{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "codex-astra-high-base-r01-redo-01-2026-09-09T00-00-30", "taskChecksum": "7c62bd47fe98d9620e80f37eaff3f7ce880939f6e00d26f3c4a75ab3f2c9c62f", "source": { "format": "codex-rollout", "sha256": "a73f8c718f33504bdbad7fb3772a627fe892de96d06b817c8029fcdf8df960f2", "records": 834, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:AgentMessage": 25, "token_count": 102, "item_completed:Reasoning": 124, "task_complete": 1 } }, "sessionId": "01a0838b-5647-7e93-a0b3-c13238bc7211", "agentVersion": "0.153.4", "model": "gpt-6-astra", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-09T00:22:22.047Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:22.047Z", "text": "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.\n\n# When to ask the user for permission\n\nUse 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.\n\nUser 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.\n\nYou 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.\n\nDo not use tools to send messages to others (e.g. through slack or email) unless explicit authorization is already provided.\n\nThe 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.\n\n# Autonomy and persistence\n\nThe 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.\n\nWhen 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.\n\nWhen 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.\n\nIf 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.\n\nDo 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. \n\n# Personality\n\nAs 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.\n\n## Writing style\n\nYour 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. \n\nUse 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:...\".\n\nInclude 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.\n\nDefault 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. \n\nAvoid 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. \n\nState 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.\n\n## Technical communication\n\nIn 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.\n\nLead 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. \n\nPresent 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.\n\n### Writing PR descriptions\n\nLead 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.\n\nDescribe 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.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nWhen 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.\n\nThe 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.\n\nWhen 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.\n\nCompaction 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.\n\n## Intermediate commentary\n\nAs 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.\n\nIf the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work.\n\nDo NOT 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.\n\nNever 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 \".\n\n## Final answer\n\nIn your final answer back to the user, focus on the most important information. \n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\nIf you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering.\n\n### Visualizations\n\nUse 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. \n\nFor 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. \n\nUse 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. \n\nUsually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations.\n\n# Rules for getting work done\n\n- When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss.\n- 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.\n- 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.\n- Do not chain shell commands with separators like `echo \"====\";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- 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.\n- Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk.\n- Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision\n- 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.\n- 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.\n\n# Using skills\n\nA 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\".\n\nEach entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n\nThe 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. \n\nThe first time in a conversation that you decide to apply a skill, inform the user in the commentary channel.\n\nIf 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.\n\n## When to use a skill\n\nIf the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why.\n\nIf your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill.\n\n## How to use skills\n\nOpen and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling `skills.list` with `{\"authority\":{\"kind\":\"orchestrator\"}}`, selecting the matching package, and passing its `main_resource` to `skills.read`. Avoid re-reading skills when possible. \n\nWhen a `SKILL.md` file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths.\n\n# Apps (Connectors)\n\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{{connector_id}})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n\n# Plugins\n\nA plugin is a local bundle of skills, MCP servers, and apps.\n\n## How to use plugins\n\n- Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list.\n- 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.\n- Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.\n- Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.\n- 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.\n- 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.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-09T00:22:23.860Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "# Collaboration Mode: Default\n\nYou are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.\n\nYour 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.\n\n## request_user_input availability\n\nUse the `request_user_input` tool only when it is listed in the available tools for this turn.\n\nIn Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions.\n\nUse the `request_user_input` tool only for optional questions where the answer would materially improve the quality of the work.\n\nIf `request_user_input` returns no answers, continue with best judgment instead of asking again or treating the turn as blocked.\n\nNever use the `request_user_input` tool for permission requests or permission-related escalations.\n\nIf explicit user input is required for another reason before progress can safely continue, do not use the `request_user_input` tool. Ask the user directly with one concise plain-text question instead. Never write a multiple choice question as a textual assistant message." } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-09T00:22:23.860Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.\n\nYou can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn.\n`send_message` calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-history forks (`fork_turns` omitted or `\"all\"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `\"none\"` or a positive integer string." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-09T00:22:23.860Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-09T00:22:23.860Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Airtable (airtable@openai-curated-remote)\n- Alpaca (alpaca@openai-curated-remote)\n- Apollo.io (apollo@openai-curated-remote)\n- Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote)\n- AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote)\n- Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote)\n- Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote)\n- LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote)\n- SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote)\n- Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote)\n- Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote)\n- Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote)\n- Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote)\n- True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote)\n- Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote)\n- Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote)\n- Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote)\n- Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote)\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote)\n- Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote)\n- Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote)\n- Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote)\n- vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote)\n- TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote)\n- Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote)\n- Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote)\n- Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote)\n- Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote)\n- COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote)\n- TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote)\n- CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote)\n- Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote)\n- Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote)\n- freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote)\n- Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote)\n- Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote)\n- Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote)\n- CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote)\n- Asana (asana@openai-curated-remote)\n- Atlassian Rovo (atlassian-rovo@openai-curated-remote)\n- Base44 (base44@openai-curated-remote)\n- Binance (binance@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Canva (canva@openai-curated-remote)\n- ClickUp (clickup@openai-curated-remote)\n- Cloudflare (cloudflare@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-09T00:22:23.860Z", "text": "\n /app\n sh\n 2026-09-09\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-09T00:22:23.861Z", "data": { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": false, "collaboration_mode": { "mode": "default", "model": "gpt-6-astra", "instructions": "f6a6d5eb73bbc12c3bdfeb3b3170124c19ff3a77" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-09", "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 } } } }, { "kind": "context", "timestamp": "2026-09-09T00:22:23.862Z", "data": { "type": "turn_context", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-09", "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" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-09T00:22:23.878Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:23.878Z", "text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-09T00:22:27.390Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T00:22:27.390Z", "text": "I’ll inspect the data and evaluator, train a history-based two-tower model, and tune it on the public window. Then I’ll refit on all observed history and generate the eligible top 100 for each user.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:22:29.260Z", "callId": "call_uhNntlqZJokVpMYz8jgVlVFx", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -la /app; ls -lh /task/data; cat /task/requirements.txt; cat /task/evaluate_public.py; cat /task/ranking_evaluation.py\",\"max_output_tokens\":12000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:22:29.335Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"155c1c\",\"wall_time_seconds\":0.000007661,\"exit_code\":0,\"original_token_count\":4136,\"output\":\"total 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\\ntotal 340M\\n-r--r--r-- 1 root root 342K Sep 9 00:05 candidate_catalog.parquet\\n-r--r--r-- 1 root root 83K Sep 9 00:05 target_users.parquet\\n-r--r--r-- 1 root root 7.4M Sep 9 00:05 test_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 test_preference_state.parquet\\n-r--r--r-- 1 root root 838K Sep 9 00:05 train_dislikes.parquet\\n-r--r--r-- 1 root root 5.2M Sep 9 00:05 train_likes.parquet\\n-r--r--r-- 1 root root 305M Sep 9 00:05 train_listens.parquet\\n-r--r--r-- 1 root root 153K Sep 9 00:05 train_undislikes.parquet\\n-r--r--r-- 1 root root 1.8M Sep 9 00:05 train_unlikes.parquet\\n-r--r--r-- 1 root root 36K Sep 9 00:05 val_dislikes.parquet\\n-r--r--r-- 1 root root 292K Sep 9 00:05 val_likes.parquet\\n-r--r--r-- 1 root root 9.5M Sep 9 00:05 val_listens.parquet\\n-r--r--r-- 1 root root 7.7K Sep 9 00:05 val_undislikes.parquet\\n-r--r--r-- 1 root root 71K Sep 9 00:05 val_unlikes.parquet\\n-r--r--r-- 1 root root 7.3M Sep 9 00:05 validation_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 validation_preference_state.parquet\\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#!/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 primitives for Yambda discovery-ranking evaluation.\\n\\nBoth the solver-visible validation evaluator and the private submission judge\\nimport this module from the generated task's ``public`` directory. It owns the\\nstage-independent contract: reading a ranking, validating its shape, converting\\nit to per-user rank order, looking up packed candidate eligibility, and\\ncomputing graded NDCG@10.\\n\\nThe module deliberately contains no event-derived labels, private artifact\\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\\nper-cutoff eligibility checks around these common operations.\\n\\\"\\\"\\\"\\nfrom __future__ import annotations\\n\\nfrom pathlib import Path\\n\\nimport numpy as np\\nimport pandas as pd\\n\\n\\n__all__ = [\\n \\\"CandidateSet\\\",\\n \\\"QUALITY_K\\\",\\n \\\"RankingValidationError\\\",\\n \\\"REQUIRED_COLUMNS\\\",\\n \\\"TOP_N\\\",\\n \\\"ndcg_at_k\\\",\\n \\\"ranked_items\\\",\\n \\\"read_ranking\\\",\\n \\\"score_rankings\\\",\\n \\\"validate_ranking\\\",\\n]\\n\\nTOP_N = 100\\nQUALITY_K = 10\\nREQUIRED_COLUMNS = (\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\")\\n\\n\\nclass RankingValidationError(ValueError):\\n \\\"\\\"\\\"Report a ranking-schema or structural-contract violation.\\\"\\\"\\\"\\n\\n\\nclass CandidateSet:\\n \\\"\\\"\\\"Provide membership lookups over one packed candidate-set archive.\\n\\n The archive contains aligned ``uids``, ``item_ids``, and\\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\\n copied into memory so they remain valid after the archive is closed.\\n \\\"\\\"\\\"\\n\\n def __init__(self, path: Path):\\n \\\"\\\"\\\"Load an eligibility archive and build ID-to-position indexes.\\n\\n Args:\\n path: Path to a ``.npz`` archive with the candidate-set schema.\\n \\\"\\\"\\\"\\n with np.load(path, allow_pickle=False) as artifact:\\n self.uids = artifact[\\\"uids\\\"].astype(np.int64, copy=True)\\n self.item_ids = artifact[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n self.packed = artifact[\\\"packed_eligible\\\"].astype(np.uint8, copy=True)\\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\\n self.item_column = {\\n int(item): column for column, item in enumerate(self.item_ids)\\n }\\n\\n def is_eligible(self, uid: int, item_id: int) -> bool:\\n \\\"\\\"\\\"Return whether ``item_id`` is eligible for ``uid``.\\n\\n IDs absent from the target-user or candidate-item arrays are treated as\\n ineligible rather than raising an exception.\\n\\n Args:\\n uid: Target user ID to query.\\n item_id: Candidate item ID to query.\\n\\n Returns:\\n ``True`` only when the corresponding packed eligibility bit is set.\\n \\\"\\\"\\\"\\n row = self.uid_row.get(int(uid))\\n column = self.item_column.get(int(item_id))\\n if row is None or column is None:\\n return False\\n byte, bit = divmod(column, 8)\\n return bool((int(self.packed[row, byte]) >> bit) & 1)\\n\\n\\ndef read_ranking(path: Path) -> pd.DataFrame:\\n \\\"\\\"\\\"Read a parquet or CSV ranking into a pandas frame.\\n\\n Args:\\n path: Input path. A ``.parquet`` suffix selects parquet; every other\\n suffix is interpreted as CSV.\\n\\n Returns:\\n The ranking frame as loaded by pandas.\\n \\\"\\\"\\\"\\n return pd.read_parquet(path) if path.suffix == \\\".parquet\\\" else pd.read_csv(path)\\n\\n\\ndef validate_ranking(\\n frame: pd.DataFrame,\\n targets: set[int],\\n) -> pd.DataFrame:\\n \\\"\\\"\\\"Normalize and validate the stage-independent ranking contract.\\n\\n Required values must be finite integers. The user set must exactly match\\n ``targets``; every user must have 100 rows, distinct items, and each rank\\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\\n intentionally left to the calling evaluator.\\n\\n Args:\\n frame: Raw ranking frame. Extra columns are discarded from an internal\\n copy; the caller's frame is not modified.\\n targets: Exact set of target user IDs the ranking must cover.\\n\\n Returns:\\n A normalized frame containing only the required ``int64`` columns.\\n\\n Raises:\\n RankingValidationError: If the ranking violates the shared contract.\\n \\\"\\\"\\\"\\n required = list(REQUIRED_COLUMNS)\\n if not all(column in frame for column in required):\\n raise RankingValidationError(f\\\"submission requires columns {required}\\\")\\n\\n normalized = frame[required].copy()\\n for column in required:\\n numeric = pd.to_numeric(normalized[column], errors=\\\"coerce\\\")\\n values = numeric.to_numpy(dtype=float)\\n if numeric.isna().any() or not np.isfinite(values).all():\\n raise RankingValidationError(\\n f\\\"submission {column} must be finite numeric values\\\"\\n )\\n if not np.equal(numeric, np.floor(numeric)).all():\\n raise RankingValidationError(\\n f\\\"submission {column} must contain integers\\\"\\n )\\n normalized[column] = numeric.astype(np.int64)\\n\\n submitted_users = set(map(int, normalized.uid.unique()))\\n if submitted_users != targets:\\n raise RankingValidationError(\\n f\\\"submission user set mismatch: missing={len(targets - submitted_users)} \\\"\\n f\\\"extra={len(submitted_users - targets)}\\\"\\n )\\n counts = normalized.groupby(\\\"uid\\\").size()\\n if not (counts == TOP_N).all():\\n raise RankingValidationError(\\n f\\\"submission must contain exactly {TOP_N} rows for every target user\\\"\\n )\\n if normalized.duplicated([\\\"uid\\\", \\\"item_id\\\"]).any():\\n raise RankingValidationError(\\n \\\"submission contains duplicate (uid, item_id) pairs\\\"\\n )\\n valid_ranks = set(range(1, TOP_N + 1))\\n ranks_are_valid = normalized.groupby(\\\"uid\\\")[\\\"rank\\\"].apply(\\n lambda values: set(map(int, values)) == valid_ranks\\n )\\n if not ranks_are_valid.all():\\n raise RankingValidationError(\\n f\\\"each user must have every rank from 1 through {TOP_N} exactly once\\\"\\n )\\n return normalized\\n\\n\\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\\n \\\"\\\"\\\"Convert a validated ranking frame to ordered items keyed by user.\\n\\n Args:\\n frame: Frame that has passed :func:`validate_ranking`.\\n\\n Returns:\\n A ``uid -> item IDs`` mapping ordered by ascending rank.\\n \\\"\\\"\\\"\\n ordered = frame.sort_values([\\\"uid\\\", \\\"rank\\\"])\\n return {\\n int(uid): list(map(int, values))\\n for uid, values in ordered.groupby(\\\"uid\\\").item_id\\n }\\n\\n\\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\\n \\\"\\\"\\\"Compute graded normalized discounted cumulative gain at ``k``.\\n\\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\\n\\n Args:\\n ranked: Item IDs in predicted rank order.\\n gold: Mapping from relevant item IDs to integer relevance grades.\\n k: Maximum number of predicted positions to score.\\n\\n Returns:\\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\\n ``0.0`` when the ideal gain is zero.\\n \\\"\\\"\\\"\\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\\n return dcg / idcg if idcg else 0.0\\n\\n\\ndef score_rankings(\\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 relevance labels.\\n\\n Args:\\n ranked: Ranked item IDs for every target user.\\n graded: Relevant item grades keyed by user and item.