { "input_tokens": 56460, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 0, "total_tokens": 56515 }, "turn_token_usage": { "input_tokens": 1484729, "cached_input_tokens": 1434880, "cache_write_input_tokens": 0, "output_tokens": 8937, "reasoning_output_tokens": 2968, "total_tokens": 1493666 }, "thread_token_usage": { "input_tokens": 1484729, "cached_input_tokens": 1434880, "cache_write_input_tokens": 0, "output_tokens": 8937, "reasoning_output_tokens": 2968, "total_tokens": 1493666 } } ## Step 29 · agent · 2026-09-23T05:28:46.201Z ### Thinking unavailable — no text recorded The baseline finished training in about four minutes and passed its chronological loss evaluation. It is now generating the public ranking. I’ll use graded NDCG@10 from that ranking to judge retrieval quality; sampled BCE alone won’t establish an improvement. ### sleep · 2026-09-23T05:28:48.373Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:29:33.395Z Wall time: 45.0104 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3638954d087d2ab0e8eda1f7a687c", "usage": { "input_tokens": 56781, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 189, "reasoning_output_tokens": 112, "total_tokens": 56970 }, "turn_token_usage": { "input_tokens": 1541510, "cached_input_tokens": 1491200, "cache_write_input_tokens": 0, "output_tokens": 9126, "reasoning_output_tokens": 3080, "total_tokens": 1550636 }, "thread_token_usage": { "input_tokens": 1541510, "cached_input_tokens": 1491200, "cache_write_input_tokens": 0, "output_tokens": 9126, "reasoning_output_tokens": 3080, "total_tokens": 1550636 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_aJBW5hHwx3si62zqHsbF1vRN", "durationMs": 45000 } } ### exec · 2026-09-23T05:29:36.639Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:29:36.714Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"29da93","wall_time_seconds":0.000005881,"exit_code":0,"original_token_count":122,"output":"full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a562e8b-54f6-47ae-9920-63f431cf473e", "process_id": "49612", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "stderr": "", "aggregated_output": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5881 }, "formatted_output": "full-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=211.2s\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=225.9 validation_seconds=16.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363bdb7a487d2b8c1dec9bdc52eb5", "usage": { "input_tokens": 56993, "cached_input_tokens": 56576, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 57055 }, "turn_token_usage": { "input_tokens": 1598503, "cached_input_tokens": 1547776, "cache_write_input_tokens": 0, "output_tokens": 9188, "reasoning_output_tokens": 3080, "total_tokens": 1607691 }, "thread_token_usage": { "input_tokens": 1598503, "cached_input_tokens": 1547776, "cache_write_input_tokens": 0, "output_tokens": 9188, "reasoning_output_tokens": 3080, "total_tokens": 1607691 } } ## Step 30 · agent · 2026-09-23T05:29:50.094Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:29:50.158Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T05:30:00.176Z Wall time: 10.0089 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363c0ed1087d2a01783dafc5f6a2d", "usage": { "input_tokens": 57265, "cached_input_tokens": 56832, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 329, "total_tokens": 57615 }, "turn_token_usage": { "input_tokens": 1655768, "cached_input_tokens": 1604608, "cache_write_input_tokens": 0, "output_tokens": 9538, "reasoning_output_tokens": 3409, "total_tokens": 1665306 }, "thread_token_usage": { "input_tokens": 1655768, "cached_input_tokens": 1604608, "cache_write_input_tokens": 0, "output_tokens": 9538, "reasoning_output_tokens": 3409, "total_tokens": 1665306 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-859b9925-ab78-4c79-9737-eef29ffe0108", "process_id": "77730", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/baseline_checkpoints --threads 4 > /app/solution/baseline.json 2> /app/solution/baseline.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 336, "nanos": 52287487 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_i633txvMOzOgYskTVXB3zhGA", "durationMs": 10000 } } ## Step 31 · agent · 2026-09-23T05:30:03.088Z The new sampler leaves request-time scoring unchanged: candidate vectors are still computed offline, and ranking uses one history-derived user vector and dot products. The shared-pool search happens only during training. ### sleep · 2026-09-23T05:30:03.406Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:30:48.452Z Wall time: 45.0099 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab363d858b087d2a52cc93209abd2c4", "usage": { "input_tokens": 57638, "cached_input_tokens": 57088, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 57702 }, "turn_token_usage": { "input_tokens": 1713406, "cached_input_tokens": 1661696, "cache_write_input_tokens": 0, "output_tokens": 9602, "reasoning_output_tokens": 3409, "total_tokens": 1723008 }, "thread_token_usage": { "input_tokens": 1713406, "cached_input_tokens": 1661696, "cache_write_input_tokens": 0, "output_tokens": 9602, "reasoning_output_tokens": 3409, "total_tokens": 1723008 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_sJC5wtSFb7SFCHlZpBoQyyWJ", "durationMs": 45000 } } ### exec · 2026-09-23T05:30:51.786Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:30:51.898Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"595315","wall_time_seconds":0.00000469,"exit_code":0,"original_token_count":73,"output":"ranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:30:51 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8d7ec01e-6419-4c63-ab82-b181ec420900", "process_id": "74527", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/baseline.log && test ! -f /app/solution/baseline_score.json || cat /app/solution/baseline_score.json && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:30:51 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:30:51 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4690 }, "formatted_output": "ranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:30:51 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364089fac87d28c3abf2200acc829", "usage": { "input_tokens": 57725, "cached_input_tokens": 57472, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 57791 }, "turn_token_usage": { "input_tokens": 1771131, "cached_input_tokens": 1719168, "cache_write_input_tokens": 0, "output_tokens": 9668, "reasoning_output_tokens": 3409, "total_tokens": 1780799 }, "thread_token_usage": { "input_tokens": 1771131, "cached_input_tokens": 1719168, "cache_write_input_tokens": 0, "output_tokens": 9668, "reasoning_output_tokens": 3409, "total_tokens": 1780799 } } ## Step 32 · agent · 2026-09-23T05:31:01.405Z ### Thinking unavailable — no text recorded The reproduced baseline scores **0.021280 NDCG@10** on 4,263 users with eligible relevant outcomes. It’s now producing the test-cutoff fallback; the hard-negative validation run follows automatically. ### exec · 2026-09-23T05:31:04.464Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/fallback.log && cat /app/solution/baseline.json | tail -15 && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:31:04.550Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"b52b50","wall_time_seconds":0.00000519,"exit_code":0,"original_token_count":134,"output":"loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n \"validation_loss\": 0.4966362175098898,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.92760972899987\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nWed Sep 23 05:31:04 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-11c0b339-cfd7-47dd-94e1-5b9fa8941bdd", "process_id": "75374", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/fallback.log && cat /app/solution/baseline.json | tail -15 && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/fallback.log && cat /app/solution/baseline.json | tail -15 && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n \"validation_loss\": 0.4966362175098898,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.92760972899987\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nWed Sep 23 05:31:04 UTC 2026\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n \"validation_loss\": 0.4966362175098898,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.92760972899987\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nWed Sep 23 05:31:04 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5190 }, "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\n \"validation_loss\": 0.4966362175098898,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 16.92760972899987\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279485106468201\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\nWed Sep 23 05:31:04 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3640c1b3c87d2803f0630d2fb3bfb", "usage": { "input_tokens": 57973, "cached_input_tokens": 57600, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 251, "total_tokens": 58331 }, "turn_token_usage": { "input_tokens": 1829104, "cached_input_tokens": 1776768, "cache_write_input_tokens": 0, "output_tokens": 10026, "reasoning_output_tokens": 3660, "total_tokens": 1839130 }, "thread_token_usage": { "input_tokens": 1829104, "cached_input_tokens": 1776768, "cache_write_input_tokens": 0, "output_tokens": 10026, "reasoning_output_tokens": 3660, "total_tokens": 1839130 } } ## Step 33 · agent · 2026-09-23T05:31:07.428Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:31:12.128Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nf=pd.read_parquet('/app/solution/production_validation.parquet')\\nt=f[f['rank']<=10]\\nc=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\\nprint('Top10 unique items:',t.item_id.nunique())\\nprint('Top10 median catalog popularity rank:',t.item_id.map(c.popularity_rank).median())\\nprint('Most frequently recommended item user fractions:',(t.item_id.value_counts().head(5)/f.uid.nunique()).round(3).to_list())\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:31:14.403Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"a3682e","wall_time_seconds":2.097858386,"exit_code":0,"original_token_count":39,"output":"Top10 unique items: 592\nTop10 median catalog popularity rank: 111.0\nMost frequently recommended item user fractions: [0.345, 0.331, 0.321, 0.314, 0.306]\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36418e76c87d2948d9bcb14119d79", "usage": { "input_tokens": 58590, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 203, "reasoning_output_tokens": 37, "total_tokens": 58793 }, "turn_token_usage": { "input_tokens": 1887694, "cached_input_tokens": 1834624, "cache_write_input_tokens": 0, "output_tokens": 10229, "reasoning_output_tokens": 3697, "total_tokens": 1897923 }, "thread_token_usage": { "input_tokens": 1887694, "cached_input_tokens": 1834624, "cache_write_input_tokens": 0, "output_tokens": 10229, "reasoning_output_tokens": 3697, "total_tokens": 1897923 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-155b1c05-b030-4f58-806f-ce5d7c91cd7c", "process_id": "2443", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nf=pd.read_parquet('/app/solution/production_validation.parquet')\nt=f[f['rank']<=10]\nc=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\nprint('Top10 unique items:',t.item_id.nunique())\nprint('Top10 median catalog popularity rank:',t.item_id.map(c.popularity_rank).median())\nprint('Most frequently recommended item user fractions:',(t.item_id.value_counts().head(5)/f.uid.nunique()).round(3).to_list())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nf=pd.read_parquet('/app/solution/production_validation.parquet')\nt=f[f['rank']<=10]\nc=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\nprint('Top10 unique items:',t.item_id.nunique())\nprint('Top10 median catalog popularity rank:',t.item_id.map(c.popularity_rank).median())\nprint('Most frequently recommended item user fractions:',(t.item_id.value_counts().head(5)/f.uid.nunique()).round(3).to_list())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Top10 unique items: 592\nTop10 median catalog popularity rank: 111.0\nMost frequently recommended item user fractions: [0.345, 0.331, 0.321, 0.314, 0.306]\n", "stderr": "", "aggregated_output": "Top10 unique items: 592\nTop10 median catalog popularity rank: 111.0\nMost frequently recommended item user fractions: [0.345, 0.331, 0.321, 0.314, 0.306]\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 97834255 }, "formatted_output": "Top10 unique items: 592\nTop10 median catalog popularity rank: 111.0\nMost frequently recommended item user fractions: [0.345, 0.331, 0.321, 0.314, 0.306]\n" } } ## Step 34 · agent · 2026-09-23T05:31:29.380Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:31:29.643Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:32:04.652Z Wall time: 35.0048 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364229a0c87d2ac27192135fad201", "usage": { "input_tokens": 58902, "cached_input_tokens": 58368, "cache_write_input_tokens": 0, "output_tokens": 418, "reasoning_output_tokens": 397, "total_tokens": 59320 }, "turn_token_usage": { "input_tokens": 1946596, "cached_input_tokens": 1892992, "cache_write_input_tokens": 0, "output_tokens": 10647, "reasoning_output_tokens": 4094, "total_tokens": 1957243 }, "thread_token_usage": { "input_tokens": 1946596, "cached_input_tokens": 1892992, "cache_write_input_tokens": 0, "output_tokens": 10647, "reasoning_output_tokens": 4094, "total_tokens": 1957243 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_n37vbTGzuM7nHvASg5NsQeCK", "durationMs": 35000 } } ## Step 35 · agent · 2026-09-23T05:32:08.161Z The baseline’s top-10 recommendations contain only 592 distinct tracks across 8,456 users, with a median catalog popularity rank of 111. That is consistent with a popularity-heavy ranker and supports testing harder competitors, though the public score will determine whether the change helps. ### sleep · 2026-09-23T05:32:08.458Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:32:53.468Z Wall time: 45.0039 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36454d4e487d2acad25cfb61636ed", "usage": { "input_tokens": 59343, "cached_input_tokens": 58752, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 0, "total_tokens": 59425 }, "turn_token_usage": { "input_tokens": 2005939, "cached_input_tokens": 1951744, "cache_write_input_tokens": 0, "output_tokens": 10729, "reasoning_output_tokens": 4094, "total_tokens": 2016668 }, "thread_token_usage": { "input_tokens": 2005939, "cached_input_tokens": 1951744, "cache_write_input_tokens": 0, "output_tokens": 10729, "reasoning_output_tokens": 4094, "total_tokens": 2016668 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_n4LbvxUKJX3iO3iEyrv4gb0o", "durationMs": 45000 } } ### exec · 2026-09-23T05:32:56.074Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/fallback.log && tail -5 /app/solution/hard_bce.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:32:56.151Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"17eacc","wall_time_seconds":0.00000345,"exit_code":0,"original_token_count":142,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\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=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nWed Sep 23 05:32:56 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-17c8a277-f9b3-42bb-8425-9be9bcfc4eef", "process_id": "70437", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/fallback.log && tail -5 /app/solution/hard_bce.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/fallback.log && tail -5 /app/solution/hard_bce.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\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=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nWed Sep 23 05:32:56 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\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=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nWed Sep 23 05:32:56 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3450 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\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=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nWed Sep 23 05:32:56 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36485a27487d2999718c1dcacb4f7", "usage": { "input_tokens": 59448, "cached_input_tokens": 59136, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 0, "total_tokens": 59501 }, "turn_token_usage": { "input_tokens": 2065387, "cached_input_tokens": 2010880, "cache_write_input_tokens": 0, "output_tokens": 10782, "reasoning_output_tokens": 4094, "total_tokens": 2076169 }, "thread_token_usage": { "input_tokens": 2065387, "cached_input_tokens": 2010880, "cache_write_input_tokens": 0, "output_tokens": 10782, "reasoning_output_tokens": 4094, "total_tokens": 2076169 } } ## Step 36 · agent · 2026-09-23T05:32:59.146Z ### Thinking unavailable — no text recorded A valid fallback is now saved at `submission.parquet`: all 8,456 users have 100 distinct, test-eligible recommendations. The hard-negative model is training. Its higher early BCE loss is expected when negatives become harder; it does not yet tell us whether retrieval improves. ### exec · 2026-09-23T05:33:11.354Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/fallback_check.txt && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\ns=s.replace('''The 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.''','''The model is a pure two-tower retriever: no user-ID embedding, no output\\nnormalization, and raw dot-product retrieval. A shared candidate pool is scored\\nby the current towers during training to mix hard competitors with random\\nnegatives. Complete training-positive and active-like filters are preserved.\\nEvery selected adjacent target retains a nonzero weight. Only files under the\\nsupplied public directory are read.''')\\ns=s.replace('\\\"negative_sampling\\\": (\\\\n \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\\\n ),','\\\"negative_sampling\\\": f\\\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\\\",')\\ns=s.replace('\\\"objective\\\": \\\"sample-weighted-BCE-at-every-timestep\\\",','\\\"objective\\\": f\\\"sample-weighted-{config.ranking_loss}-at-every-timestep\\\",')\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:33:11.498Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"8f1368","wall_time_seconds":0.00000488,"exit_code":0,"original_token_count":21,"output":"Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364885e6087d2aaa5c77c245028cc", "usage": { "input_tokens": 59770, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 467, "reasoning_output_tokens": 56, "total_tokens": 60237 }, "turn_token_usage": { "input_tokens": 2125157, "cached_input_tokens": 2070144, "cache_write_input_tokens": 0, "output_tokens": 11249, "reasoning_output_tokens": 4150, "total_tokens": 2136406 }, "thread_token_usage": { "input_tokens": 2125157, "cached_input_tokens": 2070144, "cache_write_input_tokens": 0, "output_tokens": 11249, "reasoning_output_tokens": 4150, "total_tokens": 2136406 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bf6f689c-0a5f-4056-a7f3-153344ebb822", "process_id": "37727", "command": [ "/bin/sh", "-lc", "cat /app/solution/fallback_check.txt && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace('''The 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.''','''The model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, and raw dot-product retrieval. A shared candidate pool is scored\nby the current towers during training to mix hard competitors with random\nnegatives. Complete training-positive and active-like filters are preserved.\nEvery selected adjacent target retains a nonzero weight. Only files under the\nsupplied public directory are read.''')\ns=s.replace('\"negative_sampling\": (\\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\\n ),','\"negative_sampling\": f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\",')\ns=s.replace('\"objective\": \"sample-weighted-BCE-at-every-timestep\",','\"objective\": f\"sample-weighted-{config.ranking_loss}-at-every-timestep\",')\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/fallback_check.txt && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace('''The 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.''','''The model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, and raw dot-product retrieval. A shared candidate pool is scored\nby the current towers during training to mix hard competitors with random\nnegatives. Complete training-positive and active-like filters are preserved.\nEvery selected adjacent target retains a nonzero weight. Only files under the\nsupplied public directory are read.''')\ns=s.replace('\"negative_sampling\": (\\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\\n ),','\"negative_sampling\": f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\",')\ns=s.replace('\"objective\": \"sample-weighted-BCE-at-every-timestep\",','\"objective\": f\"sample-weighted-{config.ranking_loss}-at-every-timestep\",')\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n", "stderr": "", "aggregated_output": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4880 }, "formatted_output": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n" } } ## Step 37 · agent · 2026-09-23T05:33:25.410Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:33:32.094Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/softmax_experiment.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nPY=/opt/task-venv/bin/python\\nwhile ! test -s /app/solution/hard_bce_score.json; do sleep 5; done\\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_softmax_checkpoints --threads 4 --ranking-loss softmax > /app/solution/hard_softmax.json 2> /app/solution/hard_softmax.log\\n$PY /task/evaluate_public.py /app/solution/hard_softmax_validation.parquet > /app/solution/hard_softmax_score.json\\nSH\\nsh /app/solution/softmax_experiment.sh\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:33:33.269Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"2e06d0","wall_time_seconds":1.001192549,"session_id":76123,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36497b34087d2a33a6eea004c10e5", "usage": { "input_tokens": 60316, "cached_input_tokens": 59648, "cache_write_input_tokens": 0, "output_tokens": 622, "reasoning_output_tokens": 387, "total_tokens": 60938 }, "turn_token_usage": { "input_tokens": 2185473, "cached_input_tokens": 2129792, "cache_write_input_tokens": 0, "output_tokens": 11871, "reasoning_output_tokens": 4537, "total_tokens": 2197344 }, "thread_token_usage": { "input_tokens": 2185473, "cached_input_tokens": 2129792, "cache_write_input_tokens": 0, "output_tokens": 11871, "reasoning_output_tokens": 4537, "total_tokens": 2197344 } } ## Step 38 · agent · 2026-09-23T05:33:36.296Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:33:36.556Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:34:11.566Z Wall time: 35.0049 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364ad785c87d28041c940af76eaaf", "usage": { "input_tokens": 60997, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 29, "total_tokens": 61047 }, "turn_token_usage": { "input_tokens": 2246470, "cached_input_tokens": 2189952, "cache_write_input_tokens": 0, "output_tokens": 11921, "reasoning_output_tokens": 4566, "total_tokens": 2258391 }, "thread_token_usage": { "input_tokens": 2246470, "cached_input_tokens": 2189952, "cache_write_input_tokens": 0, "output_tokens": 11921, "reasoning_output_tokens": 4566, "total_tokens": 2258391 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_MV1LGUdm4jVdO7SbXmGzUu1p", "durationMs": 35000 } } ### exec · 2026-09-23T05:34:14.063Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard_bce.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:34:14.152Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"09373b","wall_time_seconds":0.000007061,"exit_code":0,"original_token_count":100,"output":"full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nWed Sep 23 05:34:14 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1bb1b4cd-e663-48c4-bab4-9be29a8e0d4b", "process_id": "95294", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard_bce.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard_bce.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nWed Sep 23 05:34:14 UTC 2026\n", "stderr": "", "aggregated_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nWed Sep 23 05:34:14 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7061 }, "formatted_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nWed Sep 23 05:34:14 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364d3beb487d2b99b3816e5597d86", "usage": { "input_tokens": 61070, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 42, "reasoning_output_tokens": 0, "total_tokens": 61112 }, "turn_token_usage": { "input_tokens": 2307540, "cached_input_tokens": 2250752, "cache_write_input_tokens": 0, "output_tokens": 11963, "reasoning_output_tokens": 4566, "total_tokens": 2319503 }, "thread_token_usage": { "input_tokens": 2307540, "cached_input_tokens": 2250752, "cache_write_input_tokens": 0, "output_tokens": 11963, "reasoning_output_tokens": 4566, "total_tokens": 2319503 } } ## Step 39 · agent · 2026-09-23T05:34:17.132Z ### Thinking unavailable — no text recorded Hard-negative training is progressing at roughly the baseline’s speed. I’ve queued one follow-up that keeps the same sampler but uses a sampled-softmax loss, which directly compares the positive’s score with its competitors. ### sleep · 2026-09-23T05:34:18.993Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:35:04.005Z Wall time: 45.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab364d669b887d2a74c3537afed7e55", "usage": { "input_tokens": 61321, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 114, "reasoning_output_tokens": 44, "total_tokens": 61435 }, "turn_token_usage": { "input_tokens": 2368861, "cached_input_tokens": 2311680, "cache_write_input_tokens": 0, "output_tokens": 12077, "reasoning_output_tokens": 4610, "total_tokens": 2380938 }, "thread_token_usage": { "input_tokens": 2368861, "cached_input_tokens": 2311680, "cache_write_input_tokens": 0, "output_tokens": 12077, "reasoning_output_tokens": 4610, "total_tokens": 2380938 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_4mi4VJD7wPM7Q5QvuROXJvDB", "durationMs": 45000 } } ### exec · 2026-09-23T05:35:06.461Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard_bce.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:35:06.546Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"16f24f","wall_time_seconds":0.000005991,"exit_code":0,"original_token_count":100,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nWed Sep 23 05:35:06 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a3a34e37-c85a-4bff-a207-1a771d961c09", "process_id": "49325", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard_bce.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard_bce.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nWed Sep 23 05:35:06 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nWed Sep 23 05:35:06 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5991 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=0.694383 running_bce=0.681004 elapsed=45.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=0.682938 running_bce=0.685821 elapsed=91.3s\nepoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nWed Sep 23 05:35:06 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3650825d887d282bca215096b33c2", "usage": { "input_tokens": 61458, "cached_input_tokens": 61184, "cache_write_input_tokens": 0, "output_tokens": 42, "reasoning_output_tokens": 0, "total_tokens": 61500 }, "turn_token_usage": { "input_tokens": 2430319, "cached_input_tokens": 2372864, "cache_write_input_tokens": 0, "output_tokens": 12119, "reasoning_output_tokens": 4610, "total_tokens": 2442438 }, "thread_token_usage": { "input_tokens": 2430319, "cached_input_tokens": 2372864, "cache_write_input_tokens": 0, "output_tokens": 12119, "reasoning_output_tokens": 4610, "total_tokens": 2442438 } } ## Step 40 · agent · 2026-09-23T05:35:20.813Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:35:24.402Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(''' if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":''',''' if args.hard_pool_size < max(args.hard_negatives, 1) or args.hard_warmup_batches < 0:\\n parser.error(\\\"hard pool must cover the hard count and warmup must be nonnegative\\\")\\n if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":''')\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:35:24.592Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"43bf95","wall_time_seconds":0.000002721,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3650b324c87d28cb77cbfec83a71c", "usage": { "input_tokens": 61718, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 514, "reasoning_output_tokens": 343, "total_tokens": 62232 }, "turn_token_usage": { "input_tokens": 2492037, "cached_input_tokens": 2434176, "cache_write_input_tokens": 0, "output_tokens": 12633, "reasoning_output_tokens": 4953, "total_tokens": 2504670 }, "thread_token_usage": { "input_tokens": 2492037, "cached_input_tokens": 2434176, "cache_write_input_tokens": 0, "output_tokens": 12633, "reasoning_output_tokens": 4953, "total_tokens": 2504670 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-65c130e8-58e2-46e9-a684-3832f068cbf9", "process_id": "8474", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''',''' if args.hard_pool_size < max(args.hard_negatives, 1) or args.hard_warmup_batches < 0:\n parser.error(\"hard pool must cover the hard count and warmup must be nonnegative\")\n if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''')\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''',''' if args.hard_pool_size < max(args.hard_negatives, 1) or args.hard_warmup_batches < 0:\n parser.error(\"hard pool must cover the hard count and warmup must be nonnegative\")\n if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":''')\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 2721 }, "formatted_output": "" } } ## Step 41 · agent · 2026-09-23T05:35:26.721Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:35:27.043Z { "duration_ms": 25000 } ### Tool output · 2026-09-23T05:35:52.052Z Wall time: 25.0046 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3651cc2d887d2a10c7a28d88770c4", "usage": { "input_tokens": 62288, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 33, "reasoning_output_tokens": 12, "total_tokens": 62321 }, "turn_token_usage": { "input_tokens": 2554325, "cached_input_tokens": 2495744, "cache_write_input_tokens": 0, "output_tokens": 12666, "reasoning_output_tokens": 4965, "total_tokens": 2566991 }, "thread_token_usage": { "input_tokens": 2554325, "cached_input_tokens": 2495744, "cache_write_input_tokens": 0, "output_tokens": 12666, "reasoning_output_tokens": 4965, "total_tokens": 2566991 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_mpVn3tZ0i3KkYQyaehAvsL5t", "durationMs": 25000 } } ## Step 42 · agent · 2026-09-23T05:35:56.151Z The mining pool combines uniform catalog samples with targets from the current training batch. This gives it both broad catalog coverage and tracks occurring in current listening sequences. All pool construction and masking use fitting history only. ### sleep · 2026-09-23T05:35:56.419Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:36:41.428Z Wall time: 45.0047 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36538356887d2a1e1a7b19ab92c3e", "usage": { "input_tokens": 62344, "cached_input_tokens": 62080, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 62410 }, "turn_token_usage": { "input_tokens": 2616669, "cached_input_tokens": 2557824, "cache_write_input_tokens": 0, "output_tokens": 12732, "reasoning_output_tokens": 4965, "total_tokens": 2629401 }, "thread_token_usage": { "input_tokens": 2616669, "cached_input_tokens": 2557824, "cache_write_input_tokens": 0, "output_tokens": 12732, "reasoning_output_tokens": 4965, "total_tokens": 2629401 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_btTnAS0a6oHBFME4zokDoMUe", "durationMs": 45000 } } ### exec · 2026-09-23T05:36:44.857Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/hard_bce.log && date -u && cat /sys/fs/cgroup/memory.current\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:36:44.950Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"af5967","wall_time_seconds":0.000004381,"exit_code":0,"original_token_count":156,"output":"epoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678781 running_bce=0.678300 elapsed=225.9s\nsaved epoch checkpoint: /app/solution/hard_bce_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.67776 validation_loss=0.60584 pair_accuracy=0.465 train_targets=1,052,953 validation_targets=130,103 train_seconds=241.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.605840\nWed Sep 23 05:36:44 UTC 2026\n6093316096\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be815c29-b10e-4a76-b31b-4ffc5032038b", "process_id": "49936", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/hard_bce.log && date -u && cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/hard_bce.log && date -u && cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678781 running_bce=0.678300 elapsed=225.9s\nsaved epoch checkpoint: /app/solution/hard_bce_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.67776 validation_loss=0.60584 pair_accuracy=0.465 train_targets=1,052,953 validation_targets=130,103 train_seconds=241.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.605840\nWed Sep 23 05:36:44 UTC 2026\n6093316096\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678781 running_bce=0.678300 elapsed=225.9s\nsaved epoch checkpoint: /app/solution/hard_bce_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.67776 validation_loss=0.60584 pair_accuracy=0.465 train_targets=1,052,953 validation_targets=130,103 train_seconds=241.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.605840\nWed Sep 23 05:36:44 UTC 2026\n6093316096\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4381 }, "formatted_output": "epoch=1/1 batches=150 targets=596,745 batch_bce=0.673950 running_bce=0.682763 elapsed=135.4s\nepoch=1/1 batches=200 targets=790,005 batch_bce=0.676104 running_bce=0.680535 elapsed=180.5s\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.678781 running_bce=0.678300 elapsed=225.9s\nsaved epoch checkpoint: /app/solution/hard_bce_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.67776 validation_loss=0.60584 pair_accuracy=0.465 train_targets=1,052,953 validation_targets=130,103 train_seconds=241.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.605840\nWed Sep 23 05:36:44 UTC 2026\n6093316096\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36569a10887d2b1dbb0b2e99749a6", "usage": { "input_tokens": 62433, "cached_input_tokens": 62208, "cache_write_input_tokens": 0, "output_tokens": 52, "reasoning_output_tokens": 0, "total_tokens": 62485 }, "turn_token_usage": { "input_tokens": 2679102, "cached_input_tokens": 2620032, "cache_write_input_tokens": 0, "output_tokens": 12784, "reasoning_output_tokens": 4965, "total_tokens": 2691886 }, "thread_token_usage": { "input_tokens": 2679102, "cached_input_tokens": 2620032, "cache_write_input_tokens": 0, "output_tokens": 12784, "reasoning_output_tokens": 4965, "total_tokens": 2691886 } } ## Step 43 · agent · 2026-09-23T05:36:57.120Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:36:57.395Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T05:37:07.404Z Wall time: 10.0041 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3656d382487d2b04a20aa8bbf360f", "usage": { "input_tokens": 62761, "cached_input_tokens": 62208, "cache_write_input_tokens": 0, "output_tokens": 363, "reasoning_output_tokens": 342, "total_tokens": 63124 }, "turn_token_usage": { "input_tokens": 2741863, "cached_input_tokens": 2682240, "cache_write_input_tokens": 0, "output_tokens": 13147, "reasoning_output_tokens": 5307, "total_tokens": 2755010 }, "thread_token_usage": { "input_tokens": 2741863, "cached_input_tokens": 2682240, "cache_write_input_tokens": 0, "output_tokens": 13147, "reasoning_output_tokens": 5307, "total_tokens": 2755010 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_aXXcxVdDmgPp7WujOfQjQF8J", "durationMs": 10000 } } ## Step 44 · agent · 2026-09-23T05:37:10.634Z The hard-negative BCE run finished training in 241 seconds, versus 226 seconds for the baseline. The added training cost is modest, and it is now generating the public ranking for the quality comparison. ### sleep · 2026-09-23T05:37:10.908Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:37:55.917Z Wall time: 45.0047 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36583945087d29e16cb576251352f", "usage": { "input_tokens": 63147, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 63213 }, "turn_token_usage": { "input_tokens": 2805010, "cached_input_tokens": 2744832, "cache_write_input_tokens": 0, "output_tokens": 13213, "reasoning_output_tokens": 5307, "total_tokens": 2818223 }, "thread_token_usage": { "input_tokens": 2805010, "cached_input_tokens": 2744832, "cache_write_input_tokens": 0, "output_tokens": 13213, "reasoning_output_tokens": 5307, "total_tokens": 2818223 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6f4d0da8-6a4e-4d1f-afdf-e104fa9fd338", "process_id": "42335", "command": [ "/bin/sh", "-lc", "cat > /app/solution/first_experiments.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/baseline.json; do sleep 5; done\n$PY /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json\n$PY /app/solution/rank_checkpoint.py --checkpoint /app/solution/baseline_checkpoints/best.pt --stage submission --output /app/solution/submission.parquet --threads 4 > /app/solution/fallback.json 2> /app/solution/fallback.log\n$PY /app/solution/check_submission.py > /app/solution/fallback_check.txt\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log\n$PY /task/evaluate_public.py /app/solution/hard_bce_validation.parquet > /app/solution/hard_bce_score.json\nSH\nsh /app/solution/first_experiments.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/first_experiments.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/baseline.json; do sleep 5; done\n$PY /task/evaluate_public.py /app/solution/production_validation.parquet > /app/solution/baseline_score.json\n$PY /app/solution/rank_checkpoint.py --checkpoint /app/solution/baseline_checkpoints/best.pt --stage submission --output /app/solution/submission.parquet --threads 4 > /app/solution/fallback.json 2> /app/solution/fallback.log\n$PY /app/solution/check_submission.py > /app/solution/fallback_check.txt\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_bce_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_bce_checkpoints --threads 4 > /app/solution/hard_bce.json 2> /app/solution/hard_bce.log\n$PY /task/evaluate_public.py /app/solution/hard_bce_validation.parquet > /app/solution/hard_bce_score.json\nSH\nsh /app/solution/first_experiments.sh" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 626, "nanos": 470472245 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_oh2Ip20KYa9NRKNnjV7j9tXV", "durationMs": 45000 } } ### exec · 2026-09-23T05:37:59.426Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/hard_bce.log && test ! -f /app/solution/hard_bce_score.json || cat /app/solution/hard_bce_score.json && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:37:59.516Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"bdd2ee","wall_time_seconds":0.00001141,"exit_code":0,"original_token_count":67,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/hard_bce_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003568,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:37:59 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8e29c320-d69d-47c1-811e-af353ab2606e", "process_id": "45509", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/hard_bce.log && test ! -f /app/solution/hard_bce_score.json || cat /app/solution/hard_bce_score.json && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/hard_bce.log && test ! -f /app/solution/hard_bce_score.json || cat /app/solution/hard_bce_score.json && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/hard_bce_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003568,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:37:59 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/hard_bce_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003568,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:37:59 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11410 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/hard_bce_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003568,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:37:59 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab365b413b887d2a4a3affc499bf6ce", "usage": { "input_tokens": 63236, "cached_input_tokens": 62976, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 0, "total_tokens": 63305 }, "turn_token_usage": { "input_tokens": 2868246, "cached_input_tokens": 2807808, "cache_write_input_tokens": 0, "output_tokens": 13282, "reasoning_output_tokens": 5307, "total_tokens": 2881528 }, "thread_token_usage": { "input_tokens": 2868246, "cached_input_tokens": 2807808, "cache_write_input_tokens": 0, "output_tokens": 13282, "reasoning_output_tokens": 5307, "total_tokens": 2881528 } } ## Step 45 · agent · 2026-09-23T05:38:11.850Z ### Thinking unavailable — no text recorded The first hard-negative mixture performed much worse: **0.003568 NDCG@10**, versus **0.021280** for the baseline. Replacing half the negatives early appears too aggressive. I’ll reject that setting and test a gentler schedule with fewer hard negatives introduced later; the loss-comparison run is also underway. ### exec · 2026-09-23T05:38:18.547Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/hard_softmax.log && /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nfor name in ['production','hard_bce']:\\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\\n t=f[f['rank']<=10]\\n c=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\\n print(name,'unique top10',t.item_id.nunique(),'median popularity rank',t.item_id.map(c.popularity_rank).median())\\nPY\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:38:20.585Z Script completed Wall time 2.0 seconds Output: {"chunk_id":"849ae4","wall_time_seconds":1.863483795,"exit_code":0,"original_token_count":138,"output":"chronological 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=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nproduction unique top10 592 median popularity rank 111.0\nhard_bce unique top10 1284 median popularity rank 5424.0\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab365b7b88487d28d380a0ef3d2fc8f", "usage": { "input_tokens": 63478, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 583, "reasoning_output_tokens": 355, "total_tokens": 64061 }, "turn_token_usage": { "input_tokens": 2931724, "cached_input_tokens": 2870912, "cache_write_input_tokens": 0, "output_tokens": 13865, "reasoning_output_tokens": 5662, "total_tokens": 2945589 }, "thread_token_usage": { "input_tokens": 2931724, "cached_input_tokens": 2870912, "cache_write_input_tokens": 0, "output_tokens": 13865, "reasoning_output_tokens": 5662, "total_tokens": 2945589 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-56f493b3-ca5d-4192-95ef-acc861bbecae", "process_id": "19953", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/hard_softmax.log && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor name in ['production','hard_bce']:\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\n t=f[f['rank']<=10]\n c=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n print(name,'unique top10',t.item_id.nunique(),'median popularity rank',t.item_id.map(c.popularity_rank).median())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/hard_softmax.log && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfor name in ['production','hard_bce']:\n f=pd.read_parquet('/app/solution/'+name+'_validation.parquet')\n t=f[f['rank']<=10]\n c=pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n print(name,'unique top10',t.item_id.nunique(),'median popularity rank',t.item_id.map(c.popularity_rank).median())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "production unique top10 592 median popularity rank 111.0\nhard_bce unique top10 1284 median popularity rank 5424.0\n", "stderr": "", "aggregated_output": "production unique top10 592 median popularity rank 111.0\nhard_bce unique top10 1284 median popularity rank 5424.0\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 863471814 }, "formatted_output": "production unique top10 592 median popularity rank 111.0\nhard_bce unique top10 1284 median popularity rank 5424.0\n" } } ## Step 46 · agent · 2026-09-23T05:38:37.540Z ### Thinking unavailable — no text recorded ## Step 47 · agent · 2026-09-23T05:38:53.280Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:39:24.839Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/add_shared_softmax.py <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\ns=s.replace(' ranking_loss: str = \\\"bce\\\"',' ranking_loss: str = \\\"bce\\\"\\\\n shared_negatives: int = 2048')\\ns=s.replace(''' backward: bool,\\n) -> tuple[dict[str, float], torch.Tensor | None]:''',''' backward: bool,\\n user_rows: np.ndarray | None = None,\\n known_positive_packed: np.ndarray | None = None,\\n) -> tuple[dict[str, float], torch.Tensor | None]:''')\\nstart=s.index(' positive_vectors, negative_vectors = candidate_vectors(',s.index('def score_target_microbatches'))\\nend=s.index(' selected_weights = torch.from_numpy(',start)\\ns=s[:start]+''' if config.ranking_loss == \\\"shared-softmax\\\" and backward:\\n # Uniform proposals need no item-dependent log-Q correction.\\n # A shared pool amortizes item-tower encoding and matrix scoring.\\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))\\n all_items = torch.cat([positive_items[begin:end], pool])\\n unique, inverse = torch.unique(all_items, return_inverse=True)\\n encoded = model.encode_items(unique)\\n positive_vectors = encoded[inverse[:end-begin]]\\n pool_vectors = encoded[inverse[end-begin:]]\\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\\n pool_logits = query_vectors @ pool_vectors.T\\n pn = pool.cpu().numpy()\\n rows = user_rows[begin:end]\\n invalid = ((known_positive_packed[rows[:, None], pn[None, :] >> 3] >> (pn[None, :] & 7)) & 1).astype(bool)\\n lookup = np.full(len(prepared.item_ids)+1, -1, dtype=np.int64)\\n lookup[pn] = np.arange(len(pn))\\n excluded = np.concatenate([positive_items[begin:end].cpu().numpy()[:, None], prepared.liked_states[selected_states]], axis=1)\\n cols = lookup[excluded]\\n rr, cc = np.nonzero(cols >= 0)\\n invalid[rr, cols[rr, cc]] = True\\n masked = pool_logits.masked_fill(torch.from_numpy(invalid).to(device), -torch.inf)\\n shared_loss = torch.logsumexp(torch.cat([positive_logits[:, None], masked], dim=1), dim=1) - positive_logits\\n # Fixed-size unmasked score sample for comparable scale diagnostics.\\n negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]\\n else:\\n positive_vectors, negative_vectors = candidate_vectors(\\n model, positive_items[begin:end], negatives[begin:end],\\n )\\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\\n negative_logits = torch.einsum(\\\"bd,bkd->bk\\\", query_vectors, negative_vectors)\\n positive_loss = F.softplus(-positive_logits)\\n negative_loss = corrected_negative_loss(\\n negative_logits, negative_weights[begin:end], config\\n )\\n''' + s[end:]\\ns=s.replace(''' if config.ranking_loss == \\\"softmax\\\" and backward:''',''' if config.ranking_loss == \\\"shared-softmax\\\" and backward:\\n row_loss = shared_loss\\n elif config.ranking_loss == \\\"softmax\\\" and backward:''')\\ns=s.replace(''' backward=True,\\n )''',''' backward=True,\\n user_rows=flat_user_rows,\\n known_positive_packed=known_positive_packed,\\n )''')\\ns=s.replace('choices=(\\\"bce\\\", \\\"pairwise\\\", \\\"softmax\\\")','choices=(\\\"bce\\\", \\\"pairwise\\\", \\\"softmax\\\", \\\"shared-softmax\\\")')\\ns=s.replace(' parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)',' parser.add_argument(\\\"--shared-negatives\\\", type=int, default=Config.shared_negatives)\\\\n parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)')\\ns=s.replace(' ranking_loss=args.ranking_loss,',' ranking_loss=args.ranking_loss,\\\\n shared_negatives=args.shared_negatives,')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/add_shared_softmax.py\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\ntail -3 /app/solution/hard_softmax.log\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:39:25.142Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"304fb0","wall_time_seconds":0.129929992,"exit_code":0,"original_token_count":69,"output":"full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab365ccbed887d29eb2078a7613931f", "usage": { "input_tokens": 64299, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 2096, "reasoning_output_tokens": 1034, "total_tokens": 66395 }, "turn_token_usage": { "input_tokens": 2996023, "cached_input_tokens": 2934272, "cache_write_input_tokens": 0, "output_tokens": 15961, "reasoning_output_tokens": 6696, "total_tokens": 3011984 }, "thread_token_usage": { "input_tokens": 2996023, "cached_input_tokens": 2934272, "cache_write_input_tokens": 0, "output_tokens": 15961, "reasoning_output_tokens": 6696, "total_tokens": 3011984 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bd4286a8-ffec-438d-9cc4-649dfca9d9c3", "process_id": "33261", "command": [ "/bin/sh", "-lc", "cat > /app/solution/add_shared_softmax.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"bce\"\\n shared_negatives: int = 2048')\ns=s.replace(''' backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:''',''' backward: bool,\n user_rows: np.ndarray | None = None,\n known_positive_packed: np.ndarray | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:''')\nstart=s.index(' positive_vectors, negative_vectors = candidate_vectors(',s.index('def score_target_microbatches'))\nend=s.index(' selected_weights = torch.from_numpy(',start)\ns=s[:start]+''' if config.ranking_loss == \"shared-softmax\" and backward:\n # Uniform proposals need no item-dependent log-Q correction.\n # A shared pool amortizes item-tower encoding and matrix scoring.\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))\n all_items = torch.cat([positive_items[begin:end], pool])\n unique, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique)\n positive_vectors = encoded[inverse[:end-begin]]\n pool_vectors = encoded[inverse[end-begin:]]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n pool_logits = query_vectors @ pool_vectors.T\n pn = pool.cpu().numpy()\n rows = user_rows[begin:end]\n invalid = ((known_positive_packed[rows[:, None], pn[None, :] >> 3] >> (pn[None, :] & 7)) & 1).astype(bool)\n lookup = np.full(len(prepared.item_ids)+1, -1, dtype=np.int64)\n lookup[pn] = np.arange(len(pn))\n excluded = np.concatenate([positive_items[begin:end].cpu().numpy()[:, None], prepared.liked_states[selected_states]], axis=1)\n cols = lookup[excluded]\n rr, cc = np.nonzero(cols >= 0)\n invalid[rr, cols[rr, cc]] = True\n masked = pool_logits.masked_fill(torch.from_numpy(invalid).to(device), -torch.inf)\n shared_loss = torch.logsumexp(torch.cat([positive_logits[:, None], masked], dim=1), dim=1) - positive_logits\n # Fixed-size unmasked score sample for comparable scale diagnostics.\n negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]\n else:\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\"bd,bkd->bk\", query_vectors, negative_vectors)\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n''' + s[end:]\ns=s.replace(''' if config.ranking_loss == \"softmax\" and backward:''',''' if config.ranking_loss == \"shared-softmax\" and backward:\n row_loss = shared_loss\n elif config.ranking_loss == \"softmax\" and backward:''')\ns=s.replace(''' backward=True,\n )''',''' backward=True,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n )''')\ns=s.replace('choices=(\"bce\", \"pairwise\", \"softmax\")','choices=(\"bce\", \"pairwise\", \"softmax\", \"shared-softmax\")')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)',' parser.add_argument(\"--shared-negatives\", type=int, default=Config.shared_negatives)\\n parser.add_argument(\"--seed\", type=int, default=Config.seed)')\ns=s.replace(' ranking_loss=args.ranking_loss,',' ranking_loss=args.ranking_loss,\\n shared_negatives=args.shared_negatives,')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_shared_softmax.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -3 /app/solution/hard_softmax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/add_shared_softmax.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"bce\"\\n shared_negatives: int = 2048')\ns=s.replace(''' backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:''',''' backward: bool,\n user_rows: np.ndarray | None = None,\n known_positive_packed: np.ndarray | None = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:''')\nstart=s.index(' positive_vectors, negative_vectors = candidate_vectors(',s.index('def score_target_microbatches'))\nend=s.index(' selected_weights = torch.from_numpy(',start)\ns=s[:start]+''' if config.ranking_loss == \"shared-softmax\" and backward:\n # Uniform proposals need no item-dependent log-Q correction.\n # A shared pool amortizes item-tower encoding and matrix scoring.\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))\n all_items = torch.cat([positive_items[begin:end], pool])\n unique, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique)\n positive_vectors = encoded[inverse[:end-begin]]\n pool_vectors = encoded[inverse[end-begin:]]\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n pool_logits = query_vectors @ pool_vectors.T\n pn = pool.cpu().numpy()\n rows = user_rows[begin:end]\n invalid = ((known_positive_packed[rows[:, None], pn[None, :] >> 3] >> (pn[None, :] & 7)) & 1).astype(bool)\n lookup = np.full(len(prepared.item_ids)+1, -1, dtype=np.int64)\n lookup[pn] = np.arange(len(pn))\n excluded = np.concatenate([positive_items[begin:end].cpu().numpy()[:, None], prepared.liked_states[selected_states]], axis=1)\n cols = lookup[excluded]\n rr, cc = np.nonzero(cols >= 0)\n invalid[rr, cols[rr, cc]] = True\n masked = pool_logits.masked_fill(torch.from_numpy(invalid).to(device), -torch.inf)\n shared_loss = torch.logsumexp(torch.cat([positive_logits[:, None], masked], dim=1), dim=1) - positive_logits\n # Fixed-size unmasked score sample for comparable scale diagnostics.\n negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]\n else:\n positive_vectors, negative_vectors = candidate_vectors(\n model, positive_items[begin:end], negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\"bd,bkd->bk\", query_vectors, negative_vectors)\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n''' + s[end:]\ns=s.replace(''' if config.ranking_loss == \"softmax\" and backward:''',''' if config.ranking_loss == \"shared-softmax\" and backward:\n row_loss = shared_loss\n elif config.ranking_loss == \"softmax\" and backward:''')\ns=s.replace(''' backward=True,\n )''',''' backward=True,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n )''')\ns=s.replace('choices=(\"bce\", \"pairwise\", \"softmax\")','choices=(\"bce\", \"pairwise\", \"softmax\", \"shared-softmax\")')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)',' parser.add_argument(\"--shared-negatives\", type=int, default=Config.shared_negatives)\\n parser.add_argument(\"--seed\", type=int, default=Config.seed)')\ns=s.replace(' ranking_loss=args.ranking_loss,',' ranking_loss=args.ranking_loss,\\n shared_negatives=args.shared_negatives,')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_shared_softmax.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -3 /app/solution/hard_softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\n", "stderr": "", "aggregated_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 129557348 }, "formatted_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\n" } } ## Step 48 · agent · 2026-09-23T05:39:31.113Z ### Thinking unavailable — no text recorded The failed run shifted too far toward obscure tracks: median top-10 popularity rank moved from 111 to 5,424. I’m also testing a broad shared uniform pool with softmax weighting. It exposes each positive to many more competitors, gives high-scoring negatives more gradient, and avoids explicit top-k selection’s sampling bias. ### exec · 2026-09-23T05:39:39.797Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/shared_experiment.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nPY=/opt/task-venv/bin/python\\nwhile ! test -s /app/solution/hard_softmax_score.json; do sleep 5; done\\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 > /app/solution/shared.json 2> /app/solution/shared.log\\n$PY /task/evaluate_public.py /app/solution/shared_validation.parquet > /app/solution/shared_score.json\\nSH\\nsh /app/solution/shared_experiment.sh\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:39:40.965Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"70a724","wall_time_seconds":1.000554119,"session_id":86513,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3660d4e4487d2bfd2d88756b3df4c", "usage": { "input_tokens": 66552, "cached_input_tokens": 64128, "cache_write_input_tokens": 0, "output_tokens": 434, "reasoning_output_tokens": 140, "total_tokens": 66986 }, "turn_token_usage": { "input_tokens": 3062575, "cached_input_tokens": 2998400, "cache_write_input_tokens": 0, "output_tokens": 16395, "reasoning_output_tokens": 6836, "total_tokens": 3078970 }, "thread_token_usage": { "input_tokens": 3062575, "cached_input_tokens": 2998400, "cache_write_input_tokens": 0, "output_tokens": 16395, "reasoning_output_tokens": 6836, "total_tokens": 3078970 } } ## Step 49 · agent · 2026-09-23T05:39:48.588Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:39:56.117Z { "code": "text(await tools.exec_command({cmd:\"cat >> /app/solution/test_hard_sampler.py <<'PY'\\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=128)\\nmodel.zero_grad(set_to_none=True)\\nsequence=torch.randn(2,192)\\nstats,grad=m.score_target_microbatches(model,data,c,sequence,np.arange(2),np.zeros((2,4),np.float32),pos,neg,torch.ones((2,32)),np.ones(2,np.float32),torch.device('cpu'),True,user_rows=np.arange(2),known_positive_packed=known)\\nassert np.isfinite(stats['loss_sum']) and stats['loss_sum']>0\\nassert grad is not None and torch.isfinite(grad).all() and grad.abs().sum()>0\\nassert model.candidate_embedding.weight.grad.abs().sum()>0\\nassert model.context_embedding.weight.grad.abs().sum()>0\\nprint('Shared softmax finite loss and both-tower gradient checks passed')\\nPY\\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\\ntail -4 /app/solution/hard_softmax.log\\ndate -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:40:01.295Z Script completed Wall time 5.2 seconds Output: {"chunk_id":"f38893","wall_time_seconds":5.004393589,"exit_code":0,"original_token_count":124,"output":"Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nWed Sep 23 05:40:01 UTC 2026\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3661d261c87d2aa4df827b78a32d9", "usage": { "input_tokens": 67043, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 465, "reasoning_output_tokens": 190, "total_tokens": 67508 }, "turn_token_usage": { "input_tokens": 3129618, "cached_input_tokens": 3064832, "cache_write_input_tokens": 0, "output_tokens": 16860, "reasoning_output_tokens": 7026, "total_tokens": 3146478 }, "thread_token_usage": { "input_tokens": 3129618, "cached_input_tokens": 3064832, "cache_write_input_tokens": 0, "output_tokens": 16860, "reasoning_output_tokens": 7026, "total_tokens": 3146478 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-543fb51d-08ff-44b0-9e12-d82d7661b74c", "process_id": "84891", "command": [ "/bin/sh", "-lc", "cat >> /app/solution/test_hard_sampler.py <<'PY'\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=128)\nmodel.zero_grad(set_to_none=True)\nsequence=torch.randn(2,192)\nstats,grad=m.score_target_microbatches(model,data,c,sequence,np.arange(2),np.zeros((2,4),np.float32),pos,neg,torch.ones((2,32)),np.ones(2,np.float32),torch.device('cpu'),True,user_rows=np.arange(2),known_positive_packed=known)\nassert np.isfinite(stats['loss_sum']) and stats['loss_sum']>0\nassert grad is not None and torch.isfinite(grad).all() and grad.abs().sum()>0\nassert model.candidate_embedding.weight.grad.abs().sum()>0\nassert model.context_embedding.weight.grad.abs().sum()>0\nprint('Shared softmax finite loss and both-tower gradient checks passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -4 /app/solution/hard_softmax.log\ndate -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat >> /app/solution/test_hard_sampler.py <<'PY'\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=128)\nmodel.zero_grad(set_to_none=True)\nsequence=torch.randn(2,192)\nstats,grad=m.score_target_microbatches(model,data,c,sequence,np.arange(2),np.zeros((2,4),np.float32),pos,neg,torch.ones((2,32)),np.ones(2,np.float32),torch.device('cpu'),True,user_rows=np.arange(2),known_positive_packed=known)\nassert np.isfinite(stats['loss_sum']) and stats['loss_sum']>0\nassert grad is not None and torch.isfinite(grad).all() and grad.abs().sum()>0\nassert model.candidate_embedding.weight.grad.abs().sum()>0\nassert model.context_embedding.weight.grad.abs().sum()>0\nprint('Shared softmax finite loss and both-tower gradient checks passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -4 /app/solution/hard_softmax.log\ndate -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nWed Sep 23 05:40:01 UTC 2026\n", "stderr": "", "aggregated_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nWed Sep 23 05:40:01 UTC 2026\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 4382308 }, "formatted_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nWed Sep 23 05:40:01 UTC 2026\n" } } ## Step 50 · agent · 2026-09-23T05:40:19.114Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:40:19.687Z { "code": "text(await tools.exec_command({cmd:\"sed -n '1525,1585p' /app/solution/train_current_model.py\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:40:19.764Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"92f7c6","wall_time_seconds":0.000005361,"exit_code":0,"original_token_count":552,"output":" )[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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fde617c2-d58d-4f33-bf8f-dc5f37e9e790", "process_id": "88193", "command": [ "/bin/sh", "-lc", "sed -n '1525,1585p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1525,1585p' /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": " )[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", "stderr": "", "aggregated_output": " )[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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5361 }, "formatted_output": " )[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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3663173f087d2b4de9b9d188e446e", "usage": { "input_tokens": 67736, "cached_input_tokens": 66816, "cache_write_input_tokens": 0, "output_tokens": 547, "reasoning_output_tokens": 500, "total_tokens": 68283 }, "turn_token_usage": { "input_tokens": 3197354, "cached_input_tokens": 3131648, "cache_write_input_tokens": 0, "output_tokens": 17407, "reasoning_output_tokens": 7526, "total_tokens": 3214761 }, "thread_token_usage": { "input_tokens": 3197354, "cached_input_tokens": 3131648, "cache_write_input_tokens": 0, "output_tokens": 17407, "reasoning_output_tokens": 7526, "total_tokens": 3214761 } } ### exec · 2026-09-23T05:40:31.001Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace('if config.ranking_loss == \\\"shared-softmax\\\" and backward:', 'if config.ranking_loss == \\\"shared-softmax\\\":')\\ns=s.replace(''' pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))''',''' pool_generator = None if backward else torch.Generator(device=device).manual_seed(config.seed + 70001 + begin)\\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), generator=pool_generator, device=device))''')\\ns=s.replace(''' backward=False,\\n )''',''' backward=False,\\n user_rows=flat_user_rows,\\n known_positive_packed=known_positive_packed,\\n )''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\\ntail -3 /app/solution/hard_softmax.log\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:40:35.659Z Script completed Wall time 4.7 seconds Output: {"chunk_id":"444748","wall_time_seconds":4.483190416,"exit_code":0,"original_token_count":94,"output":"Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36643ff5087d29ea124a2c08544a4", "usage": { "input_tokens": 68840, "cached_input_tokens": 67584, "cache_write_input_tokens": 0, "output_tokens": 264, "reasoning_output_tokens": 0, "total_tokens": 69104 }, "turn_token_usage": { "input_tokens": 3266194, "cached_input_tokens": 3199232, "cache_write_input_tokens": 0, "output_tokens": 17671, "reasoning_output_tokens": 7526, "total_tokens": 3283865 }, "thread_token_usage": { "input_tokens": 3266194, "cached_input_tokens": 3199232, "cache_write_input_tokens": 0, "output_tokens": 17671, "reasoning_output_tokens": 7526, "total_tokens": 3283865 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5d8bc76-fd68-4724-923e-ee187ffaa7be", "process_id": "54301", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('if config.ranking_loss == \"shared-softmax\" and backward:', 'if config.ranking_loss == \"shared-softmax\":')\ns=s.replace(''' pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))''',''' pool_generator = None if backward else torch.Generator(device=device).manual_seed(config.seed + 70001 + begin)\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), generator=pool_generator, device=device))''')\ns=s.replace(''' backward=False,\n )''',''' backward=False,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n )''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -3 /app/solution/hard_softmax.