\\n\\n Returns:\\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\\n \\\"\\\"\\\"\\n quality = float(\\n np.mean(\\n [\\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\\n for uid, gold in graded.items()\\n ]\\n )\\n )\\n return {\\n \\\"quality_ndcg@10\\\": round(quality, 6),\\n \\\"quality_users\\\": len(graded),\\n }\\n\"}\n{\"current_time\":\"2026-09-09 00:22:29 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:22:29.324Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76774610-0d84-40a4-b8fd-117a456725b0", "process_id": "64553", "command": [ "/bin/sh", "-lc", "ls -la /app; ls -lh /task/data; cat /task/requirements.txt; cat /task/evaluate_public.py; cat /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -la /app", "path": "app" }, { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "cat /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" }, { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "cat /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 9 00:05 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 9 00:05 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 9 00:05 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 9 00:05 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 9 00:05 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 9 00:05 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 9 00:05 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 9 00:05 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 9 00:05 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 9 00:05 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 9 00:05 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 9 00:05 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 9 00:05 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 9 00:05 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 validation_preference_state.parquet\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#!/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 primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "stderr": "", "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 9 00:05 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 9 00:05 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 9 00:05 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 9 00:05 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 9 00:05 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 9 00:05 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 9 00:05 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 9 00:05 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 9 00:05 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 9 00:05 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 9 00:05 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 9 00:05 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 9 00:05 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 9 00:05 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 validation_preference_state.parquet\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#!/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 primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7661 }, "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 9 00:05 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 9 00:05 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 9 00:05 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 9 00:05 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 9 00:05 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 9 00:05 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 9 00:05 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 9 00:05 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 9 00:05 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 9 00:05 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 9 00:05 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 9 00:05 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 9 00:05 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 9 00:05 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 9 00:06 validation_preference_state.parquet\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#!/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 primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\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 relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:22:29.330Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6c00d6887d1afc3e7d073625048", "usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 0, "total_tokens": 16928 }, "turn_token_usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 0, "total_tokens": 16928 }, "thread_token_usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 123, "reasoning_output_tokens": 0, "total_tokens": 16928 } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-09T00:22:32.613Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:22:32.613Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:22:40.208Z", "callId": "call_AWrGgR4Fb1VT2SS8KIscluQd", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -la /app/solution; find /app -name AGENTS.md; python - <<'PY'\\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\\nprint('cpu',os.cpu_count(),torch.get_num_threads())\\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\\nfor s in ['validation','test']:\\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\\nPY\",\"max_output_tokens\":4500}));\n" }, "result": { "timestamp": "2026-09-09T00:22:40.264Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"87b36f\",\"wall_time_seconds\":0.00000361,\"exit_code\":1,\"original_token_count\":55,\"output\":\"total 8\\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 .\\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 ..\\nTraceback (most recent call last):\\n File \\\"\\\", line 1, in \\nModuleNotFoundError: No module named 'pandas'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:22:40.254Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6c6432087d1b271ec46d3525ca1", "usage": { "input_tokens": 21834, "cached_input_tokens": 16640, "cache_write_input_tokens": 0, "output_tokens": 282, "reasoning_output_tokens": 29, "total_tokens": 22116 }, "turn_token_usage": { "input_tokens": 38639, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 405, "reasoning_output_tokens": 29, "total_tokens": 39044 }, "thread_token_usage": { "input_tokens": 38639, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 405, "reasoning_output_tokens": 29, "total_tokens": 39044 } } }, { "kind": "context", "timestamp": "2026-09-09T00:22:40.260Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-01894c83-a1b4-4a39-8f03-7f37ee62626d", "process_id": "28044", "command": [ "/bin/sh", "-lc", "ls -la /app/solution; find /app -name AGENTS.md; python - <<'PY'\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\nprint('cpu',os.cpu_count(),torch.get_num_threads())\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /app/solution; find /app -name AGENTS.md; python - <<'PY'\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\nprint('cpu',os.cpu_count(),torch.get_num_threads())\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 ..\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 ..\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3610 }, "formatted_output": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 ..\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } }, { "kind": "tool", "timestamp": "2026-09-09T00:22:43.619Z", "callId": "call_kdprytpKRgHd8wQdL0Nfi78u", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"which python; ls -la /opt; ls -la /app; printenv PATH; ls /usr/local/bin | head; ls /opt/venv/bin\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-09T00:22:43.669Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"4f2d1f\",\"wall_time_seconds\":0.00000398,\"exit_code\":2,\"original_token_count\":162,\"output\":\"/usr/local/bin/python\\ntotal 16\\ndrwxr-xr-x 1 root root 4096 Sep 9 00:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\\ndrwxr-xr-x 4 root root 4096 Sep 9 00:19 codex-home\\ndr-xr-xr-x 1 root root 4096 Sep 9 00:19 task-venv\\ntotal 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\\n/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\\n2to3\\n2to3-3.12\\ncodex\\nidle\\nidle3\\nidle3.12\\npip\\npip3\\npip3.12\\npydoc\\nls: cannot access '/opt/venv/bin': No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:22:43.662Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6d0890c87d1aad6883f7d37de2a", "usage": { "input_tokens": 22262, "cached_input_tokens": 21632, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 0, "total_tokens": 22322 }, "turn_token_usage": { "input_tokens": 60901, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 465, "reasoning_output_tokens": 29, "total_tokens": 61366 }, "thread_token_usage": { "input_tokens": 60901, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 465, "reasoning_output_tokens": 29, "total_tokens": 61366 } } }, { "kind": "context", "timestamp": "2026-09-09T00:22:43.664Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-312cd856-c9c8-4c7f-b9e4-4fe42eb1ee25", "process_id": "37967", "command": [ "/bin/sh", "-lc", "which python; ls -la /opt; ls -la /app; printenv PATH; ls /usr/local/bin | head; ls /opt/venv/bin" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; ls -la /opt; ls -la /app; printenv PATH; ls /usr/local/bin | head; ls /opt/venv/bin" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 4 root root 4096 Sep 9 00:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 9 00:19 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\n/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\nls: cannot access '/opt/venv/bin': No such file or directory\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 4 root root 4096 Sep 9 00:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 9 00:19 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\n/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\nls: cannot access '/opt/venv/bin': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3980 }, "formatted_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 4 root root 4096 Sep 9 00:19 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 9 00:19 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 9 00:19 .\ndrwxr-xr-x 1 root root 4096 Sep 9 00:22 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 9 00:19 solution\n/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\n2to3\n2to3-3.12\ncodex\nidle\nidle3\nidle3.12\npip\npip3\npip3.12\npydoc\nls: cannot access '/opt/venv/bin': No such file or directory\n" } } }, { "kind": "tool", "timestamp": "2026-09-09T00:22:52.361Z", "callId": "call_MYdguAsVdvncpOr2n4LtqVSV", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\\nprint('cpu',os.cpu_count(),torch.get_num_threads())\\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\\nfor s in ['validation','test']:\\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\\nPY\",\"max_output_tokens\":5000}));\n" }, "result": { "timestamp": "2026-09-09T00:22:56.988Z", "content": "Script completed\nWall time 4.6 seconds\nOutput:\n\n{\"chunk_id\":\"307482\",\"wall_time_seconds\":4.458661005,\"exit_code\":0,\"original_token_count\":1372,\"output\":\"cpu 8 4\\ncandidate_catalog 30000 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int64 field_id=-1 positive_listens;\\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\\n optional int32 field_id=-1 popularity_rank;\\n}\\n\\n item_id positive_listens organic_positive_listens popularity_rank\\n0 5862961 26615 20620 1\\n1 6901374 25536 17347 2\\n2 5635052 24691 17194 3\\n3 3542184 24075 17699 4\\n4 9378983 24036 18195 5\\ntarget_users 8456 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional double field_id=-1 recent_positive_listens;\\n optional int64 field_id=-1 history_positive_listens;\\n}\\n\\n uid recent_positive_listens history_positive_listens\\n0 100 844.0 2604\\n1 200 50.0 286\\n2 300 168.0 359\\n3 600 1985.0 2423\\n4 700 1776.0 6114\\nvalidation_preference_state 469124 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional binary field_id=-1 state (String);\\n}\\n\\n uid item_id state\\n0 100 410868 liked\\n1 100 732449 liked\\n2 100 982903 liked\\n3 100 1730273 liked\\n4 100 2621343 liked\\ntest_preference_state 484776 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional binary field_id=-1 state (String);\\n}\\n\\n uid item_id state\\n0 100 410868 liked\\n1 100 732449 liked\\n2 100 982903 liked\\n3 100 1730273 liked\\n4 100 2621343 liked\\ntrain_listens 43484159 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\\n}\\n\\ntrain_likes 814831 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic\\n0 100 44755 732449 1\\n1 100 1155860 6568592 0\\n2 100 1259125 5411243 1\\n3 100 1260005 7371186 0\\n4 100 1263935 4943655 0\\nval_listens 1501697 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\\n0 100 24943430 9321269 0 100 195\\n1 100 24943605 5452713 0 100 175\\n2 100 24943765 6448777 0 100 160\\n3 100 24951960 6008056 0 8 175\\n4 100 25246895 7968587 0 45 565\\nval_likes 37795 \\nrequired group field_id=-1 schema {\\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\\n}\\n\\n uid timestamp item_id is_organic\\n0 100 25249855 3526521 1\\n1 300 25046245 4867327 1\\n2 300 25046970 2674136 1\\n3 400 25030730 2331315 1\\n4 500 24861395 1005397 1\\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\\neligible 24657\\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\\neligible 24552\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:22:52.396Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6d3f66c87d1ae61de626f2865fb", "usage": { "input_tokens": 22669, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 241, "reasoning_output_tokens": 0, "total_tokens": 22910 }, "turn_token_usage": { "input_tokens": 83570, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 706, "reasoning_output_tokens": 29, "total_tokens": 84276 }, "thread_token_usage": { "input_tokens": 83570, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 706, "reasoning_output_tokens": 29, "total_tokens": 84276 } } }, { "kind": "context", "timestamp": "2026-09-09T00:22:56.983Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f9366bd1-fecf-46e9-bdb1-e6f4cb568375", "process_id": "85465", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\nprint('cpu',os.cpu_count(),torch.get_num_threads())\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,os,pyarrow.parquet as pq,torch\nprint('cpu',os.cpu_count(),torch.get_num_threads())\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens','val_likes']:\n p='/task/data/'+f+'.parquet'; pf=pq.ParquetFile(p); print(f,pf.metadata.num_rows,pf.schema)\n if f not in ['train_listens']: print(pd.read_parquet(p).head())\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); print(s,[(k,a[k].shape) for k in a.files]); print('eligible',np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little').sum(1).min())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "cpu 8 4\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_preference_state 484776 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntrain_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\nval_likes 37795 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 25249855 3526521 1\n1 300 25046245 4867327 1\n2 300 25046970 2674136 1\n3 400 25030730 2331315 1\n4 500 24861395 1005397 1\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24552\n", "stderr": "", "aggregated_output": "cpu 8 4\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_preference_state 484776 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntrain_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\nval_likes 37795 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 25249855 3526521 1\n1 300 25046245 4867327 1\n2 300 25046970 2674136 1\n3 400 25030730 2331315 1\n4 500 24861395 1005397 1\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24552\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 458652825 }, "formatted_output": "cpu 8 4\ncandidate_catalog 30000 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int64 field_id=-1 positive_listens;\n optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n optional int32 field_id=-1 popularity_rank;\n}\n\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntarget_users 8456 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional double field_id=-1 recent_positive_listens;\n optional int64 field_id=-1 history_positive_listens;\n}\n\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nvalidation_preference_state 469124 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_preference_state 484776 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional binary field_id=-1 state (String);\n}\n\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntrain_listens 43484159 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_likes 814831 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nval_listens 1501697 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n3 100 24951960 6008056 0 8 175\n4 100 25246895 7968587 0 45 565\nval_likes 37795 \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n uid timestamp item_id is_organic\n0 100 25249855 3526521 1\n1 300 25046245 4867327 1\n2 300 25046970 2674136 1\n3 400 25030730 2331315 1\n4 500 24861395 1005397 1\nvalidation [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24657\ntest [('uids', (8456,)), ('item_ids', (30000,)), ('packed_eligible', (8456, 3750))]\neligible 24552\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-09T00:23:00.811Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:23:00.811Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:23:11.456Z", "callId": "call_jiGCfUyPm4wwl22g7Y7HR04F", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"free -h; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\ns=pd.read_parquet('/task/data/validation_preference_state.parquet'); a=np.load('/task/data/validation_eligible_candidates.npz'); us=pd.Index(a['uids']); it=pd.Index(a['item_ids']); u=us.get_indexer(s.uid);i=it.get_indexer(s.item_id);m=(u>=0)&(i>=0);s=s[m];u=u[m];i=i[m];e=(a['packed_eligible'][u,i//8]>>(i%8))&1; print(s.groupby('state').size(),pd.crosstab(s.state,e));\\nf=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','timestamp','item_id','played_ratio_pct','is_organic']); print(f.shape,'users',f.uid.nunique(),'items',f.item_id.nunique(),'time',f.timestamp.min(),f.timestamp.max());m=f.item_id.isin(it); print('catalog',m.mean(),'organic',f.is_organic.mean()); f=f[m]; u=us.get_indexer(f.uid);i=it.get_indexer(f.item_id);m=u>=0; e=(a['packed_eligible'][u[m],i[m]//8]>>(i[m]%8))&1; print(pd.crosstab(f[m].played_ratio_pct>=50,e));\\nprint('recent',pd.crosstab(f[m].timestamp>24786800-86400*7,e));\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-09T00:23:12.622Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"25664c\",\"wall_time_seconds\":1.001699396,\"session_id\":8441,\"original_token_count\":14,\"output\":\"/bin/sh: 1: free: not found\\n8589934592\\n800000 100000\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:23:11.512Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6e1299487d194162eaa02d6dd13", "usage": { "input_tokens": 24738, "cached_input_tokens": 22528, "cache_write_input_tokens": 0, "output_tokens": 439, "reasoning_output_tokens": 52, "total_tokens": 25177 }, "turn_token_usage": { "input_tokens": 108308, "cached_input_tokens": 95104, "cache_write_input_tokens": 0, "output_tokens": 1145, "reasoning_output_tokens": 81, "total_tokens": 109453 }, "thread_token_usage": { "input_tokens": 108308, "cached_input_tokens": 95104, "cache_write_input_tokens": 0, "output_tokens": 1145, "reasoning_output_tokens": 81, "total_tokens": 109453 } } }, { "kind": "context", "timestamp": "2026-09-09T00:23:20.288Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-37c39bee-7e77-4d64-bc6e-c320516b9120", "process_id": "8441", "command": [ "/bin/sh", "-lc", "free -h; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\ns=pd.read_parquet('/task/data/validation_preference_state.parquet'); a=np.load('/task/data/validation_eligible_candidates.npz'); us=pd.Index(a['uids']); it=pd.Index(a['item_ids']); u=us.get_indexer(s.uid);i=it.get_indexer(s.item_id);m=(u>=0)&(i>=0);s=s[m];u=u[m];i=i[m];e=(a['packed_eligible'][u,i//8]>>(i%8))&1; print(s.groupby('state').size(),pd.crosstab(s.state,e));\nf=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','timestamp','item_id','played_ratio_pct','is_organic']); print(f.shape,'users',f.uid.nunique(),'items',f.item_id.nunique(),'time',f.timestamp.min(),f.timestamp.max());m=f.item_id.isin(it); print('catalog',m.mean(),'organic',f.is_organic.mean()); f=f[m]; u=us.get_indexer(f.uid);i=it.get_indexer(f.item_id);m=u>=0; e=(a['packed_eligible'][u[m],i[m]//8]>>(i[m]%8))&1; print(pd.crosstab(f[m].played_ratio_pct>=50,e));\nprint('recent',pd.crosstab(f[m].timestamp>24786800-86400*7,e));\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\ns=pd.read_parquet('/task/data/validation_preference_state.parquet'); a=np.load('/task/data/validation_eligible_candidates.npz'); us=pd.Index(a['uids']); it=pd.Index(a['item_ids']); u=us.get_indexer(s.uid);i=it.get_indexer(s.item_id);m=(u>=0)&(i>=0);s=s[m];u=u[m];i=i[m];e=(a['packed_eligible'][u,i//8]>>(i%8))&1; print(s.groupby('state').size(),pd.crosstab(s.state,e));\nf=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','timestamp','item_id','played_ratio_pct','is_organic']); print(f.shape,'users',f.uid.nunique(),'items',f.item_id.nunique(),'time',f.timestamp.min(),f.timestamp.max());m=f.item_id.isin(it); print('catalog',m.mean(),'organic',f.is_organic.mean()); f=f[m]; u=us.get_indexer(f.uid);i=it.get_indexer(f.item_id);m=u>=0; e=(a['packed_eligible'][u[m],i[m]//8]>>(i[m]%8))&1; print(pd.crosstab(f[m].played_ratio_pct>=50,e));\nprint('recent',pd.crosstab(f[m].timestamp>24786800-86400*7,e));\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "state\ndisliked 50689\nliked 418435\ndtype: int64 col_0 0\nstate \ndisliked 50689\nliked 418435\n(43484159, 5) users 9238 items 845395 time 0 24786795\ncatalog 0.7186436099638032 organic 0.5169151828370419\ncol_0 0\nplayed_ratio_pct \nFalse 11102687\nTrue 19959853\nrecent col_0 0\ntimestamp \nFalse 30016839\nTrue 1045701\n", "stderr": "", "aggregated_output": "state\ndisliked 50689\nliked 418435\ndtype: int64 col_0 0\nstate \ndisliked 50689\nliked 418435\n(43484159, 5) users 9238 items 845395 time 0 24786795\ncatalog 0.7186436099638032 organic 0.5169151828370419\ncol_0 0\nplayed_ratio_pct \nFalse 11102687\nTrue 19959853\nrecent col_0 0\ntimestamp \nFalse 30016839\nTrue 1045701\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 668800830 }, "formatted_output": "state\ndisliked 50689\nliked 418435\ndtype: int64 col_0 0\nstate \ndisliked 50689\nliked 418435\n(43484159, 5) users 9238 items 845395 time 0 24786795\ncatalog 0.7186436099638032 organic 0.5169151828370419\ncol_0 0\nplayed_ratio_pct \nFalse 11102687\nTrue 19959853\nrecent col_0 0\ntimestamp \nFalse 30016839\nTrue 1045701\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-09T00:23:20.836Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:23:20.836Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:23:21.555Z", "callId": "call_pv47Sb32GgYyBbawQnQcatVo", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:8441,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-09T00:23:21.598Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"f07a4e\",\"wall_time_seconds\":0.00000198,\"exit_code\":0,\"original_token_count\":107,\"output\":\"state\\ndisliked 50689\\nliked 418435\\ndtype: int64 col_0 0\\nstate \\ndisliked 50689\\nliked 418435\\n(43484159, 5) users 9238 items 845395 time 0 24786795\\ncatalog 0.7186436099638032 organic 0.5169151828370419\\ncol_0 0\\nplayed_ratio_pct \\nFalse 11102687\\nTrue 19959853\\nrecent col_0 0\\ntimestamp \\nFalse 30016839\\nTrue 1045701\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:23:21.597Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6f0dc7087d1b0d02f529203bc7e", "usage": { "input_tokens": 25256, "cached_input_tokens": 24576, "cache_write_input_tokens": 0, "output_tokens": 190, "reasoning_output_tokens": 148, "total_tokens": 25446 }, "turn_token_usage": { "input_tokens": 133564, "cached_input_tokens": 119680, "cache_write_input_tokens": 0, "output_tokens": 1335, "reasoning_output_tokens": 229, "total_tokens": 134899 }, "thread_token_usage": { "input_tokens": 133564, "cached_input_tokens": 119680, "cache_write_input_tokens": 0, "output_tokens": 1335, "reasoning_output_tokens": 229, "total_tokens": 134899 } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-09T00:23:47.138Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:23:47.138Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:23:49.313Z", "text": "The candidate sets exclude every previously interacted-with track, so the model must learn which new tracks fit a listener’s taste. I’m building a neural history encoder trained to predict held-out tracks from other tracks in a user’s history, without constructing a global user-item matrix.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:24:01.171Z", "callId": "call_bXe4Et7pBlg84JLXoftGdjGv", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare.py <<'PY'\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\\nROOT=Path('/app/solution'); DATA=Path('/task/data')\\nt=time.time()\\nitems=pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy()\\nusers=np.sort(pd.read_parquet(DATA/'train_likes.parquet').uid.unique())\\n# All training users, including users without explicit actions.\\nus=[]\\nfor b in pq.ParquetFile(DATA/'train_listens.parquet').iter_batches(columns=['uid'],batch_size=2000000):us.append(np.unique(b.column(0).to_numpy()))\\nusers=np.unique(np.concatenate(us)); np.savez(ROOT/'ids.npz',users=users,items=items)\\nui=pd.Index(users); ii=pd.Index(items)\\nfor stage in ['train','val']:\\n parts=[]\\n for family in ['listens','likes','dislikes','unlikes','undislikes']:\\n for b in pq.ParquetFile(DATA/f'{stage}_{family}.parquet').iter_batches(batch_size=1000000):\\n f=b.to_pandas();i=ii.get_indexer(f.item_id);u=ui.get_indexer(f.uid);keep=(i>=0)&(u>=0)\\n v=np.empty(keep.sum(),dtype=[('u','i4'),('i','i4'),('t','i4'),('o','i1'),('k','i1'),('q','i2')])\\n v['u']=u[keep];v['i']=i[keep];v['t']=f.timestamp.to_numpy()[keep];v['o']=f.is_organic.to_numpy()[keep]\\n v['k']=['listens','likes','dislikes','unlikes','undislikes'].index(family)\\n v['q']=f.played_ratio_pct.to_numpy()[keep] if family=='listens' else 100\\n parts.append(v)\\n print(stage,family,'loaded',time.time()-t,flush=True)\\n v=np.concatenate(parts);del parts\\n order=np.lexsort((v['t'],v['u']));v=v[order];np.save(ROOT/f'{stage}_events.npy',v)\\n print(stage,len(v),'saved',time.time()-t,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:24:02.339Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"dbe91e\",\"wall_time_seconds\":1.001844007,\"session_id\":83669,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:24:01.228Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a6fa3c8087d1b4e7d523f4bd3dd9", "usage": { "input_tokens": 25647, "cached_input_tokens": 25088, "cache_write_input_tokens": 0, "output_tokens": 1154, "reasoning_output_tokens": 516, "total_tokens": 26801 }, "turn_token_usage": { "input_tokens": 159211, "cached_input_tokens": 144768, "cache_write_input_tokens": 0, "output_tokens": 2489, "reasoning_output_tokens": 745, "total_tokens": 161700 }, "thread_token_usage": { "input_tokens": 159211, "cached_input_tokens": 144768, "cache_write_input_tokens": 0, "output_tokens": 2489, "reasoning_output_tokens": 745, "total_tokens": 161700 } } }, { "kind": "context", "timestamp": "2026-09-09T00:24:09.255Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ae6ec42-bcf5-4998-b500-54aee2586919", "process_id": "83669", "command": [ "/bin/sh", "-lc", "cat > /app/solution/prepare.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nROOT=Path('/app/solution'); DATA=Path('/task/data')\nt=time.time()\nitems=pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy()\nusers=np.sort(pd.read_parquet(DATA/'train_likes.parquet').uid.unique())\n# All training users, including users without explicit actions.\nus=[]\nfor b in pq.ParquetFile(DATA/'train_listens.parquet').iter_batches(columns=['uid'],batch_size=2000000):us.append(np.unique(b.column(0).to_numpy()))\nusers=np.unique(np.concatenate(us)); np.savez(ROOT/'ids.npz',users=users,items=items)\nui=pd.Index(users); ii=pd.Index(items)\nfor stage in ['train','val']:\n parts=[]\n for family in ['listens','likes','dislikes','unlikes','undislikes']:\n for b in pq.ParquetFile(DATA/f'{stage}_{family}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();i=ii.get_indexer(f.item_id);u=ui.get_indexer(f.uid);keep=(i>=0)&(u>=0)\n v=np.empty(keep.sum(),dtype=[('u','i4'),('i','i4'),('t','i4'),('o','i1'),('k','i1'),('q','i2')])\n v['u']=u[keep];v['i']=i[keep];v['t']=f.timestamp.to_numpy()[keep];v['o']=f.is_organic.to_numpy()[keep]\n v['k']=['listens','likes','dislikes','unlikes','undislikes'].index(family)\n v['q']=f.played_ratio_pct.to_numpy()[keep] if family=='listens' else 100\n parts.append(v)\n print(stage,family,'loaded',time.time()-t,flush=True)\n v=np.concatenate(parts);del parts\n order=np.lexsort((v['t'],v['u']));v=v[order];np.save(ROOT/f'{stage}_events.npy',v)\n print(stage,len(v),'saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/prepare.py <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nROOT=Path('/app/solution'); DATA=Path('/task/data')\nt=time.time()\nitems=pd.read_parquet(DATA/'candidate_catalog.parquet').item_id.to_numpy()\nusers=np.sort(pd.read_parquet(DATA/'train_likes.parquet').uid.unique())\n# All training users, including users without explicit actions.\nus=[]\nfor b in pq.ParquetFile(DATA/'train_listens.parquet').iter_batches(columns=['uid'],batch_size=2000000):us.append(np.unique(b.column(0).to_numpy()))\nusers=np.unique(np.concatenate(us)); np.savez(ROOT/'ids.npz',users=users,items=items)\nui=pd.Index(users); ii=pd.Index(items)\nfor stage in ['train','val']:\n parts=[]\n for family in ['listens','likes','dislikes','unlikes','undislikes']:\n for b in pq.ParquetFile(DATA/f'{stage}_{family}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();i=ii.get_indexer(f.item_id);u=ui.get_indexer(f.uid);keep=(i>=0)&(u>=0)\n v=np.empty(keep.sum(),dtype=[('u','i4'),('i','i4'),('t','i4'),('o','i1'),('k','i1'),('q','i2')])\n v['u']=u[keep];v['i']=i[keep];v['t']=f.timestamp.to_numpy()[keep];v['o']=f.is_organic.to_numpy()[keep]\n v['k']=['listens','likes','dislikes','unlikes','undislikes'].index(family)\n v['q']=f.played_ratio_pct.to_numpy()[keep] if family=='listens' else 100\n parts.append(v)\n print(stage,family,'loaded',time.time()-t,flush=True)\n v=np.concatenate(parts);del parts\n order=np.lexsort((v['t'],v['u']));v=v[order];np.save(ROOT/f'{stage}_events.npy',v)\n print(stage,len(v),'saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 918537203 }, "formatted_output": "" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-09T00:24:24.895Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:24:24.895Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:24:35.667Z", "callId": "call_heoFLiumGDZbNTL6Y2fh2piJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\\n\\\"\\\"\\\"CPU neural retrieval: both outputs are independent and score is exactly u @ v.T.\\\"\\\"\\\"\\nimport torch\\nfrom torch import nn\\nfrom torch.nn import functional as F\\nclass UserTower(nn.Module):\\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\\n super().__init__()\\n self.history=nn.EmbeddingBag(n_items,hidden,mode='sum')\\n self.network=nn.Sequential(nn.Linear(hidden,hidden),nn.Tanh(),nn.Linear(hidden,dim))\\n self.dropout=dropout\\n nn.init.normal_(self.history.weight,std=.02)\\n def forward(self,ids,offsets,weights):\\n if self.training:weights=F.dropout(weights,p=self.dropout)\\n x=self.history(ids,offsets,per_sample_weights=weights)\\n x=F.normalize(x,dim=-1)\\n x=self.network(x)\\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\\nclass ItemTower(nn.Module):\\n def __init__(self,n_items,dim=192):\\n super().__init__()\\n self.embedding=nn.Embedding(n_items,dim)\\n self.projection=nn.Linear(dim,dim,bias=False)\\n self.bias=nn.Embedding(n_items,1)\\n nn.init.normal_(self.embedding.weight,std=.02)\\n nn.init.eye_(self.projection.weight)\\n nn.init.zeros_(self.bias.weight)\\n def forward(self,ids):\\n return torch.cat([self.projection(self.embedding(ids)),self.bias(ids)],dim=-1)\\nclass TwoTower(nn.Module):\\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\\n super().__init__();self.user=UserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\\n def forward(self,ids,offsets,weights,item_ids):\\n return self.user(ids,offsets,weights) @ self.item(item_ids).T\\nPY\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-09T00:24:35.723Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"626a6b\",\"wall_time_seconds\":0.00000648,\"exit_code\":0,\"original_token_count\":189,\"output\":\"train listens loaded 2.076504707336426\\ntrain likes loaded 2.1107561588287354\\ntrain dislikes loaded 2.116408109664917\\ntrain unlikes loaded 2.1297097206115723\\ntrain undislikes loaded 2.1318728923797607\\ntrain 31869321 saved 6.3718695640563965\\nval listens loaded 6.440881729125977\\nval likes loaded 6.4440083503723145\\nval dislikes loaded 6.4452667236328125\\nval unlikes loaded 6.446715593338013\\nval undislikes loaded 6.447751522064209\\nval 1034429 saved 6.560548543930054\\ntotal 503M\\n-rw-r--r-- 1 agent agent 154K Sep 9 00:24 ids.npz\\n-rw-r--r-- 1 agent agent 465 Sep 9 00:24 prepare.log\\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 prepare.py\\n-rw-r--r-- 1 agent agent 487M Sep 9 00:24 train_events.npy\\n-rw-r--r-- 1 agent agent 16M Sep 9 00:24 val_events.npy\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:24:35.709Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ee24fab-899f-4ce9-8e3a-9af9656fc5e0", "process_id": "65005", "command": [ "/bin/sh", "-lc", "cat /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural retrieval: both outputs are independent and score is exactly u @ v.T.\"\"\"\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nclass UserTower(nn.Module):\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\n super().__init__()\n self.history=nn.EmbeddingBag(n_items,hidden,mode='sum')\n self.network=nn.Sequential(nn.Linear(hidden,hidden),nn.Tanh(),nn.Linear(hidden,dim))\n self.dropout=dropout\n nn.init.normal_(self.history.weight,std=.02)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n x=self.history(ids,offsets,per_sample_weights=weights)\n x=F.normalize(x,dim=-1)\n x=self.network(x)\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\nclass ItemTower(nn.Module):\n def __init__(self,n_items,dim=192):\n super().__init__()\n self.embedding=nn.Embedding(n_items,dim)\n self.projection=nn.Linear(dim,dim,bias=False)\n self.bias=nn.Embedding(n_items,1)\n nn.init.normal_(self.embedding.weight,std=.02)\n nn.init.eye_(self.projection.weight)\n nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n return torch.cat([self.projection(self.embedding(ids)),self.bias(ids)],dim=-1)\nclass TwoTower(nn.Module):\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\n super().__init__();self.user=UserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\n def forward(self,ids,offsets,weights,item_ids):\n return self.user(ids,offsets,weights) @ self.item(item_ids).T\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural retrieval: both outputs are independent and score is exactly u @ v.T.\"\"\"\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nclass UserTower(nn.Module):\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\n super().__init__()\n self.history=nn.EmbeddingBag(n_items,hidden,mode='sum')\n self.network=nn.Sequential(nn.Linear(hidden,hidden),nn.Tanh(),nn.Linear(hidden,dim))\n self.dropout=dropout\n nn.init.normal_(self.history.weight,std=.02)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n x=self.history(ids,offsets,per_sample_weights=weights)\n x=F.normalize(x,dim=-1)\n x=self.network(x)\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\nclass ItemTower(nn.Module):\n def __init__(self,n_items,dim=192):\n super().__init__()\n self.embedding=nn.Embedding(n_items,dim)\n self.projection=nn.Linear(dim,dim,bias=False)\n self.bias=nn.Embedding(n_items,1)\n nn.init.normal_(self.embedding.weight,std=.02)\n nn.init.eye_(self.projection.weight)\n nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n return torch.cat([self.projection(self.embedding(ids)),self.bias(ids)],dim=-1)\nclass TwoTower(nn.Module):\n def __init__(self,n_items,hidden=384,dim=192,dropout=.5):\n super().__init__();self.user=UserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\n def forward(self,ids,offsets,weights,item_ids):\n return self.user(ids,offsets,weights) @ self.item(item_ids).T\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train listens loaded 2.076504707336426\ntrain likes loaded 2.1107561588287354\ntrain dislikes loaded 2.116408109664917\ntrain unlikes loaded 2.1297097206115723\ntrain undislikes loaded 2.1318728923797607\ntrain 31869321 saved 6.3718695640563965\nval listens loaded 6.440881729125977\nval likes loaded 6.4440083503723145\nval dislikes loaded 6.4452667236328125\nval unlikes loaded 6.446715593338013\nval undislikes loaded 6.447751522064209\nval 1034429 saved 6.560548543930054\ntotal 503M\n-rw-r--r-- 1 agent agent 154K Sep 9 00:24 ids.npz\n-rw-r--r-- 1 agent agent 465 Sep 9 00:24 prepare.log\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 prepare.py\n-rw-r--r-- 1 agent agent 487M Sep 9 00:24 train_events.npy\n-rw-r--r-- 1 agent agent 16M Sep 9 00:24 val_events.npy\n", "stderr": "", "aggregated_output": "train listens loaded 2.076504707336426\ntrain likes loaded 2.1107561588287354\ntrain dislikes loaded 2.116408109664917\ntrain unlikes loaded 2.1297097206115723\ntrain undislikes loaded 2.1318728923797607\ntrain 31869321 saved 6.3718695640563965\nval listens loaded 6.440881729125977\nval likes loaded 6.4440083503723145\nval dislikes loaded 6.4452667236328125\nval unlikes loaded 6.446715593338013\nval undislikes loaded 6.447751522064209\nval 1034429 saved 6.560548543930054\ntotal 503M\n-rw-r--r-- 1 agent agent 154K Sep 9 00:24 ids.npz\n-rw-r--r-- 1 agent agent 465 Sep 9 00:24 prepare.log\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 prepare.py\n-rw-r--r-- 1 agent agent 487M Sep 9 00:24 train_events.npy\n-rw-r--r-- 1 agent agent 16M Sep 9 00:24 val_events.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6480 }, "formatted_output": "train listens loaded 2.076504707336426\ntrain likes loaded 2.1107561588287354\ntrain dislikes loaded 2.116408109664917\ntrain unlikes loaded 2.1297097206115723\ntrain undislikes loaded 2.1318728923797607\ntrain 31869321 saved 6.3718695640563965\nval listens loaded 6.440881729125977\nval likes loaded 6.4440083503723145\nval dislikes loaded 6.4452667236328125\nval unlikes loaded 6.446715593338013\nval undislikes loaded 6.447751522064209\nval 1034429 saved 6.560548543930054\ntotal 503M\n-rw-r--r-- 1 agent agent 154K Sep 9 00:24 ids.npz\n-rw-r--r-- 1 agent agent 465 Sep 9 00:24 prepare.log\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 prepare.py\n-rw-r--r-- 1 agent agent 487M Sep 9 00:24 train_events.npy\n-rw-r--r-- 1 agent agent 16M Sep 9 00:24 val_events.npy\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:24:35.721Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a722879c87d1966c365ef9162479", "usage": { "input_tokens": 26859, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 1035, "reasoning_output_tokens": 516, "total_tokens": 27894 }, "turn_token_usage": { "input_tokens": 186070, "cached_input_tokens": 170240, "cache_write_input_tokens": 0, "output_tokens": 3524, "reasoning_output_tokens": 1261, "total_tokens": 189594 }, "thread_token_usage": { "input_tokens": 186070, "cached_input_tokens": 170240, "cache_write_input_tokens": 0, "output_tokens": 3524, "reasoning_output_tokens": 1261, "total_tokens": 189594 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-09T00:24:46.224Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:24:46.224Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:25:51.172Z", "callId": "call_EGoMj6Dkey0U7HvInqUkvdoO", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/train.py <<'PY'\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nfrom pathlib import Path\\nimport argparse,json,time,sys\\nimport numpy as np,pandas as pd,torch\\nfrom torch.nn import functional as F\\nfrom model import TwoTower\\nROOT=Path('/app/solution');DATA=Path('/task/data')\\n\\ndef make_bags(stage,half=90,like_weight=3.,organic_weight=1.,recent_days=0):\\n cache=ROOT/f'bags_{stage}_{half}_{like_weight}_{organic_weight}_{recent_days}.npz'\\n if cache.exists():return dict(np.load(cache))\\n ids=np.load(ROOT/'ids.npz'); n=len(ids['users'])\\n events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\\n cutoff=24786800+(606600 if stage=='test' else 0)\\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\\n bagids=[];inw=[];outw=[];times=[];offsets=[0]\\n for u in range(n):\\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)])\\n pos=((v['k']==0)&(v['q']>=50))|(v['k']==1)\\n if recent_days:pos&=v['t']>=cutoff-recent_days*86400\\n v=v[pos]\\n it,ix=np.unique(v['i'],return_inverse=True); k=len(it)\\n age=(cutoff-v['t'])/86400\\n decay=np.power(2.,-age/half) if half else np.ones(len(v))\\n iw=np.where(v['k']==1,3.,1.)