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('if config.ranking_loss == \"shared-softmax\" and backward:', 'if config.ranking_loss == \"shared-softmax\":')\ns=s.replace(''' pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), device=device))''',''' pool_generator = None if backward else torch.Generator(device=device).manual_seed(config.seed + 70001 + begin)\n pool = torch.unique(torch.randint(len(prepared.item_ids), (config.shared_negatives,), generator=pool_generator, device=device))''')\ns=s.replace(''' backward=False,\n )''',''' backward=False,\n user_rows=flat_user_rows,\n known_positive_packed=known_positive_packed,\n )''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\ntail -3 /app/solution/hard_softmax.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\n", "stderr": "", "aggregated_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 483178165 }, "formatted_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\n" } } ## Step 51 · agent · 2026-09-23T05:40:46.964Z ### Thinking unavailable — no text recorded The broad-pool loss passes finite-loss and gradient checks for both towers. I’ve also made its chronological holdout use the same loss with a fixed sampling seed, so epoch selection can compare checkpoints consistently without changing the data split. ### sleep · 2026-09-23T05:40:49.055Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:41:34.070Z Wall time: 45.0098 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36653d41887d28f28019fe1480dac", "usage": { "input_tokens": 69288, "cached_input_tokens": 68608, "cache_write_input_tokens": 0, "output_tokens": 359, "reasoning_output_tokens": 285, "total_tokens": 69647 }, "turn_token_usage": { "input_tokens": 3335482, "cached_input_tokens": 3267840, "cache_write_input_tokens": 0, "output_tokens": 18030, "reasoning_output_tokens": 7811, "total_tokens": 3353512 }, "thread_token_usage": { "input_tokens": 3335482, "cached_input_tokens": 3267840, "cache_write_input_tokens": 0, "output_tokens": 18030, "reasoning_output_tokens": 7811, "total_tokens": 3353512 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_29j4idcyz9YVZJ65yn3PRh5m", "durationMs": 45000 } } ### exec · 2026-09-23T05:41:39.144Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/hard_softmax.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:41:39.244Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"e1e9ff","wall_time_seconds":0.000005431,"exit_code":0,"original_token_count":123,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nWed Sep 23 05:41:39 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3756e4d-06aa-408a-9970-bec054c900cc", "process_id": "90114", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/hard_softmax.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/hard_softmax.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nWed Sep 23 05:41:39 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nWed Sep 23 05:41:39 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5431 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=3.496041 running_bce=3.397639 elapsed=45.3s\nepoch=1/1 batches=100 targets=402,892 batch_bce=3.447907 running_bce=3.438271 elapsed=90.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=3.429821 running_bce=3.436601 elapsed=135.2s\nepoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nWed Sep 23 05:41:39 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3668e3d7487d2b67fc65f4c120e7c", "usage": { "input_tokens": 69670, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 42, "reasoning_output_tokens": 0, "total_tokens": 69712 }, "turn_token_usage": { "input_tokens": 3405152, "cached_input_tokens": 3336960, "cache_write_input_tokens": 0, "output_tokens": 18072, "reasoning_output_tokens": 7811, "total_tokens": 3423224 }, "thread_token_usage": { "input_tokens": 3405152, "cached_input_tokens": 3336960, "cache_write_input_tokens": 0, "output_tokens": 18072, "reasoning_output_tokens": 7811, "total_tokens": 3423224 } } ## Step 52 · agent · 2026-09-23T05:41:52.952Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:41:53.237Z { "duration_ms": 15000 } ### Tool output · 2026-09-23T05:42:08.261Z Wall time: 15.0083 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36693701887d2a373b9ac5f8d891b", "usage": { "input_tokens": 69968, "cached_input_tokens": 69504, "cache_write_input_tokens": 0, "output_tokens": 367, "reasoning_output_tokens": 346, "total_tokens": 70335 }, "turn_token_usage": { "input_tokens": 3475120, "cached_input_tokens": 3406464, "cache_write_input_tokens": 0, "output_tokens": 18439, "reasoning_output_tokens": 8157, "total_tokens": 3493559 }, "thread_token_usage": { "input_tokens": 3475120, "cached_input_tokens": 3406464, "cache_write_input_tokens": 0, "output_tokens": 18439, "reasoning_output_tokens": 8157, "total_tokens": 3493559 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_d4kBprIYgVicdBlqntJe8o0p", "durationMs": 15000 } } ## Step 53 · agent · 2026-09-23T05:42:12.521Z The softmax run with explicit top-k hard negatives has nearly finished training. I’ll compare it with the broad-pool version to separate the effect of the loss from the effect of biased negative selection. ### sleep · 2026-09-23T05:42:12.820Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:42:57.828Z Wall time: 45.0040 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab366b06f3087d2b3b964ebad4e0097", "usage": { "input_tokens": 70358, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 70424 }, "turn_token_usage": { "input_tokens": 3545478, "cached_input_tokens": 3476224, "cache_write_input_tokens": 0, "output_tokens": 18505, "reasoning_output_tokens": 8157, "total_tokens": 3563983 }, "thread_token_usage": { "input_tokens": 3545478, "cached_input_tokens": 3476224, "cache_write_input_tokens": 0, "output_tokens": 18505, "reasoning_output_tokens": 8157, "total_tokens": 3563983 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_Syf7vRl0WWUX4G6zmR8QvIiT", "durationMs": 45000 } } ### exec · 2026-09-23T05:43:01.331Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/hard_softmax.log && test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:43:01.461Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"f5f766","wall_time_seconds":0.00000515,"exit_code":0,"original_token_count":131,"output":"epoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nsaved epoch checkpoint: /app/solution/hard_softmax_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=3.43008 validation_loss=0.60431 pair_accuracy=0.474 train_targets=1,052,953 validation_targets=130,103 train_seconds=240.5 validation_seconds=17.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604307\nWed Sep 23 05:43:01 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aafbb0eb-257c-4fa6-9417-9afe10320909", "process_id": "50577", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/hard_softmax.log && test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/hard_softmax.log && test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nsaved epoch checkpoint: /app/solution/hard_softmax_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=3.43008 validation_loss=0.60431 pair_accuracy=0.474 train_targets=1,052,953 validation_targets=130,103 train_seconds=240.5 validation_seconds=17.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604307\nWed Sep 23 05:43:01 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nsaved epoch checkpoint: /app/solution/hard_softmax_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=3.43008 validation_loss=0.60431 pair_accuracy=0.474 train_targets=1,052,953 validation_targets=130,103 train_seconds=240.5 validation_seconds=17.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604307\nWed Sep 23 05:43:01 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5150 }, "formatted_output": "epoch=1/1 batches=200 targets=790,005 batch_bce=3.427172 running_bce=3.434864 elapsed=180.0s\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.449210 running_bce=3.430944 elapsed=225.3s\nsaved epoch checkpoint: /app/solution/hard_softmax_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=3.43008 validation_loss=0.60431 pair_accuracy=0.474 train_targets=1,052,953 validation_targets=130,103 train_seconds=240.5 validation_seconds=17.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604307\nWed Sep 23 05:43:01 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab366e1fdfc87d2a8dc1c2a97d6d3e2", "usage": { "input_tokens": 70447, "cached_input_tokens": 70144, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 0, "total_tokens": 70516 }, "turn_token_usage": { "input_tokens": 3615925, "cached_input_tokens": 3546368, "cache_write_input_tokens": 0, "output_tokens": 18574, "reasoning_output_tokens": 8157, "total_tokens": 3634499 }, "thread_token_usage": { "input_tokens": 3615925, "cached_input_tokens": 3546368, "cache_write_input_tokens": 0, "output_tokens": 18574, "reasoning_output_tokens": 8157, "total_tokens": 3634499 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c3525f8-3ecb-493a-80e6-2b8a074787c1", "process_id": "76123", "command": [ "/bin/sh", "-lc", "cat > /app/solution/softmax_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/hard_bce_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_softmax_checkpoints --threads 4 --ranking-loss softmax > /app/solution/hard_softmax.json 2> /app/solution/hard_softmax.log\n$PY /task/evaluate_public.py /app/solution/hard_softmax_validation.parquet > /app/solution/hard_softmax_score.json\nSH\nsh /app/solution/softmax_experiment.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/softmax_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/hard_bce_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/hard_softmax_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/hard_softmax_checkpoints --threads 4 --ranking-loss softmax > /app/solution/hard_softmax.json 2> /app/solution/hard_softmax.log\n$PY /task/evaluate_public.py /app/solution/hard_softmax_validation.parquet > /app/solution/hard_softmax_score.json\nSH\nsh /app/solution/softmax_experiment.sh" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 581, "nanos": 177173899 }, "formatted_output": "" } } ## Step 54 · agent · 2026-09-23T05:43:19.861Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:43:20.019Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T05:43:30.026Z Wall time: 10.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab366e5bbc887d289c82190bb04cded", "usage": { "input_tokens": 70751, "cached_input_tokens": 70272, "cache_write_input_tokens": 0, "output_tokens": 495, "reasoning_output_tokens": 474, "total_tokens": 71246 }, "turn_token_usage": { "input_tokens": 3686676, "cached_input_tokens": 3616640, "cache_write_input_tokens": 0, "output_tokens": 19069, "reasoning_output_tokens": 8631, "total_tokens": 3705745 }, "thread_token_usage": { "input_tokens": 3686676, "cached_input_tokens": 3616640, "cache_write_input_tokens": 0, "output_tokens": 19069, "reasoning_output_tokens": 8631, "total_tokens": 3705745 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_mSBAZxeAzGrjvOxmsfkLpys0", "durationMs": 10000 } } ## Step 55 · agent · 2026-09-23T05:43:33.520Z The broad-pool implementation encodes roughly 2,000 shared negatives once per block, then scores them with a matrix multiplication. That makes a much larger negative set practical on CPU without changing the model architecture. ### sleep · 2026-09-23T05:43:33.811Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:44:18.819Z Wall time: 45.0041 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36702322887d2871b212c8c508127", "usage": { "input_tokens": 71269, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 68, "reasoning_output_tokens": 0, "total_tokens": 71337 }, "turn_token_usage": { "input_tokens": 3757945, "cached_input_tokens": 3687168, "cache_write_input_tokens": 0, "output_tokens": 19137, "reasoning_output_tokens": 8631, "total_tokens": 3777082 }, "thread_token_usage": { "input_tokens": 3757945, "cached_input_tokens": 3687168, "cache_write_input_tokens": 0, "output_tokens": 19137, "reasoning_output_tokens": 8631, "total_tokens": 3777082 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_e5JamqMDMjsgOpVe7iy2uhF2", "durationMs": 45000 } } ### exec · 2026-09-23T05:44:26.511Z { "code": "text(await tools.exec_command({cmd:\"test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json; tail -6 /app/solution/shared.log; date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:44:26.604Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"7d13fa","wall_time_seconds":0.00000474,"exit_code":0,"original_token_count":175,"output":"{\n \"model\": \"/app/solution/hard_softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005014,\n \"quality_users\": 4263\n }\n}\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=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nWed Sep 23 05:44:26 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-addaa205-8598-4933-97f4-568f6c6066fd", "process_id": "6425", "command": [ "/bin/sh", "-lc", "test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json; tail -6 /app/solution/shared.log; date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "test ! -f /app/solution/hard_softmax_score.json || cat /app/solution/hard_softmax_score.json; tail -6 /app/solution/shared.log; date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/hard_softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005014,\n \"quality_users\": 4263\n }\n}\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=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nWed Sep 23 05:44:26 UTC 2026\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/hard_softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005014,\n \"quality_users\": 4263\n }\n}\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=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nWed Sep 23 05:44:26 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4740 }, "formatted_output": "{\n \"model\": \"/app/solution/hard_softmax_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005014,\n \"quality_users\": 4263\n }\n}\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=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nWed Sep 23 05:44:26 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3673314f487d28cf1055e5f9b0876", "usage": { "input_tokens": 71360, "cached_input_tokens": 71040, "cache_write_input_tokens": 0, "output_tokens": 66, "reasoning_output_tokens": 0, "total_tokens": 71426 }, "turn_token_usage": { "input_tokens": 3829305, "cached_input_tokens": 3758208, "cache_write_input_tokens": 0, "output_tokens": 19203, "reasoning_output_tokens": 8631, "total_tokens": 3848508 }, "thread_token_usage": { "input_tokens": 3829305, "cached_input_tokens": 3758208, "cache_write_input_tokens": 0, "output_tokens": 19203, "reasoning_output_tokens": 8631, "total_tokens": 3848508 } } ## Step 56 · agent · 2026-09-23T05:44:42.323Z ### Thinking unavailable — no text recorded Explicit top-k mining also underperformed with softmax: **0.005014 NDCG@10**. I’ll drop that approach. The broad-pool model is training about 40% faster than the baseline so far, which leaves room to test whether another epoch improves retrieval. ### exec · 2026-09-23T05:44:54.340Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text()\\ns=s.replace('''\\\"model_selection\\\": \\\"chronological-validation-BCE-early-stopping\\\",''','''\\\"model_selection\\\": \\\"chronological-validation-objective-early-stopping\\\",''')\\ns=s.replace('''\\\"negative_sampling\\\": f\\\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\\\",''','''\\\"negative_sampling\\\": (\\n f\\\"shared-uniform-pool-{config.shared_negatives}-with-softmax-weighting\\\"\\n if config.ranking_loss == \\\"shared-softmax\\\" else\\n f\\\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\\\"\\n ),''')\\ns=s.replace('if args.hard_negatives and args.negative_filter_scope != \\\"full-training-history\\\":','if (args.hard_negatives or args.ranking_loss == \\\"shared-softmax\\\") and args.negative_filter_scope != \\\"full-training-history\\\":')\\ns=s.replace('parser.error(\\\"hard mining requires the full training-history filter\\\")','parser.error(\\\"hard mining and shared softmax require the full training-history filter\\\")')\\ns=s.replace(''' public = args.public_dir.resolve()''',''' if args.shared_negatives < Config.uniform_negatives + Config.inbatch_negatives:\\n parser.error(\\\"shared pool must contain at least 32 proposals\\\")\\n public = args.public_dir.resolve()''')\\np.write_text(s)\\nPY\\ntail -3 /app/solution/shared.log\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:44:54.526Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"bae186","wall_time_seconds":0.01291809,"exit_code":0,"original_token_count":69,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36741de6087d2be0c6dfce2fdcaf6", "usage": { "input_tokens": 71746, "cached_input_tokens": 64128, "cache_write_input_tokens": 0, "output_tokens": 623, "reasoning_output_tokens": 184, "total_tokens": 72369 }, "turn_token_usage": { "input_tokens": 3901051, "cached_input_tokens": 3822336, "cache_write_input_tokens": 0, "output_tokens": 19826, "reasoning_output_tokens": 8815, "total_tokens": 3920877 }, "thread_token_usage": { "input_tokens": 3901051, "cached_input_tokens": 3822336, "cache_write_input_tokens": 0, "output_tokens": 19826, "reasoning_output_tokens": 8815, "total_tokens": 3920877 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c78a8d50-f6fc-46b7-909b-a735fde8b540", "process_id": "39081", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace('''\"model_selection\": \"chronological-validation-BCE-early-stopping\",''','''\"model_selection\": \"chronological-validation-objective-early-stopping\",''')\ns=s.replace('''\"negative_sampling\": f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\",''','''\"negative_sampling\": (\n f\"shared-uniform-pool-{config.shared_negatives}-with-softmax-weighting\"\n if config.ranking_loss == \"shared-softmax\" else\n f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\"\n ),''')\ns=s.replace('if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":','if (args.hard_negatives or args.ranking_loss == \"shared-softmax\") and args.negative_filter_scope != \"full-training-history\":')\ns=s.replace('parser.error(\"hard mining requires the full training-history filter\")','parser.error(\"hard mining and shared softmax require the full training-history filter\")')\ns=s.replace(''' public = args.public_dir.resolve()''',''' if args.shared_negatives < Config.uniform_negatives + Config.inbatch_negatives:\n parser.error(\"shared pool must contain at least 32 proposals\")\n public = args.public_dir.resolve()''')\np.write_text(s)\nPY\ntail -3 /app/solution/shared.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\ns=s.replace('''\"model_selection\": \"chronological-validation-BCE-early-stopping\",''','''\"model_selection\": \"chronological-validation-objective-early-stopping\",''')\ns=s.replace('''\"negative_sampling\": f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\",''','''\"negative_sampling\": (\n f\"shared-uniform-pool-{config.shared_negatives}-with-softmax-weighting\"\n if config.ranking_loss == \"shared-softmax\" else\n f\"mixed-random-with-{config.hard_negatives}-model-mined-negatives\"\n ),''')\ns=s.replace('if args.hard_negatives and args.negative_filter_scope != \"full-training-history\":','if (args.hard_negatives or args.ranking_loss == \"shared-softmax\") and args.negative_filter_scope != \"full-training-history\":')\ns=s.replace('parser.error(\"hard mining requires the full training-history filter\")','parser.error(\"hard mining and shared softmax require the full training-history filter\")')\ns=s.replace(''' public = args.public_dir.resolve()''',''' if args.shared_negatives < Config.uniform_negatives + Config.inbatch_negatives:\n parser.error(\"shared pool must contain at least 32 proposals\")\n public = args.public_dir.resolve()''')\np.write_text(s)\nPY\ntail -3 /app/solution/shared.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 12918610 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\n" } } ## Step 57 · agent · 2026-09-23T05:45:14.057Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:45:14.195Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:45:49.204Z Wall time: 35.0033 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36756c53487d290a4b5a7fb2ca7d4", "usage": { "input_tokens": 72533, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 522, "reasoning_output_tokens": 501, "total_tokens": 73055 }, "turn_token_usage": { "input_tokens": 3973584, "cached_input_tokens": 3893888, "cache_write_input_tokens": 0, "output_tokens": 20348, "reasoning_output_tokens": 9316, "total_tokens": 3993932 }, "thread_token_usage": { "input_tokens": 3973584, "cached_input_tokens": 3893888, "cache_write_input_tokens": 0, "output_tokens": 20348, "reasoning_output_tokens": 9316, "total_tokens": 3993932 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_DhqiaaKt65wFtOiJcgML53yd", "durationMs": 35000 } } ### exec · 2026-09-23T05:45:53.133Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/shared.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:45:53.233Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c1a97c","wall_time_seconds":0.000005271,"exit_code":0,"original_token_count":123,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nWed Sep 23 05:45:53 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60249a68-dbec-4464-a2d5-ce72346c4d5e", "process_id": "94200", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/shared.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/shared.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nWed Sep 23 05:45:53 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nWed Sep 23 05:45:53 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5271 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.682729 running_bce=6.888840 elapsed=26.9s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.507079 running_bce=6.808819 elapsed=52.8s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.614733 running_bce=6.748461 elapsed=78.1s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nWed Sep 23 05:45:53 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3678d5ef087d28f9dc51e5a77c974", "usage": { "input_tokens": 73078, "cached_input_tokens": 72320, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 73117 }, "turn_token_usage": { "input_tokens": 4046662, "cached_input_tokens": 3966208, "cache_write_input_tokens": 0, "output_tokens": 20387, "reasoning_output_tokens": 9316, "total_tokens": 4067049 }, "thread_token_usage": { "input_tokens": 4046662, "cached_input_tokens": 3966208, "cache_write_input_tokens": 0, "output_tokens": 20387, "reasoning_output_tokens": 9316, "total_tokens": 4067049 } } ## Step 58 · agent · 2026-09-23T05:46:00.229Z ### Thinking unavailable — no text recorded The broad-pool model has completed nearly all training targets in about 140 seconds, versus 226 seconds for the baseline. If its public score improves, I’ll use the remaining budget for a small epoch extension rather than further architecture changes. ### sleep · 2026-09-23T05:46:01.948Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:46:46.956Z Wall time: 45.0043 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36791cb7487d2939b9d9dbbfa4ae9", "usage": { "input_tokens": 73373, "cached_input_tokens": 72960, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 148, "total_tokens": 73597 }, "turn_token_usage": { "input_tokens": 4120035, "cached_input_tokens": 4039168, "cache_write_input_tokens": 0, "output_tokens": 20611, "reasoning_output_tokens": 9464, "total_tokens": 4140646 }, "thread_token_usage": { "input_tokens": 4120035, "cached_input_tokens": 4039168, "cache_write_input_tokens": 0, "output_tokens": 20611, "reasoning_output_tokens": 9464, "total_tokens": 4140646 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_vjEpmqmuHmqbRn1dFHJPrMLH", "durationMs": 45000 } } ### exec · 2026-09-23T05:46:49.647Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/shared.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:46:49.750Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"73fa95","wall_time_seconds":0.000005591,"exit_code":0,"original_token_count":129,"output":"epoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.65878 validation_loss=6.56110 pair_accuracy=0.196 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.9 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=1 loss=6.561099\nWed Sep 23 05:46:49 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-372476e5-7416-4fbf-81f1-67f06ad4abff", "process_id": "12426", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/shared.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/shared.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.65878 validation_loss=6.56110 pair_accuracy=0.196 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.9 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=1 loss=6.561099\nWed Sep 23 05:46:49 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.65878 validation_loss=6.56110 pair_accuracy=0.196 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.9 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=1 loss=6.561099\nWed Sep 23 05:46:49 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5591 }, "formatted_output": "epoch=1/1 batches=200 targets=790,005 batch_bce=6.371556 running_bce=6.709281 elapsed=103.8s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.843338 running_bce=6.665521 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.65878 validation_loss=6.56110 pair_accuracy=0.196 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.9 validation_seconds=12.4\nrestored best validation-BCE checkpoint: epoch=1 loss=6.561099\nWed Sep 23 05:46:49 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab367c72d7087d2b6225a7f8e3dd3c5", "usage": { "input_tokens": 73620, "cached_input_tokens": 73216, "cache_write_input_tokens": 0, "output_tokens": 39, "reasoning_output_tokens": 0, "total_tokens": 73659 }, "turn_token_usage": { "input_tokens": 4193655, "cached_input_tokens": 4112384, "cache_write_input_tokens": 0, "output_tokens": 20650, "reasoning_output_tokens": 9464, "total_tokens": 4214305 }, "thread_token_usage": { "input_tokens": 4193655, "cached_input_tokens": 4112384, "cache_write_input_tokens": 0, "output_tokens": 20650, "reasoning_output_tokens": 9464, "total_tokens": 4214305 } } ## Step 59 · agent · 2026-09-23T05:47:10.259Z ### Thinking unavailable — no text recorded ## Step 60 · agent · 2026-09-23T05:47:18.725Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a8490877-3a84-458d-8ded-448cec799759", "process_id": "86513", "command": [ "/bin/sh", "-lc", "cat > /app/solution/shared_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/hard_softmax_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 > /app/solution/shared.json 2> /app/solution/shared.log\n$PY /task/evaluate_public.py /app/solution/shared_validation.parquet > /app/solution/shared_score.json\nSH\nsh /app/solution/shared_experiment.