*np.where(v['o']==1,1.,.5)*decay\\n ow=np.where(v['k']==1,like_weight,np.where((v['o']==1)&(v['q']>=80),organic_weight,.15))*decay\\n a=np.bincount(ix,weights=iw,minlength=k); b=np.bincount(ix,weights=ow,minlength=k)\\n a=np.sqrt(a);b=np.log1p(b)\\n a=a/np.sqrt((a*a).sum()+1.e-9)\\n bagids.append(it.astype('int32'));inw.append(a.astype('float32'));outw.append(b.astype('float32'))\\n offsets.append(offsets[-1]+k)\\n bags=dict(items=np.concatenate(bagids),weights=np.concatenate(inw),targets=np.concatenate(outw),offsets=np.array(offsets,dtype='int64'))\\n np.savez(cache,**bags);print('bags',stage,len(bags['items']),flush=True);return bags\\n\\ndef batch(bags,users):\\n off=bags['offsets']; lens=off[users+1]-off[users];offs=np.r_[0,np.cumsum(lens)[:-1]]\\n ix=np.repeat(off[users]-offs,lens)+np.arange(lens.sum())\\n return torch.from_numpy(bags['items'][ix].astype('int64')),torch.from_numpy(offs),torch.from_numpy(bags['weights'][ix]),ix,np.repeat(np.arange(len(users)),lens)\\n\\ndef gold_data(elig,ids):\\n sys.path.insert(0,'/task');from evaluate_public import validation_truth\\n from ranking_evaluation import CandidateSet\\n truth=validation_truth(CandidateSet(DATA/'validation_eligible_candidates.npz'),set(ids['users']))\\n itemidx={int(x):i for i,x in enumerate(ids['items'])}\\n gold={u:{itemidx[i]:(2**g-1) for i,g in vals.items()} for u,vals in truth.items()}\\n return gold\\n\\ndef infer(model,bags,stage,out,ids,gold=None,bs=256,return_scores=False):\\n model.eval(); a=np.load(DATA/f'{stage}_eligible_candidates.npz'); userix=pd.Index(ids['users']).get_indexer(a['uids']); col=pd.Index(ids['items']).get_indexer(a['item_ids'])\\n top=[];ndcg=[];discount=1/np.log2(np.arange(2,12));scoreparts=[]\\n with torch.no_grad():\\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\\n for st in range(0,len(userix),bs):\\n x,o,w,_,_=batch(bags,userix[st:st+bs]);scores=(model.user(x,o,w)@iv).numpy()\\n mask=np.unpackbits(a['packed_eligible'][st:st+bs],axis=1,count=len(col),bitorder='little').astype(bool);scores[~mask]=-1.e9\\n if return_scores:scoreparts.append(scores.copy())\\n rank=torch.topk(torch.from_numpy(scores),100,dim=1).indices.numpy();top.append(a['item_ids'][rank])\\n if gold:\\n for j,rr in enumerate(rank):\\n g=gold.get(int(a['uids'][st+j]))\\n if g:\\n gains=np.array([g.get(int(col[c]),0) for c in rr[:10]]);ideal=sorted(g.values(),reverse=True)[:10]\\n ndcg.append(float(gains@discount/(np.array(ideal)@discount[:len(ideal)])))\\n score=float(np.mean(ndcg)) if ndcg else 0.\\n if out:\\n frame=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':np.concatenate(top).ravel(),'rank':np.tile(np.arange(1,101),len(userix))})\\n tmp=ROOT/(out+'.tmp.parquet');frame.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\\n return (score,np.concatenate(scoreparts)) if return_scores else score\\n\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation',choices=['validation','test']);p.add_argument('--name',default='base');p.add_argument('--epochs',type=int,default=60);p.add_argument('--hidden',type=int,default=384);p.add_argument('--dim',type=int,default=192);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--half',type=float,default=90);p.add_argument('--likes',type=float,default=3.);p.add_argument('--organic',type=float,default=1.);p.add_argument('--recent-days',type=int,default=0);p.add_argument('--lr',type=float,default=.001);p.add_argument('--batch',type=int,default=256);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--seed',type=int,default=123);p.add_argument('--resume');p.add_argument('--threads',type=int,default=4)\\n args=p.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);t=time.time()\\n ids=dict(np.load(ROOT/'ids.npz'));bags=make_bags('train' if args.stage=='validation' else 'test',args.half,args.likes,args.organic,args.recent_days)\\n model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\\n if args.resume:model.load_state_dict(torch.load(ROOT/args.resume,weights_only=False)['model'])\\n opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\\n gold=gold_data(None,ids) if args.stage=='validation' else None\\n item_ids=torch.arange(len(ids['items'])); users=np.flatnonzero(np.diff(bags['offsets'])>1);best=-1\\n for epoch in range(1,args.epochs+1):\\n model.train(); losses=[];rng.shuffle(users)\\n for st in range(0,len(users),args.batch):\\n uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\\n print(args.name,args.stage,'epoch',epoch,'loss',round(np.mean(losses),5),'seconds',round(time.time()-t,1),flush=True)\\n if epoch%args.eval_every==0 or epoch==1 or epoch==args.epochs:\\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\\n score=infer(model,bags,args.stage,out,ids,gold)\\n print('SCORE',epoch,score,flush=True)\\n if score>best or args.stage=='test':\\n best=score;torch.save(dict(model=model.state_dict(),args=vars(args),epoch=epoch,score=score),ROOT/f'{args.name}_{args.stage}.pt')\\n if args.stage=='validation':\\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet')\\n (ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':epoch,'score':score,'args':vars(args)},indent=2))\\n print('DONE',best,flush=True)\\nif __name__=='__main__':main()\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --epochs 80 --eval-every 5 > /app/solution/base.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:25:52.339Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"7aee07\",\"wall_time_seconds\":1.000923358,\"session_id\":33145,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:25:51.219Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a744234487d1bcb0d86901b1c355", "usage": { "input_tokens": 28242, "cached_input_tokens": 26624, "cache_write_input_tokens": 0, "output_tokens": 2472, "reasoning_output_tokens": 225, "total_tokens": 30714 }, "turn_token_usage": { "input_tokens": 214312, "cached_input_tokens": 196864, "cache_write_input_tokens": 0, "output_tokens": 5996, "reasoning_output_tokens": 1486, "total_tokens": 220308 }, "thread_token_usage": { "input_tokens": 214312, "cached_input_tokens": 196864, "cache_write_input_tokens": 0, "output_tokens": 5996, "reasoning_output_tokens": 1486, "total_tokens": 220308 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-09T00:25:54.227Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:25:54.227Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:25:59.235Z", "callId": "call_yyVoBfFaz1KbI0NBX4wABjK8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/base.log; ls -lh /app/solution; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,sys\\nsys.path.insert(0,'/task');from evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet\\nusers=set(pd.read_parquet('/task/data/target_users.parquet').uid)\\ng=validation_truth(CandidateSet('/task/data/validation_eligible_candidates.npz'),users)\\nprint('gold users',len(g),'pairs',sum(map(len,g.values())),'likes',sum(sum(x==3 for x in v.values()) for v in g.values()))\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:26:00.402Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"ab897c\",\"wall_time_seconds\":1.001820516,\"session_id\":29578,\"original_token_count\":134,\"output\":\"bags train 5618006\\ntotal 567M\\n-rw-r--r-- 1 agent agent 65M Sep 9 00:25 bags_train_90_3.0_1.0_0.npz\\n-rw-r--r-- 1 agent agent 19 Sep 9 00:25 base.log\\n-rw-r--r-- 1 agent agent 154K Sep 9 00:24 ids.npz\\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 model.py\\n-rw-r--r-- 1 agent agent 465 Sep 9 00:24 prepare.log\\n-rw-r--r-- 1 agent agent 1.6K Sep 9 00:24 prepare.py\\n-rw-r--r-- 1 agent agent 7.2K Sep 9 00:25 train.py\\n-rw-r--r-- 1 agent agent 487M Sep 9 00:24 train_events.npy\\n-rw-r--r-- 1 agent agent 16M Sep 9 00:24 val_events.npy\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:25:59.338Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a7909cc087d1839b41726279de04", "usage": { "input_tokens": 30772, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 185, "reasoning_output_tokens": 8, "total_tokens": 30957 }, "turn_token_usage": { "input_tokens": 245084, "cached_input_tokens": 224896, "cache_write_input_tokens": 0, "output_tokens": 6181, "reasoning_output_tokens": 1494, "total_tokens": 251265 }, "thread_token_usage": { "input_tokens": 245084, "cached_input_tokens": 224896, "cache_write_input_tokens": 0, "output_tokens": 6181, "reasoning_output_tokens": 1494, "total_tokens": 251265 } } }, { "kind": "context", "timestamp": "2026-09-09T00:26:01.365Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e9b5a3a2-aeba-4b00-b051-96e3cdeea816", "process_id": "29578", "command": [ "/bin/sh", "-lc", "cat /app/solution/base.log; ls -lh /app/solution; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys\nsys.path.insert(0,'/task');from evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nusers=set(pd.read_parquet('/task/data/target_users.parquet').uid)\ng=validation_truth(CandidateSet('/task/data/validation_eligible_candidates.npz'),users)\nprint('gold users',len(g),'pairs',sum(map(len,g.values())),'likes',sum(sum(x==3 for x in v.values()) for v in g.values()))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/base.log; ls -lh /app/solution; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys\nsys.path.insert(0,'/task');from evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\nusers=set(pd.read_parquet('/task/data/target_users.parquet').uid)\ng=validation_truth(CandidateSet('/task/data/validation_eligible_candidates.npz'),users)\nprint('gold users',len(g),'pairs',sum(map(len,g.values())),'likes',sum(sum(x==3 for x in v.values()) for v in g.values()))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "gold users 4263 pairs 37769 likes 4417\n", "stderr": "", "aggregated_output": "gold users 4263 pairs 37769 likes 4417\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 965924228 }, "formatted_output": "gold users 4263 pairs 37769 likes 4417\n" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-09T00:26:05.279Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:26:05.279Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:26:07.754Z", "callId": "call_rIbGPBwaAb9ph1QFpatWbHZS", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/base.log; ps -eo pid,pcpu,rss,args | head -12\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:26:07.964Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2a2dd0\",\"wall_time_seconds\":0.00000374,\"exit_code\":0,\"original_token_count\":24,\"output\":\"bags train 5618006\\nbase validation epoch 1 loss 9.76416 seconds 12.9\\n/bin/sh: 1: ps: not found\\n\"}\n{\"current_time\":\"2026-09-09 00:26:07 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:26:07.823Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8fb8e3dd-79ab-4dbc-8c5c-2f18e0430cef", "process_id": "10216", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/base.log; ps -eo pid,pcpu,rss,args | head -12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/base.log; ps -eo pid,pcpu,rss,args | head -12" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3740 }, "formatted_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:26:07.963Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a79893b087d18eea82d24260299a", "usage": { "input_tokens": 31267, "cached_input_tokens": 30592, "cache_write_input_tokens": 0, "output_tokens": 145, "reasoning_output_tokens": 84, "total_tokens": 31412 }, "turn_token_usage": { "input_tokens": 276351, "cached_input_tokens": 255488, "cache_write_input_tokens": 0, "output_tokens": 6326, "reasoning_output_tokens": 1578, "total_tokens": 282677 }, "thread_token_usage": { "input_tokens": 276351, "cached_input_tokens": 255488, "cache_write_input_tokens": 0, "output_tokens": 6326, "reasoning_output_tokens": 1578, "total_tokens": 282677 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-09T00:26:12.741Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:26:12.741Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:26:14.443Z", "callId": "call_Z1QlQ5mEGBCV5LCNWa0Hkam5", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:29578,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1200}));\ntext(await tools.exec_command({cmd:\"tail -8 /app/solution/base.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:26:14.544Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"d0c6a0\",\"wall_time_seconds\":0.00000171,\"exit_code\":0,\"original_token_count\":10,\"output\":\"gold users 4263 pairs 37769 likes 4417\\n\"}\n{\"chunk_id\":\"af4c39\",\"wall_time_seconds\":0.000004121,\"exit_code\":0,\"original_token_count\":18,\"output\":\"bags train 5618006\\nbase validation epoch 1 loss 9.76416 seconds 12.9\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:26:14.482Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ba33f945-b56e-4ac7-96db-6ecbcce422aa", "process_id": "20227", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n", "stderr": "", "aggregated_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4121 }, "formatted_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:26:14.542Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a7a0802087d1b5a80c5bd1f2e0ce", "usage": { "input_tokens": 31522, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 82, "total_tokens": 31671 }, "turn_token_usage": { "input_tokens": 307873, "cached_input_tokens": 286592, "cache_write_input_tokens": 0, "output_tokens": 6475, "reasoning_output_tokens": 1660, "total_tokens": 314348 }, "thread_token_usage": { "input_tokens": 307873, "cached_input_tokens": 286592, "cache_write_input_tokens": 0, "output_tokens": 6475, "reasoning_output_tokens": 1660, "total_tokens": 314348 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-09T00:26:28.745Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:26:28.745Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:26:31.004Z", "text": "The training history contains 5.6 million distinct positive user-track pairs. The first model is training with randomly dropped history entries and a weighted softmax objective that emphasizes organic completions and likes. Item vectors will be computed separately; retrieval will use only their dot product with the user vector.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:26:31.760Z", "callId": "call_CxqraU548bvwLG7wnHSDqZ5H", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/base.log; ls -lh /app/solution/*parquet\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:26:31.829Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"e06e43\",\"wall_time_seconds\":0.00000446,\"exit_code\":0,\"original_token_count\":92,\"output\":\"bags train 5618006\\nbase validation epoch 1 loss 9.76416 seconds 12.9\\nSCORE 1 0.013686379892513763\\nbase validation epoch 2 loss 9.50096 seconds 25.6\\nbase validation epoch 3 loss 9.42088 seconds 31.7\\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_best_validation.parquet\\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_validation.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:26:31.815Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-03feeb5b-d9d8-487c-8823-4429d5947fc9", "process_id": "80981", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/base.log; ls -lh /app/solution/*parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/base.log; ls -lh /app/solution/*parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\nSCORE 1 0.013686379892513763\nbase validation epoch 2 loss 9.50096 seconds 25.6\nbase validation epoch 3 loss 9.42088 seconds 31.7\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_best_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_validation.parquet\n", "stderr": "", "aggregated_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\nSCORE 1 0.013686379892513763\nbase validation epoch 2 loss 9.50096 seconds 25.6\nbase validation epoch 3 loss 9.42088 seconds 31.7\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_best_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4460 }, "formatted_output": "bags train 5618006\nbase validation epoch 1 loss 9.76416 seconds 12.9\nSCORE 1 0.013686379892513763\nbase validation epoch 2 loss 9.50096 seconds 25.6\nbase validation epoch 3 loss 9.42088 seconds 31.7\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_best_validation.parquet\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:26 /app/solution/base_validation.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:26:31.822Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a7a6c96087d1a251e7a57d40fad8", "usage": { "input_tokens": 31800, "cached_input_tokens": 31360, "cache_write_input_tokens": 0, "output_tokens": 400, "reasoning_output_tokens": 287, "total_tokens": 32200 }, "turn_token_usage": { "input_tokens": 339673, "cached_input_tokens": 317952, "cache_write_input_tokens": 0, "output_tokens": 6875, "reasoning_output_tokens": 1947, "total_tokens": 346548 }, "thread_token_usage": { "input_tokens": 339673, "cached_input_tokens": 317952, "cache_write_input_tokens": 0, "output_tokens": 6875, "reasoning_output_tokens": 1947, "total_tokens": 346548 } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-09T00:27:00.776Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:27:00.776Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:27:40.110Z", "callId": "call_WtdP7CbMFte6OPeNsJutk4Z6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/temporal.py <<'PY'\\n\\\"\\\"\\\"Prequential supervised training examples from observed windows only.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nfrom pathlib import Path\\nimport argparse,time,json\\nimport numpy as np,torch\\nfrom torch.nn import functional as F\\nfrom model import TwoTower\\nfrom train import ROOT,DATA,make_bags,batch,gold_data,infer\\n\\ndef prepare(stage,half=90,days=168,stride=14):\\n dest=ROOT/f'temporal_{stage}_{half}_{days}_{stride}.npz'\\n if dest.exists():return dict(np.load(dest))\\n n=len(np.load(ROOT/'ids.npz')['users']);events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\\n end=24786800+(606600 if stage=='test' else 0)\\n cuts=end-np.arange(7,days+1,stride)*86400-1800\\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\\n data={k:[] for k in ['items','weights','yitems','yweights']};off=[0];yo=[0];sampleusers=[]\\n for u in range(n):\\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)]); v=v[np.argsort(v['t'])]\\n allitems,inverse=np.unique(v['i'],return_inverse=True);first=np.full(len(allitems),2**31-1,dtype='int32');np.minimum.at(first,inverse,v['t'])\\n for cut in cuts:\\n target=(v['t']>cut+1800)&(v['t']<=cut+1800+7*86400)&(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))&(first[inverse]>cut)\\n if not target.any():continue\\n yi,yinv=np.unique(v['i'][target],return_inverse=True);yw=np.zeros(len(yi),dtype='float32');np.maximum.at(yw,yinv,np.where(v['k'][target]==1,7.,1.))