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/shared_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/hard_softmax_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 > /app/solution/shared.json 2> /app/solution/shared.log\n$PY /task/evaluate_public.py /app/solution/shared_validation.parquet > /app/solution/shared_score.json\nSH\nsh /app/solution/shared_experiment.sh" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 459, "nanos": 791548180 }, "formatted_output": "" } } ### exec · 2026-09-23T05:47:21.613Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(''' negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]''',''' diagnostic_columns = torch.linspace(0, len(pool) - 1, config.uniform_negatives + config.inbatch_negatives, device=device).long()\\n negative_logits = pool_logits[:, diagnostic_columns]''')\\ns=s.replace('\\\"restored best validation-BCE checkpoint:', '\\\"restored best chronological-validation checkpoint:')\\ns=s.replace('\\\"chronological BCE window:', '\\\"chronological target window:')\\np.write_text(s)\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:47:21.754Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"00d48e","wall_time_seconds":0.00000274,"exit_code":0,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab367c9fda887d2849514d8d170f74c", "usage": { "input_tokens": 73890, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 908, "reasoning_output_tokens": 722, "total_tokens": 74798 }, "turn_token_usage": { "input_tokens": 4267545, "cached_input_tokens": 4185856, "cache_write_input_tokens": 0, "output_tokens": 21558, "reasoning_output_tokens": 10186, "total_tokens": 4289103 }, "thread_token_usage": { "input_tokens": 4267545, "cached_input_tokens": 4185856, "cache_write_input_tokens": 0, "output_tokens": 21558, "reasoning_output_tokens": 10186, "total_tokens": 4289103 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dbf8279d-307b-444f-a578-0515ef47f337", "process_id": "21885", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]''',''' diagnostic_columns = torch.linspace(0, len(pool) - 1, config.uniform_negatives + config.inbatch_negatives, device=device).long()\n negative_logits = pool_logits[:, diagnostic_columns]''')\ns=s.replace('\"restored best validation-BCE checkpoint:', '\"restored best chronological-validation checkpoint:')\ns=s.replace('\"chronological BCE window:', '\"chronological target window:')\np.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' negative_logits = pool_logits[:, :config.uniform_negatives + config.inbatch_negatives]''',''' diagnostic_columns = torch.linspace(0, len(pool) - 1, config.uniform_negatives + config.inbatch_negatives, device=device).long()\n negative_logits = pool_logits[:, diagnostic_columns]''')\ns=s.replace('\"restored best validation-BCE checkpoint:', '\"restored best chronological-validation checkpoint:')\ns=s.replace('\"chronological BCE window:', '\"chronological target window:')\np.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 2740 }, "formatted_output": "" } } ## Step 61 · agent · 2026-09-23T05:47:27.696Z ### Thinking unavailable — no text recorded The first broad-pool run finished training in 139 seconds and is generating its public ranking. Its chronological holdout uses a fixed, filtered candidate pool, so an epoch extension can be selected using training-window data alone. ### sleep · 2026-09-23T05:47:29.736Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T05:47:59.744Z Wall time: 30.0041 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab367e9f4e087d2863d65aa3c71774a", "usage": { "input_tokens": 74855, "cached_input_tokens": 73728, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 18, "total_tokens": 74945 }, "turn_token_usage": { "input_tokens": 4342400, "cached_input_tokens": 4259584, "cache_write_input_tokens": 0, "output_tokens": 21648, "reasoning_output_tokens": 10204, "total_tokens": 4364048 }, "thread_token_usage": { "input_tokens": 4342400, "cached_input_tokens": 4259584, "cache_write_input_tokens": 0, "output_tokens": 21648, "reasoning_output_tokens": 10204, "total_tokens": 4364048 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_7qEXOave1HsGV7cvE5fGAP5j", "durationMs": 30000 } } ### exec · 2026-09-23T05:48:02.908Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/shared.log && test ! -f /app/solution/shared_score.json || cat /app/solution/shared_score.json && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:48:03.003Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"6bf559","wall_time_seconds":0.00000435,"exit_code":0,"original_token_count":66,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017989,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:48:02 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b6324825-7f08-49d4-9c8f-8e8baea778c8", "process_id": "25526", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/shared.log && test ! -f /app/solution/shared_score.json || cat /app/solution/shared_score.json && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/shared.log && test ! -f /app/solution/shared_score.json || cat /app/solution/shared_score.json && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017989,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:48:02 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017989,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:48:02 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4350 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017989,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:48:02 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3680fe69c87d29a35b110818f51d7", "usage": { "input_tokens": 74968, "cached_input_tokens": 74624, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 75028 }, "turn_token_usage": { "input_tokens": 4417368, "cached_input_tokens": 4334208, "cache_write_input_tokens": 0, "output_tokens": 21708, "reasoning_output_tokens": 10204, "total_tokens": 4439076 }, "thread_token_usage": { "input_tokens": 4417368, "cached_input_tokens": 4334208, "cache_write_input_tokens": 0, "output_tokens": 21708, "reasoning_output_tokens": 10204, "total_tokens": 4439076 } } ## Step 62 · agent · 2026-09-23T05:48:15.831Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:48:18.186Z { "code": "text(await tools.exec_command({cmd:\"sed -n '1590,1625p' /app/solution/train_current_model.py && sed -n '1725,1748p' /app/solution/train_current_model.py && sed -n '1840,1862p' /app/solution/train_current_model.py\",max_output_tokens:2500}));\n" } ### Tool output · 2026-09-23T05:48:18.277Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"35e86e","wall_time_seconds":0.00000313,"exit_code":0,"original_token_count":830,"output":"\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 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 \"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"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8548dc84-3dbf-423b-b718-1d7cb85cfdac", "process_id": "7265", "command": [ "/bin/sh", "-lc", "sed -n '1590,1625p' /app/solution/train_current_model.py && sed -n '1725,1748p' /app/solution/train_current_model.py && sed -n '1840,1862p' /app/solution/train_current_model.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1590,1625p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1725,1748p' /app/solution/train_current_model.py", "name": "train_current_model.py", "path": "/app/solution/train_current_model.py" }, { "type": "read", "cmd": "sed -n '1840,1862p' /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": "\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 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 \"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", "stderr": "", "aggregated_output": "\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 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 \"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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3130 }, "formatted_output": "\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 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 \"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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab368133f3c87d2a8e3d6755ab2920b", "usage": { "input_tokens": 75198, "cached_input_tokens": 74752, "cache_write_input_tokens": 0, "output_tokens": 433, "reasoning_output_tokens": 346, "total_tokens": 75631 }, "turn_token_usage": { "input_tokens": 4492566, "cached_input_tokens": 4408960, "cache_write_input_tokens": 0, "output_tokens": 22141, "reasoning_output_tokens": 10550, "total_tokens": 4514707 }, "thread_token_usage": { "input_tokens": 4492566, "cached_input_tokens": 4408960, "cache_write_input_tokens": 0, "output_tokens": 22141, "reasoning_output_tokens": 10550, "total_tokens": 4514707 } } ## Step 63 · agent · 2026-09-23T05:48:26.474Z ### Thinking unavailable — no text recorded One broad-pool epoch scores **0.017989 NDCG@10**, still below the baseline. It is much better than explicit top-k mining, but not yet a replacement. I’ll check a second epoch to see whether the faster objective benefits from more training. ### exec · 2026-09-23T05:48:39.583Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace('\\\"generator_state\\\": generator.get_state(),','\\\"generator_state\\\": generator.get_state(),\\\\n \\\"torch_rng_state\\\": torch.get_rng_state(),')\\ns=s.replace(''' start_epoch = int(payload[\\\"epoch\\\"])''',''' if \\\"torch_rng_state\\\" in payload and not objective_changed:\\n torch.set_rng_state(payload[\\\"torch_rng_state\\\"].cpu())\\n start_epoch = int(payload[\\\"epoch\\\"])''')\\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope\\n''',''' stored_filter_scope != config.negative_filter_scope\\n or stored_config.get(\\\"ranking_loss\\\", \\\"bce\\\") != config.ranking_loss\\n or stored_config.get(\\\"shared_negatives\\\", 2048) != config.shared_negatives\\n''')\\np.write_text(s)\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --epochs 2 --resume-checkpoint /app/solution/shared_checkpoints/epoch-01.pt > /app/solution/shared2.json 2> /app/solution/shared2.log\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/shared2_validation.parquet > /app/solution/shared2_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:48:40.750Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"cd9272","wall_time_seconds":1.000967736,"session_id":90523,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36822874487d285995ff1847936a9", "usage": { "input_tokens": 76483, "cached_input_tokens": 75008, "cache_write_input_tokens": 0, "output_tokens": 637, "reasoning_output_tokens": 162, "total_tokens": 77120 }, "turn_token_usage": { "input_tokens": 4569049, "cached_input_tokens": 4483968, "cache_write_input_tokens": 0, "output_tokens": 22778, "reasoning_output_tokens": 10712, "total_tokens": 4591827 }, "thread_token_usage": { "input_tokens": 4569049, "cached_input_tokens": 4483968, "cache_write_input_tokens": 0, "output_tokens": 22778, "reasoning_output_tokens": 10712, "total_tokens": 4591827 } } ## Step 64 · agent · 2026-09-23T05:49:01.269Z ### Thinking unavailable — no text recorded ## Step 65 · agent · 2026-09-23T05:49:09.552Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:49:26.631Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# Music discovery two-tower experiments\\n\\nThe original production code was reproduced without changing its architecture,\\nchronological target split, user set, cutoff features, or candidate eligibility.\\nAll validation fits use train events only. Final fitting uses train + validation\\nhistory, with the supplied test-cutoff preference and eligibility artifacts.\\n\\nThe original negative objective averages BCE over only 24 uniform and eight\\nfrequency-corrected in-batch examples. Most random negatives are easy; the\\nfrequency weights further reduce frequent competitors' contributions. The\\nweights are sampling heuristics, not impression propensity estimates.\\n\\nExplicit top-k hard mining was tested with full-history, target, active-like,\\nand duplicate filtering. It was too aggressive: it pushed recommendations far\\ninto the catalog tail and substantially reduced discovery quality. It is kept\\nas an optional experimental mode, not assumed to be an improvement.\\n\\nThe alternative shared-pool objective uses 2,048 uniform item proposals per\\nblock, deduplicates and encodes them once, and applies the same positive/like\\nfilters per user. The target competes with the legal pool through log-sum-exp.\\nHigh-scoring negatives receive more gradient automatically. Uniform proposals\\navoid item-dependent sampling corrections. The chronological holdout uses this\\nsame objective and a fixed proposal seed. Scoring remains a raw dot product\\nbetween a single user vector and precomputed item vectors.\\n\\n| Public validation experiment | Graded NDCG@10 |\\n| --- | ---: |\\n| Production, 1 epoch | 0.021280 |\\n| 16 explicit hard negatives, BCE, 1 epoch | 0.003568 |\\n| 16 explicit hard negatives, sampled softmax, 1 epoch | 0.005014 |\\n| Shared uniform pool, softmax, 1 epoch | 0.017989 |\\n\\nAll scores use `/task/evaluate_public.py`, with 4,263 scored users.\\nThe baseline top 10 covered 592 tracks with median catalog popularity rank 111;\\naggressive hard mining shifted that median to 5,424.\\n\\nCPU training per epoch was 226 seconds for production, 241 seconds for explicit\\nhard mining, and 139 seconds for the shared pool (four PyTorch threads).\\nExperiments run sequentially because concurrent full-size fits exceeded the\\n8 GB container memory limit.\\n\\n`test_hard_sampler.py` checks filtering, uniqueness, detached deterministic\\nmining, finite shared-softmax loss, and gradients into both towers.\\n`check_submission.py` checks the final schema, users, ranks, distinct items,\\nand test-cutoff candidate membership.\\n\\nThe environment's package interpreter is `/opt/task-venv/bin/python`.\\nMD\\ntail -5 /app/solution/shared2.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:49:26.720Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"48be30","wall_time_seconds":0.000004421,"exit_code":0,"original_token_count":102,"output":"target 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\nrestored optimizer state from /app/solution/shared_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nWed Sep 23 05:49:26 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-277e0c96-f742-480a-ac98-058453508120", "process_id": "67782", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# Music discovery two-tower experiments\n\nThe original production code was reproduced without changing its architecture,\nchronological target split, user set, cutoff features, or candidate eligibility.\nAll validation fits use train events only. Final fitting uses train + validation\nhistory, with the supplied test-cutoff preference and eligibility artifacts.\n\nThe original negative objective averages BCE over only 24 uniform and eight\nfrequency-corrected in-batch examples. Most random negatives are easy; the\nfrequency weights further reduce frequent competitors' contributions. The\nweights are sampling heuristics, not impression propensity estimates.\n\nExplicit top-k hard mining was tested with full-history, target, active-like,\nand duplicate filtering. It was too aggressive: it pushed recommendations far\ninto the catalog tail and substantially reduced discovery quality. It is kept\nas an optional experimental mode, not assumed to be an improvement.\n\nThe alternative shared-pool objective uses 2,048 uniform item proposals per\nblock, deduplicates and encodes them once, and applies the same positive/like\nfilters per user. The target competes with the legal pool through log-sum-exp.\nHigh-scoring negatives receive more gradient automatically. Uniform proposals\navoid item-dependent sampling corrections. The chronological holdout uses this\nsame objective and a fixed proposal seed. Scoring remains a raw dot product\nbetween a single user vector and precomputed item vectors.\n\n| Public validation experiment | Graded NDCG@10 |\n| --- | ---: |\n| Production, 1 epoch | 0.021280 |\n| 16 explicit hard negatives, BCE, 1 epoch | 0.003568 |\n| 16 explicit hard negatives, sampled softmax, 1 epoch | 0.005014 |\n| Shared uniform pool, softmax, 1 epoch | 0.017989 |\n\nAll scores use `/task/evaluate_public.py`, with 4,263 scored users.\nThe baseline top 10 covered 592 tracks with median catalog popularity rank 111;\naggressive hard mining shifted that median to 5,424.\n\nCPU training per epoch was 226 seconds for production, 241 seconds for explicit\nhard mining, and 139 seconds for the shared pool (four PyTorch threads).\nExperiments run sequentially because concurrent full-size fits exceeded the\n8 GB container memory limit.\n\n`test_hard_sampler.py` checks filtering, uniqueness, detached deterministic\nmining, finite shared-softmax loss, and gradients into both towers.\n`check_submission.py` checks the final schema, users, ranks, distinct items,\nand test-cutoff candidate membership.\n\nThe environment's package interpreter is `/opt/task-venv/bin/python`.\nMD\ntail -5 /app/solution/shared2.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# Music discovery two-tower experiments\n\nThe original production code was reproduced without changing its architecture,\nchronological target split, user set, cutoff features, or candidate eligibility.\nAll validation fits use train events only. Final fitting uses train + validation\nhistory, with the supplied test-cutoff preference and eligibility artifacts.\n\nThe original negative objective averages BCE over only 24 uniform and eight\nfrequency-corrected in-batch examples. Most random negatives are easy; the\nfrequency weights further reduce frequent competitors' contributions. The\nweights are sampling heuristics, not impression propensity estimates.\n\nExplicit top-k hard mining was tested with full-history, target, active-like,\nand duplicate filtering. It was too aggressive: it pushed recommendations far\ninto the catalog tail and substantially reduced discovery quality. It is kept\nas an optional experimental mode, not assumed to be an improvement.\n\nThe alternative shared-pool objective uses 2,048 uniform item proposals per\nblock, deduplicates and encodes them once, and applies the same positive/like\nfilters per user. The target competes with the legal pool through log-sum-exp.\nHigh-scoring negatives receive more gradient automatically. Uniform proposals\navoid item-dependent sampling corrections. The chronological holdout uses this\nsame objective and a fixed proposal seed. Scoring remains a raw dot product\nbetween a single user vector and precomputed item vectors.\n\n| Public validation experiment | Graded NDCG@10 |\n| --- | ---: |\n| Production, 1 epoch | 0.021280 |\n| 16 explicit hard negatives, BCE, 1 epoch | 0.003568 |\n| 16 explicit hard negatives, sampled softmax, 1 epoch | 0.005014 |\n| Shared uniform pool, softmax, 1 epoch | 0.017989 |\n\nAll scores use `/task/evaluate_public.py`, with 4,263 scored users.\nThe baseline top 10 covered 592 tracks with median catalog popularity rank 111;\naggressive hard mining shifted that median to 5,424.\n\nCPU training per epoch was 226 seconds for production, 241 seconds for explicit\nhard mining, and 139 seconds for the shared pool (four PyTorch threads).\nExperiments run sequentially because concurrent full-size fits exceeded the\n8 GB container memory limit.\n\n`test_hard_sampler.py` checks filtering, uniqueness, detached deterministic\nmining, finite shared-softmax loss, and gradients into both towers.\n`check_submission.py` checks the final schema, users, ranks, distinct items,\nand test-cutoff candidate membership.\n\nThe environment's package interpreter is `/opt/task-venv/bin/python`.\nMD\ntail -5 /app/solution/shared2.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target 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\nrestored optimizer state from /app/solution/shared_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nWed Sep 23 05:49:26 UTC 2026\n", "stderr": "", "aggregated_output": "target 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\nrestored optimizer state from /app/solution/shared_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nWed Sep 23 05:49:26 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4421 }, "formatted_output": "target 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\nrestored optimizer state from /app/solution/shared_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nWed Sep 23 05:49:26 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36838e62487d2b623640234a8ce77", "usage": { "input_tokens": 77177, "cached_input_tokens": 76288, "cache_write_input_tokens": 0, "output_tokens": 1367, "reasoning_output_tokens": 741, "total_tokens": 78544 }, "turn_token_usage": { "input_tokens": 4646226, "cached_input_tokens": 4560256, "cache_write_input_tokens": 0, "output_tokens": 24145, "reasoning_output_tokens": 11453, "total_tokens": 4670371 }, "thread_token_usage": { "input_tokens": 4646226, "cached_input_tokens": 4560256, "cache_write_input_tokens": 0, "output_tokens": 24145, "reasoning_output_tokens": 11453, "total_tokens": 4670371 } } ## Step 66 · agent · 2026-09-23T05:49:31.412Z ### Thinking unavailable — no text recorded The second epoch is underway, and its training loss is lower than the first epoch’s average. I’m recording all public scores, including the failed variants, so the final model choice is reviewable. ### sleep · 2026-09-23T05:49:33.360Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:50:18.374Z Wall time: 45.0042 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36867022487d28dea0aae4f9d06b1", "usage": { "input_tokens": 78736, "cached_input_tokens": 77056, "cache_write_input_tokens": 0, "output_tokens": 120, "reasoning_output_tokens": 52, "total_tokens": 78856 }, "turn_token_usage": { "input_tokens": 4724962, "cached_input_tokens": 4637312, "cache_write_input_tokens": 0, "output_tokens": 24265, "reasoning_output_tokens": 11505, "total_tokens": 4749227 }, "thread_token_usage": { "input_tokens": 4724962, "cached_input_tokens": 4637312, "cache_write_input_tokens": 0, "output_tokens": 24265, "reasoning_output_tokens": 11505, "total_tokens": 4749227 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_LumUZH1bQhBlhsj6K8OO2WZ8", "durationMs": 45000 } } ### exec · 2026-09-23T05:50:21.750Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/shared2.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:50:21.837Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"c8c4c2","wall_time_seconds":0.000005581,"exit_code":0,"original_token_count":90,"output":"resumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nepoch=2/2 batches=100 targets=401,186 batch_bce=6.337770 running_bce=6.330434 elapsed=53.2s\nepoch=2/2 batches=150 targets=603,258 batch_bce=6.520325 running_bce=6.320598 elapsed=79.4s\nWed Sep 23 05:50:21 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-77108cac-f12d-4952-bfe3-c144875b2d3d", "process_id": "80711", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/shared2.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/shared2.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nepoch=2/2 batches=100 targets=401,186 batch_bce=6.337770 running_bce=6.330434 elapsed=53.2s\nepoch=2/2 batches=150 targets=603,258 batch_bce=6.520325 running_bce=6.320598 elapsed=79.4s\nWed Sep 23 05:50:21 UTC 2026\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nepoch=2/2 batches=100 targets=401,186 batch_bce=6.337770 running_bce=6.330434 elapsed=53.2s\nepoch=2/2 batches=150 targets=603,258 batch_bce=6.520325 running_bce=6.320598 elapsed=79.4s\nWed Sep 23 05:50:21 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5581 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=6.561099\nepoch=2/2 batches=50 targets=203,757 batch_bce=6.497558 running_bce=6.369509 elapsed=27.2s\nepoch=2/2 batches=100 targets=401,186 batch_bce=6.337770 running_bce=6.330434 elapsed=53.2s\nepoch=2/2 batches=150 targets=603,258 batch_bce=6.520325 running_bce=6.320598 elapsed=79.4s\nWed Sep 23 05:50:21 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3689a9b2087d2aa498c6870c7addd", "usage": { "input_tokens": 78879, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 40, "reasoning_output_tokens": 0, "total_tokens": 78919 }, "turn_token_usage": { "input_tokens": 4803841, "cached_input_tokens": 4715904, "cache_write_input_tokens": 0, "output_tokens": 24305, "reasoning_output_tokens": 11505, "total_tokens": 4828146 }, "thread_token_usage": { "input_tokens": 4803841, "cached_input_tokens": 4715904, "cache_write_input_tokens": 0, "output_tokens": 24305, "reasoning_output_tokens": 11505, "total_tokens": 4828146 } } ## Step 67 · agent · 2026-09-23T05:50:47.302Z ### Thinking unavailable — no text recorded ## Step 68 · agent · 2026-09-23T05:50:51.087Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:50:51.181Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T05:51:01.188Z Wall time: 10.0038 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3689e07c887d2b6119dcf8a0f7cc9", "usage": { "input_tokens": 79120, "cached_input_tokens": 78720, "cache_write_input_tokens": 0, "output_tokens": 632, "reasoning_output_tokens": 611, "total_tokens": 79752 }, "turn_token_usage": { "input_tokens": 4882961, "cached_input_tokens": 4794624, "cache_write_input_tokens": 0, "output_tokens": 24937, "reasoning_output_tokens": 12116, "total_tokens": 4907898 }, "thread_token_usage": { "input_tokens": 4882961, "cached_input_tokens": 4794624, "cache_write_input_tokens": 0, "output_tokens": 24937, "reasoning_output_tokens": 12116, "total_tokens": 4907898 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_JqVNKdWh7rTu05LgebpSqIGG", "durationMs": 10000 } } ## Step 69 · agent · 2026-09-23T05:51:04.503Z Checkpoint selection still uses only the chronological holdout inside the fitting data. Public validation outcomes are used only to compare completed rankings, and their events will enter features only during the final refit. ### sleep · 2026-09-23T05:51:04.789Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:51:49.802Z Wall time: 45.0043 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab368c55d7087d2a3b50aa5b7526685", "usage": { "input_tokens": 79775, "cached_input_tokens": 78976, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 79839 }, "turn_token_usage": { "input_tokens": 4962736, "cached_input_tokens": 4873600, "cache_write_input_tokens": 0, "output_tokens": 25001, "reasoning_output_tokens": 12116, "total_tokens": 4987737 }, "thread_token_usage": { "input_tokens": 4962736, "cached_input_tokens": 4873600, "cache_write_input_tokens": 0, "output_tokens": 25001, "reasoning_output_tokens": 12116, "total_tokens": 4987737 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_gIuAgJtS86kmmLMMgzhImDZr", "durationMs": 45000 } } ### exec · 2026-09-23T05:51:53.429Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/shared2.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:51:53.680Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"e41439","wall_time_seconds":0.00000553,"exit_code":0,"original_token_count":132,"output":"epoch=2/2 batches=200 targets=787,814 batch_bce=5.932357 running_bce=6.310211 elapsed=103.5s\nepoch=2/2 batches=250 targets=988,643 batch_bce=5.984305 running_bce=6.292494 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared2_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=6.28210 validation_loss=6.33378 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.3 validation_seconds=12.3\nrestored best chronological-validation checkpoint: epoch=2 loss=6.333780\nWed Sep 23 05:51:53 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f5d32eee-2dfd-4ab7-9aaf-da3cb25f8aa3", "process_id": "31997", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/shared2.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/shared2.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=200 targets=787,814 batch_bce=5.932357 running_bce=6.310211 elapsed=103.5s\nepoch=2/2 batches=250 targets=988,643 batch_bce=5.984305 running_bce=6.292494 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared2_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=6.28210 validation_loss=6.33378 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.3 validation_seconds=12.3\nrestored best chronological-validation checkpoint: epoch=2 loss=6.333780\nWed Sep 23 05:51:53 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=5.932357 running_bce=6.310211 elapsed=103.5s\nepoch=2/2 batches=250 targets=988,643 batch_bce=5.984305 running_bce=6.292494 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared2_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=6.28210 validation_loss=6.33378 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.3 validation_seconds=12.3\nrestored best chronological-validation checkpoint: epoch=2 loss=6.333780\nWed Sep 23 05:51:53 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5530 }, "formatted_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=5.932357 running_bce=6.310211 elapsed=103.5s\nepoch=2/2 batches=250 targets=988,643 batch_bce=5.984305 running_bce=6.292494 elapsed=129.9s\nsaved epoch checkpoint: /app/solution/shared2_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=6.28210 validation_loss=6.33378 pair_accuracy=0.822 train_targets=1,052,953 validation_targets=130,103 train_seconds=138.3 validation_seconds=12.3\nrestored best chronological-validation checkpoint: epoch=2 loss=6.333780\nWed Sep 23 05:51:53 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab368f5fe2087d29eb0edbccb000150", "usage": { "input_tokens": 79862, "cached_input_tokens": 79616, "cache_write_input_tokens": 0, "output_tokens": 40, "reasoning_output_tokens": 0, "total_tokens": 79902 }, "turn_token_usage": { "input_tokens": 5042598, "cached_input_tokens": 4953216, "cache_write_input_tokens": 0, "output_tokens": 25041, "reasoning_output_tokens": 12116, "total_tokens": 5067639 }, "thread_token_usage": { "input_tokens": 5042598, "cached_input_tokens": 4953216, "cache_write_input_tokens": 0, "output_tokens": 25041, "reasoning_output_tokens": 12116, "total_tokens": 5067639 } } ## Step 70 · agent · 2026-09-23T05:52:04.431Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:52:07.125Z { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().split(b'\\\\x00')\\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\\n print(p.name,*[s for s in (p/'status').read_text().splitlines() if s.startswith('VmRSS')])\\n except OSError: pass\\nPY\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:52:07.311Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"792ab0","wall_time_seconds":0.00000326,"exit_code":0,"original_token_count":9,"output":"5882998784\n1230 VmRSS:\t 3216504 kB\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab368f9f6b087d298799ed83f0e57a5", "usage": { "input_tokens": 80133, "cached_input_tokens": 79744, "cache_write_input_tokens": 0, "output_tokens": 365, "reasoning_output_tokens": 209, "total_tokens": 80498 }, "turn_token_usage": { "input_tokens": 5122731, "cached_input_tokens": 5032960, "cache_write_input_tokens": 0, "output_tokens": 25406, "reasoning_output_tokens": 12325, "total_tokens": 5148137 }, "thread_token_usage": { "input_tokens": 5122731, "cached_input_tokens": 5032960, "cache_write_input_tokens": 0, "output_tokens": 25406, "reasoning_output_tokens": 12325, "total_tokens": 5148137 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf93d78b-ca60-4e0c-aea3-c6679b3772d6", "process_id": "48270", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\x00')\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\n print(p.name,*[s for s in (p/'status').read_text().splitlines() if s.startswith('VmRSS')])\n except OSError: pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.current && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\x00')\n if len(cmd)>1 and cmd[1].endswith(b'train_current_model.py'):\n print(p.name,*[s for s in (p/'status').read_text().splitlines() if s.startswith('VmRSS')])\n except OSError: pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "5882998784\n1230 VmRSS:\t 3216504 kB\n", "stderr": "", "aggregated_output": "5882998784\n1230 VmRSS:\t 3216504 kB\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3260 }, "formatted_output": "5882998784\n1230 VmRSS:\t 3216504 kB\n" } } ## Step 71 · agent · 2026-09-23T05:52:21.644Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:52:21.811Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T05:52:31.820Z Wall time: 10.0048 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36907911487d28beb9fe69a867a09", "usage": { "input_tokens": 