\\n keep=(v['t']<=cut)&(((v['k']==0)&(v['q']>=50))|(v['k']==1));h=v[keep]\\n if len(h)<20:continue\\n hi,ix=np.unique(h['i'],return_inverse=True)\\n iw=np.where(h['k']==1,3.,1.)*np.where(h['o']==1,1.,.5)*np.power(2.,-(cut-h['t'])/(86400*half))\\n hw=np.sqrt(np.bincount(ix,weights=iw));hw/=np.linalg.norm(hw)+1e-9\\n data['items'].append(hi);data['weights'].append(hw.astype('float32'));data['yitems'].append(yi);data['yweights'].append(yw/yw.sum());off.append(off[-1]+len(hi));yo.append(yo[-1]+len(yi));sampleusers.append(u)\\n d={k:np.concatenate(v) for k,v in data.items()};d['offsets']=np.array(off);d['yoffsets']=np.array(yo);d['users']=np.array(sampleusers)\\n np.savez(dest,**d);print('temporal',stage,'samples',len(sampleusers),'entries',len(d['items']),flush=True);return d\\n\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='temporal');p.add_argument('--resume',default='base_validation.pt');p.add_argument('--epochs',type=int,default=10);p.add_argument('--lr',type=float,default=.0002);p.add_argument('--half',type=float,default=90);p.add_argument('--dropout',type=float,default=.4);p.add_argument('--days',type=int,default=168);p.add_argument('--stride',type=int,default=14);p.add_argument('--mix',type=float,default=0.);args=p.parse_args()\\n torch.set_num_threads(4);torch.manual_seed(246);rng=np.random.default_rng(246);t=time.time();ids=dict(np.load(ROOT/'ids.npz'))\\n d=prepare('train' if args.stage=='validation' else 'test',args.half,args.days,args.stride)\\n bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\\n ck=torch.load(ROOT/args.resume,weights_only=False);model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout);model.load_state_dict(ck['model']);opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\\n gold=gold_data(None,ids) if args.stage=='validation' else None;best=-1;items=torch.arange(len(ids['items']));examples=np.arange(len(d['offsets'])-1)\\n ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\\n for ep in range(1,args.epochs+1):\\n model.train();rng.shuffle(examples);losses=[]\\n for st in range(0,len(examples),256):\\n uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\\n logits=model(x,o,w,items)\\n # Previously observed positives cannot be discovery outcomes.\\n logits[torch.from_numpy(rows),x]=-1e9\\n logp=F.log_softmax(logits,dim=1)\\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\\n opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\\n score=infer(model,bags,args.stage,out,ids,gold);print(args.name,ep,'loss',np.mean(losses),'score',score,'time',time.time()-t,flush=True)\\n if score>best or args.stage=='test':\\n best=score; cargs=ck['args'].copy();cargs.update(vars(args));torch.save(dict(model=model.state_dict(),args=cargs,epoch=ep,score=score),ROOT/f'{args.name}_{args.stage}.pt')\\n if args.stage=='validation':\\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet');(ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':ep,'score':score,'args':cargs},indent=2))\\nif __name__=='__main__':main()\\nPY\\ntail -10 /app/solution/base.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:27:40.219Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"6fbae9\",\"wall_time_seconds\":0.00000518,\"exit_code\":0,\"original_token_count\":116,\"output\":\"SCORE 5 0.013883589291707302\\nbase validation epoch 6 loss 9.11952 seconds 56.2\\nbase validation epoch 7 loss 8.88735 seconds 62.1\\nbase validation epoch 8 loss 8.74579 seconds 67.9\\nbase validation epoch 9 loss 8.69093 seconds 73.7\\nbase validation epoch 10 loss 8.66086 seconds 79.5\\nSCORE 10 0.016921171952603176\\nbase validation epoch 11 loss 8.62911 seconds 92.3\\nbase validation epoch 12 loss 8.58118 seconds 98.2\\nbase validation epoch 13 loss 8.52543 seconds 104.0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:27:40.161Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b6cd6757-f7c5-4fc1-9027-5537c15bbfd9", "process_id": "34641", "command": [ "/bin/sh", "-lc", "cat > /app/solution/temporal.py <<'PY'\n\"\"\"Prequential supervised training examples from observed windows only.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport argparse,time,json\nimport numpy as np,torch\nfrom torch.nn import functional as F\nfrom model import TwoTower\nfrom train import ROOT,DATA,make_bags,batch,gold_data,infer\n\ndef prepare(stage,half=90,days=168,stride=14):\n dest=ROOT/f'temporal_{stage}_{half}_{days}_{stride}.npz'\n if dest.exists():return dict(np.load(dest))\n n=len(np.load(ROOT/'ids.npz')['users']);events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0)\n cuts=end-np.arange(7,days+1,stride)*86400-1800\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\n data={k:[] for k in ['items','weights','yitems','yweights']};off=[0];yo=[0];sampleusers=[]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)]); v=v[np.argsort(v['t'])]\n allitems,inverse=np.unique(v['i'],return_inverse=True);first=np.full(len(allitems),2**31-1,dtype='int32');np.minimum.at(first,inverse,v['t'])\n for cut in cuts:\n target=(v['t']>cut+1800)&(v['t']<=cut+1800+7*86400)&(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))&(first[inverse]>cut)\n if not target.any():continue\n yi,yinv=np.unique(v['i'][target],return_inverse=True);yw=np.zeros(len(yi),dtype='float32');np.maximum.at(yw,yinv,np.where(v['k'][target]==1,7.,1.))\n keep=(v['t']<=cut)&(((v['k']==0)&(v['q']>=50))|(v['k']==1));h=v[keep]\n if len(h)<20:continue\n hi,ix=np.unique(h['i'],return_inverse=True)\n iw=np.where(h['k']==1,3.,1.)*np.where(h['o']==1,1.,.5)*np.power(2.,-(cut-h['t'])/(86400*half))\n hw=np.sqrt(np.bincount(ix,weights=iw));hw/=np.linalg.norm(hw)+1e-9\n data['items'].append(hi);data['weights'].append(hw.astype('float32'));data['yitems'].append(yi);data['yweights'].append(yw/yw.sum());off.append(off[-1]+len(hi));yo.append(yo[-1]+len(yi));sampleusers.append(u)\n d={k:np.concatenate(v) for k,v in data.items()};d['offsets']=np.array(off);d['yoffsets']=np.array(yo);d['users']=np.array(sampleusers)\n np.savez(dest,**d);print('temporal',stage,'samples',len(sampleusers),'entries',len(d['items']),flush=True);return d\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='temporal');p.add_argument('--resume',default='base_validation.pt');p.add_argument('--epochs',type=int,default=10);p.add_argument('--lr',type=float,default=.0002);p.add_argument('--half',type=float,default=90);p.add_argument('--dropout',type=float,default=.4);p.add_argument('--days',type=int,default=168);p.add_argument('--stride',type=int,default=14);p.add_argument('--mix',type=float,default=0.);args=p.parse_args()\n torch.set_num_threads(4);torch.manual_seed(246);rng=np.random.default_rng(246);t=time.time();ids=dict(np.load(ROOT/'ids.npz'))\n d=prepare('train' if args.stage=='validation' else 'test',args.half,args.days,args.stride)\n bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\n ck=torch.load(ROOT/args.resume,weights_only=False);model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout);model.load_state_dict(ck['model']);opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\n gold=gold_data(None,ids) if args.stage=='validation' else None;best=-1;items=torch.arange(len(ids['items']));examples=np.arange(len(d['offsets'])-1)\n ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\n for ep in range(1,args.epochs+1):\n model.train();rng.shuffle(examples);losses=[]\n for st in range(0,len(examples),256):\n uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\n logits=model(x,o,w,items)\n # Previously observed positives cannot be discovery outcomes.\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\n opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\n score=infer(model,bags,args.stage,out,ids,gold);print(args.name,ep,'loss',np.mean(losses),'score',score,'time',time.time()-t,flush=True)\n if score>best or args.stage=='test':\n best=score; cargs=ck['args'].copy();cargs.update(vars(args));torch.save(dict(model=model.state_dict(),args=cargs,epoch=ep,score=score),ROOT/f'{args.name}_{args.stage}.pt')\n if args.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet');(ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':ep,'score':score,'args':cargs},indent=2))\nif __name__=='__main__':main()\nPY\ntail -10 /app/solution/base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/temporal.py <<'PY'\n\"\"\"Prequential supervised training examples from observed windows only.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport argparse,time,json\nimport numpy as np,torch\nfrom torch.nn import functional as F\nfrom model import TwoTower\nfrom train import ROOT,DATA,make_bags,batch,gold_data,infer\n\ndef prepare(stage,half=90,days=168,stride=14):\n dest=ROOT/f'temporal_{stage}_{half}_{days}_{stride}.npz'\n if dest.exists():return dict(np.load(dest))\n n=len(np.load(ROOT/'ids.npz')['users']);events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0)\n cuts=end-np.arange(7,days+1,stride)*86400-1800\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\n data={k:[] for k in ['items','weights','yitems','yweights']};off=[0];yo=[0];sampleusers=[]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)]); v=v[np.argsort(v['t'])]\n allitems,inverse=np.unique(v['i'],return_inverse=True);first=np.full(len(allitems),2**31-1,dtype='int32');np.minimum.at(first,inverse,v['t'])\n for cut in cuts:\n target=(v['t']>cut+1800)&(v['t']<=cut+1800+7*86400)&(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))&(first[inverse]>cut)\n if not target.any():continue\n yi,yinv=np.unique(v['i'][target],return_inverse=True);yw=np.zeros(len(yi),dtype='float32');np.maximum.at(yw,yinv,np.where(v['k'][target]==1,7.,1.))\n keep=(v['t']<=cut)&(((v['k']==0)&(v['q']>=50))|(v['k']==1));h=v[keep]\n if len(h)<20:continue\n hi,ix=np.unique(h['i'],return_inverse=True)\n iw=np.where(h['k']==1,3.,1.)*np.where(h['o']==1,1.,.5)*np.power(2.,-(cut-h['t'])/(86400*half))\n hw=np.sqrt(np.bincount(ix,weights=iw));hw/=np.linalg.norm(hw)+1e-9\n data['items'].append(hi);data['weights'].append(hw.astype('float32'));data['yitems'].append(yi);data['yweights'].append(yw/yw.sum());off.append(off[-1]+len(hi));yo.append(yo[-1]+len(yi));sampleusers.append(u)\n d={k:np.concatenate(v) for k,v in data.items()};d['offsets']=np.array(off);d['yoffsets']=np.array(yo);d['users']=np.array(sampleusers)\n np.savez(dest,**d);print('temporal',stage,'samples',len(sampleusers),'entries',len(d['items']),flush=True);return d\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='temporal');p.add_argument('--resume',default='base_validation.pt');p.add_argument('--epochs',type=int,default=10);p.add_argument('--lr',type=float,default=.0002);p.add_argument('--half',type=float,default=90);p.add_argument('--dropout',type=float,default=.4);p.add_argument('--days',type=int,default=168);p.add_argument('--stride',type=int,default=14);p.add_argument('--mix',type=float,default=0.);args=p.parse_args()\n torch.set_num_threads(4);torch.manual_seed(246);rng=np.random.default_rng(246);t=time.time();ids=dict(np.load(ROOT/'ids.npz'))\n d=prepare('train' if args.stage=='validation' else 'test',args.half,args.days,args.stride)\n bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\n ck=torch.load(ROOT/args.resume,weights_only=False);model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout);model.load_state_dict(ck['model']);opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\n gold=gold_data(None,ids) if args.stage=='validation' else None;best=-1;items=torch.arange(len(ids['items']));examples=np.arange(len(d['offsets'])-1)\n ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\n for ep in range(1,args.epochs+1):\n model.train();rng.shuffle(examples);losses=[]\n for st in range(0,len(examples),256):\n uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\n logits=model(x,o,w,items)\n # Previously observed positives cannot be discovery outcomes.\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\n opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\n score=infer(model,bags,args.stage,out,ids,gold);print(args.name,ep,'loss',np.mean(losses),'score',score,'time',time.time()-t,flush=True)\n if score>best or args.stage=='test':\n best=score; cargs=ck['args'].copy();cargs.update(vars(args));torch.save(dict(model=model.state_dict(),args=cargs,epoch=ep,score=score),ROOT/f'{args.name}_{args.stage}.pt')\n if args.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet');(ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':ep,'score':score,'args':cargs},indent=2))\nif __name__=='__main__':main()\nPY\ntail -10 /app/solution/base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "SCORE 5 0.013883589291707302\nbase validation epoch 6 loss 9.11952 seconds 56.2\nbase validation epoch 7 loss 8.88735 seconds 62.1\nbase validation epoch 8 loss 8.74579 seconds 67.9\nbase validation epoch 9 loss 8.69093 seconds 73.7\nbase validation epoch 10 loss 8.66086 seconds 79.5\nSCORE 10 0.016921171952603176\nbase validation epoch 11 loss 8.62911 seconds 92.3\nbase validation epoch 12 loss 8.58118 seconds 98.2\nbase validation epoch 13 loss 8.52543 seconds 104.0\n", "stderr": "", "aggregated_output": "SCORE 5 0.013883589291707302\nbase validation epoch 6 loss 9.11952 seconds 56.2\nbase validation epoch 7 loss 8.88735 seconds 62.1\nbase validation epoch 8 loss 8.74579 seconds 67.9\nbase validation epoch 9 loss 8.69093 seconds 73.7\nbase validation epoch 10 loss 8.66086 seconds 79.5\nSCORE 10 0.016921171952603176\nbase validation epoch 11 loss 8.62911 seconds 92.3\nbase validation epoch 12 loss 8.58118 seconds 98.2\nbase validation epoch 13 loss 8.52543 seconds 104.0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5180 }, "formatted_output": "SCORE 5 0.013883589291707302\nbase validation epoch 6 loss 9.11952 seconds 56.2\nbase validation epoch 7 loss 8.88735 seconds 62.1\nbase validation epoch 8 loss 8.74579 seconds 67.9\nbase validation epoch 9 loss 8.69093 seconds 73.7\nbase validation epoch 10 loss 8.66086 seconds 79.5\nSCORE 10 0.016921171952603176\nbase validation epoch 11 loss 8.62911 seconds 92.3\nbase validation epoch 12 loss 8.58118 seconds 98.2\nbase validation epoch 13 loss 8.52543 seconds 104.0\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:27:40.217Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a7b837d087d1a0f584547d8938de", "usage": { "input_tokens": 32393, "cached_input_tokens": 31616, "cache_write_input_tokens": 0, "output_tokens": 2132, "reasoning_output_tokens": 516, "total_tokens": 34525 }, "turn_token_usage": { "input_tokens": 372066, "cached_input_tokens": 349568, "cache_write_input_tokens": 0, "output_tokens": 9007, "reasoning_output_tokens": 2463, "total_tokens": 381073 }, "thread_token_usage": { "input_tokens": 372066, "cached_input_tokens": 349568, "cache_write_input_tokens": 0, "output_tokens": 9007, "reasoning_output_tokens": 2463, "total_tokens": 381073 } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-09T00:27:43.798Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:27:43.798Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:27:46.655Z", "callId": "call_WnJz869ccKYVcdFSnsOl1JQf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -c \\\"import sys;sys.path.insert(0,'/app/solution');from temporal import prepare;prepare('train',90.,168,14)\\\" > /app/solution/temporal_prepare.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:27:47.837Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"53db3a\",\"wall_time_seconds\":1.00107764,\"session_id\":70447,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 00:27:47 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:27:46.758Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a7fca8dc87d19757fa6816551768", "usage": { "input_tokens": 34745, "cached_input_tokens": 32256, "cache_write_input_tokens": 0, "output_tokens": 124, "reasoning_output_tokens": 18, "total_tokens": 34869 }, "turn_token_usage": { "input_tokens": 406811, "cached_input_tokens": 381824, "cache_write_input_tokens": 0, "output_tokens": 9131, "reasoning_output_tokens": 2481, "total_tokens": 415942 }, "thread_token_usage": { "input_tokens": 406811, "cached_input_tokens": 381824, "cache_write_input_tokens": 0, "output_tokens": 9131, "reasoning_output_tokens": 2481, "total_tokens": 415942 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-09T00:27:55.009Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:27:55.009Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:27:56.602Z", "callId": "call_zNtCWBl2gUoyshetu2du0yMV", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name backup --epochs 30 --eval-every 30 > /app/solution/backup.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:27:57.772Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"addb5f\",\"wall_time_seconds\":1.000427636,\"session_id\":1870,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:27:56.656Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a804261887d1bae84d2cc55aa97a", "usage": { "input_tokens": 34945, "cached_input_tokens": 34560, "cache_write_input_tokens": 0, "output_tokens": 231, "reasoning_output_tokens": 147, "total_tokens": 35176 }, "turn_token_usage": { "input_tokens": 441756, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 9362, "reasoning_output_tokens": 2628, "total_tokens": 451118 }, "thread_token_usage": { "input_tokens": 441756, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 9362, "reasoning_output_tokens": 2628, "total_tokens": 451118 } } }, { "kind": "context", "timestamp": "2026-09-09T00:28:13.679Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b029f744-9ea1-43ad-b909-d2d7af5df759", "process_id": "70447", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -c \"import sys;sys.path.insert(0,'/app/solution');from temporal import prepare;prepare('train',90.,168,14)\" > /app/solution/temporal_prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python -c \"import sys;sys.path.insert(0,'/app/solution');from temporal import prepare;prepare('train',90.,168,14)\" > /app/solution/temporal_prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 26, "nanos": 853879891 }, "formatted_output": "" } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-09T00:28:15.475Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:28:15.475Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:28:18.065Z", "callId": "call_ErZIqCAeDSwrjcoN31e1Lu8d", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; cat /app/solution/temporal_prepare.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1800}));\n" }, "result": { "timestamp": "2026-09-09T00:28:18.194Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"e1fe0d\",\"wall_time_seconds\":0.00000535,\"exit_code\":0,\"original_token_count\":92,\"output\":\"base validation epoch 13 loss 8.52543 seconds 104.0\\nbase validation epoch 14 loss 8.47575 seconds 109.8\\nbase validation epoch 15 loss 8.43765 seconds 115.7\\nSCORE 15 0.018826878370829362\\nbase validation epoch 16 loss 8.39569 seconds 129.9\\nbase validation epoch 17 loss 8.34873 seconds 137.3\\nbags test 5778549\\ntemporal train samples 40369 entries 25813306\\n3426807808\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:28:18.139Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4041d5e8-3687-4997-830b-2eef1d14395c", "process_id": "10690", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; cat /app/solution/temporal_prepare.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; cat /app/solution/temporal_prepare.