80571, "cached_input_tokens": 80000, "cache_write_input_tokens": 0, "output_tokens": 388, "reasoning_output_tokens": 367, "total_tokens": 80959 }, "turn_token_usage": { "input_tokens": 5203302, "cached_input_tokens": 5112960, "cache_write_input_tokens": 0, "output_tokens": 25794, "reasoning_output_tokens": 12692, "total_tokens": 5229096 }, "thread_token_usage": { "input_tokens": 5203302, "cached_input_tokens": 5112960, "cache_write_input_tokens": 0, "output_tokens": 25794, "reasoning_output_tokens": 12692, "total_tokens": 5229096 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_mYDjyiuQ8IrjrRDP1hDWeqzq", "durationMs": 10000 } } ## Step 72 · agent · 2026-09-23T05:52:36.355Z The second epoch reduced chronological holdout loss from **6.5611 to 6.3338**, so the trainer selected it over epoch one. Its public ranking is now being generated; that score will determine the next step. ### sleep · 2026-09-23T05:52:36.625Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T05:53:21.649Z Wall time: 45.0194 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3691ff8e487d2b3a68959d9f969c8", "usage": { "input_tokens": 80982, "cached_input_tokens": 80384, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 0, "total_tokens": 81054 }, "turn_token_usage": { "input_tokens": 5284284, "cached_input_tokens": 5193344, "cache_write_input_tokens": 0, "output_tokens": 25866, "reasoning_output_tokens": 12692, "total_tokens": 5310150 }, "thread_token_usage": { "input_tokens": 5284284, "cached_input_tokens": 5193344, "cache_write_input_tokens": 0, "output_tokens": 25866, "reasoning_output_tokens": 12692, "total_tokens": 5310150 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a8091618-6477-4166-9c87-df8b82a540dc", "process_id": "90523", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('\"generator_state\": generator.get_state(),','\"generator_state\": generator.get_state(),\\n \"torch_rng_state\": torch.get_rng_state(),')\ns=s.replace(''' start_epoch = int(payload[\"epoch\"])''',''' if \"torch_rng_state\" in payload and not objective_changed:\n torch.set_rng_state(payload[\"torch_rng_state\"].cpu())\n start_epoch = int(payload[\"epoch\"])''')\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope\n''',''' stored_filter_scope != config.negative_filter_scope\n or stored_config.get(\"ranking_loss\", \"bce\") != config.ranking_loss\n or stored_config.get(\"shared_negatives\", 2048) != config.shared_negatives\n''')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --epochs 2 --resume-checkpoint /app/solution/shared_checkpoints/epoch-01.pt > /app/solution/shared2.json 2> /app/solution/shared2.log\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/shared2_validation.parquet > /app/solution/shared2_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace('\"generator_state\": generator.get_state(),','\"generator_state\": generator.get_state(),\\n \"torch_rng_state\": torch.get_rng_state(),')\ns=s.replace(''' start_epoch = int(payload[\"epoch\"])''',''' if \"torch_rng_state\" in payload and not objective_changed:\n torch.set_rng_state(payload[\"torch_rng_state\"].cpu())\n start_epoch = int(payload[\"epoch\"])''')\ns=s.replace(''' stored_filter_scope != config.negative_filter_scope\n''',''' stored_filter_scope != config.negative_filter_scope\n or stored_config.get(\"ranking_loss\", \"bce\") != config.ranking_loss\n or stored_config.get(\"shared_negatives\", 2048) != config.shared_negatives\n''')\np.write_text(s)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/shared2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/shared2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --epochs 2 --resume-checkpoint /app/solution/shared_checkpoints/epoch-01.pt > /app/solution/shared2.json 2> /app/solution/shared2.log\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/shared2_validation.parquet > /app/solution/shared2_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 242, "nanos": 828247196 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_KeUWMsS1hEmK4y5t5IneCApc", "durationMs": 45000 } } ### exec · 2026-09-23T05:53:25.207Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/shared2.log && test ! -f /app/solution/shared2_score.json || cat /app/solution/shared2_score.json && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:53:25.308Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"5fb1af","wall_time_seconds":0.00000436,"exit_code":0,"original_token_count":67,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016693,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:53:25 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab7cb223-4eb1-403f-97c6-ad27a8d2e1e8", "process_id": "65465", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/shared2.log && test ! -f /app/solution/shared2_score.json || cat /app/solution/shared2_score.json && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/shared2.log && test ! -f /app/solution/shared2_score.json || cat /app/solution/shared2_score.json && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016693,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:53:25 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016693,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:53:25 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/shared2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016693,\n \"quality_users\": 4263\n }\n}\nWed Sep 23 05:53:25 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36951e6a487d2bb22178d1f145011", "usage": { "input_tokens": 81077, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 0, "total_tokens": 81140 }, "turn_token_usage": { "input_tokens": 5365361, "cached_input_tokens": 5274112, "cache_write_input_tokens": 0, "output_tokens": 25929, "reasoning_output_tokens": 12692, "total_tokens": 5391290 }, "thread_token_usage": { "input_tokens": 5365361, "cached_input_tokens": 5274112, "cache_write_input_tokens": 0, "output_tokens": 25929, "reasoning_output_tokens": 12692, "total_tokens": 5391290 } } ## Step 73 · agent · 2026-09-23T05:53:48.420Z ### Thinking unavailable — no text recorded The second broad-pool epoch dropped to **0.016693 NDCG@10** despite better holdout loss, so I’m rejecting that extension. I’ll test the existing discovery-graded target weights with the broad pool to check whether emphasis on repeated listens is limiting discovery quality. ### exec · 2026-09-23T05:53:53.022Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 > /app/solution/graded.json 2> /app/solution/graded.log\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:53:54.188Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"ea111a","wall_time_seconds":1.001061597,"session_id":53068,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab369558dbc87d2853493c38456fab6", "usage": { "input_tokens": 81312, "cached_input_tokens": 80896, "cache_write_input_tokens": 0, "output_tokens": 755, "reasoning_output_tokens": 491, "total_tokens": 82067 }, "turn_token_usage": { "input_tokens": 5446673, "cached_input_tokens": 5355008, "cache_write_input_tokens": 0, "output_tokens": 26684, "reasoning_output_tokens": 13183, "total_tokens": 5473357 }, "thread_token_usage": { "input_tokens": 5446673, "cached_input_tokens": 5355008, "cache_write_input_tokens": 0, "output_tokens": 26684, "reasoning_output_tokens": 13183, "total_tokens": 5473357 } } ## Step 74 · agent · 2026-09-23T05:54:16.272Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:54:26.967Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(' hard_warmup_batches: int = 25',' hard_warmup_batches: int = 25\\\\n hard_start_epoch: int = 1')\\ns=s.replace('if config.hard_negatives and batches >= config.hard_warmup_batches:', 'if config.hard_negatives and epoch >= config.hard_start_epoch and batches >= config.hard_warmup_batches:')\\ns=s.replace(' parser.add_argument(\\\"--ranking-loss\\\",', ' parser.add_argument(\\\"--hard-start-epoch\\\", type=int, default=Config.hard_start_epoch)\\\\n parser.add_argument(\\\"--ranking-loss\\\",')\\ns=s.replace(' hard_warmup_batches=args.hard_warmup_batches,',' hard_warmup_batches=args.hard_warmup_batches,\\\\n hard_start_epoch=args.hard_start_epoch,')\\np.write_text(s)\\nPY\\ncat > /app/solution/gentle_experiment.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nPY=/opt/task-venv/bin/python\\nwhile ! test -s /app/solution/graded_score.json; do sleep 5; done\\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/gentle_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/gentle_checkpoints --threads 4 --ranking-loss bce --hard-negatives 2 --hard-pool-size 128 --hard-warmup-batches 0 --hard-start-epoch 2 --epochs 2 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt > /app/solution/gentle.json 2> /app/solution/gentle.log\\n$PY /task/evaluate_public.py /app/solution/gentle_validation.parquet > /app/solution/gentle_score.json\\nSH\\nsh /app/solution/gentle_experiment.sh\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:54:28.138Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"bfa4af","wall_time_seconds":1.001112181,"session_id":64246,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36972795487d2b29a89dff4cc8aa0", "usage": { "input_tokens": 82124, "cached_input_tokens": 81152, "cache_write_input_tokens": 0, "output_tokens": 1023, "reasoning_output_tokens": 516, "total_tokens": 83147 }, "turn_token_usage": { "input_tokens": 5528797, "cached_input_tokens": 5436160, "cache_write_input_tokens": 0, "output_tokens": 27707, "reasoning_output_tokens": 13699, "total_tokens": 5556504 }, "thread_token_usage": { "input_tokens": 5528797, "cached_input_tokens": 5436160, "cache_write_input_tokens": 0, "output_tokens": 27707, "reasoning_output_tokens": 13699, "total_tokens": 5556504 } } ## Step 75 · agent · 2026-09-23T05:54:30.809Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:54:31.772Z { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/graded.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:54:31.868Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"dc92db","wall_time_seconds":0.000004021,"exit_code":0,"original_token_count":139,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nWed Sep 23 05:54:31 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9387fea8-d7c3-4bc2-9f45-d16e8b33413b", "process_id": "31062", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/graded.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/graded.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nWed Sep 23 05:54:31 UTC 2026\n", "stderr": "", "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nWed Sep 23 05:54:31 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4021 }, "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological target 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\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nWed Sep 23 05:54:31 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab3699451ec87d2a40b7d16432d7c2d", "usage": { "input_tokens": 83205, "cached_input_tokens": 81920, "cache_write_input_tokens": 0, "output_tokens": 72, "reasoning_output_tokens": 30, "total_tokens": 83277 }, "turn_token_usage": { "input_tokens": 5612002, "cached_input_tokens": 5518080, "cache_write_input_tokens": 0, "output_tokens": 27779, "reasoning_output_tokens": 13729, "total_tokens": 5639781 }, "thread_token_usage": { "input_tokens": 5612002, "cached_input_tokens": 5518080, "cache_write_input_tokens": 0, "output_tokens": 27779, "reasoning_output_tokens": 13729, "total_tokens": 5639781 } } ## Step 76 · agent · 2026-09-23T05:54:52.010Z ### Thinking unavailable — no text recorded ## Step 77 · agent · 2026-09-23T05:55:13.591Z ### Thinking unavailable — no text recorded ## Step 78 · agent · 2026-09-23T05:55:34.529Z ### Thinking unavailable — no text recorded The discovery-graded run is training. I’ve also queued a conservative hard-negative test: start from the trained baseline, then introduce just two hard negatives per row in epoch two. This checks whether the earlier failure came from applying too much hard-negative pressure too soon. ### exec · 2026-09-23T05:55:36.747Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/graded.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:55:36.813Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"503370","wall_time_seconds":0.0000045,"exit_code":0,"original_token_count":100,"output":"full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nWed Sep 23 05:55:36 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-39a2ccaf-d49d-40c4-a2ad-ee19169e2b09", "process_id": "93480", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/graded.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/graded.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nWed Sep 23 05:55:36 UTC 2026\n", "stderr": "", "aggregated_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nWed Sep 23 05:55:36 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4500 }, "formatted_output": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nWed Sep 23 05:55:36 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab369981cd487d2b222533ff8ceea92", "usage": { "input_tokens": 83517, "cached_input_tokens": 83072, "cache_write_input_tokens": 0, "output_tokens": 1654, "reasoning_output_tokens": 1552, "total_tokens": 85171 }, "turn_token_usage": { "input_tokens": 5695519, "cached_input_tokens": 5601152, "cache_write_input_tokens": 0, "output_tokens": 29433, "reasoning_output_tokens": 15281, "total_tokens": 5724952 }, "thread_token_usage": { "input_tokens": 5695519, "cached_input_tokens": 5601152, "cache_write_input_tokens": 0, "output_tokens": 29433, "reasoning_output_tokens": 15281, "total_tokens": 5724952 } } ## Step 79 · agent · 2026-09-23T05:55:59.871Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:56:21.713Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/proposal_calibration.py <<'PY'\\n\\\"\\\"\\\"Measure global proposal correction while retaining an indexed dot score.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport json,sys\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nimport train_current_model as m\\nsys.path.insert(0,'/task')\\nimport evaluate_public as ev\\n\\ntorch.set_num_threads(2)\\ntorch.set_num_interop_threads(1)\\nc=m.Config(hard_negatives=0)\\nd,e=m.load_or_prepare(Path('/task'),'validation',c,Path('/app/solution/validation_cache.npz'))\\np=torch.load('/app/solution/baseline_checkpoints/best.pt',map_location='cpu',weights_only=False)\\nmodel=m.base.SequentialTwoTower(d.item_features,m.model_config(c))\\nmodel.load_state_dict(p['model_state']); model.eval()\\nwith torch.inference_mode():\\n q=m.inference_queries(model,d,c,torch.device('cpu'))\\n items=torch.cat([model.encode_items(torch.arange(s,min(s+2048,len(d.item_ids)))) for s in range(0,len(d.item_ids),2048)])\\nnp.savez('/app/solution/baseline_embeddings.npz',queries=q.numpy(),items=items.numpy())\\npartition=m.chronological_target_partition(d,c.validation_fraction,c.max_train_targets_per_user,c.max_validation_targets_per_user)\\ncounts=np.zeros(len(d.item_ids))\\nfor r,count in enumerate(partition.training_counts):\\n s=d.offsets[r]+partition.window_starts[r]+1\\n counts+=np.bincount(d.sequence_items[s:s+count],minlength=len(counts))\\np=(counts+1)/(counts.sum()+len(counts))\\ninc=-np.expm1(4096*np.log1p(-p))\\ncorrection=m.build_item_correction(d,c,partition.window_starts,partition.training_counts).numpy()\\nib=inc*correction; ib/=ib.sum()\\nproposal=.75/len(p)+.25*ib\\nbias=np.log(proposal*len(p))\\nnp.save('/app/solution/proposal_bias.npy',bias)\\ntruth=ev.validation_truth(ev.CandidateSet(Path('/task/data/validation_eligible_candidates.npz')),set(map(int,e.uids)))\\nreports=[]\\nfor alpha in [0.0,0.5,1.0,2.0]:\\n rows=[]\\n for start in range(0,len(q),64):\\n end=min(start+64,len(q))\\n scores=q[start:end]@items.T+torch.from_numpy((alpha*bias).astype(np.float32))[None,:]\\n eligible=np.unpackbits(e.packed[start:end],axis=1,count=len(e.item_ids),bitorder='little').astype(bool)\\n scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf)\\n selected=scores.topk(100,dim=1).indices.numpy()\\n for r,cols in enumerate(selected):\\n rows.extend((int(e.uids[start+r]),int(e.item_ids[col]),rank) for rank,col in enumerate(cols,1))\\n frame=pd.DataFrame(rows,columns=['uid','item_id','rank'])\\n path=Path(f'/app/solution/calibration_{alpha}_validation.parquet')\\n m.write_ranking(frame,path)\\n score=ev.score(ev.ranked_items(frame),truth)\\n reports.append(dict(alpha=alpha,**score)); print(reports[-1],flush=True)\\nPath('/app/solution/calibration_scores.json').write_text(json.dumps(reports,indent=2))\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/proposal_calibration.py > /app/solution/calibration.log 2>&1\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T05:56:22.886Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b4da7a","wall_time_seconds":1.001551625,"session_id":12512,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab369d927d487d2a6547551d610c0a5", "usage": { "input_tokens": 85379, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 1368, "reasoning_output_tokens": 516, "total_tokens": 86747 }, "turn_token_usage": { "input_tokens": 5780898, "cached_input_tokens": 5684480, "cache_write_input_tokens": 0, "output_tokens": 30801, "reasoning_output_tokens": 15797, "total_tokens": 5811699 }, "thread_token_usage": { "input_tokens": 5780898, "cached_input_tokens": 5684480, "cache_write_input_tokens": 0, "output_tokens": 30801, "reasoning_output_tokens": 15797, "total_tokens": 5811699 } } ## Step 80 · agent · 2026-09-23T05:56:29.547Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:56:30.819Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/graded.log && tail -2 /app/solution/calibration.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:56:30.951Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"338524","wall_time_seconds":0.00000794,"exit_code":0,"original_token_count":139,"output":"epoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.421542 running_bce=6.768412 elapsed=105.3s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:56:30 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-160f8152-e870-4961-a28a-bcf56e6a3b3d", "process_id": "38288", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/graded.log && tail -2 /app/solution/calibration.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/graded.log && tail -2 /app/solution/calibration.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.421542 running_bce=6.768412 elapsed=105.3s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:56:30 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.421542 running_bce=6.768412 elapsed=105.3s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:56:30 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7940 }, "formatted_output": "epoch=1/1 batches=50 targets=206,457 batch_bce=6.717603 running_bce=6.994904 elapsed=27.6s\nepoch=1/1 batches=100 targets=402,892 batch_bce=6.664186 running_bce=6.898245 elapsed=54.1s\nepoch=1/1 batches=150 targets=596,745 batch_bce=6.790597 running_bce=6.819479 elapsed=79.5s\nepoch=1/1 batches=200 targets=790,005 batch_bce=6.421542 running_bce=6.768412 elapsed=105.3s\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:56:30 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36a070e2087d2ba183fca47a38251", "usage": { "input_tokens": 86806, "cached_input_tokens": 85248, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 70, "total_tokens": 86929 }, "turn_token_usage": { "input_tokens": 5867704, "cached_input_tokens": 5769728, "cache_write_input_tokens": 0, "output_tokens": 30924, "reasoning_output_tokens": 15867, "total_tokens": 5898628 }, "thread_token_usage": { "input_tokens": 5867704, "cached_input_tokens": 5769728, "cache_write_input_tokens": 0, "output_tokens": 30924, "reasoning_output_tokens": 15867, "total_tokens": 5898628 } } ## Step 81 · agent · 2026-09-23T05:56:53.614Z ### Thinking unavailable — no text recorded ## Step 82 · agent · 2026-09-23T05:57:14.909Z ### Thinking unavailable — no text recorded ## Step 83 · agent · 2026-09-23T05:57:26.072Z ### Thinking unavailable — no text recorded I’m checking one additional sampler issue: unique in-batch draws do not follow the raw item-frequency distribution used by the baseline’s correction weights. I’ll test a global proposal correction estimated from training targets. It can be stored as one extra item-vector coordinate, preserving dot-product serving. ### exec · 2026-09-23T05:57:45.460Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/add_proposal.py <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(' shared_negatives: int = 2048',' shared_negatives: int = 2048\\\\n proposal_correction: float = 0.0')\\npos=s.index('\\\\ndef build_known_positive_filter(')\\ns=s[:pos]+'''\\ndef proposal_log_density(prepared: SequenceData, config: Config) -> np.ndarray:\\n \\\"\\\"\\\"Approximate the effective global mixed negative proposal.\\n\\n Unique in-batch sampling follows inclusion probabilities rather than raw\\n occurrence frequencies. Apply the trainer's clipped frequency weights to\\n that inclusion distribution, then mix with the uniform component. This is\\n a global calibration approximation: user-specific rejection is not modeled.\\n \\\"\\\"\\\"\\n partition = chronological_target_partition(\\n prepared, config.validation_fraction, config.max_train_targets_per_user,\\n config.max_validation_targets_per_user,\\n )\\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\\n for row, count in enumerate(partition.training_counts):\\n start = prepared.offsets[row] + partition.window_starts[row] + 1\\n counts += np.bincount(prepared.sequence_items[start:start+count], minlength=len(counts))\\n probabilities = (counts + 1) / (counts.sum() + len(counts))\\n inclusion = -np.expm1(4096 * np.log1p(-probabilities))\\n correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts).numpy()\\n weighted = inclusion * correction\\n weighted /= weighted.sum()\\n uniform_mix = config.uniform_negatives / (config.uniform_negatives + config.inbatch_negatives)\\n proposal = uniform_mix / len(counts) + (1 - uniform_mix) * weighted\\n return np.log(proposal * len(counts)).astype(np.float32)\\n\\n''' + s[pos:]\\ns=s.replace(''' items = torch.cat(item_parts)\\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\\n if config.proposal_correction:\\n # Constant user coordinate and an offline item coordinate keep this a\\n # single indexed dot product, with no request-time candidate features.\\n bias = config.proposal_correction * proposal_log_density(prepared, config)\\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\\n items = torch.cat([items, torch.from_numpy(bias)[:, None]], dim=1)\\n export_path = getattr(model, \\\"embedding_export_path\\\", None)\\n if export_path is not None:\\n np.savez(export_path, queries=queries.numpy(), items=items.numpy())\\n rows: list[tuple[int, int, int]] = []''')\\ns=s.replace(' parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)', ' parser.add_argument(\\\"--proposal-correction\\\", type=float, default=Config.proposal_correction)\\\\n parser.add_argument(\\\"--seed\\\", type=int, default=Config.seed)')\\ns=s.replace(' shared_negatives=args.shared_negatives,',' shared_negatives=args.shared_negatives,\\\\n proposal_correction=args.proposal_correction,')\\ns=s.replace(''' ranking, embedding_diagnostics = rank_candidates(''',''' model.embedding_export_path = args.output.resolve().with_suffix(\\\".vectors.npz\\\")\\n ranking, embedding_diagnostics = rank_candidates(''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/add_proposal.py\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\ntail -4 /app/solution/graded.log\\ntail -5 /app/solution/calibration.log\\ndate -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:57:45.835Z Script completed Wall time 0.4 seconds Output: {"chunk_id":"ab8789","wall_time_seconds":0.197995972,"exit_code":0,"original_token_count":125,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:57:45 UTC 2026\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36a0f32f887d29f21fe422c3f50a9", "usage": { "input_tokens": 87203, "cached_input_tokens": 86656, "cache_write_input_tokens": 0, "output_tokens": 2374, "reasoning_output_tokens": 1414, "total_tokens": 89577 }, "turn_token_usage": { "input_tokens": 5954907, "cached_input_tokens": 5856384, "cache_write_input_tokens": 0, "output_tokens": 33298, "reasoning_output_tokens": 17281, "total_tokens": 5988205 }, "thread_token_usage": { "input_tokens": 5954907, "cached_input_tokens": 5856384, "cache_write_input_tokens": 0, "output_tokens": 33298, "reasoning_output_tokens": 17281, "total_tokens": 5988205 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e76143e0-ed5d-4a0c-b92b-1d19fe6cf795", "process_id": "66764", "command": [ "/bin/sh", "-lc", "cat > /app/solution/add_proposal.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' shared_negatives: int = 2048',' shared_negatives: int = 2048\\n proposal_correction: float = 0.0')\npos=s.index('\\ndef build_known_positive_filter(')\ns=s[:pos]+'''\ndef proposal_log_density(prepared: SequenceData, config: Config) -> np.ndarray:\n \"\"\"Approximate the effective global mixed negative proposal.\n\n Unique in-batch sampling follows inclusion probabilities rather than raw\n occurrence frequencies. Apply the trainer's clipped frequency weights to\n that inclusion distribution, then mix with the uniform component. This is\n a global calibration approximation: user-specific rejection is not modeled.\n \"\"\"\n partition = chronological_target_partition(\n prepared, config.validation_fraction, config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for row, count in enumerate(partition.training_counts):\n start = prepared.offsets[row] + partition.window_starts[row] + 1\n counts += np.bincount(prepared.sequence_items[start:start+count], minlength=len(counts))\n probabilities = (counts + 1) / (counts.sum() + len(counts))\n inclusion = -np.expm1(4096 * np.log1p(-probabilities))\n correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts).numpy()\n weighted = inclusion * correction\n weighted /= weighted.sum()\n uniform_mix = config.uniform_negatives / (config.uniform_negatives + config.inbatch_negatives)\n proposal = uniform_mix / len(counts) + (1 - uniform_mix) * weighted\n return np.log(proposal * len(counts)).astype(np.float32)\n\n''' + s[pos:]\ns=s.replace(''' items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\n if config.proposal_correction:\n # Constant user coordinate and an offline item coordinate keep this a\n # single indexed dot product, with no request-time candidate features.\n bias = config.proposal_correction * proposal_log_density(prepared, config)\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\n items = torch.cat([items, torch.from_numpy(bias)[:, None]], dim=1)\n export_path = getattr(model, \"embedding_export_path\", None)\n if export_path is not None:\n np.savez(export_path, queries=queries.numpy(), items=items.numpy())\n rows: list[tuple[int, int, int]] = []''')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)', ' parser.add_argument(\"--proposal-correction\", type=float, default=Config.proposal_correction)\\n parser.add_argument(\"--seed\", type=int, default=Config.seed)')\ns=s.replace(' shared_negatives=args.shared_negatives,',' shared_negatives=args.shared_negatives,\\n proposal_correction=args.proposal_correction,')\ns=s.replace(''' ranking, embedding_diagnostics = rank_candidates(''',''' model.embedding_export_path = args.output.resolve().with_suffix(\".vectors.npz\")\n ranking, embedding_diagnostics = rank_candidates(''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_proposal.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -4 /app/solution/graded.log\ntail -5 /app/solution/calibration.log\ndate -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/add_proposal.py <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' shared_negatives: int = 2048',' shared_negatives: int = 2048\\n proposal_correction: float = 0.0')\npos=s.index('\\ndef build_known_positive_filter(')\ns=s[:pos]+'''\ndef proposal_log_density(prepared: SequenceData, config: Config) -> np.ndarray:\n \"\"\"Approximate the effective global mixed negative proposal.\n\n Unique in-batch sampling follows inclusion probabilities rather than raw\n occurrence frequencies. Apply the trainer's clipped frequency weights to\n that inclusion distribution, then mix with the uniform component. This is\n a global calibration approximation: user-specific rejection is not modeled.\n \"\"\"\n partition = chronological_target_partition(\n prepared, config.validation_fraction, config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for row, count in enumerate(partition.training_counts):\n start = prepared.offsets[row] + partition.window_starts[row] + 1\n counts += np.bincount(prepared.sequence_items[start:start+count], minlength=len(counts))\n probabilities = (counts + 1) / (counts.sum() + len(counts))\n inclusion = -np.expm1(4096 * np.log1p(-probabilities))\n correction = build_item_correction(prepared, config, partition.window_starts, partition.training_counts).numpy()\n weighted = inclusion * correction\n weighted /= weighted.sum()\n uniform_mix = config.uniform_negatives / (config.uniform_negatives + config.inbatch_negatives)\n proposal = uniform_mix / len(counts) + (1 - uniform_mix) * weighted\n return np.log(proposal * len(counts)).astype(np.float32)\n\n''' + s[pos:]\ns=s.replace(''' items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []''',''' items = torch.cat(item_parts)\n if config.proposal_correction:\n # Constant user coordinate and an offline item coordinate keep this a\n # single indexed dot product, with no request-time candidate features.\n bias = config.proposal_correction * proposal_log_density(prepared, config)\n queries = torch.cat([queries, torch.ones(len(queries), 1)], dim=1)\n items = torch.cat([items, torch.from_numpy(bias)[:, None]], dim=1)\n export_path = getattr(model, \"embedding_export_path\", None)\n if export_path is not None:\n np.savez(export_path, queries=queries.numpy(), items=items.numpy())\n rows: list[tuple[int, int, int]] = []''')\ns=s.replace(' parser.add_argument(\"--seed\", type=int, default=Config.seed)', ' parser.add_argument(\"--proposal-correction\", type=float, default=Config.proposal_correction)\\n parser.add_argument(\"--seed\", type=int, default=Config.seed)')\ns=s.replace(' shared_negatives=args.shared_negatives,',' shared_negatives=args.shared_negatives,\\n proposal_correction=args.proposal_correction,')\ns=s.replace(''' ranking, embedding_diagnostics = rank_candidates(''',''' model.embedding_export_path = args.output.resolve().with_suffix(\".vectors.npz\")\n ranking, embedding_diagnostics = rank_candidates(''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/add_proposal.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\ntail -4 /app/solution/graded.log\ntail -5 /app/solution/calibration.log\ndate -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:57:45 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:57:45 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 197575285 }, "formatted_output": "epoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nWed Sep 23 05:57:45 UTC 2026\n" } } ## Step 84 · agent · 2026-09-23T05:58:02.617Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T05:58:03.417Z { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current && tail -4 /app/solution/calibration.log && tail -4 /app/solution/graded.log\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T05:58:03.501Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"07054c","wall_time_seconds":0.000004901,"exit_code":0,"original_token_count":120,"output":"8085766144\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a0e7ffd-9ae6-4c3f-8ea6-c93e74a23cb9", "process_id": "25377", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current && tail -4 /app/solution/calibration.log && tail -4 /app/solution/graded.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.current && tail -4 /app/solution/calibration.log && tail -4 /app/solution/graded.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8085766144\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\n", "stderr": "", "aggregated_output": "8085766144\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4901 }, "formatted_output": "8085766144\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nepoch=1/1 batches=250 targets=987,417 batch_bce=6.951507 running_bce=6.726174 elapsed=131.7s\nsaved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.71730 validation_loss=6.65099 pair_accuracy=0.773 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.2 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.650992\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36a5a179c87d29cbd383846ec3d70", "usage": { "input_tokens": 89792, "cached_input_tokens": 87040, "cache_write_input_tokens": 0, "output_tokens": 410, "reasoning_output_tokens": 351, "total_tokens": 90202 }, "turn_token_usage": { "input_tokens": 6044699, "cached_input_tokens": 5943424, "cache_write_input_tokens": 0, "output_tokens": 33708, "reasoning_output_tokens": 17632, "total_tokens": 6078407 }, "thread_token_usage": { "input_tokens": 6044699, "cached_input_tokens": 5943424, "cache_write_input_tokens": 0, "output_tokens": 33708, "reasoning_output_tokens": 17632, "total_tokens": 6078407 } } ## Step 85 · agent · 2026-09-23T05:58:27.423Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T05:58:27.681Z { "duration_ms": 15000 } ### Tool output · 2026-09-23T05:58:42.690Z Wall time: 15.0039 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36a6bb81887d2ba2cc3081f02109c", "usage": { "input_tokens": 90408, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 487, "reasoning_output_tokens": 466, "total_tokens": 90895 }, "turn_token_usage": { "input_tokens": 6135107, "cached_input_tokens": 6033024, "cache_write_input_tokens": 0, "output_tokens": 34195, "reasoning_output_tokens": 18098, "total_tokens": 6169302 }, "thread_token_usage": { "input_tokens": 6135107, "cached_input_tokens": 6033024, "cache_write_input_tokens": 0, "output_tokens": 34195, "reasoning_output_tokens": 18098, "total_tokens": 6169302 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_jwt8vBqkblaexOIInuyJ3DOi", "durationMs": 15000 } } ## Step 86 · agent · 2026-09-23T05:58:47.595Z The discovery-graded model has finished training and is generating its ranking. The proposal-correction check evaluates several correction strengths on a fixed baseline checkpoint, isolating the correction from retraining effects. ### sleep · 2026-09-23T05:58:47.894Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T05:59:22.902Z Wall time: 35.0034 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36a92d9c487d29cf767cf1d254682", "usage": { "input_tokens": 90918, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 0, "total_tokens": 90982 }, "turn_token_usage": { "input_tokens": 6226025, "cached_input_tokens": 6123264, "cache_write_input_tokens": 0, "output_tokens": 34259, "reasoning_output_tokens": 18098, "total_tokens": 6260284 }, "thread_token_usage": { "input_tokens": 6226025, "cached_input_tokens": 6123264, "cache_write_input_tokens": 0, "output_tokens": 34259, "reasoning_output_tokens": 18098, "total_tokens": 6260284 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c45b0756-12d5-4244-ac30-bfa1f5d6520a", "process_id": "53068", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 > /app/solution/graded.json 2> /app/solution/graded.log\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 > /app/solution/graded.json 2> /app/solution/graded.log\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet > /app/solution/graded_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 298, "nanos": 603252737 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab5cc89e-c4e9-4be4-9ca0-cad9d8af55b1", "process_id": "12512", "command": [ "/bin/sh", "-lc", "cat > /app/solution/proposal_calibration.py <<'PY'\n\"\"\"Measure global proposal correction while retaining an indexed dot score.\"\"\"\nfrom pathlib import Path\nimport json,sys\nimport numpy as np\nimport pandas as pd\nimport torch\nimport train_current_model as m\nsys.path.insert(0,'/task')\nimport evaluate_public as ev\n\ntorch.set_num_threads(2)\ntorch.set_num_interop_threads(1)\nc=m.Config(hard_negatives=0)\nd,e=m.load_or_prepare(Path('/task'),'validation',c,Path('/app/solution/validation_cache.npz'))\np=torch.load('/app/solution/baseline_checkpoints/best.pt',map_location='cpu',weights_only=False)\nmodel=m.base.SequentialTwoTower(d.item_features,m.model_config(c))\nmodel.load_state_dict(p['model_state']); model.eval()\nwith torch.inference_mode():\n q=m.inference_queries(model,d,c,torch.device('cpu'))\n items=torch.cat([model.encode_items(torch.arange(s,min(s+2048,len(d.item_ids)))) for s in range(0,len(d.item_ids),2048)])\nnp.savez('/app/solution/baseline_embeddings.npz',queries=q.numpy(),items=items.numpy())\npartition=m.chronological_target_partition(d,c.validation_fraction,c.max_train_targets_per_user,c.max_validation_targets_per_user)\ncounts=np.zeros(len(d.item_ids))\nfor r,count in enumerate(partition.training_counts):\n s=d.offsets[r]+partition.window_starts[r]+1\n counts+=np.bincount(d.sequence_items[s:s+count],minlength=len(counts))\np=(counts+1)/(counts.sum()+len(counts))\ninc=-np.expm1(4096*np.log1p(-p))\ncorrection=m.build_item_correction(d,c,partition.window_starts,partition.training_counts).numpy()\nib=inc*correction; ib/=ib.sum()\nproposal=.75/len(p)+.25*ib\nbias=np.log(proposal*len(p))\nnp.save('/app/solution/proposal_bias.npy',bias)\ntruth=ev.validation_truth(ev.CandidateSet(Path('/task/data/validation_eligible_candidates.npz')),set(map(int,e.uids)))\nreports=[]\nfor alpha in [0.0,0.5,1.0,2.0]:\n rows=[]\n for start in range(0,len(q),64):\n end=min(start+64,len(q))\n scores=q[start:end]@items.T+torch.from_numpy((alpha*bias).astype(np.float32))[None,:]\n eligible=np.unpackbits(e.packed[start:end],axis=1,count=len(e.item_ids),bitorder='little').astype(bool)\n scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf)\n selected=scores.topk(100,dim=1).indices.numpy()\n for r,cols in enumerate(selected):\n rows.extend((int(e.uids[start+r]),int(e.item_ids[col]),rank) for rank,col in enumerate(cols,1))\n frame=pd.DataFrame(rows,columns=['uid','item_id','rank'])\n path=Path(f'/app/solution/calibration_{alpha}_validation.parquet')\n m.write_ranking(frame,path)\n score=ev.score(ev.ranked_items(frame),truth)\n reports.append(dict(alpha=alpha,**score)); print(reports[-1],flush=True)\nPath('/app/solution/calibration_scores.json').write_text(json.dumps(reports,indent=2))\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/proposal_calibration.py > /app/solution/calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/proposal_calibration.py <<'PY'\n\"\"\"Measure global proposal correction while retaining an indexed dot score.\"\"\"\nfrom pathlib import Path\nimport json,sys\nimport numpy as np\nimport pandas as pd\nimport torch\nimport train_current_model as m\nsys.path.insert(0,'/task')\nimport evaluate_public as ev\n\ntorch.set_num_threads(2)\ntorch.set_num_interop_threads(1)\nc=m.Config(hard_negatives=0)\nd,e=m.load_or_prepare(Path('/task'),'validation',c,Path('/app/solution/validation_cache.npz'))\np=torch.load('/app/solution/baseline_checkpoints/best.pt',map_location='cpu',weights_only=False)\nmodel=m.base.SequentialTwoTower(d.item_features,m.model_config(c))\nmodel.load_state_dict(p['model_state']); model.eval()\nwith torch.inference_mode():\n q=m.inference_queries(model,d,c,torch.device('cpu'))\n items=torch.cat([model.encode_items(torch.arange(s,min(s+2048,len(d.item_ids)))) for s in range(0,len(d.item_ids),2048)])\nnp.savez('/app/solution/baseline_embeddings.npz',queries=q.numpy(),items=items.numpy())\npartition=m.chronological_target_partition(d,c.validation_fraction,c.max_train_targets_per_user,c.max_validation_targets_per_user)\ncounts=np.zeros(len(d.item_ids))\nfor r,count in enumerate(partition.training_counts):\n s=d.offsets[r]+partition.window_starts[r]+1\n counts+=np.bincount(d.sequence_items[s:s+count],minlength=len(counts))\np=(counts+1)/(counts.sum()+len(counts))\ninc=-np.expm1(4096*np.log1p(-p))\ncorrection=m.build_item_correction(d,c,partition.window_starts,partition.training_counts).numpy()\nib=inc*correction; ib/=ib.sum()\nproposal=.75/len(p)+.25*ib\nbias=np.log(proposal*len(p))\nnp.save('/app/solution/proposal_bias.npy',bias)\ntruth=ev.validation_truth(ev.CandidateSet(Path('/task/data/validation_eligible_candidates.npz')),set(map(int,e.uids)))\nreports=[]\nfor alpha in [0.0,0.5,1.0,2.0]:\n rows=[]\n for start in range(0,len(q),64):\n end=min(start+64,len(q))\n scores=q[start:end]@items.T+torch.from_numpy((alpha*bias).astype(np.float32))[None,:]\n eligible=np.unpackbits(e.packed[start:end],axis=1,count=len(e.item_ids),bitorder='little').astype(bool)\n scores.masked_fill_(~torch.from_numpy(eligible),-torch.inf)\n selected=scores.topk(100,dim=1).indices.numpy()\n for r,cols in enumerate(selected):\n rows.extend((int(e.uids[start+r]),int(e.item_ids[col]),rank) for rank,col in enumerate(cols,1))\n frame=pd.DataFrame(rows,columns=['uid','item_id','rank'])\n path=Path(f'/app/solution/calibration_{alpha}_validation.parquet')\n m.write_ranking(frame,path)\n score=ev.score(ev.ranked_items(frame),truth)\n reports.append(dict(alpha=alpha,**score)); print(reports[-1],flush=True)\nPath('/app/solution/calibration_scores.json').write_text(json.dumps(reports,indent=2))\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/proposal_calibration.py > /app/solution/calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 155, "nanos": 135525588 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_SD9ze2hd5k6YCK8Lrwz4V2iY", "durationMs": 35000 } } ### exec · 2026-09-23T05:59:27.172Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/calibration.log && test ! -f /app/solution/graded_score.json || cat /app/solution/graded_score.json && tail -5 /app/solution/gentle.log 2>/dev/null && date -u\",max_output_tokens:2000}));\n" } ### Tool output · 2026-09-23T05:59:27.294Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"1413c4","wall_time_seconds":0.000023181,"exit_code":0,"original_token_count":211,"output":"inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\n{'alpha': 0.0, 'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n{'alpha': 0.5, 'quality_ndcg@10': 0.021131, 'quality_users': 4263}\n{'alpha': 1.0, 'quality_ndcg@10': 0.021063, 'quality_users': 4263}\n{'alpha': 2.0, 'quality_ndcg@10': 0.020748, 'quality_users': 4263}\n{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023759,\n \"quality_users\": 4263\n }\n}\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\nrestored optimizer state from /app/solution/baseline_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.496636\nWed Sep 23 05:59:27 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b3466cb-7348-4e43-a432-b7b9a290d6dc", "process_id": "18753", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/calibration.log && test ! -f /app/solution/graded_score.json || cat /app/solution/graded_score.json && tail -5 /app/solution/gentle.log 2>/dev/null && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/calibration.log && test ! -f /app/solution/graded_score.json || cat /app/solution/graded_score.json && tail -5 /app/solution/gentle.log 2>/dev/null && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\n{'alpha': 0.0, 'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n{'alpha': 0.5, 'quality_ndcg@10': 0.021131, 'quality_users': 4263}\n{'alpha': 1.0, 'quality_ndcg@10': 0.021063, 'quality_users': 4263}\n{'alpha': 2.0, 'quality_ndcg@10': 0.020748, 'quality_users': 4263}\n{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023759,\n \"quality_users\": 4263\n }\n}\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\nrestored optimizer state from /app/solution/baseline_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.496636\nWed Sep 23 05:59:27 UTC 2026\n", "stderr": "", "aggregated_output": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\n{'alpha': 0.0, 'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n{'alpha': 0.5, 'quality_ndcg@10': 0.021131, 'quality_users': 4263}\n{'alpha': 1.0, 'quality_ndcg@10': 0.021063, 'quality_users': 4263}\n{'alpha': 2.0, 'quality_ndcg@10': 0.020748, 'quality_users': 4263}\n{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023759,\n \"quality_users\": 4263\n }\n}\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\nrestored optimizer state from /app/solution/baseline_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.496636\nWed Sep 23 05:59:27 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 23181 }, "formatted_output": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\n{'alpha': 0.0, 'quality_ndcg@10': 0.02128, 'quality_users': 4263}\n{'alpha': 0.5, 'quality_ndcg@10': 0.021131, 'quality_users': 4263}\n{'alpha': 1.0, 'quality_ndcg@10': 0.021063, 'quality_users': 4263}\n{'alpha': 2.0, 'quality_ndcg@10': 0.020748, 'quality_users': 4263}\n{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023759,\n \"quality_users\": 4263\n }\n}\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\nrestored optimizer state from /app/solution/baseline_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.496636\nWed Sep 23 05:59:27 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36abb250487d2bda2088ece68d29d", "usage": { "input_tokens": 91005, "cached_input_tokens": 90752, "cache_write_input_tokens": 0, "output_tokens": 80, "reasoning_output_tokens": 0, "total_tokens": 91085 }, "turn_token_usage": { "input_tokens": 6317030, "cached_input_tokens": 6214016, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 18098, "total_tokens": 6351369 }, "thread_token_usage": { "input_tokens": 6317030, "cached_input_tokens": 6214016, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 18098, "total_tokens": 6351369 } } ## Step 87 · agent · 2026-09-23T05:59:47.492Z ### Thinking unavailable — no text recorded ## Step 88 · agent · 2026-09-23T06:00:04.439Z ### Thinking unavailable — no text recorded The joint change improves the public score: shared-pool softmax with discovery-graded weights reaches **0.023759 NDCG@10**, versus **0.021280** for production—about **11.6% higher**. Proposal correction did not help and will stay disabled. The conservative hard-negative run is the remaining comparison before final selection. ### exec · 2026-09-23T06:00:08.357Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/gentle.log && date -u && /opt/task-venv/bin/python /app/solution/check_submission.py\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:00:10.539Z Script completed Wall time 2.2 seconds Output: {"chunk_id":"eaa322","wall_time_seconds":2.008323939,"exit_code":0,"original_token_count":123,"output":"target 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\nrestored optimizer state from /app/solution/baseline_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nWed Sep 23 06:00:08 UTC 2026\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36abf9db887d2a3f3ec0756e20da5", "usage": { "input_tokens": 91460, "cached_input_tokens": 90880, "cache_write_input_tokens": 0, "output_tokens": 1172, "reasoning_output_tokens": 1034, "total_tokens": 92632 }, "turn_token_usage": { "input_tokens": 6408490, "cached_input_tokens": 6304896, "cache_write_input_tokens": 0, "output_tokens": 35511, "reasoning_output_tokens": 19132, "total_tokens": 6444001 }, "thread_token_usage": { "input_tokens": 6408490, "cached_input_tokens": 6304896, "cache_write_input_tokens": 0, "output_tokens": 35511, "reasoning_output_tokens": 19132, "total_tokens": 6444001 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-df2de8b3-8a74-4a05-af97-00ffb6eb59ea", "process_id": "70370", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/gentle.log && date -u && /opt/task-venv/bin/python /app/solution/check_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/gentle.log && date -u && /opt/task-venv/bin/python /app/solution/check_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n", "stderr": "", "aggregated_output": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 8317538 }, "formatted_output": "Validated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n" } } ## Step 89 · agent · 2026-09-23T06:00:29.309Z ### Thinking unavailable — no text recorded ## Step 90 · agent · 2026-09-23T06:00:47.406Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:00:47.565Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T06:01:17.575Z Wall time: 30.0057 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36aeaf32087d2980bb66145085bc2", "usage": { "input_tokens": 92849, "cached_input_tokens": 91264, "cache_write_input_tokens": 0, "output_tokens": 1055, "reasoning_output_tokens": 1034, "total_tokens": 93904 }, "turn_token_usage": { "input_tokens": 6501339, "cached_input_tokens": 6396160, "cache_write_input_tokens": 0, "output_tokens": 36566, "reasoning_output_tokens": 20166, "total_tokens": 6537905 }, "thread_token_usage": { "input_tokens": 6501339, "cached_input_tokens": 6396160, "cache_write_input_tokens": 0, "output_tokens": 36566, "reasoning_output_tokens": 20166, "total_tokens": 6537905 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_EGGnBtA83XHCodzTiFLMJKdA", "durationMs": 30000 } } ### exec · 2026-09-23T06:01:21.522Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/gentle.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:01:21.646Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"201316","wall_time_seconds":0.000004241,"exit_code":0,"original_token_count":90,"output":"resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nWed Sep 23 06:01:21 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-29837d41-745e-44f3-a437-3bb716f92a69", "process_id": "88153", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/gentle.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/gentle.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nWed Sep 23 06:01:21 UTC 2026\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nWed Sep 23 06:01:21 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4241 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nWed Sep 23 06:01:21 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36b2dce2487d283e7aa6ea0e277b0", "usage": { "input_tokens": 93927, "cached_input_tokens": 92672, "cache_write_input_tokens": 0, "output_tokens": 41, "reasoning_output_tokens": 0, "total_tokens": 93968 }, "turn_token_usage": { "input_tokens": 6595266, "cached_input_tokens": 6488832, "cache_write_input_tokens": 0, "output_tokens": 36607, "reasoning_output_tokens": 20166, "total_tokens": 6631873 }, "thread_token_usage": { "input_tokens": 6595266, "cached_input_tokens": 6488832, "cache_write_input_tokens": 0, "output_tokens": 36607, "reasoning_output_tokens": 20166, "total_tokens": 6631873 } } ## Step 91 · agent · 2026-09-23T06:01:42.485Z ### Thinking unavailable — no text recorded The best measured model now combines broader negative competition with weights aligned to novel organic outcomes, and it remains faster to train than production. Once the conservative comparison finishes, I’ll refit the best configuration on `train + val` and replace the fallback only after the new file passes validation. ### exec · 2026-09-23T06:01:53.741Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(' hard_negatives: int = 16',' hard_negatives: int = 0').replace(' ranking_loss: str = \\\"bce\\\"',' ranking_loss: str = \\\"shared-softmax\\\"').replace(' target_weighting: str = \\\"organic-strong\\\"',' target_weighting: str = \\\"discovery-graded\\\"').replace(' ordinary_target_weight: float = 0.25',' ordinary_target_weight: float = 0.1').replace(' organic_like_target_weight: float = 3.0',' organic_like_target_weight: float = 7.0')\\ns=s.replace('''normalization, and raw dot-product retrieval. A shared candidate pool is scored\\nby the current towers during training to mix hard competitors with random\\nnegatives. Complete training-positive and active-like filters are preserved.''','''normalization, and raw dot-product retrieval. The default objective scores a\\nshared uniform candidate pool with filtered sampled softmax and discovery-graded\\ntarget weights. High-scoring negatives automatically receive larger gradients.\\nComplete training-positive and active-like filters are preserved.''')\\ns=s.replace('Compute one logical block\\\\'s exact BCE in bounded-memory slices.', 'Compute one logical block\\\\'s weighted objective in bounded-memory slices.')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\",max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:01:58.755Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"07bedb","wall_time_seconds":4.841645929,"exit_code":0,"original_token_count":24,"output":"Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36b31f6dc87d2948f7b5c19cfa69e", "usage": { "input_tokens": 94165, "cached_input_tokens": 93696, "cache_write_input_tokens": 0, "output_tokens": 939, "reasoning_output_tokens": 516, "total_tokens": 95104 }, "turn_token_usage": { "input_tokens": 6689431, "cached_input_tokens": 6582528, "cache_write_input_tokens": 0, "output_tokens": 37546, "reasoning_output_tokens": 20682, "total_tokens": 6726977 }, "thread_token_usage": { "input_tokens": 6689431, "cached_input_tokens": 6582528, "cache_write_input_tokens": 0, "output_tokens": 37546, "reasoning_output_tokens": 20682, "total_tokens": 6726977 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4cba48ab-968b-4e84-858e-1ab8c4a0fdf9", "process_id": "58101", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' hard_negatives: int = 16',' hard_negatives: int = 0').replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"shared-softmax\"').replace(' target_weighting: str = \"organic-strong\"',' target_weighting: str = \"discovery-graded\"').replace(' ordinary_target_weight: float = 0.25',' ordinary_target_weight: float = 0.1').replace(' organic_like_target_weight: float = 3.0',' organic_like_target_weight: float = 7.0')\ns=s.replace('''normalization, and raw dot-product retrieval. A shared candidate pool is scored\nby the current towers during training to mix hard competitors with random\nnegatives. Complete training-positive and active-like filters are preserved.''','''normalization, and raw dot-product retrieval. The default objective scores a\nshared uniform candidate pool with filtered sampled softmax and discovery-graded\ntarget weights. High-scoring negatives automatically receive larger gradients.\nComplete training-positive and active-like filters are preserved.''')\ns=s.replace('Compute one logical block\\'s exact BCE in bounded-memory slices.', 'Compute one logical block\\'s weighted objective in bounded-memory slices.')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' hard_negatives: int = 16',' hard_negatives: int = 0').replace(' ranking_loss: str = \"bce\"',' ranking_loss: str = \"shared-softmax\"').replace(' target_weighting: str = \"organic-strong\"',' target_weighting: str = \"discovery-graded\"').replace(' ordinary_target_weight: float = 0.25',' ordinary_target_weight: float = 0.1').replace(' organic_like_target_weight: float = 3.0',' organic_like_target_weight: float = 7.0')\ns=s.replace('''normalization, and raw dot-product retrieval. A shared candidate pool is scored\nby the current towers during training to mix hard competitors with random\nnegatives. Complete training-positive and active-like filters are preserved.''','''normalization, and raw dot-product retrieval. The default objective scores a\nshared uniform candidate pool with filtered sampled softmax and discovery-graded\ntarget weights. High-scoring negatives automatically receive larger gradients.\nComplete training-positive and active-like filters are preserved.''')\ns=s.replace('Compute one logical block\\'s exact BCE in bounded-memory slices.', 'Compute one logical block\\'s weighted objective in bounded-memory slices.')\np.write_text(s)\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\n", "stderr": "", "aggregated_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 841637918 }, "formatted_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\n" } } ## Step 92 · agent · 2026-09-23T06:02:22.429Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:02:22.665Z { "duration_ms": 30000 } ### Tool output · 2026-09-23T06:02:52.673Z Wall time: 30.0032 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36b56efa087d2ada0de1ea5a2dede", "usage": { "input_tokens": 95178, "cached_input_tokens": 93952, "cache_write_input_tokens": 0, "output_tokens": 532, "reasoning_output_tokens": 511, "total_tokens": 95710 }, "turn_token_usage": { "input_tokens": 6784609, "cached_input_tokens": 6676480, "cache_write_input_tokens": 0, "output_tokens": 38078, "reasoning_output_tokens": 21193, "total_tokens": 6822687 }, "thread_token_usage": { "input_tokens": 6784609, "cached_input_tokens": 6676480, "cache_write_input_tokens": 0, "output_tokens": 38078, "reasoning_output_tokens": 21193, "total_tokens": 6822687 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_8eDVjOzTHSLtHno9TM5DkD3s", "durationMs": 30000 } } ### exec · 2026-09-23T06:02:56.650Z { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/gentle.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:02:56.794Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"ce5d4d","wall_time_seconds":0.00000516,"exit_code":0,"original_token_count":137,"output":"resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nepoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nWed Sep 23 06:02:56 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b0097178-f589-4564-bc9f-f7eb8e748568", "process_id": "70402", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/gentle.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/gentle.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nepoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nWed Sep 23 06:02:56 UTC 2026\n", "stderr": "", "aggregated_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nepoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nWed Sep 23 06:02:56 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5160 }, "formatted_output": "resumed validation-BCE training: epoch=1 loss=0.496636\nepoch=2/2 batches=50 targets=203,757 batch_bce=0.527338 running_bce=0.524277 elapsed=44.7s\nepoch=2/2 batches=100 targets=401,186 batch_bce=0.526281 running_bce=0.517997 elapsed=88.4s\nepoch=2/2 batches=150 targets=603,258 batch_bce=0.513967 running_bce=0.513837 elapsed=132.8s\nepoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nWed Sep 23 06:02:56 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36b8ce53487d2a3c4bc90c3909b97", "usage": { "input_tokens": 95733, "cached_input_tokens": 94976, "cache_write_input_tokens": 0, "output_tokens": 41, "reasoning_output_tokens": 0, "total_tokens": 95774 }, "turn_token_usage": { "input_tokens": 6880342, "cached_input_tokens": 6771456, "cache_write_input_tokens": 0, "output_tokens": 38119, "reasoning_output_tokens": 21193, "total_tokens": 6918461 }, "thread_token_usage": { "input_tokens": 6880342, "cached_input_tokens": 6771456, "cache_write_input_tokens": 0, "output_tokens": 38119, "reasoning_output_tokens": 21193, "total_tokens": 6918461 } } ## Step 93 · agent · 2026-09-23T06:03:13.498Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:03:29.108Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/refit_best.py <<'PY'\\n\\\"\\\"\\\"Refit the selected public configuration and atomically install a valid artifact.\\\"\\\"\\\"\\nimport json, os, shlex, subprocess, time\\nfrom pathlib import Path\\nroot=Path('/app/solution')\\nwhile not (root/'gentle_score.json').is_file():\\n time.sleep(3)\\nnames=['graded','gentle']\\nbest=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\\nreport=json.loads((root/f'{best}.json').read_text())\\nscore=json.loads((root/f'{best}_score.json').read_text())['scores']['quality_ndcg@10']\\nc=report['config']\\n(root/'selection.json').write_text(json.dumps({'experiment':best,'public_ndcg10':score,'baseline_ndcg10':0.021280,'config':c},indent=2))\\npy='/opt/task-venv/bin/python'\\ncmd=[py,str(root/'train_current_model.py'),'--stage','submission','--public-dir','/task','--output',str(root/'final_candidate.parquet'),'--prepared-cache',str(root/'submission_cache.npz'),'--checkpoint-dir',str(root/'final_checkpoints'),'--threads','4']\\nfor field in ['epochs','ranking_loss','hard_negatives','hard_pool_size','hard_warmup_batches','hard_start_epoch','target_weighting','ordinary_target_weight','organic_strong_target_weight','organic_like_target_weight','shared_negatives']:\\n if field in c: cmd += ['--'+field.replace('_','-'),str(c[field])]\\n(root/'submission_command.txt').write_text(shlex.join(cmd)+'\\\\n')\\nprint('Refitting',best,'public NDCG',score,flush=True)\\nwith (root/'final.json').open('w') as out,(root/'final.log').open('w') as err:\\n subprocess.run(cmd,stdout=out,stderr=err,check=True)\\nresult=subprocess.run([py,str(root/'check_submission.py'),str(root/'final_candidate.parquet')],capture_output=True,text=True,check=True)\\n(root/'submission_check.txt').write_text(result.stdout)\\nos.replace(root/'final_candidate.parquet',root/'submission.parquet')\\nprint(result.stdout,flush=True)\\nPY\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:03:30.278Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"5ccdac","wall_time_seconds":1.001479547,"session_id":65839,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36b9112bc87d284749bc979c59316", "usage": { "input_tokens": 96048, "cached_input_tokens": 95616, "cache_write_input_tokens": 0, "output_tokens": 946, "reasoning_output_tokens": 364, "total_tokens": 96994 }, "turn_token_usage": { "input_tokens": 6976390, "cached_input_tokens": 6867072, "cache_write_input_tokens": 0, "output_tokens": 39065, "reasoning_output_tokens": 21557, "total_tokens": 7015455 }, "thread_token_usage": { "input_tokens": 6976390, "cached_input_tokens": 6867072, "cache_write_input_tokens": 0, "output_tokens": 39065, "reasoning_output_tokens": 21557, "total_tokens": 7015455 } } ## Step 94 · agent · 2026-09-23T06:03:38.496Z ### Thinking unavailable — no text recorded The final refit is queued to select the higher public score from the discovery-graded model and the conservative hard-negative model. It will write a temporary ranking, validate every required invariant, and then atomically replace `submission.parquet`. ### sleep · 2026-09-23T06:03:39.963Z { "duration_ms": 45000 } ### Tool output · 2026-09-23T06:04:24.972Z Wall time: 45.0045 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36bb274a887d2b14a1cbb63419888", "usage": { "input_tokens": 97051, "cached_input_tokens": 95872, "cache_write_input_tokens": 0, "output_tokens": 263, "reasoning_output_tokens": 188, "total_tokens": 97314 }, "turn_token_usage": { "input_tokens": 7073441, "cached_input_tokens": 6962944, "cache_write_input_tokens": 0, "output_tokens": 39328, "reasoning_output_tokens": 21745, "total_tokens": 7112769 }, "thread_token_usage": { "input_tokens": 7073441, "cached_input_tokens": 6962944, "cache_write_input_tokens": 0, "output_tokens": 39328, "reasoning_output_tokens": 21745, "total_tokens": 7112769 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_yRpNdeWnfspb5LSI5Ywvnqtb", "durationMs": 45000 } } ### exec · 2026-09-23T06:04:28.212Z { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/gentle.log && cat /app/solution/refit.