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base validation epoch 13 loss 8.52543 seconds 104.0\nbase validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbags test 5778549\ntemporal train samples 40369 entries 25813306\n3426807808\n", "stderr": "", "aggregated_output": "base validation epoch 13 loss 8.52543 seconds 104.0\nbase validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbags test 5778549\ntemporal train samples 40369 entries 25813306\n3426807808\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5350 }, "formatted_output": "base validation epoch 13 loss 8.52543 seconds 104.0\nbase validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbags test 5778549\ntemporal train samples 40369 entries 25813306\n3426807808\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:28:18.184Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a80e454487d1b68a1a63578e1278", "usage": { "input_tokens": 35234, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 394, "reasoning_output_tokens": 325, "total_tokens": 35628 }, "turn_token_usage": { "input_tokens": 476990, "cached_input_tokens": 451200, "cache_write_input_tokens": 0, "output_tokens": 9756, "reasoning_output_tokens": 2953, "total_tokens": 486746 }, "thread_token_usage": { "input_tokens": 476990, "cached_input_tokens": 451200, "cache_write_input_tokens": 0, "output_tokens": 9756, "reasoning_output_tokens": 2953, "total_tokens": 486746 } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-09T00:28:41.014Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:28:41.014Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:28:43.205Z", "text": "The first validation results are improving as training progresses. I’m also preparing temporal examples that predict first-time organic interactions in a later seven-day window. A refit on train plus validation is running alongside this, so a valid final submission will be saved early.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:28:44.076Z", "callId": "call_wmZDuNMbNCuxg2Tog8EZGwfw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/base.log; tail -8 /app/solution/backup.log; df -h /app/solution\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:28:44.147Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"8a2a4d\",\"wall_time_seconds\":0.00000949,\"exit_code\":0,\"original_token_count\":151,\"output\":\"base validation epoch 14 loss 8.47575 seconds 109.8\\nbase validation epoch 15 loss 8.43765 seconds 115.7\\nSCORE 15 0.018826878370829362\\nbase validation epoch 16 loss 8.39569 seconds 129.9\\nbase validation epoch 17 loss 8.34873 seconds 137.3\\nbase validation epoch 18 loss 8.3034 seconds 146.5\\nbase validation epoch 19 loss 8.26295 seconds 153.5\\nbase validation epoch 20 loss 8.22548 seconds 162.2\\nbags test 5778549\\nbackup test epoch 1 loss 9.76858 seconds 19.6\\nSCORE 1 0.0\\nbackup test epoch 2 loss 9.50931 seconds 35.1\\nFilesystem Size Used Avail Use% Mounted on\\noverlay 96G 15G 82G 16% /\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:28:44.100Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a82294a087d1af2eabe5dc63b976", "usage": { "input_tokens": 35809, "cached_input_tokens": 35072, "cache_write_input_tokens": 0, "output_tokens": 624, "reasoning_output_tokens": 510, "total_tokens": 36433 }, "turn_token_usage": { "input_tokens": 512799, "cached_input_tokens": 486272, "cache_write_input_tokens": 0, "output_tokens": 10380, "reasoning_output_tokens": 3463, "total_tokens": 523179 }, "thread_token_usage": { "input_tokens": 512799, "cached_input_tokens": 486272, "cache_write_input_tokens": 0, "output_tokens": 10380, "reasoning_output_tokens": 3463, "total_tokens": 523179 } } }, { "kind": "context", "timestamp": "2026-09-09T00:28:44.125Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c26aa11-c1b0-496d-85da-db94df902b10", "process_id": "27966", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/base.log; tail -8 /app/solution/backup.log; df -h /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/base.log; tail -8 /app/solution/backup.log; df -h /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbase validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nbags test 5778549\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n", "stderr": "", "aggregated_output": "base validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbase validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nbags test 5778549\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9490 }, "formatted_output": "base validation epoch 14 loss 8.47575 seconds 109.8\nbase validation epoch 15 loss 8.43765 seconds 115.7\nSCORE 15 0.018826878370829362\nbase validation epoch 16 loss 8.39569 seconds 129.9\nbase validation epoch 17 loss 8.34873 seconds 137.3\nbase validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nbags test 5778549\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 15G 82G 16% /\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-09T00:29:09.221Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:29:09.221Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:29:10.331Z", "callId": "call_GW2JXR3slLp0ZeZ7bjw665EY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; ls -lh /app/solution/submission.parquet\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:29:10.435Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"92df78\",\"wall_time_seconds\":0.00001174,\"exit_code\":0,\"original_token_count\":152,\"output\":\"base validation epoch 18 loss 8.3034 seconds 146.5\\nbase validation epoch 19 loss 8.26295 seconds 153.5\\nbase validation epoch 20 loss 8.22548 seconds 162.2\\nSCORE 20 0.019055932335779405\\nbase validation epoch 21 loss 8.19684 seconds 179.6\\nbase validation epoch 22 loss 8.16531 seconds 188.6\\nbackup test epoch 1 loss 9.76858 seconds 19.6\\nSCORE 1 0.0\\nbackup test epoch 2 loss 9.50931 seconds 35.1\\nbackup test epoch 3 loss 9.39116 seconds 42.1\\nbackup test epoch 4 loss 9.22728 seconds 51.3\\nbackup test epoch 5 loss 9.18958 seconds 60.6\\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:28 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-09 00:29:10 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:29:10.393Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2837b2a2-ce49-4a2a-8bdd-cf059ad406bb", "process_id": "5097", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/base.log; tail -6 /app/solution/backup.log; ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nSCORE 20 0.019055932335779405\nbase validation epoch 21 loss 8.19684 seconds 179.6\nbase validation epoch 22 loss 8.16531 seconds 188.6\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nbackup test epoch 3 loss 9.39116 seconds 42.1\nbackup test epoch 4 loss 9.22728 seconds 51.3\nbackup test epoch 5 loss 9.18958 seconds 60.6\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:28 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "base validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nSCORE 20 0.019055932335779405\nbase validation epoch 21 loss 8.19684 seconds 179.6\nbase validation epoch 22 loss 8.16531 seconds 188.6\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nbackup test epoch 3 loss 9.39116 seconds 42.1\nbackup test epoch 4 loss 9.22728 seconds 51.3\nbackup test epoch 5 loss 9.18958 seconds 60.6\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:28 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11740 }, "formatted_output": "base validation epoch 18 loss 8.3034 seconds 146.5\nbase validation epoch 19 loss 8.26295 seconds 153.5\nbase validation epoch 20 loss 8.22548 seconds 162.2\nSCORE 20 0.019055932335779405\nbase validation epoch 21 loss 8.19684 seconds 179.6\nbase validation epoch 22 loss 8.16531 seconds 188.6\nbackup test epoch 1 loss 9.76858 seconds 19.6\nSCORE 1 0.0\nbackup test epoch 2 loss 9.50931 seconds 35.1\nbackup test epoch 3 loss 9.39116 seconds 42.1\nbackup test epoch 4 loss 9.22728 seconds 51.3\nbackup test epoch 5 loss 9.18958 seconds 60.6\n-rw-r--r-- 1 agent agent 1.2M Sep 9 00:28 /app/solution/submission.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:29:10.414Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a83c65c887d1aa31e636a39cb32b", "usage": { "input_tokens": 36706, "cached_input_tokens": 35584, "cache_write_input_tokens": 0, "output_tokens": 585, "reasoning_output_tokens": 516, "total_tokens": 37291 }, "turn_token_usage": { "input_tokens": 549505, "cached_input_tokens": 521856, "cache_write_input_tokens": 0, "output_tokens": 10965, "reasoning_output_tokens": 3979, "total_tokens": 560470 }, "thread_token_usage": { "input_tokens": 549505, "cached_input_tokens": 521856, "cache_write_input_tokens": 0, "output_tokens": 10965, "reasoning_output_tokens": 3979, "total_tokens": 560470 } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-09T00:29:39.036Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:29:39.036Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:30:00.416Z", "callId": "call_xe5pIQo44QU4fYFKRU4ynVMk", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/analyze.py <<'PY'\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport argparse,time,json\\nimport torch,numpy as np,pandas as pd\\nfrom model import TwoTower\\nfrom train import ROOT,DATA,make_bags,batch,gold_data\\n\\ndef score_cache(ckpath,half=None,stage='validation',dest=None):\\n torch.set_num_threads(2); ck=torch.load(ROOT/ckpath,weights_only=False);args=ck['args'];ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz')\\n model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout']);model.load_state_dict(ck['model']);model.eval()\\n bags=make_bags('train' if stage=='validation' else 'test',half or args['half'],args['likes'],args['organic'],args.get('recent_days',0))\\n userix=pd.Index(ids['users']).get_indexer(a['uids']);col=pd.Index(ids['items']).get_indexer(a['item_ids'])\\n if dest is None:dest=ckpath.replace('.pt','')+f'_h{half or args[\\\"half\\\"]}_scores.npy'\\n scores=np.lib.format.open_memmap(ROOT/dest,mode='w+',dtype='float32',shape=(len(userix),len(col)))\\n with torch.no_grad():\\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\\n for st in range(0,len(userix),256):\\n x,o,w,_,_=batch(bags,userix[st:st+256]);s=(model.user(x,o,w)@iv).numpy();mask=np.unpackbits(a['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);s[~mask]=-1e9;scores[st:st+len(s)]=s\\n scores.flush();print(dest,flush=True);return scores\\n\\ndef evaluate(scores,stage='validation',popgrid=True):\\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz');gold=gold_data(None,ids);rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]); g=[gold[int(a['uids'][r])] for r in rows];discount=1/np.log2(np.arange(2,12));idcg=np.array([np.array(sorted(v.values(),reverse=True)[:10])@discount[:min(10,len(v))] for v in g]);pop=np.log1p(pd.read_parquet(DATA/'candidate_catalog.parquet').organic_positive_listens.to_numpy()).astype('float32'); results=[]\\n for p in ([-1.,-.6,-.3,0.,.3,.6,1.] if popgrid else [0.]):\\n dcg=[]\\n for st in range(0,len(rows),256):\\n ss=np.asarray(scores[rows[st:st+256]])+p*pop[None,:];tops=torch.topk(torch.from_numpy(ss),10,dim=1).indices.numpy()\\n dcg.extend([np.array([gg.get(int(i),0) for i in rr])@discount for gg,rr in zip(g[st:st+256],tops)])\\n val=float(np.mean(np.array(dcg)/idcg));results.append((p,val));print('pop',p,'score',val,flush=True)\\n return results\\nif __name__=='__main__':\\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--half',type=float);p.add_argument('--dest');p.add_argument('--stage',default='validation');p.add_argument('--no-grid',action='store_true');a=p.parse_args();s=score_cache(a.checkpoint,a.half,a.stage,a.dest)\\n if a.stage=='validation':evaluate(s,popgrid=not a.no_grid)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py base_validation.pt --dest early_scores.npy > /app/solution/early_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:30:01.630Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"f222f4\",\"wall_time_seconds\":1.00114245,\"session_id\":31238,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:30:00.488Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a856ce5087d1879bdf52fcba6be0", "usage": { "input_tokens": 37589, "cached_input_tokens": 36480, "cache_write_input_tokens": 0, "output_tokens": 1472, "reasoning_output_tokens": 516, "total_tokens": 39061 }, "turn_token_usage": { "input_tokens": 587094, "cached_input_tokens": 558336, "cache_write_input_tokens": 0, "output_tokens": 12437, "reasoning_output_tokens": 4495, "total_tokens": 599531 }, "thread_token_usage": { "input_tokens": 587094, "cached_input_tokens": 558336, "cache_write_input_tokens": 0, "output_tokens": 12437, "reasoning_output_tokens": 4495, "total_tokens": 599531 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-09T00:30:07.884Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:30:07.884Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:30:09.469Z", "callId": "call_YyfpNFVb3LM2kWAvmCyjOwlY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/early_analyze.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/base_best_validation.parquet\",\"yield_time_ms\":1000,\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:30:10.664Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"0ea434\",\"wall_time_seconds\":1.001172345,\"session_id\":4211,\"original_token_count\":120,\"output\":\"base validation epoch 24 loss 8.12489 seconds 207.3\\nbase validation epoch 25 loss 8.11084 seconds 216.5\\nSCORE 25 0.018289660127746944\\nbase validation epoch 26 loss 8.08843 seconds 234.0\\nbase validation epoch 27 loss 8.07989 seconds 243.2\\nbase validation epoch 28 loss 8.06579 seconds 253.0\\nbackup test epoch 9 loss 8.68817 seconds 95.5\\nbackup test epoch 10 loss 8.6636 seconds 104.3\\nbackup test epoch 11 loss 8.62074 seconds 113.5\\nbackup test epoch 12 loss 8.5599 seconds 123.6\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:30:09.545Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a88a068c87d198cae22357dad83c", "usage": { "input_tokens": 39119, "cached_input_tokens": 37376, "cache_write_input_tokens": 0, "output_tokens": 195, "reasoning_output_tokens": 98, "total_tokens": 39314 }, "turn_token_usage": { "input_tokens": 626213, "cached_input_tokens": 595712, "cache_write_input_tokens": 0, "output_tokens": 12632, "reasoning_output_tokens": 4593, "total_tokens": 638845 }, "thread_token_usage": { "input_tokens": 626213, "cached_input_tokens": 595712, "cache_write_input_tokens": 0, "output_tokens": 12632, "reasoning_output_tokens": 4593, "total_tokens": 638845 } } }, { "kind": "context", "timestamp": "2026-09-09T00:30:15.703Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-812531b5-f2c1-40d6-acc1-dc5443f54442", "process_id": "4211", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/early_analyze.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/base_best_validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/early_analyze.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/base_best_validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/base_best_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019056,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/base_best_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019056,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 43948590 }, "formatted_output": "{\n \"model\": \"/app/solution/base_best_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019056,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-09T00:30:20.695Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:30:20.695Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:30:22.756Z", "text": "A final-format submission is now saved. The current public validation score is 0.0191 NDCG@10 over 4,263 users with relevant eligible outcomes. I’m checking popularity calibration and temporal training before choosing the final model.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:30:24.652Z", "callId": "call_FJ9ccfZ1j9td5bYOOHviXAJv", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/early_analyze.log; tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\ntext(await tools.write_stdin({session_id:4211,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:30:24.782Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"7f4f74\",\"wall_time_seconds\":0.00000918,\"exit_code\":0,\"original_token_count\":116,\"output\":\"early_scores.npy\\npop -1.0 score 0.003021419881219965\\nbase validation epoch 26 loss 8.08843 seconds 234.0\\nbase validation epoch 27 loss 8.07989 seconds 243.2\\nbase validation epoch 28 loss 8.06579 seconds 253.0\\nbase validation epoch 29 loss 8.05627 seconds 264.8\\nbackup test epoch 10 loss 8.6636 seconds 104.3\\nbackup test epoch 11 loss 8.62074 seconds 113.5\\nbackup test epoch 12 loss 8.5599 seconds 123.6\\nbackup test epoch 13 loss 8.49824 seconds 136.1\\n5183369216\\n\"}\n{\"chunk_id\":\"34eceb\",\"wall_time_seconds\":0.00000218,\"exit_code\":0,\"original_token_count\":35,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/base_best_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.019056,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:30:24.720Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a892f8b887d19b933b41afcf628d", "usage": { "input_tokens": 39537, "cached_input_tokens": 38912, "cache_write_input_tokens": 0, "output_tokens": 329, "reasoning_output_tokens": 174, "total_tokens": 39866 }, "turn_token_usage": { "input_tokens": 665750, "cached_input_tokens": 634624, "cache_write_input_tokens": 0, "output_tokens": 12961, "reasoning_output_tokens": 4767, "total_tokens": 678711 }, "thread_token_usage": { "input_tokens": 665750, "cached_input_tokens": 634624, "cache_write_input_tokens": 0, "output_tokens": 12961, "reasoning_output_tokens": 4767, "total_tokens": 678711 } } }, { "kind": "context", "timestamp": "2026-09-09T00:30:24.743Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0432ad30-fd97-47d9-a2ac-902ffb59a30c", "process_id": "21251", "command": [ "/bin/sh", "-lc", "cat /app/solution/early_analyze.log; tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/early_analyze.log; tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "early_scores.npy\npop -1.0 score 0.003021419881219965\nbase validation epoch 26 loss 8.08843 seconds 234.0\nbase validation epoch 27 loss 8.07989 seconds 243.2\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbackup test epoch 10 loss 8.6636 seconds 104.3\nbackup test epoch 11 loss 8.62074 seconds 113.5\nbackup test epoch 12 loss 8.5599 seconds 123.6\nbackup test epoch 13 loss 8.49824 seconds 136.1\n5183369216\n", "stderr": "", "aggregated_output": "early_scores.npy\npop -1.0 score 0.003021419881219965\nbase validation epoch 26 loss 8.08843 seconds 234.0\nbase validation epoch 27 loss 8.07989 seconds 243.2\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbackup test epoch 10 loss 8.6636 seconds 104.3\nbackup test epoch 11 loss 8.62074 seconds 113.5\nbackup test epoch 12 loss 8.5599 seconds 123.6\nbackup test epoch 13 loss 8.49824 seconds 136.1\n5183369216\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9180 }, "formatted_output": "early_scores.npy\npop -1.0 score 0.003021419881219965\nbase validation epoch 26 loss 8.08843 seconds 234.0\nbase validation epoch 27 loss 8.07989 seconds 243.2\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbackup test epoch 10 loss 8.6636 seconds 104.3\nbackup test epoch 11 loss 8.62074 seconds 113.5\nbackup test epoch 12 loss 8.5599 seconds 123.6\nbackup test epoch 13 loss 8.49824 seconds 136.1\n5183369216\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:30:27.490Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-de96c7e1-aacf-4d1a-a8bf-f5ae97bf93fe", "process_id": "31238", "command": [ "/bin/sh", "-lc", "cat > /app/solution/analyze.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json\nimport torch,numpy as np,pandas as pd\nfrom model import TwoTower\nfrom train import ROOT,DATA,make_bags,batch,gold_data\n\ndef score_cache(ckpath,half=None,stage='validation',dest=None):\n torch.set_num_threads(2); ck=torch.load(ROOT/ckpath,weights_only=False);args=ck['args'];ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz')\n model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout']);model.load_state_dict(ck['model']);model.eval()\n bags=make_bags('train' if stage=='validation' else 'test',half or args['half'],args['likes'],args['organic'],args.get('recent_days',0))\n userix=pd.Index(ids['users']).get_indexer(a['uids']);col=pd.Index(ids['items']).get_indexer(a['item_ids'])\n if dest is None:dest=ckpath.replace('.pt','')+f'_h{half or args[\"half\"]}_scores.npy'\n scores=np.lib.format.open_memmap(ROOT/dest,mode='w+',dtype='float32',shape=(len(userix),len(col)))\n with torch.no_grad():\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\n for st in range(0,len(userix),256):\n x,o,w,_,_=batch(bags,userix[st:st+256]);s=(model.user(x,o,w)@iv).numpy();mask=np.unpackbits(a['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);s[~mask]=-1e9;scores[st:st+len(s)]=s\n scores.flush();print(dest,flush=True);return scores\n\ndef evaluate(scores,stage='validation',popgrid=True):\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz');gold=gold_data(None,ids);rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]); g=[gold[int(a['uids'][r])] for r in rows];discount=1/np.log2(np.arange(2,12));idcg=np.array([np.array(sorted(v.values(),reverse=True)[:10])@discount[:min(10,len(v))] for v in g]);pop=np.log1p(pd.read_parquet(DATA/'candidate_catalog.parquet').organic_positive_listens.to_numpy()).astype('float32'); results=[]\n for p in ([-1.,-.6,-.3,0.,.3,.6,1.] if popgrid else [0.]):\n dcg=[]\n for st in range(0,len(rows),256):\n ss=np.asarray(scores[rows[st:st+256]])+p*pop[None,:];tops=torch.topk(torch.from_numpy(ss),10,dim=1).indices.numpy()\n dcg.extend([np.array([gg.get(int(i),0) for i in rr])@discount for gg,rr in zip(g[st:st+256],tops)])\n val=float(np.mean(np.array(dcg)/idcg));results.append((p,val));print('pop',p,'score',val,flush=True)\n return results\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--half',type=float);p.add_argument('--dest');p.add_argument('--stage',default='validation');p.add_argument('--no-grid',action='store_true');a=p.parse_args();s=score_cache(a.checkpoint,a.half,a.stage,a.dest)\n if a.stage=='validation':evaluate(s,popgrid=not a.no_grid)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py base_validation.pt --dest early_scores.npy > /app/solution/early_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/analyze.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json\nimport torch,numpy as np,pandas as pd\nfrom model import TwoTower\nfrom train import ROOT,DATA,make_bags,batch,gold_data\n\ndef score_cache(ckpath,half=None,stage='validation',dest=None):\n torch.set_num_threads(2); ck=torch.load(ROOT/ckpath,weights_only=False);args=ck['args'];ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz')\n model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout']);model.load_state_dict(ck['model']);model.eval()\n bags=make_bags('train' if stage=='validation' else 'test',half or args['half'],args['likes'],args['organic'],args.get('recent_days',0))\n userix=pd.Index(ids['users']).get_indexer(a['uids']);col=pd.Index(ids['items']).get_indexer(a['item_ids'])\n if dest is None:dest=ckpath.replace('.pt','')+f'_h{half or args[\"half\"]}_scores.npy'\n scores=np.lib.format.open_memmap(ROOT/dest,mode='w+',dtype='float32',shape=(len(userix),len(col)))\n with torch.no_grad():\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\n for st in range(0,len(userix),256):\n x,o,w,_,_=batch(bags,userix[st:st+256]);s=(model.user(x,o,w)@iv).numpy();mask=np.unpackbits(a['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);s[~mask]=-1e9;scores[st:st+len(s)]=s\n scores.flush();print(dest,flush=True);return scores\n\ndef evaluate(scores,stage='validation',popgrid=True):\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/f'{stage}_eligible_candidates.npz');gold=gold_data(None,ids);rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]); g=[gold[int(a['uids'][r])] for r in rows];discount=1/np.log2(np.arange(2,12));idcg=np.array([np.array(sorted(v.values(),reverse=True)[:10])@discount[:min(10,len(v))] for v in g]);pop=np.log1p(pd.read_parquet(DATA/'candidate_catalog.parquet').organic_positive_listens.to_numpy()).astype('float32'); results=[]\n for p in ([-1.,-.6,-.3,0.,.3,.6,1.] if popgrid else [0.]):\n dcg=[]\n for st in range(0,len(rows),256):\n ss=np.asarray(scores[rows[st:st+256]])+p*pop[None,:];tops=torch.topk(torch.from_numpy(ss),10,dim=1).indices.numpy()\n dcg.extend([np.array([gg.get(int(i),0) for i in rr])@discount for gg,rr in zip(g[st:st+256],tops)])\n val=float(np.mean(np.array(dcg)/idcg));results.append((p,val));print('pop',p,'score',val,flush=True)\n return results\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--half',type=float);p.add_argument('--dest');p.add_argument('--stage',default='validation');p.add_argument('--no-grid',action='store_true');a=p.parse_args();s=score_cache(a.checkpoint,a.half,a.stage,a.dest)\n if a.stage=='validation':evaluate(s,popgrid=not a.no_grid)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py base_validation.pt --dest early_scores.npy > /app/solution/early_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 26, "nanos": 865456919 }, "formatted_output": "" } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-09T00:30:50.989Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:30:50.989Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:30:52.437Z", "callId": "call_XEmqiVPJSdQvQMQEAauhO8LO", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/early_analyze.log; tail -5 /app/solution/base.log; tail -4 /app/solution/backup.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:30:52.543Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ade25e\",\"wall_time_seconds\":0.00000529,\"exit_code\":0,\"original_token_count\":174,\"output\":\"early_scores.npy\\npop -1.0 score 0.003021419881219965\\npop -0.6 score 0.012800659604146634\\npop -0.3 score 0.018324392528552107\\npop 0.0 score 0.019055932335779405\\npop 0.3 score 0.019228891268496293\\npop 0.6 score 0.018177915937801763\\npop 1.0 score 0.016902743767331915\\nbase validation epoch 28 loss 8.06579 seconds 253.0\\nbase validation epoch 29 loss 8.05627 seconds 264.8\\nbase validation epoch 30 loss 8.04619 seconds 275.4\\nSCORE 30 0.018148997524716225\\nbase validation epoch 31 loss 8.02268 seconds 292.7\\nbackup test epoch 13 loss 8.49824 seconds 136.1\\nbackup test epoch 14 loss 8.44821 seconds 146.6\\nbackup test epoch 15 loss 8.40418 seconds 153.7\\nbackup test epoch 16 loss 8.36035 seconds 162.3\\n\"}\n{\"current_time\":\"2026-09-09 00:30:52 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:30:52.501Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a8a111bc87d19e8367dfd97bf091", "usage": { "input_tokens": 40169, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 515, "reasoning_output_tokens": 445, "total_tokens": 40684 }, "turn_token_usage": { "input_tokens": 705919, "cached_input_tokens": 673920, "cache_write_input_tokens": 0, "output_tokens": 13476, "reasoning_output_tokens": 5212, "total_tokens": 719395 }, "thread_token_usage": { "input_tokens": 705919, "cached_input_tokens": 673920, "cache_write_input_tokens": 0, "output_tokens": 13476, "reasoning_output_tokens": 5212, "total_tokens": 719395 } } }, { "kind": "context", "timestamp": "2026-09-09T00:30:52.523Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6017e4e1-91e5-4ffd-a7e9-2ccc94b599e7", "process_id": "46641", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/early_analyze.log; tail -5 /app/solution/base.log; tail -4 /app/solution/backup.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/early_analyze.log; tail -5 /app/solution/base.log; tail -4 /app/solution/backup.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "early_scores.npy\npop -1.0 score 0.003021419881219965\npop -0.6 score 0.012800659604146634\npop -0.3 score 0.018324392528552107\npop 0.0 score 0.019055932335779405\npop 0.3 score 0.019228891268496293\npop 0.6 score 0.018177915937801763\npop 1.0 score 0.016902743767331915\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbase validation epoch 30 loss 8.04619 seconds 275.4\nSCORE 30 0.018148997524716225\nbase validation epoch 31 loss 8.02268 seconds 292.7\nbackup test epoch 13 loss 8.49824 seconds 136.1\nbackup test epoch 14 loss 8.44821 seconds 146.6\nbackup test epoch 15 loss 8.40418 seconds 153.7\nbackup test epoch 16 loss 8.36035 seconds 162.3\n", "stderr": "", "aggregated_output": "early_scores.npy\npop -1.0 score 0.003021419881219965\npop -0.6 score 0.012800659604146634\npop -0.3 score 0.018324392528552107\npop 0.0 score 0.019055932335779405\npop 0.3 score 0.019228891268496293\npop 0.6 score 0.018177915937801763\npop 1.0 score 0.016902743767331915\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbase validation epoch 30 loss 8.04619 seconds 275.4\nSCORE 30 0.018148997524716225\nbase validation epoch 31 loss 8.02268 seconds 292.7\nbackup test epoch 13 loss 8.49824 seconds 136.1\nbackup test epoch 14 loss 8.44821 seconds 146.6\nbackup test epoch 15 loss 8.40418 seconds 153.7\nbackup test epoch 16 loss 8.36035 seconds 162.3\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5290 }, "formatted_output": "early_scores.npy\npop -1.0 score 0.003021419881219965\npop -0.6 score 0.012800659604146634\npop -0.3 score 0.018324392528552107\npop 0.0 score 0.019055932335779405\npop 0.3 score 0.019228891268496293\npop 0.6 score 0.018177915937801763\npop 1.0 score 0.016902743767331915\nbase validation epoch 28 loss 8.06579 seconds 253.0\nbase validation epoch 29 loss 8.05627 seconds 264.8\nbase validation epoch 30 loss 8.04619 seconds 275.4\nSCORE 30 0.018148997524716225\nbase validation epoch 31 loss 8.02268 seconds 292.7\nbackup test epoch 13 loss 8.49824 seconds 136.1\nbackup test epoch 14 loss 8.44821 seconds 146.6\nbackup test epoch 15 loss 8.40418 seconds 153.7\nbackup test epoch 16 loss 8.36035 seconds 162.3\n" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-09T00:31:04.840Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:31:04.840Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:31:06.320Z", "callId": "call_hiKcWU4QeXorjFQHEHLZcQpI", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal1 --epochs 12 > /app/solution/temporal1.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:31:07.511Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"125022\",\"wall_time_seconds\":1.001709641,\"session_id\":23057,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:31:06.408Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a8bcc5a087d1a839ba9674d19a8b", "usage": { "input_tokens": 41023, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 351, "reasoning_output_tokens": 271, "total_tokens": 41374 }, "turn_token_usage": { "input_tokens": 746942, "cached_input_tokens": 713856, "cache_write_input_tokens": 0, "output_tokens": 13827, "reasoning_output_tokens": 5483, "total_tokens": 760769 }, "thread_token_usage": { "input_tokens": 746942, "cached_input_tokens": 713856, "cache_write_input_tokens": 0, "output_tokens": 13827, "reasoning_output_tokens": 5483, "total_tokens": 760769 } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-09T00:31:35.630Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:31:35.630Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:31:53.068Z", "callId": "call_wSu1LfLKA6TT6314DRDEMgyY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat >> /app/solution/model.py <<'PY'\\n\\nclass VariationalUserTower(nn.Module):\\n \\\"\\\"\\\"Denoising variational history encoder; deterministic posterior mean for serving.\\\"\\\"\\\"\\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.hidden_bias=nn.Parameter(torch.zeros(hidden));self.posterior=nn.Linear(hidden,2*dim);self.dropout=dropout;self.kl=None\\n nn.init.normal_(self.history.weight,std=(2/(n_items+hidden))**.5)\\n nn.init.xavier_normal_(self.posterior.weight);nn.init.zeros_(self.posterior.bias)\\n def forward(self,ids,offsets,weights):\\n if self.training:weights=F.dropout(weights,p=self.dropout)\\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights)+self.hidden_bias)\\n mu,lv=self.posterior(h).chunk(2,dim=1);lv=lv.clamp(-12,8)\\n self.kl=.5*(mu.square()+lv.exp()-1-lv).sum(1).mean()\\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\\n return torch.cat([z,torch.ones_like(z[:,:1])],dim=1)\\nclass VariationalTwoTower(TwoTower):\\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\\n nn.Module.__init__(self);self.user=VariationalUserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/train.py'\\ns=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower')\\ns=s.replace(\\\"p.add_argument('--resume');\\\", \\\"p.add_argument('--resume');p.add_argument('--vae',action='store_true');p.add_argument('--beta',type=float,default=.0002);\\\")\\ns=s.replace(\\\"model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\\\",\\\"model=(VariationalTwoTower if args.vae else TwoTower)(len(ids['items']),args.hidden,args.dim,args.dropout)\\\")\\ns=s.replace(\\\"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad\\\", \\\"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();loss=loss+(min(1.,epoch/20)*args.beta*model.user.kl if args.vae else 0.);opt.zero_grad\\\")\\nopen(p,'w').write(s)\\np='/app/solution/analyze.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\\\"model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\\\",\\\"model=(VariationalTwoTower if args.get('vae') else TwoTower)(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\\\");open(p,'w').write(s)\\nPY\\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/temporal1.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:31:53.274Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"353cea\",\"wall_time_seconds\":0.00000454,\"exit_code\":0,\"original_token_count\":106,\"output\":\"base validation epoch 34 loss 7.98799 seconds 322.3\\nbase validation epoch 35 loss 7.97471 seconds 332.4\\nSCORE 35 0.019167930531766787\\nbase validation epoch 36 loss 7.96356 seconds 351.6\\nbackup test epoch 20 loss 8.22079 seconds 201.8\\nbackup test epoch 21 loss 8.1986 seconds 210.4\\nbackup test epoch 22 loss 8.17659 seconds 219.7\\nbackup test epoch 23 loss 8.15462 seconds 230.8\\ntemporal train samples 40369 entries 25813306\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:31:53.113Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a8cbb0f887d1a75aae6e4dbdf945", "usage": { "input_tokens": 41430, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 1308, "reasoning_output_tokens": 516, "total_tokens": 42738 }, "turn_token_usage": { "input_tokens": 788372, "cached_input_tokens": 754688, "cache_write_input_tokens": 0, "output_tokens": 15135, "reasoning_output_tokens": 5999, "total_tokens": 803507 }, "thread_token_usage": { "input_tokens": 788372, "cached_input_tokens": 754688, "cache_write_input_tokens": 0, "output_tokens": 15135, "reasoning_output_tokens": 5999, "total_tokens": 803507 } } }, { "kind": "context", "timestamp": "2026-09-09T00:31:53.266Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a39a446f-fc5a-41d3-8c74-fb334f0b774e", "process_id": "96882", "command": [ "/bin/sh", "-lc", "cat >> /app/solution/model.py <<'PY'\n\nclass VariationalUserTower(nn.Module):\n \"\"\"Denoising variational history encoder; deterministic posterior mean for serving.\"\"\"\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.hidden_bias=nn.Parameter(torch.zeros(hidden));self.posterior=nn.Linear(hidden,2*dim);self.dropout=dropout;self.kl=None\n nn.init.normal_(self.history.weight,std=(2/(n_items+hidden))**.5)\n nn.init.xavier_normal_(self.posterior.weight);nn.init.zeros_(self.posterior.bias)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights)+self.hidden_bias)\n mu,lv=self.posterior(h).chunk(2,dim=1);lv=lv.clamp(-12,8)\n self.kl=.5*(mu.square()+lv.exp()-1-lv).sum(1).mean()\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\n return torch.cat([z,torch.ones_like(z[:,:1])],dim=1)\nclass VariationalTwoTower(TwoTower):\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\n nn.Module.__init__(self);self.user=VariationalUserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py'\ns=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower')\ns=s.replace(\"p.add_argument('--resume');\", \"p.add_argument('--resume');p.add_argument('--vae',action='store_true');p.add_argument('--beta',type=float,default=.0002);\")\ns=s.replace(\"model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\",\"model=(VariationalTwoTower if args.vae else TwoTower)(len(ids['items']),args.hidden,args.dim,args.dropout)\")\ns=s.replace(\"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad\", \"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();loss=loss+(min(1.,epoch/20)*args.beta*model.user.kl if args.vae else 0.);opt.zero_grad\")\nopen(p,'w').write(s)\np='/app/solution/analyze.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\"model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\",\"model=(VariationalTwoTower if args.get('vae') else TwoTower)(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\");open(p,'w').write(s)\nPY\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/temporal1.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat >> /app/solution/model.py <<'PY'\n\nclass VariationalUserTower(nn.Module):\n \"\"\"Denoising variational history encoder; deterministic posterior mean for serving.\"\"\"\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.hidden_bias=nn.Parameter(torch.zeros(hidden));self.posterior=nn.Linear(hidden,2*dim);self.dropout=dropout;self.kl=None\n nn.init.normal_(self.history.weight,std=(2/(n_items+hidden))**.5)\n nn.init.xavier_normal_(self.posterior.weight);nn.init.zeros_(self.posterior.bias)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights)+self.hidden_bias)\n mu,lv=self.posterior(h).chunk(2,dim=1);lv=lv.clamp(-12,8)\n self.kl=.5*(mu.square()+lv.exp()-1-lv).sum(1).mean()\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\n return torch.cat([z,torch.ones_like(z[:,:1])],dim=1)\nclass VariationalTwoTower(TwoTower):\n def __init__(self,n_items,hidden=600,dim=256,dropout=.5):\n nn.Module.__init__(self);self.user=VariationalUserTower(n_items,hidden,dim,dropout);self.item=ItemTower(n_items,dim)\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py'\ns=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower')\ns=s.replace(\"p.add_argument('--resume');\", \"p.add_argument('--resume');p.add_argument('--vae',action='store_true');p.add_argument('--beta',type=float,default=.0002);\")\ns=s.replace(\"model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\",\"model=(VariationalTwoTower if args.vae else TwoTower)(len(ids['items']),args.hidden,args.dim,args.dropout)\")\ns=s.replace(\"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad\", \"loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();loss=loss+(min(1.,epoch/20)*args.beta*model.user.kl if args.vae else 0.);opt.zero_grad\")\nopen(p,'w').write(s)\np='/app/solution/analyze.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\"model=TwoTower(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\",\"model=(VariationalTwoTower if args.get('vae') else TwoTower)(len(ids['items']),args['hidden'],args['dim'],args['dropout'])\");open(p,'w').write(s)\nPY\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; cat /app/solution/temporal1.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base validation epoch 34 loss 7.98799 seconds 322.3\nbase validation epoch 35 loss 7.97471 seconds 332.4\nSCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbackup test epoch 20 loss 8.22079 seconds 201.8\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\ntemporal train samples 40369 entries 25813306\n", "stderr": "", "aggregated_output": "base validation epoch 34 loss 7.98799 seconds 322.3\nbase validation epoch 35 loss 7.97471 seconds 332.4\nSCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbackup test epoch 20 loss 8.22079 seconds 201.8\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\ntemporal train samples 40369 entries 25813306\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4540 }, "formatted_output": "base validation epoch 34 loss 7.98799 seconds 322.3\nbase validation epoch 35 loss 7.97471 seconds 332.4\nSCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbackup test epoch 20 loss 8.22079 seconds 201.8\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\ntemporal train samples 40369 entries 25813306\n" } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-09T00:32:19.278Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:32:19.278Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:32:21.092Z", "text": "I’m testing two extensions: temporal training on unseen future tracks, and a variational history encoder to regularize the learned representation. Both retain the same serving contract: independently computed user and item vectors, scored by a dot product.