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:04:28.342Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"48185d","wall_time_seconds":0.00000497,"exit_code":0,"original_token_count":132,"output":"epoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nsaved epoch checkpoint: /app/solution/gentle_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50658 validation_loss=0.44758 pair_accuracy=0.780 train_targets=1,052,953 validation_targets=130,103 train_seconds=234.3 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=2 loss=0.447583\nWed Sep 23 06:04:28 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-05cc7473-dc48-4b1c-b063-725b9ef23a62", "process_id": "21154", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/gentle.log && cat /app/solution/refit.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/gentle.log && cat /app/solution/refit.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nsaved epoch checkpoint: /app/solution/gentle_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50658 validation_loss=0.44758 pair_accuracy=0.780 train_targets=1,052,953 validation_targets=130,103 train_seconds=234.3 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=2 loss=0.447583\nWed Sep 23 06:04:28 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nsaved epoch checkpoint: /app/solution/gentle_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50658 validation_loss=0.44758 pair_accuracy=0.780 train_targets=1,052,953 validation_targets=130,103 train_seconds=234.3 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=2 loss=0.447583\nWed Sep 23 06:04:28 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4970 }, "formatted_output": "epoch=2/2 batches=200 targets=787,814 batch_bce=0.497838 running_bce=0.511153 elapsed=174.6s\nepoch=2/2 batches=250 targets=988,643 batch_bce=0.467255 running_bce=0.508173 elapsed=219.8s\nsaved epoch checkpoint: /app/solution/gentle_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.50658 validation_loss=0.44758 pair_accuracy=0.780 train_targets=1,052,953 validation_targets=130,103 train_seconds=234.3 validation_seconds=16.6\nrestored best chronological-validation checkpoint: epoch=2 loss=0.447583\nWed Sep 23 06:04:28 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36be932c487d2a60eca1b7ff2ecbc", "usage": { "input_tokens": 97337, "cached_input_tokens": 96896, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 97387 }, "turn_token_usage": { "input_tokens": 7170778, "cached_input_tokens": 7059840, "cache_write_input_tokens": 0, "output_tokens": 39378, "reasoning_output_tokens": 21745, "total_tokens": 7210156 }, "thread_token_usage": { "input_tokens": 7170778, "cached_input_tokens": 7059840, "cache_write_input_tokens": 0, "output_tokens": 39378, "reasoning_output_tokens": 21745, "total_tokens": 7210156 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b8bbb787-0dee-4e5e-af33-33f0c085bdf0", "process_id": "65839", "command": [ "/bin/sh", "-lc", "cat > /app/solution/refit_best.py <<'PY'\n\"\"\"Refit the selected public configuration and atomically install a valid artifact.\"\"\"\nimport json, os, shlex, subprocess, time\nfrom pathlib import Path\nroot=Path('/app/solution')\nwhile not (root/'gentle_score.json').is_file():\n time.sleep(3)\nnames=['graded','gentle']\nbest=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\nreport=json.loads((root/f'{best}.json').read_text())\nscore=json.loads((root/f'{best}_score.json').read_text())['scores']['quality_ndcg@10']\nc=report['config']\n(root/'selection.json').write_text(json.dumps({'experiment':best,'public_ndcg10':score,'baseline_ndcg10':0.021280,'config':c},indent=2))\npy='/opt/task-venv/bin/python'\ncmd=[py,str(root/'train_current_model.py'),'--stage','submission','--public-dir','/task','--output',str(root/'final_candidate.parquet'),'--prepared-cache',str(root/'submission_cache.npz'),'--checkpoint-dir',str(root/'final_checkpoints'),'--threads','4']\nfor field in ['epochs','ranking_loss','hard_negatives','hard_pool_size','hard_warmup_batches','hard_start_epoch','target_weighting','ordinary_target_weight','organic_strong_target_weight','organic_like_target_weight','shared_negatives']:\n if field in c: cmd += ['--'+field.replace('_','-'),str(c[field])]\n(root/'submission_command.txt').write_text(shlex.join(cmd)+'\\n')\nprint('Refitting',best,'public NDCG',score,flush=True)\nwith (root/'final.json').open('w') as out,(root/'final.log').open('w') as err:\n subprocess.run(cmd,stdout=out,stderr=err,check=True)\nresult=subprocess.run([py,str(root/'check_submission.py'),str(root/'final_candidate.parquet')],capture_output=True,text=True,check=True)\n(root/'submission_check.txt').write_text(result.stdout)\nos.replace(root/'final_candidate.parquet',root/'submission.parquet')\nprint(result.stdout,flush=True)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/refit_best.py <<'PY'\n\"\"\"Refit the selected public configuration and atomically install a valid artifact.\"\"\"\nimport json, os, shlex, subprocess, time\nfrom pathlib import Path\nroot=Path('/app/solution')\nwhile not (root/'gentle_score.json').is_file():\n time.sleep(3)\nnames=['graded','gentle']\nbest=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\nreport=json.loads((root/f'{best}.json').read_text())\nscore=json.loads((root/f'{best}_score.json').read_text())['scores']['quality_ndcg@10']\nc=report['config']\n(root/'selection.json').write_text(json.dumps({'experiment':best,'public_ndcg10':score,'baseline_ndcg10':0.021280,'config':c},indent=2))\npy='/opt/task-venv/bin/python'\ncmd=[py,str(root/'train_current_model.py'),'--stage','submission','--public-dir','/task','--output',str(root/'final_candidate.parquet'),'--prepared-cache',str(root/'submission_cache.npz'),'--checkpoint-dir',str(root/'final_checkpoints'),'--threads','4']\nfor field in ['epochs','ranking_loss','hard_negatives','hard_pool_size','hard_warmup_batches','hard_start_epoch','target_weighting','ordinary_target_weight','organic_strong_target_weight','organic_like_target_weight','shared_negatives']:\n if field in c: cmd += ['--'+field.replace('_','-'),str(c[field])]\n(root/'submission_command.txt').write_text(shlex.join(cmd)+'\\n')\nprint('Refitting',best,'public NDCG',score,flush=True)\nwith (root/'final.json').open('w') as out,(root/'final.log').open('w') as err:\n subprocess.run(cmd,stdout=out,stderr=err,check=True)\nresult=subprocess.run([py,str(root/'check_submission.py'),str(root/'final_candidate.parquet')],capture_output=True,text=True,check=True)\n(root/'submission_check.txt').write_text(result.stdout)\nos.replace(root/'final_candidate.parquet',root/'submission.parquet')\nprint(result.stdout,flush=True)\nPY\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 63, "nanos": 71626125 }, "formatted_output": "" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7e9f04a7-3459-48b3-8a70-6bbe1fb57855", "process_id": "64246", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' hard_warmup_batches: int = 25',' hard_warmup_batches: int = 25\\n hard_start_epoch: int = 1')\ns=s.replace('if config.hard_negatives and batches >= config.hard_warmup_batches:', 'if config.hard_negatives and epoch >= config.hard_start_epoch and batches >= config.hard_warmup_batches:')\ns=s.replace(' parser.add_argument(\"--ranking-loss\",', ' parser.add_argument(\"--hard-start-epoch\", type=int, default=Config.hard_start_epoch)\\n parser.add_argument(\"--ranking-loss\",')\ns=s.replace(' hard_warmup_batches=args.hard_warmup_batches,',' hard_warmup_batches=args.hard_warmup_batches,\\n hard_start_epoch=args.hard_start_epoch,')\np.write_text(s)\nPY\ncat > /app/solution/gentle_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/graded_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/gentle_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/gentle_checkpoints --threads 4 --ranking-loss bce --hard-negatives 2 --hard-pool-size 128 --hard-warmup-batches 0 --hard-start-epoch 2 --epochs 2 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt > /app/solution/gentle.json 2> /app/solution/gentle.log\n$PY /task/evaluate_public.py /app/solution/gentle_validation.parquet > /app/solution/gentle_score.json\nSH\nsh /app/solution/gentle_experiment.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(' hard_warmup_batches: int = 25',' hard_warmup_batches: int = 25\\n hard_start_epoch: int = 1')\ns=s.replace('if config.hard_negatives and batches >= config.hard_warmup_batches:', 'if config.hard_negatives and epoch >= config.hard_start_epoch and batches >= config.hard_warmup_batches:')\ns=s.replace(' parser.add_argument(\"--ranking-loss\",', ' parser.add_argument(\"--hard-start-epoch\", type=int, default=Config.hard_start_epoch)\\n parser.add_argument(\"--ranking-loss\",')\ns=s.replace(' hard_warmup_batches=args.hard_warmup_batches,',' hard_warmup_batches=args.hard_warmup_batches,\\n hard_start_epoch=args.hard_start_epoch,')\np.write_text(s)\nPY\ncat > /app/solution/gentle_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/graded_score.json; do sleep 5; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/gentle_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/gentle_checkpoints --threads 4 --ranking-loss bce --hard-negatives 2 --hard-pool-size 128 --hard-warmup-batches 0 --hard-start-epoch 2 --epochs 2 --resume-checkpoint /app/solution/baseline_checkpoints/epoch-01.pt > /app/solution/gentle.json 2> /app/solution/gentle.log\n$PY /task/evaluate_public.py /app/solution/gentle_validation.parquet > /app/solution/gentle_score.json\nSH\nsh /app/solution/gentle_experiment.sh" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 607, "nanos": 847340710 }, "formatted_output": "" } } ## Step 95 · agent · 2026-09-23T06:04:51.149Z ### Thinking unavailable — no text recorded ### sleep · 2026-09-23T06:04:51.200Z { "duration_ms": 10000 } ### Tool output · 2026-09-23T06:05:01.207Z Wall time: 10.0036 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36beca5d887d2836dff94ca00217e", "usage": { "input_tokens": 97619, "cached_input_tokens": 97152, "cache_write_input_tokens": 0, "output_tokens": 537, "reasoning_output_tokens": 516, "total_tokens": 98156 }, "turn_token_usage": { "input_tokens": 7268397, "cached_input_tokens": 7156992, "cache_write_input_tokens": 0, "output_tokens": 39915, "reasoning_output_tokens": 22261, "total_tokens": 7308312 }, "thread_token_usage": { "input_tokens": 7268397, "cached_input_tokens": 7156992, "cache_write_input_tokens": 0, "output_tokens": 39915, "reasoning_output_tokens": 22261, "total_tokens": 7308312 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_h4O8M4tA2kWGyhC9oRBfYKYy", "durationMs": 10000 } } ## Step 96 · agent · 2026-09-23T06:05:04.789Z The conservative hard-negative run improved chronological BCE from **0.4966 to 0.4476** and selected epoch two. Its public ranking is still being generated; the discovery-graded model remains the best measured option until that score arrives. ### sleep · 2026-09-23T06:05:05.681Z { "duration_ms": 35000 } ### Tool output · 2026-09-23T06:05:40.688Z Wall time: 35.0037 seconds Sleep completed. ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36c0d5ed087d297c59e99259134bf", "usage": { "input_tokens": 98179, "cached_input_tokens": 97408, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 0, "total_tokens": 98254 }, "turn_token_usage": { "input_tokens": 7366576, "cached_input_tokens": 7254400, "cache_write_input_tokens": 0, "output_tokens": 39990, "reasoning_output_tokens": 22261, "total_tokens": 7406566 }, "thread_token_usage": { "input_tokens": 7366576, "cached_input_tokens": 7254400, "cache_write_input_tokens": 0, "output_tokens": 39990, "reasoning_output_tokens": 22261, "total_tokens": 7406566 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_VANLcFDUgVqybFY3nMqQzg4x", "durationMs": 35000 } } ### exec · 2026-09-23T06:05:47.723Z { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/gentle.log && test ! -f /app/solution/gentle_score.json || cat /app/solution/gentle_score.json && cat /app/solution/refit.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:05:47.813Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"138f8b","wall_time_seconds":0.00000316,"exit_code":0,"original_token_count":345,"output":"ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/gentle_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00912,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 338, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 356, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\nWed Sep 23 06:05:47 UTC 2026\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1ee4418a-34ac-4291-8d5b-d7a33e5486b9", "process_id": "71045", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/gentle.log && test ! -f /app/solution/gentle_score.json || cat /app/solution/gentle_score.json && cat /app/solution/refit.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/gentle.log && test ! -f /app/solution/gentle_score.json || cat /app/solution/gentle_score.json && cat /app/solution/refit.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/gentle_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00912,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 338, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 356, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\nWed Sep 23 06:05:47 UTC 2026\n", "stderr": "", "aggregated_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/gentle_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00912,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 338, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 356, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\nWed Sep 23 06:05:47 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3160 }, "formatted_output": "ranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"model\": \"/app/solution/gentle_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00912,\n \"quality_users\": 4263\n }\n}\nTraceback (most recent call last):\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/refit_best.py\", line 8, in \n best=max(names,key=lambda n: json.loads((root/f'{n}_score.json').read_text())['scores']['quality_ndcg@10'])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/__init__.py\", line 346, in loads\n return _default_decoder.decode(s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 338, in decode\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/json/decoder.py\", line 356, in raw_decode\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\nWed Sep 23 06:05:47 UTC 2026\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36c34e2c087d2b3122a4f252f15ca", "usage": { "input_tokens": 98277, "cached_input_tokens": 98048, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 0, "total_tokens": 98352 }, "turn_token_usage": { "input_tokens": 7464853, "cached_input_tokens": 7352448, "cache_write_input_tokens": 0, "output_tokens": 40065, "reasoning_output_tokens": 22261, "total_tokens": 7504918 }, "thread_token_usage": { "input_tokens": 7464853, "cached_input_tokens": 7352448, "cache_write_input_tokens": 0, "output_tokens": 40065, "reasoning_output_tokens": 22261, "total_tokens": 7504918 } } ## Step 97 · agent · 2026-09-23T06:06:06.218Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:06:18.731Z { "code": "text(await tools.exec_command({cmd:\"PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/graded2_experiment.sh <<'SH'\\n#!/bin/sh\\nset -eu\\nexport PYTHONDONTWRITEBYTECODE=1\\nPY=/opt/task-venv/bin/python\\nwhile ! test -s /app/solution/submission_check.txt; do sleep 3; done\\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --epochs 2 --resume-checkpoint /app/solution/graded_checkpoints/epoch-01.pt > /app/solution/graded2.json 2> /app/solution/graded2.log\\n$PY /task/evaluate_public.py /app/solution/graded2_validation.parquet > /app/solution/graded2_score.json\\nSH\\nsh /app/solution/graded2_experiment.sh\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:06:21.059Z Script completed Wall time 2.3 seconds Output: {"chunk_id":"7417b0","wall_time_seconds":1.000852785,"session_id":50987,"original_token_count":0,"output":""} {"chunk_id":"eacdbc","wall_time_seconds":1.001170176,"session_id":34324,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36c3c0ca887d283d37ea74e7fd4ed", "usage": { "input_tokens": 98829, "cached_input_tokens": 98048, "cache_write_input_tokens": 0, "output_tokens": 852, "reasoning_output_tokens": 516, "total_tokens": 99681 }, "turn_token_usage": { "input_tokens": 7563682, "cached_input_tokens": 7450496, "cache_write_input_tokens": 0, "output_tokens": 40917, "reasoning_output_tokens": 22777, "total_tokens": 7604599 }, "thread_token_usage": { "input_tokens": 7563682, "cached_input_tokens": 7450496, "cache_write_input_tokens": 0, "output_tokens": 40917, "reasoning_output_tokens": 22777, "total_tokens": 7604599 } } ## Step 98 · agent · 2026-09-23T06:06:44.267Z ### Thinking unavailable — no text recorded ## Step 99 · agent · 2026-09-23T06:06:45.314Z ### Thinking unavailable — no text recorded The conservative hard-negative model also failed the public test (**0.009120 NDCG@10**). I’ve selected the discovery-graded shared-pool model at **0.023759**, and its final refit on `train + val` is running. The baseline submission remains saved until the replacement passes validation. ### exec · 2026-09-23T06:06:49.653Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np\\nwith np.load('/app/solution/submission_cache.npz') as z:\\n lens=np.diff(z['offsets']); rows=z['inference_user_rows']; lens=np.where(rows>=0,lens[np.maximum(rows,0)],0); lens.sort()\\n cost=sum(len(lens[s:s+64])*lens[s:s+64].max() for s in range(0,len(lens),64))\\n print('tokens',lens.sum(),'padded',cost,'longest',lens[-10:])\\nPY\\ntail -5 /app/solution/final.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:06:50.188Z Script completed Wall time 0.5 seconds Output: {"chunk_id":"48df44","wall_time_seconds":0.360615917,"exit_code":0,"original_token_count":115,"output":"tokens 20610148 padded 21311128 longest [18988 19103 19321 19337 19380 20170 20174 20307 23381 25779]\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nWed Sep 23 06:06:50 UTC 2026\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36c5d59dc87d2b0cfe5a310b5d13a", "usage": { "input_tokens": 99773, "cached_input_tokens": 98688, "cache_write_input_tokens": 0, "output_tokens": 793, "reasoning_output_tokens": 550, "total_tokens": 100566 }, "turn_token_usage": { "input_tokens": 7663455, "cached_input_tokens": 7549184, "cache_write_input_tokens": 0, "output_tokens": 41710, "reasoning_output_tokens": 23327, "total_tokens": 7705165 }, "thread_token_usage": { "input_tokens": 7663455, "cached_input_tokens": 7549184, "cache_write_input_tokens": 0, "output_tokens": 41710, "reasoning_output_tokens": 23327, "total_tokens": 7705165 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-52c4c945-4d4e-4180-b9bc-d2762281b15f", "process_id": "5634", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nwith np.load('/app/solution/submission_cache.npz') as z:\n lens=np.diff(z['offsets']); rows=z['inference_user_rows']; lens=np.where(rows>=0,lens[np.maximum(rows,0)],0); lens.sort()\n cost=sum(len(lens[s:s+64])*lens[s:s+64].max() for s in range(0,len(lens),64))\n print('tokens',lens.sum(),'padded',cost,'longest',lens[-10:])\nPY\ntail -5 /app/solution/final.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nwith np.load('/app/solution/submission_cache.npz') as z:\n lens=np.diff(z['offsets']); rows=z['inference_user_rows']; lens=np.where(rows>=0,lens[np.maximum(rows,0)],0); lens.sort()\n cost=sum(len(lens[s:s+64])*lens[s:s+64].max() for s in range(0,len(lens),64))\n print('tokens',lens.sum(),'padded',cost,'longest',lens[-10:])\nPY\ntail -5 /app/solution/final.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "tokens 20610148 padded 21311128 longest [18988 19103 19321 19337 19380 20170 20174 20307 23381 25779]\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nWed Sep 23 06:06:50 UTC 2026\n", "stderr": "", "aggregated_output": "tokens 20610148 padded 21311128 longest [18988 19103 19321 19337 19380 20170 20174 20307 23381 25779]\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nWed Sep 23 06:06:50 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 360602526 }, "formatted_output": "tokens 20610148 padded 21311128 longest [18988 19103 19321 19337 19380 20170 20174 20307 23381 25779]\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\ngrade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.100 mean=0.180 max=7.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nWed Sep 23 06:06:50 UTC 2026\n" } } ## Step 100 · agent · 2026-09-23T06:07:17.589Z ### Thinking unavailable — no text recorded ## Step 101 · agent · 2026-09-23T06:10:22.438Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:10:26.731Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,time\\nsys.path.insert(0,'/app/solution')\\nimport train_current_model as m\\nimport torch,numpy as np\\nmodel=m.base.SequentialTwoTower(np.zeros((30000,4),np.float32),m.model_config(m.Config())).eval()\\nitems=torch.randint(30000,(64,100)); features=torch.zeros(64,100,3); hidden=torch.zeros(1,64,192)\\nwith torch.inference_mode():\\n for threads in [1,2,4,8]:\\n torch.set_num_threads(threads)\\n for _ in range(2):m.event_sequence(model,items,features,hidden)\\n t=time.monotonic()\\n for _ in range(15):m.event_sequence(model,items,features,hidden)\\n print(threads,(time.monotonic()-t)/15)\\nPY\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:10:27.901Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"49ab59","wall_time_seconds":1.001859201,"session_id":42913,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36d3fba0c87d2bd555ea3ed0d7b7f", "usage": { "input_tokens": 101409, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 542, "reasoning_output_tokens": 309, "total_tokens": 101951 }, "turn_token_usage": { "input_tokens": 7764864, "cached_input_tokens": 7620736, "cache_write_input_tokens": 0, "output_tokens": 42252, "reasoning_output_tokens": 23636, "total_tokens": 7807116 }, "thread_token_usage": { "input_tokens": 7764864, "cached_input_tokens": 7620736, "cache_write_input_tokens": 0, "output_tokens": 42252, "reasoning_output_tokens": 23636, "total_tokens": 7807116 } } ## Step 102 · agent · 2026-09-23T06:10:30.945Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:10:32.752Z { "code": "text(await tools.write_stdin({session_id:42913,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/final.log && date -u\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:10:37.861Z Script completed Wall time 5.1 seconds Output: {"chunk_id":"68e433","wall_time_seconds":5.001607265,"session_id":42913,"original_token_count":0,"output":""} {"chunk_id":"2a2bc5","wall_time_seconds":0.00000545,"exit_code":0,"original_token_count":108,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=6.158849 running_bce=6.732735 elapsed=136.3s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.70993 validation_loss=6.67942 pair_accuracy=0.774 train_targets=1,067,606 validation_targets=131,997 train_seconds=146.0 validation_seconds=12.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.679416\nWed Sep 23 06:10:37 UTC 2026\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36d5423d887d2ad4947b8bcb75b54", "usage": { "input_tokens": 102008, "cached_input_tokens": 101248, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 27, "total_tokens": 102106 }, "turn_token_usage": { "input_tokens": 7866872, "cached_input_tokens": 7721984, "cache_write_input_tokens": 0, "output_tokens": 42350, "reasoning_output_tokens": 23663, "total_tokens": 7909222 }, "thread_token_usage": { "input_tokens": 7866872, "cached_input_tokens": 7721984, "cache_write_input_tokens": 0, "output_tokens": 42350, "reasoning_output_tokens": 23663, "total_tokens": 7909222 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d10604a1-ef8a-48a9-b298-1860c0b48177", "process_id": "49906", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch=1/1 batches=250 targets=994,228 batch_bce=6.158849 running_bce=6.732735 elapsed=136.3s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.70993 validation_loss=6.67942 pair_accuracy=0.774 train_targets=1,067,606 validation_targets=131,997 train_seconds=146.0 validation_seconds=12.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.679416\nWed Sep 23 06:10:37 UTC 2026\n", "stderr": "", "aggregated_output": "epoch=1/1 batches=250 targets=994,228 batch_bce=6.158849 running_bce=6.732735 elapsed=136.3s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.70993 validation_loss=6.67942 pair_accuracy=0.774 train_targets=1,067,606 validation_targets=131,997 train_seconds=146.0 validation_seconds=12.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.679416\nWed Sep 23 06:10:37 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5450 }, "formatted_output": "epoch=1/1 batches=250 targets=994,228 batch_bce=6.158849 running_bce=6.732735 elapsed=136.3s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=6.70993 validation_loss=6.67942 pair_accuracy=0.774 train_targets=1,067,606 validation_targets=131,997 train_seconds=146.0 validation_seconds=12.6\nrestored best chronological-validation checkpoint: epoch=1 loss=6.679416\nWed Sep 23 06:10:37 UTC 2026\n" } } ## Step 103 · agent · 2026-09-23T06:10:49.242Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6cdc65fe-dd68-4d9b-81c6-1e61eb58599a", "process_id": "42913", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,time\nsys.path.insert(0,'/app/solution')\nimport train_current_model as m\nimport torch,numpy as np\nmodel=m.base.SequentialTwoTower(np.zeros((30000,4),np.float32),m.model_config(m.Config())).eval()\nitems=torch.randint(30000,(64,100)); features=torch.zeros(64,100,3); hidden=torch.zeros(1,64,192)\nwith torch.inference_mode():\n for threads in [1,2,4,8]:\n torch.set_num_threads(threads)\n for _ in range(2):m.event_sequence(model,items,features,hidden)\n t=time.monotonic()\n for _ in range(15):m.event_sequence(model,items,features,hidden)\n print(threads,(time.monotonic()-t)/15)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,time\nsys.path.insert(0,'/app/solution')\nimport train_current_model as m\nimport torch,numpy as np\nmodel=m.base.SequentialTwoTower(np.zeros((30000,4),np.float32),m.model_config(m.Config())).eval()\nitems=torch.randint(30000,(64,100)); features=torch.zeros(64,100,3); hidden=torch.zeros(1,64,192)\nwith torch.inference_mode():\n for threads in [1,2,4,8]:\n torch.set_num_threads(threads)\n for _ in range(2):m.event_sequence(model,items,features,hidden)\n t=time.monotonic()\n for _ in range(15):m.event_sequence(model,items,features,hidden)\n print(threads,(time.monotonic()-t)/15)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "1 0.07223139360000157\n2 0.05433370880000439\n4 0.03970680766666798\n8 0.6958207814666518\n", "stderr": "", "aggregated_output": "1 0.07223139360000157\n2 0.05433370880000439\n4 0.03970680766666798\n8 0.6958207814666518\n", "exit_code": 0, "duration": { "secs": 22, "nanos": 813683977 }, "formatted_output": "1 0.07223139360000157\n2 0.05433370880000439\n4 0.03970680766666798\n8 0.6958207814666518\n" } } The final refit has completed training and is generating the submission ranking. I’m stopping further model experiments to leave time for the final artifact checks and experiment record. ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-85fe4d0d-e85e-4e40-9f28-55ac81f66de8", "process_id": "50987", "command": [ "/bin/sh", "-lc", "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "PYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/refit_best.py > /app/solution/refit.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 273, "nanos": 545908821 }, "formatted_output": "" } } ### exec · 2026-09-23T06:10:56.479Z { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,signal\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().split(b'\\\\0')\\n if len(cmd)>1 and cmd[0]==b'sh' and cmd[1]==b'/app/solution/graded2_experiment.sh':\\n os.kill(int(p.name),signal.SIGTERM)\\n print('Stopped optional experiment queue',p.name)\\n except (OSError,ProcessLookupError):pass\\nPY\\ntail -4 /app/solution/final.log && date -u\",max_output_tokens:1500}));\ntext(await tools.write_stdin({session_id:42913,chars:\"\",yield_time_ms:1000,max_output_tokens:1000}));\n" } ### Tool output · 2026-09-23T06:10:56.619Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"55cb67","wall_time_seconds":0.00000335,"exit_code":0,"original_token_count":42,"output":"Stopped optional experiment queue 1712\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nWed Sep 23 06:10:56 UTC 2026\n"} {"chunk_id":"c07947","wall_time_seconds":0.00000208,"exit_code":0,"original_token_count":22,"output":"1 0.07223139360000157\n2 0.05433370880000439\n4 0.03970680766666798\n8 0.6958207814666518\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36d5e2b2487d2bdced11faa2b6523", "usage": { "input_tokens": 102336, "cached_input_tokens": 101888, "cache_write_input_tokens": 0, "output_tokens": 569, "reasoning_output_tokens": 324, "total_tokens": 102905 }, "turn_token_usage": { "input_tokens": 7969208, "cached_input_tokens": 7823872, "cache_write_input_tokens": 0, "output_tokens": 42919, "reasoning_output_tokens": 23987, "total_tokens": 8012127 }, "thread_token_usage": { "input_tokens": 7969208, "cached_input_tokens": 7823872, "cache_write_input_tokens": 0, "output_tokens": 42919, "reasoning_output_tokens": 23987, "total_tokens": 8012127 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6b0f35d8-fd62-4fde-a3c6-d522d4493a8e", "process_id": "34324", "command": [ "/bin/sh", "-lc", "cat > /app/solution/graded2_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/submission_check.txt; do sleep 3; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --epochs 2 --resume-checkpoint /app/solution/graded_checkpoints/epoch-01.pt > /app/solution/graded2.json 2> /app/solution/graded2.log\n$PY /task/evaluate_public.py /app/solution/graded2_validation.parquet > /app/solution/graded2_score.json\nSH\nsh /app/solution/graded2_experiment.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/graded2_experiment.sh <<'SH'\n#!/bin/sh\nset -eu\nexport PYTHONDONTWRITEBYTECODE=1\nPY=/opt/task-venv/bin/python\nwhile ! test -s /app/solution/submission_check.txt; do sleep 3; done\n$PY /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/graded2_validation.parquet --prepared-cache /app/solution/validation_cache.npz --checkpoint-dir /app/solution/graded2_checkpoints --threads 4 --ranking-loss shared-softmax --hard-negatives 0 --target-weighting discovery-graded --organic-like-target-weight 7 --ordinary-target-weight 0.1 --epochs 2 --resume-checkpoint /app/solution/graded_checkpoints/epoch-01.pt > /app/solution/graded2.json 2> /app/solution/graded2.log\n$PY /task/evaluate_public.py /app/solution/graded2_validation.parquet > /app/solution/graded2_score.json\nSH\nsh /app/solution/graded2_experiment.sh" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Terminated\n", "stderr": "", "aggregated_output": "Terminated\n", "exit_code": 143, "duration": { "secs": 276, "nanos": 528160034 }, "formatted_output": "Terminated\n" } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f2ce9c49-d704-4de5-bfe0-518acb61fe9a", "process_id": "1286", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\0')\n if len(cmd)>1 and cmd[0]==b'sh' and cmd[1]==b'/app/solution/graded2_experiment.sh':\n os.kill(int(p.name),signal.SIGTERM)\n print('Stopped optional experiment queue',p.name)\n except (OSError,ProcessLookupError):pass\nPY\ntail -4 /app/solution/final.log && date -u" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().split(b'\\0')\n if len(cmd)>1 and cmd[0]==b'sh' and cmd[1]==b'/app/solution/graded2_experiment.sh':\n os.kill(int(p.name),signal.SIGTERM)\n print('Stopped optional experiment queue',p.name)\n except (OSError,ProcessLookupError):pass\nPY\ntail -4 /app/solution/final.log && date -u" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Stopped optional experiment queue 1712\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nWed Sep 23 06:10:56 UTC 2026\n", "stderr": "", "aggregated_output": "Stopped optional experiment queue 1712\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nWed Sep 23 06:10:56 UTC 2026\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3350 }, "formatted_output": "Stopped optional experiment queue 1712\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\nWed Sep 23 06:10:56 UTC 2026\n" } } ## Step 104 · agent · 2026-09-23T06:11:05.857Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:11:06.761Z { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/refit.log && cat /app/solution/submission_check.txt && cat /app/solution/selection.json && cat /app/solution/submission_command.txt\",max_output_tokens:3500}));\n" } ### Tool output · 2026-09-23T06:11:06.859Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"75f221","wall_time_seconds":0.00000395,"exit_code":0,"original_token_count":489,"output":"Refitting graded public NDCG 0.023759\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n{\n \"experiment\": \"graded\",\n \"public_ndcg10\": 0.023759,\n \"baseline_ndcg10\": 0.02128,\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 \"hard_negatives\": 0,\n \"hard_pool_size\": 1024,\n \"hard_warmup_batches\": 25,\n \"ranking_loss\": \"shared-softmax\",\n \"shared_negatives\": 2048,\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.1,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 7.0,\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}/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/final_candidate.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_checkpoints --threads 4 --epochs 1 --ranking-loss shared-softmax --hard-negatives 0 --hard-pool-size 1024 --hard-warmup-batches 25 --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-strong-target-weight 1.0 --organic-like-target-weight 7.0 --shared-negatives 2048\n"} ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-335cad23-7804-436f-80f4-e94de70a93f4", "process_id": "49506", "command": [ "/bin/sh", "-lc", "cat /app/solution/refit.log && cat /app/solution/submission_check.txt && cat /app/solution/selection.json && cat /app/solution/submission_command.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/refit.log", "name": "refit.log", "path": "/app/solution/refit.log" }, { "type": "read", "cmd": "cat /app/solution/submission_check.txt", "name": "submission_check.txt", "path": "/app/solution/submission_check.txt" }, { "type": "read", "cmd": "cat /app/solution/selection.json", "name": "selection.json", "path": "/app/solution/selection.json" }, { "type": "read", "cmd": "cat /app/solution/submission_command.txt", "name": "submission_command.txt", "path": "/app/solution/submission_command.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Refitting graded public NDCG 0.023759\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n{\n \"experiment\": \"graded\",\n \"public_ndcg10\": 0.023759,\n \"baseline_ndcg10\": 0.02128,\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 \"hard_negatives\": 0,\n \"hard_pool_size\": 1024,\n \"hard_warmup_batches\": 25,\n \"ranking_loss\": \"shared-softmax\",\n \"shared_negatives\": 2048,\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.1,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 7.0,\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}/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/final_candidate.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_checkpoints --threads 4 --epochs 1 --ranking-loss shared-softmax --hard-negatives 0 --hard-pool-size 1024 --hard-warmup-batches 25 --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-strong-target-weight 1.0 --organic-like-target-weight 7.0 --shared-negatives 2048\n", "stderr": "", "aggregated_output": "Refitting graded public NDCG 0.023759\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n{\n \"experiment\": \"graded\",\n \"public_ndcg10\": 0.023759,\n \"baseline_ndcg10\": 0.02128,\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 \"hard_negatives\": 0,\n \"hard_pool_size\": 1024,\n \"hard_warmup_batches\": 25,\n \"ranking_loss\": \"shared-softmax\",\n \"shared_negatives\": 2048,\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.1,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 7.0,\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}/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/final_candidate.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_checkpoints --threads 4 --epochs 1 --ranking-loss shared-softmax --hard-negatives 0 --hard-pool-size 1024 --hard-warmup-batches 25 --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-strong-target-weight 1.0 --organic-like-target-weight 7.0 --shared-negatives 2048\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3950 }, "formatted_output": "Refitting graded public NDCG 0.023759\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\n{\n \"experiment\": \"graded\",\n \"public_ndcg10\": 0.023759,\n \"baseline_ndcg10\": 0.02128,\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 \"hard_negatives\": 0,\n \"hard_pool_size\": 1024,\n \"hard_warmup_batches\": 25,\n \"ranking_loss\": \"shared-softmax\",\n \"shared_negatives\": 2048,\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.1,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 7.0,\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}/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/final_candidate.parquet --prepared-cache /app/solution/submission_cache.npz --checkpoint-dir /app/solution/final_checkpoints --threads 4 --epochs 1 --ranking-loss shared-softmax --hard-negatives 0 --hard-pool-size 1024 --hard-warmup-batches 25 --target-weighting discovery-graded --ordinary-target-weight 0.1 --organic-strong-target-weight 1.0 --organic-like-target-weight 7.0 --shared-negatives 2048\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36d713b9887d2aa309b0692f1c524", "usage": { "input_tokens": 103107, "cached_input_tokens": 102144, "cache_write_input_tokens": 0, "output_tokens": 176, "reasoning_output_tokens": 111, "total_tokens": 103283 }, "turn_token_usage": { "input_tokens": 8072315, "cached_input_tokens": 7926016, "cache_write_input_tokens": 0, "output_tokens": 43095, "reasoning_output_tokens": 24098, "total_tokens": 8115410 }, "thread_token_usage": { "input_tokens": 8072315, "cached_input_tokens": 7926016, "cache_write_input_tokens": 0, "output_tokens": 43095, "reasoning_output_tokens": 24098, "total_tokens": 8115410 } } ## Step 105 · agent · 2026-09-23T06:11:29.624Z ### Thinking unavailable — no text recorded ## Step 106 · agent · 2026-09-23T06:11:43.966Z ### Thinking unavailable — no text recorded The improved submission is saved and validated: **845,600 rows**, covering **8,456 users**, with exactly 100 distinct, test-eligible items and ranks per user. The final model was refit on `train + val`. I’m finishing the README and a final regression check. ### exec · 2026-09-23T06:11:52.462Z { "code": "text(await tools.exec_command({cmd:\"cat >> /app/solution/test_hard_sampler.py <<'PY'\\n# Known positives and active likes must receive no negative gradient, even\\n# when the shared proposal is large enough to cover the entire toy catalog.\\nmodel.zero_grad(set_to_none=True)\\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=4096)\\nstats,grad=m.score_target_microbatches(model,data,c,sequence[:1],np.array([0]),np.zeros((1,4),np.float32),pos[:1],neg[:1],torch.ones((1,32)),np.ones(1,np.float32),torch.device('cpu'),True,user_rows=np.array([0]),known_positive_packed=known)\\ncg=model.candidate_embedding.weight.grad\\nassert cg[:10].abs().sum()==0\\nassert cg[12:14].abs().sum()==0\\nassert cg[11].abs().sum()>0 and cg[30].abs().sum()>0\\nprint('Shared-pool false-negative gradient checks passed')\\nPY\\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\\n/opt/task-venv/bin/python /app/solution/check_submission.py\\n/opt/task-venv/bin/python - <<'PY'\\nimport json\\nfor name in ['baseline','graded','final']:\\n d=json.load(open('/app/solution/'+name+'.json'))\\n print(name,'runtime',d['runtime_seconds'],'epochs',len(d['training_history']),'mode',d['config'].get('ranking_loss','bce'))\\nPY\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:11:59.512Z Script completed Wall time 7.0 seconds Output: {"chunk_id":"5d9470","wall_time_seconds":6.87960575,"exit_code":0,"original_token_count":93,"output":"Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nShared-pool false-negative gradient checks passed\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\nbaseline runtime 331.572 epochs 1 mode bce\ngraded runtime 290.433 epochs 1 mode shared-softmax\nfinal runtime 267.22 epochs 1 mode shared-softmax\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36d7b268c87d28236066f27467f57", "usage": { "input_tokens": 103993, "cached_input_tokens": 102912, "cache_write_input_tokens": 0, "output_tokens": 1326, "reasoning_output_tokens": 894, "total_tokens": 105319 }, "turn_token_usage": { "input_tokens": 8176308, "cached_input_tokens": 8028928, "cache_write_input_tokens": 0, "output_tokens": 44421, "reasoning_output_tokens": 24992, "total_tokens": 8220729 }, "thread_token_usage": { "input_tokens": 8176308, "cached_input_tokens": 8028928, "cache_write_input_tokens": 0, "output_tokens": 44421, "reasoning_output_tokens": 24992, "total_tokens": 8220729 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aa9b9a32-7a21-4028-9c75-95b97c9e4d95", "process_id": "51511", "command": [ "/bin/sh", "-lc", "cat >> /app/solution/test_hard_sampler.py <<'PY'\n# Known positives and active likes must receive no negative gradient, even\n# when the shared proposal is large enough to cover the entire toy catalog.\nmodel.zero_grad(set_to_none=True)\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=4096)\nstats,grad=m.score_target_microbatches(model,data,c,sequence[:1],np.array([0]),np.zeros((1,4),np.float32),pos[:1],neg[:1],torch.ones((1,32)),np.ones(1,np.float32),torch.device('cpu'),True,user_rows=np.array([0]),known_positive_packed=known)\ncg=model.candidate_embedding.weight.grad\nassert cg[:10].abs().sum()==0\nassert cg[12:14].abs().sum()==0\nassert cg[11].abs().sum()>0 and cg[30].abs().sum()>0\nprint('Shared-pool false-negative gradient checks passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\n/opt/task-venv/bin/python /app/solution/check_submission.py\n/opt/task-venv/bin/python - <<'PY'\nimport json\nfor name in ['baseline','graded','final']:\n d=json.load(open('/app/solution/'+name+'.json'))\n print(name,'runtime',d['runtime_seconds'],'epochs',len(d['training_history']),'mode',d['config'].get('ranking_loss','bce'))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat >> /app/solution/test_hard_sampler.py <<'PY'\n# Known positives and active likes must receive no negative gradient, even\n# when the shared proposal is large enough to cover the entire toy catalog.\nmodel.zero_grad(set_to_none=True)\nc=m.Config(ranking_loss='shared-softmax',hard_negatives=0,shared_negatives=4096)\nstats,grad=m.score_target_microbatches(model,data,c,sequence[:1],np.array([0]),np.zeros((1,4),np.float32),pos[:1],neg[:1],torch.ones((1,32)),np.ones(1,np.float32),torch.device('cpu'),True,user_rows=np.array([0]),known_positive_packed=known)\ncg=model.candidate_embedding.weight.grad\nassert cg[:10].abs().sum()==0\nassert cg[12:14].abs().sum()==0\nassert cg[11].abs().sum()>0 and cg[30].abs().sum()>0\nprint('Shared-pool false-negative gradient checks passed')\nPY\n/opt/task-venv/bin/python /app/solution/test_hard_sampler.py\n/opt/task-venv/bin/python /app/solution/check_submission.py\n/opt/task-venv/bin/python - <<'PY'\nimport json\nfor name in ['baseline','graded','final']:\n d=json.load(open('/app/solution/'+name+'.json'))\n print(name,'runtime',d['runtime_seconds'],'epochs',len(d['training_history']),'mode',d['config'].get('ranking_loss','bce'))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nShared-pool false-negative gradient checks passed\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\nbaseline runtime 331.572 epochs 1 mode bce\ngraded runtime 290.433 epochs 1 mode shared-softmax\nfinal runtime 267.22 epochs 1 mode shared-softmax\n", "stderr": "", "aggregated_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nShared-pool false-negative gradient checks passed\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\nbaseline runtime 331.572 epochs 1 mode bce\ngraded runtime 290.433 epochs 1 mode shared-softmax\nfinal runtime 267.22 epochs 1 mode shared-softmax\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 879599160 }, "formatted_output": "Hard sampler invariants passed\nShared softmax finite loss and both-tower gradient checks passed\nShared-pool false-negative gradient checks passed\nValidated 845,600 rows: 8,456 users, 100 distinct eligible items and ranks each.\nbaseline runtime 331.572 epochs 1 mode bce\ngraded runtime 290.433 epochs 1 mode shared-softmax\nfinal runtime 267.22 epochs 1 mode shared-softmax\n" } } ## Step 107 · agent · 2026-09-23T06:12:14.132Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T06:12:48.815Z { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# Improved music-discovery two-tower\\n\\n**Selected public NDCG@10: 0.023759 versus production 0.021280 (+11.6%).**\\nThe final model was refit on `train_* + val_*`. `submission.parquet` contains\\n845,600 validated rows: 8,456 users, each with 100 distinct eligible items and\\nranks 1–100. Final weights are in `final_checkpoints/best.pt`.\\n\\n## Diagnosis and selected change\\n\\nProduction averages BCE over 24 uniform and eight frequency-corrected in-batch\\nnegatives. This gives limited exposure to strong competitors. However, simply\\nreplacing random draws with high-scoring negatives was harmful: it excessively\\npenalized popular tracks and shifted recommendations toward the catalog tail.\\nSampled BCE improvements also failed to predict public discovery quality.\\n\\nThe selected objective uses **2,048 shared uniform proposals per block**,\\ndeduplicated and encoded once. A filtered sampled-softmax loss automatically\\ngives larger gradients to high-scoring negatives. Uniform proposals avoid\\nitem-dependent sampling corrections. Targets, complete training-history\\npositives, and active likes are excluded from each negative pool.\\n\\nThis required discovery-aligned target weighting as well: the existing\\n`discovery-graded` mode assigns weight 7 to qualifying novel organic-like\\noutcomes, 1 to novel organic strong listens, and 0.1 to ordinary/repeated\\ntargets. Every selected target retains positive weight. The measured gain is\\nfor this **joint change**, not shared negatives alone.\\n\\nThe architecture remains the original 192-dimensional GRU/MLP two-tower with\\nprecomputed item vectors and raw dot-product scoring. No user-ID embeddings,\\ncandidate-conditioned user features, or serving-time hard mining were added.\\nChronological splits, cutoffs, target users, eligibility, and evaluation labels\\nare unchanged. The chronological holdout uses the selected objective with\\nfixed proposal seeds. Explicit hard mining and proposal correction are disabled.\\n\\n## Public experiments\\n\\nAll scores use the supplied evaluator, over 4,263 users with relevant outcomes.\\n\\n| Configuration | NDCG@10 |\\n| --- | ---: |\\n| Production, one epoch | 0.021280 |\\n| 16 hard negatives, BCE | 0.003568 |\\n| 16 hard negatives, softmax | 0.005014 |\\n| Shared uniform softmax, ordinary production weights, one epoch | 0.017989 |\\n| Same, two epochs | 0.016693 |\\n| **Shared softmax + discovery weights, one epoch** | **0.023759** |\\n| Two hard negatives introduced in epoch two | 0.009120 |\\n\\nGlobal proposal corrections also failed to improve production. Full logs,\\nconfiguration JSON, public rankings, and checkpoints are retained. Selected\\nvalidation training took 142 seconds versus 226 seconds for production on four\\nCPU threads; complete validation runtime was 290 versus 332 seconds.\\n\\n## Reproduce the final submission\\n\\nThe implementation defaults match the selected configuration. The installed\\npackage interpreter is `/opt/task-venv/bin/python`.\\n\\n```sh\\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py \\\\\\n --stage submission --public-dir /task \\\\\\n --output /app/solution/submission.parquet \\\\\\n --prepared-cache /app/solution/submission_cache.npz \\\\\\n --checkpoint-dir /app/solution/final_checkpoints --threads 4 \\\\\\n --epochs 1 --ranking-loss shared-softmax --shared-negatives 2048 \\\\\\n --hard-negatives 0 --target-weighting discovery-graded \\\\\\n --ordinary-target-weight 0.1 --organic-like-target-weight 7\\n/opt/task-venv/bin/python /app/solution/check_submission.py\\n```\\n\\nFor public reproduction, change stage to `validation`, use `validation_cache.npz`\\nand a different output/checkpoint path, then run `/task/evaluate_public.py`.\\n\\n`test_hard_sampler.py` passed filtering, uniqueness, detached deterministic\\nmining, finite-loss, both-tower gradient, and masked-item zero-gradient checks.\\n`check_submission.py` passed all final schema and test-eligibility checks.\\nMD\\n/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/refit_best.py')\\ns=p.read_text().replace(\\\"while not (root/'gentle_score.json').is_file():\\\\n time.sleep(3)\\\\nnames=['graded','gentle']\\\", \\\"names=['graded','gentle']\\\\nwhile True:\\\\n try:\\\\n for name in names: json.loads((root/f'{name}_score.json').read_text())\\\\n break\\\\n except (FileNotFoundError,json.JSONDecodeError): time.sleep(3)\\\")\\np.write_text(s)\\np=Path('/app/solution/train_current_model.py')\\ns=p.read_text().replace(''' \\\"generator_state\\\": generator.get_state(),\\n \\\"torch_rng_state\\\": torch.get_rng_state(),''',''' \\\"generator_state\\\": generator.get_state(),\\n \\\"torch_rng_state\\\": torch.get_rng_state(),''')\\np.write_text(s)\\nPY\\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/refit_best.py\\nls -lh /app/solution/submission.parquet /app/solution/final_checkpoints/best.pt\",max_output_tokens:1500}));\n" } ### Tool output · 2026-09-23T06:12:49.097Z Script completed Wall time 0.3 seconds Output: {"chunk_id":"7e70df","wall_time_seconds":0.108228676,"exit_code":0,"original_token_count":40,"output":"-rw-r--r-- 1 agent agent 145M Sep 23 06:09 /app/solution/final_checkpoints/best.pt\n-rw-r--r-- 1 agent agent 1.1M Sep 23 06:10 /app/solution/submission.parquet\n"} ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36dafac7487d294e545743e4b731a", "usage": { "input_tokens": 105470, "cached_input_tokens": 103808, "cache_write_input_tokens": 0, "output_tokens": 1576, "reasoning_output_tokens": 327, "total_tokens": 107046 }, "turn_token_usage": { "input_tokens": 8281778, "cached_input_tokens": 8132736, "cache_write_input_tokens": 0, "output_tokens": 45997, "reasoning_output_tokens": 25319, "total_tokens": 8327775 }, "thread_token_usage": { "input_tokens": 8281778, "cached_input_tokens": 8132736, "cache_write_input_tokens": 0, "output_tokens": 45997, "reasoning_output_tokens": 25319, "total_tokens": 8327775 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b062ee6-4e98-45f6-8df9-f60dac54a38a", "process_id": "55528", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# Improved music-discovery two-tower\n\n**Selected public NDCG@10: 0.023759 versus production 0.021280 (+11.6%).**\nThe final model was refit on `train_* + val_*`. `submission.parquet` contains\n845,600 validated rows: 8,456 users, each with 100 distinct eligible items and\nranks 1–100. Final weights are in `final_checkpoints/best.pt`.\n\n## Diagnosis and selected change\n\nProduction averages BCE over 24 uniform and eight frequency-corrected in-batch\nnegatives. This gives limited exposure to strong competitors. However, simply\nreplacing random draws with high-scoring negatives was harmful: it excessively\npenalized popular tracks and shifted recommendations toward the catalog tail.\nSampled BCE improvements also failed to predict public discovery quality.\n\nThe selected objective uses **2,048 shared uniform proposals per block**,\ndeduplicated and encoded once. A filtered sampled-softmax loss automatically\ngives larger gradients to high-scoring negatives. Uniform proposals avoid\nitem-dependent sampling corrections. Targets, complete training-history\npositives, and active likes are excluded from each negative pool.\n\nThis required discovery-aligned target weighting as well: the existing\n`discovery-graded` mode assigns weight 7 to qualifying novel organic-like\noutcomes, 1 to novel organic strong listens, and 0.1 to ordinary/repeated\ntargets. Every selected target retains positive weight. The measured gain is\nfor this **joint change**, not shared negatives alone.\n\nThe architecture remains the original 192-dimensional GRU/MLP two-tower with\nprecomputed item vectors and raw dot-product scoring. No user-ID embeddings,\ncandidate-conditioned user features, or serving-time hard mining were added.\nChronological splits, cutoffs, target users, eligibility, and evaluation labels\nare unchanged. The chronological holdout uses the selected objective with\nfixed proposal seeds. Explicit hard mining and proposal correction are disabled.\n\n## Public experiments\n\nAll scores use the supplied evaluator, over 4,263 users with relevant outcomes.\n\n| Configuration | NDCG@10 |\n| --- | ---: |\n| Production, one epoch | 0.021280 |\n| 16 hard negatives, BCE | 0.003568 |\n| 16 hard negatives, softmax | 0.005014 |\n| Shared uniform softmax, ordinary production weights, one epoch | 0.017989 |\n| Same, two epochs | 0.016693 |\n| **Shared softmax + discovery weights, one epoch** | **0.023759** |\n| Two hard negatives introduced in epoch two | 0.009120 |\n\nGlobal proposal corrections also failed to improve production. Full logs,\nconfiguration JSON, public rankings, and checkpoints are retained. Selected\nvalidation training took 142 seconds versus 226 seconds for production on four\nCPU threads; complete validation runtime was 290 versus 332 seconds.\n\n## Reproduce the final submission\n\nThe implementation defaults match the selected configuration. The installed\npackage interpreter is `/opt/task-venv/bin/python`.\n\n```sh\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --output /app/solution/submission.parquet \\\n --prepared-cache /app/solution/submission_cache.npz \\\n --checkpoint-dir /app/solution/final_checkpoints --threads 4 \\\n --epochs 1 --ranking-loss shared-softmax --shared-negatives 2048 \\\n --hard-negatives 0 --target-weighting discovery-graded \\\n --ordinary-target-weight 0.1 --organic-like-target-weight 7\n/opt/task-venv/bin/python /app/solution/check_submission.py\n```\n\nFor public reproduction, change stage to `validation`, use `validation_cache.npz`\nand a different output/checkpoint path, then run `/task/evaluate_public.py`.\n\n`test_hard_sampler.py` passed filtering, uniqueness, detached deterministic\nmining, finite-loss, both-tower gradient, and masked-item zero-gradient checks.\n`check_submission.py` passed all final schema and test-eligibility checks.\nMD\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/refit_best.py')\ns=p.read_text().replace(\"while not (root/'gentle_score.json').is_file():\\n time.sleep(3)\\nnames=['graded','gentle']\", \"names=['graded','gentle']\\nwhile True:\\n try:\\n for name in names: json.loads((root/f'{name}_score.json').read_text())\\n break\\n except (FileNotFoundError,json.JSONDecodeError): time.sleep(3)\")\np.write_text(s)\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' \"generator_state\": generator.get_state(),\n \"torch_rng_state\": torch.get_rng_state(),''',''' \"generator_state\": generator.get_state(),\n \"torch_rng_state\": torch.get_rng_state(),''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/refit_best.py\nls -lh /app/solution/submission.parquet /app/solution/final_checkpoints/best.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# Improved music-discovery two-tower\n\n**Selected public NDCG@10: 0.023759 versus production 0.021280 (+11.6%).**\nThe final model was refit on `train_* + val_*`. `submission.parquet` contains\n845,600 validated rows: 8,456 users, each with 100 distinct eligible items and\nranks 1–100. Final weights are in `final_checkpoints/best.pt`.\n\n## Diagnosis and selected change\n\nProduction averages BCE over 24 uniform and eight frequency-corrected in-batch\nnegatives. This gives limited exposure to strong competitors. However, simply\nreplacing random draws with high-scoring negatives was harmful: it excessively\npenalized popular tracks and shifted recommendations toward the catalog tail.\nSampled BCE improvements also failed to predict public discovery quality.\n\nThe selected objective uses **2,048 shared uniform proposals per block**,\ndeduplicated and encoded once. A filtered sampled-softmax loss automatically\ngives larger gradients to high-scoring negatives. Uniform proposals avoid\nitem-dependent sampling corrections. Targets, complete training-history\npositives, and active likes are excluded from each negative pool.\n\nThis required discovery-aligned target weighting as well: the existing\n`discovery-graded` mode assigns weight 7 to qualifying novel organic-like\noutcomes, 1 to novel organic strong listens, and 0.1 to ordinary/repeated\ntargets. Every selected target retains positive weight. The measured gain is\nfor this **joint change**, not shared negatives alone.\n\nThe architecture remains the original 192-dimensional GRU/MLP two-tower with\nprecomputed item vectors and raw dot-product scoring. No user-ID embeddings,\ncandidate-conditioned user features, or serving-time hard mining were added.\nChronological splits, cutoffs, target users, eligibility, and evaluation labels\nare unchanged. The chronological holdout uses the selected objective with\nfixed proposal seeds. Explicit hard mining and proposal correction are disabled.\n\n## Public experiments\n\nAll scores use the supplied evaluator, over 4,263 users with relevant outcomes.\n\n| Configuration | NDCG@10 |\n| --- | ---: |\n| Production, one epoch | 0.021280 |\n| 16 hard negatives, BCE | 0.003568 |\n| 16 hard negatives, softmax | 0.005014 |\n| Shared uniform softmax, ordinary production weights, one epoch | 0.017989 |\n| Same, two epochs | 0.016693 |\n| **Shared softmax + discovery weights, one epoch** | **0.023759** |\n| Two hard negatives introduced in epoch two | 0.009120 |\n\nGlobal proposal corrections also failed to improve production. Full logs,\nconfiguration JSON, public rankings, and checkpoints are retained. Selected\nvalidation training took 142 seconds versus 226 seconds for production on four\nCPU threads; complete validation runtime was 290 versus 332 seconds.\n\n## Reproduce the final submission\n\nThe implementation defaults match the selected configuration. The installed\npackage interpreter is `/opt/task-venv/bin/python`.\n\n```sh\nPYTHONDONTWRITEBYTECODE=1 /opt/task-venv/bin/python /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --output /app/solution/submission.parquet \\\n --prepared-cache /app/solution/submission_cache.npz \\\n --checkpoint-dir /app/solution/final_checkpoints --threads 4 \\\n --epochs 1 --ranking-loss shared-softmax --shared-negatives 2048 \\\n --hard-negatives 0 --target-weighting discovery-graded \\\n --ordinary-target-weight 0.1 --organic-like-target-weight 7\n/opt/task-venv/bin/python /app/solution/check_submission.py\n```\n\nFor public reproduction, change stage to `validation`, use `validation_cache.npz`\nand a different output/checkpoint path, then run `/task/evaluate_public.py`.\n\n`test_hard_sampler.py` passed filtering, uniqueness, detached deterministic\nmining, finite-loss, both-tower gradient, and masked-item zero-gradient checks.\n`check_submission.py` passed all final schema and test-eligibility checks.\nMD\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/refit_best.py')\ns=p.read_text().replace(\"while not (root/'gentle_score.json').is_file():\\n time.sleep(3)\\nnames=['graded','gentle']\", \"names=['graded','gentle']\\nwhile True:\\n try:\\n for name in names: json.loads((root/f'{name}_score.json').read_text())\\n break\\n except (FileNotFoundError,json.JSONDecodeError): time.sleep(3)\")\np.write_text(s)\np=Path('/app/solution/train_current_model.py')\ns=p.read_text().replace(''' \"generator_state\": generator.get_state(),\n \"torch_rng_state\": torch.get_rng_state(),''',''' \"generator_state\": generator.get_state(),\n \"torch_rng_state\": torch.get_rng_state(),''')\np.write_text(s)\nPY\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/refit_best.py\nls -lh /app/solution/submission.parquet /app/solution/final_checkpoints/best.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 145M Sep 23 06:09 /app/solution/final_checkpoints/best.pt\n-rw-r--r-- 1 agent agent 1.1M Sep 23 06:10 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 145M Sep 23 06:09 /app/solution/final_checkpoints/best.pt\n-rw-r--r-- 1 agent agent 1.1M Sep 23 06:10 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 107765917 }, "formatted_output": "-rw-r--r-- 1 agent agent 145M Sep 23 06:09 /app/solution/final_checkpoints/best.pt\n-rw-r--r-- 1 agent agent 1.1M Sep 23 06:10 /app/solution/submission.parquet\n" } } ## Step 108 · agent · 2026-09-23T06:13:00.184Z ### Thinking unavailable — no text recorded Created [submission.parquet](/app/solution/submission.parquet). Public NDCG@10 improved from **0.021280 to 0.023759 (+11.6%)**. Refit on `train + val`; all **845,600 rows** passed schema and eligibility checks. [Implementation](/app/solution/train_current_model.py) · [Experiment record](/app/solution/README.md) ### Harness context { "type": "token_usage_record", "thread_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "session_id": "01a0ccb4-3366-7b12-abd4-46dd71cdc20f", "root_turn_id": "01a0ccb4-337c-7982-a2e6-cd288761a3e7", "response_id": "resp_063e9ada2082cf78016ab36de147d087d2a56d2aff2d0e8606", "usage": { "input_tokens": 107165, "cached_input_tokens": 105344, "cache_write_input_tokens": 0, "output_tokens": 324, "reasoning_output_tokens": 231, "total_tokens": 107489 }, "turn_token_usage": { "input_tokens": 8388943, "cached_input_tokens": 8238080, "cache_write_input_tokens": 0, "output_tokens": 46321, "reasoning_output_tokens": 25550, "total_tokens": 8435264 }, "thread_token_usage": { "input_tokens": 8388943, "cached_input_tokens": 8238080, "cache_write_input_tokens": 0, "output_tokens": 46321, "reasoning_output_tokens": 25550, "total_tokens": 8435264 } }