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:32:22.353Z", "callId": "call_6D9o4DKd3SdRFvvGldBneYo2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:32:23.197Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"791914\",\"wall_time_seconds\":0.0000048,\"exit_code\":0,\"original_token_count\":109,\"output\":\"SCORE 35 0.019167930531766787\\nbase validation epoch 36 loss 7.96356 seconds 351.6\\nbase validation epoch 37 loss 7.95674 seconds 365.2\\nbase validation epoch 38 loss 7.94652 seconds 382.4\\nbackup test epoch 21 loss 8.1986 seconds 210.4\\nbackup test epoch 22 loss 8.17659 seconds 219.7\\nbackup test epoch 23 loss 8.15462 seconds 230.8\\nbackup test epoch 24 loss 8.1351 seconds 247.9\\ntemporal train samples 40369 entries 25813306\\n6216966144\\n\"}\n{\"current_time\":\"2026-09-09 00:32:22 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:32:22.433Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-819dc132-efd2-4443-8c85-63cc94f23a11", "process_id": "20889", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "SCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbase validation epoch 37 loss 7.95674 seconds 365.2\nbase validation epoch 38 loss 7.94652 seconds 382.4\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\nbackup test epoch 24 loss 8.1351 seconds 247.9\ntemporal train samples 40369 entries 25813306\n6216966144\n", "stderr": "", "aggregated_output": "SCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbase validation epoch 37 loss 7.95674 seconds 365.2\nbase validation epoch 38 loss 7.94652 seconds 382.4\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\nbackup test epoch 24 loss 8.1351 seconds 247.9\ntemporal train samples 40369 entries 25813306\n6216966144\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4800 }, "formatted_output": "SCORE 35 0.019167930531766787\nbase validation epoch 36 loss 7.96356 seconds 351.6\nbase validation epoch 37 loss 7.95674 seconds 365.2\nbase validation epoch 38 loss 7.94652 seconds 382.4\nbackup test epoch 21 loss 8.1986 seconds 210.4\nbackup test epoch 22 loss 8.17659 seconds 219.7\nbackup test epoch 23 loss 8.15462 seconds 230.8\nbackup test epoch 24 loss 8.1351 seconds 247.9\ntemporal train samples 40369 entries 25813306\n6216966144\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:32:23.188Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a8f9849c87d19e6df4cb81b3c410", "usage": { "input_tokens": 42938, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 649, "reasoning_output_tokens": 516, "total_tokens": 43587 }, "turn_token_usage": { "input_tokens": 831310, "cached_input_tokens": 795904, "cache_write_input_tokens": 0, "output_tokens": 15784, "reasoning_output_tokens": 6515, "total_tokens": 847094 }, "thread_token_usage": { "input_tokens": 831310, "cached_input_tokens": 795904, "cache_write_input_tokens": 0, "output_tokens": 15784, "reasoning_output_tokens": 6515, "total_tokens": 847094 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-09T00:32:35.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:32:35.805Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:32:43.034Z", "callId": "call_aZAIlstteal41xSn7MPE8bV6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/verify.py <<'PY'\\n\\\"\\\"\\\"Validate final schema and the supplied cutoff-specific eligibility service.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\\nstage=sys.argv[2] if len(sys.argv)>2 else 'test'\\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet('/task/data/target_users.parquet').uid))\\na=np.load(f'/task/data/{stage}_eligible_candidates.npz');u=pd.Index(a['uids']).get_indexer(f.uid);i=pd.Index(a['item_ids']).get_indexer(f.item_id)\\nassert (u>=0).all() and (i>=0).all();assert (((a['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\\nprint(f'VALID: {len(f):,} rows, {f.uid.nunique():,} users, 100 distinct eligible items and ranks 1–100 each.')\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:32:44.248Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"7a596f\",\"wall_time_seconds\":1.001024952,\"session_id\":43255,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:32:43.099Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a917751087d1b04f3f352428658d", "usage": { "input_tokens": 43810, "cached_input_tokens": 42752, "cache_write_input_tokens": 0, "output_tokens": 560, "reasoning_output_tokens": 240, "total_tokens": 44370 }, "turn_token_usage": { "input_tokens": 875120, "cached_input_tokens": 838656, "cache_write_input_tokens": 0, "output_tokens": 16344, "reasoning_output_tokens": 6755, "total_tokens": 891464 }, "thread_token_usage": { "input_tokens": 875120, "cached_input_tokens": 838656, "cache_write_input_tokens": 0, "output_tokens": 16344, "reasoning_output_tokens": 6755, "total_tokens": 891464 } } }, { "kind": "context", "timestamp": "2026-09-09T00:32:47.393Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c21045c0-3bda-46fd-ba92-d6a446603dbd", "process_id": "43255", "command": [ "/bin/sh", "-lc", "cat > /app/solution/verify.py <<'PY'\n\"\"\"Validate final schema and the supplied cutoff-specific eligibility service.\"\"\"\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\nstage=sys.argv[2] if len(sys.argv)>2 else 'test'\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet('/task/data/target_users.parquet').uid))\na=np.load(f'/task/data/{stage}_eligible_candidates.npz');u=pd.Index(a['uids']).get_indexer(f.uid);i=pd.Index(a['item_ids']).get_indexer(f.item_id)\nassert (u>=0).all() and (i>=0).all();assert (((a['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\nprint(f'VALID: {len(f):,} rows, {f.uid.nunique():,} users, 100 distinct eligible items and ranks 1–100 each.')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/verify.py <<'PY'\n\"\"\"Validate final schema and the supplied cutoff-specific eligibility service.\"\"\"\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\np=Path(sys.argv[1] if len(sys.argv)>1 else '/app/solution/submission.parquet')\nstage=sys.argv[2] if len(sys.argv)>2 else 'test'\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet('/task/data/target_users.parquet').uid))\na=np.load(f'/task/data/{stage}_eligible_candidates.npz');u=pd.Index(a['uids']).get_indexer(f.uid);i=pd.Index(a['item_ids']).get_indexer(f.item_id)\nassert (u>=0).all() and (i>=0).all();assert (((a['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\nprint(f'VALID: {len(f):,} rows, {f.uid.nunique():,} users, 100 distinct eligible items and ranks 1–100 each.')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 148249647 }, "formatted_output": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n" } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-09T00:33:11.971Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:33:11.971Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:33:27.415Z", "callId": "call_Bfyi2Tw6jxnKgvzTO2XoX9Ch", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\\\ndef main():')\\nf='''\\\\ndef training_loss(model,bags,uu,rng,item_ids):\\n \\\"\\\"\\\"Exact minibatch softmax here; bounded sampled softmax for large catalogs.\\n\\n Both paths consume ragged event histories. Neither builds a global\\n user-item matrix. The large-catalog path costs O(batch * 257 * dimension).\\n \\\"\\\"\\\"\\n x,o,w,ix,rows=batch(bags,uu)\\n if len(item_ids)<=50000:\\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix])\\n target/=target.sum(1,keepdim=True).clamp_min(1e-8)\\n return -(target*F.log_softmax(logits,dim=1)).sum(1).mean()\\n positive=[]\\n for u in uu:\\n start,end=bags['offsets'][u:u+2];weight=bags['targets'][start:end]\\n positive.append(rng.choice(bags['items'][start:end],p=weight/weight.sum()))\\n sampled=rng.integers(0,len(item_ids),size=(len(uu),257),dtype=np.int64)\\n sampled[:,0]=positive\\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled))\\n logits=torch.einsum('bd,bkd->bk',uv,iv)\\n # Uniform negative sampling has a constant log-Q correction, which cancels.\\n collision=torch.from_numpy(sampled[:,1:]==sampled[:,:1]);logits[:,1:].masked_fill_(collision,-1e9)\\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\\n'''\\ns=s[:pos]+f+s[pos:]\\nold=\\\"\\\"\\\"uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();\\\"\\\"\\\"\\ns=s.replace(old,\\\"uu=users[st:st+args.batch];loss=training_loss(model,bags,uu,rng,item_ids);\\\")\\nassert old not in s\\nopen(p,'w').write(s)\\nPY\\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log\",\"max_output_tokens\":1200}));\ntext(await tools.write_stdin({session_id:43255,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:33:27.577Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"52c9d3\",\"wall_time_seconds\":0.00000557,\"exit_code\":0,\"original_token_count\":128,\"output\":\"base validation epoch 39 loss 7.94009 seconds 398.2\\nbase validation epoch 40 loss 7.93392 seconds 416.9\\nSCORE 40 0.018313029854196243\\nbase validation epoch 41 loss 7.92147 seconds 440.5\\nbackup test epoch 26 loss 8.10617 seconds 283.8\\nbackup test epoch 27 loss 8.09714 seconds 297.9\\nbackup test epoch 28 loss 8.08969 seconds 310.5\\nbackup test epoch 29 loss 8.07145 seconds 324.6\\ntemporal train samples 40369 entries 25813306\\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\\n\"}\n{\"chunk_id\":\"e2667d\",\"wall_time_seconds\":0.00000256,\"exit_code\":0,\"original_token_count\":22,\"output\":\"VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:33:27.477Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a92c74bc87d18a97a7591c998df6", "usage": { "input_tokens": 44428, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 1207, "reasoning_output_tokens": 516, "total_tokens": 45635 }, "turn_token_usage": { "input_tokens": 919548, "cached_input_tokens": 882304, "cache_write_input_tokens": 0, "output_tokens": 17551, "reasoning_output_tokens": 7271, "total_tokens": 937099 }, "thread_token_usage": { "input_tokens": 919548, "cached_input_tokens": 882304, "cache_write_input_tokens": 0, "output_tokens": 17551, "reasoning_output_tokens": 7271, "total_tokens": 937099 } } }, { "kind": "context", "timestamp": "2026-09-09T00:33:27.543Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-628b9782-9cd4-4b0a-987a-d11aa71297af", "process_id": "33381", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\ndef main():')\nf='''\\ndef training_loss(model,bags,uu,rng,item_ids):\n \"\"\"Exact minibatch softmax here; bounded sampled softmax for large catalogs.\n\n Both paths consume ragged event histories. Neither builds a global\n user-item matrix. The large-catalog path costs O(batch * 257 * dimension).\n \"\"\"\n x,o,w,ix,rows=batch(bags,uu)\n if len(item_ids)<=50000:\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix])\n target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n return -(target*F.log_softmax(logits,dim=1)).sum(1).mean()\n positive=[]\n for u in uu:\n start,end=bags['offsets'][u:u+2];weight=bags['targets'][start:end]\n positive.append(rng.choice(bags['items'][start:end],p=weight/weight.sum()))\n sampled=rng.integers(0,len(item_ids),size=(len(uu),257),dtype=np.int64)\n sampled[:,0]=positive\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled))\n logits=torch.einsum('bd,bkd->bk',uv,iv)\n # Uniform negative sampling has a constant log-Q correction, which cancels.\n collision=torch.from_numpy(sampled[:,1:]==sampled[:,:1]);logits[:,1:].masked_fill_(collision,-1e9)\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\n'''\ns=s[:pos]+f+s[pos:]\nold=\"\"\"uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();\"\"\"\ns=s.replace(old,\"uu=users[st:st+args.batch];loss=training_loss(model,bags,uu,rng,item_ids);\")\nassert old not in s\nopen(p,'w').write(s)\nPY\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\ndef main():')\nf='''\\ndef training_loss(model,bags,uu,rng,item_ids):\n \"\"\"Exact minibatch softmax here; bounded sampled softmax for large catalogs.\n\n Both paths consume ragged event histories. Neither builds a global\n user-item matrix. The large-catalog path costs O(batch * 257 * dimension).\n \"\"\"\n x,o,w,ix,rows=batch(bags,uu)\n if len(item_ids)<=50000:\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix])\n target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n return -(target*F.log_softmax(logits,dim=1)).sum(1).mean()\n positive=[]\n for u in uu:\n start,end=bags['offsets'][u:u+2];weight=bags['targets'][start:end]\n positive.append(rng.choice(bags['items'][start:end],p=weight/weight.sum()))\n sampled=rng.integers(0,len(item_ids),size=(len(uu),257),dtype=np.int64)\n sampled[:,0]=positive\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled))\n logits=torch.einsum('bd,bkd->bk',uv,iv)\n # Uniform negative sampling has a constant log-Q correction, which cancels.\n collision=torch.from_numpy(sampled[:,1:]==sampled[:,:1]);logits[:,1:].masked_fill_(collision,-1e9)\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\n'''\ns=s[:pos]+f+s[pos:]\nold=\"\"\"uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();\"\"\"\ns=s.replace(old,\"uu=users[st:st+args.batch];loss=training_loss(model,bags,uu,rng,item_ids);\")\nassert old not in s\nopen(p,'w').write(s)\nPY\ntail -4 /app/solution/base.log; tail -4 /app/solution/backup.log; tail -4 /app/solution/temporal1.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "base validation epoch 39 loss 7.94009 seconds 398.2\nbase validation epoch 40 loss 7.93392 seconds 416.9\nSCORE 40 0.018313029854196243\nbase validation epoch 41 loss 7.92147 seconds 440.5\nbackup test epoch 26 loss 8.10617 seconds 283.8\nbackup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\n", "stderr": "", "aggregated_output": "base validation epoch 39 loss 7.94009 seconds 398.2\nbase validation epoch 40 loss 7.93392 seconds 416.9\nSCORE 40 0.018313029854196243\nbase validation epoch 41 loss 7.92147 seconds 440.5\nbackup test epoch 26 loss 8.10617 seconds 283.8\nbackup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5570 }, "formatted_output": "base validation epoch 39 loss 7.94009 seconds 398.2\nbase validation epoch 40 loss 7.93392 seconds 416.9\nSCORE 40 0.018313029854196243\nbase validation epoch 41 loss 7.92147 seconds 440.5\nbackup test epoch 26 loss 8.10617 seconds 283.8\nbackup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-09T00:33:43.401Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:33:43.401Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:33:48.580Z", "callId": "call_5ujoqn6HR2Epip7NeTc3tAhB", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/train.py' in c and b'80' in c:\\n print('Stopping completed baseline experiment',f);os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 > /app/solution/vae.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:33:49.813Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"c8fd39\",\"wall_time_seconds\":1.001629824,\"session_id\":2801,\"original_token_count\":11,\"output\":\"Stopping completed baseline experiment 331\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:33:48.656Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a957cd1c87d1a8fdc825078fb5c0", "usage": { "input_tokens": 45933, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 590, "reasoning_output_tokens": 356, "total_tokens": 46523 }, "turn_token_usage": { "input_tokens": 965481, "cached_input_tokens": 926592, "cache_write_input_tokens": 0, "output_tokens": 18141, "reasoning_output_tokens": 7627, "total_tokens": 983622 }, "thread_token_usage": { "input_tokens": 965481, "cached_input_tokens": 926592, "cache_write_input_tokens": 0, "output_tokens": 18141, "reasoning_output_tokens": 7627, "total_tokens": 983622 } } }, { "kind": "context", "timestamp": "2026-09-09T00:33:48.852Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-85cac2ef-f9f2-44cc-b898-1e0a21cbda75", "process_id": "33145", "command": [ "/bin/sh", "-lc", "cat > /app/solution/train.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport argparse,json,time,sys\nimport numpy as np,pandas as pd,torch\nfrom torch.nn import functional as F\nfrom model import TwoTower\nROOT=Path('/app/solution');DATA=Path('/task/data')\n\ndef make_bags(stage,half=90,like_weight=3.,organic_weight=1.,recent_days=0):\n cache=ROOT/f'bags_{stage}_{half}_{like_weight}_{organic_weight}_{recent_days}.npz'\n if cache.exists():return dict(np.load(cache))\n ids=np.load(ROOT/'ids.npz'); n=len(ids['users'])\n events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n cutoff=24786800+(606600 if stage=='test' else 0)\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\n bagids=[];inw=[];outw=[];times=[];offsets=[0]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)])\n pos=((v['k']==0)&(v['q']>=50))|(v['k']==1)\n if recent_days:pos&=v['t']>=cutoff-recent_days*86400\n v=v[pos]\n it,ix=np.unique(v['i'],return_inverse=True); k=len(it)\n age=(cutoff-v['t'])/86400\n decay=np.power(2.,-age/half) if half else np.ones(len(v))\n iw=np.where(v['k']==1,3.,1.)*np.where(v['o']==1,1.,.5)*decay\n ow=np.where(v['k']==1,like_weight,np.where((v['o']==1)&(v['q']>=80),organic_weight,.15))*decay\n a=np.bincount(ix,weights=iw,minlength=k); b=np.bincount(ix,weights=ow,minlength=k)\n a=np.sqrt(a);b=np.log1p(b)\n a=a/np.sqrt((a*a).sum()+1.e-9)\n bagids.append(it.astype('int32'));inw.append(a.astype('float32'));outw.append(b.astype('float32'))\n offsets.append(offsets[-1]+k)\n bags=dict(items=np.concatenate(bagids),weights=np.concatenate(inw),targets=np.concatenate(outw),offsets=np.array(offsets,dtype='int64'))\n np.savez(cache,**bags);print('bags',stage,len(bags['items']),flush=True);return bags\n\ndef batch(bags,users):\n off=bags['offsets']; lens=off[users+1]-off[users];offs=np.r_[0,np.cumsum(lens)[:-1]]\n ix=np.repeat(off[users]-offs,lens)+np.arange(lens.sum())\n return torch.from_numpy(bags['items'][ix].astype('int64')),torch.from_numpy(offs),torch.from_numpy(bags['weights'][ix]),ix,np.repeat(np.arange(len(users)),lens)\n\ndef gold_data(elig,ids):\n sys.path.insert(0,'/task');from evaluate_public import validation_truth\n from ranking_evaluation import CandidateSet\n truth=validation_truth(CandidateSet(DATA/'validation_eligible_candidates.npz'),set(ids['users']))\n itemidx={int(x):i for i,x in enumerate(ids['items'])}\n gold={u:{itemidx[i]:(2**g-1) for i,g in vals.items()} for u,vals in truth.items()}\n return gold\n\ndef infer(model,bags,stage,out,ids,gold=None,bs=256,return_scores=False):\n model.eval(); a=np.load(DATA/f'{stage}_eligible_candidates.npz'); userix=pd.Index(ids['users']).get_indexer(a['uids']); col=pd.Index(ids['items']).get_indexer(a['item_ids'])\n top=[];ndcg=[];discount=1/np.log2(np.arange(2,12));scoreparts=[]\n with torch.no_grad():\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\n for st in range(0,len(userix),bs):\n x,o,w,_,_=batch(bags,userix[st:st+bs]);scores=(model.user(x,o,w)@iv).numpy()\n mask=np.unpackbits(a['packed_eligible'][st:st+bs],axis=1,count=len(col),bitorder='little').astype(bool);scores[~mask]=-1.e9\n if return_scores:scoreparts.append(scores.copy())\n rank=torch.topk(torch.from_numpy(scores),100,dim=1).indices.numpy();top.append(a['item_ids'][rank])\n if gold:\n for j,rr in enumerate(rank):\n g=gold.get(int(a['uids'][st+j]))\n if g:\n gains=np.array([g.get(int(col[c]),0) for c in rr[:10]]);ideal=sorted(g.values(),reverse=True)[:10]\n ndcg.append(float(gains@discount/(np.array(ideal)@discount[:len(ideal)])))\n score=float(np.mean(ndcg)) if ndcg else 0.\n if out:\n frame=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':np.concatenate(top).ravel(),'rank':np.tile(np.arange(1,101),len(userix))})\n tmp=ROOT/(out+'.tmp.parquet');frame.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\n return (score,np.concatenate(scoreparts)) if return_scores else score\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation',choices=['validation','test']);p.add_argument('--name',default='base');p.add_argument('--epochs',type=int,default=60);p.add_argument('--hidden',type=int,default=384);p.add_argument('--dim',type=int,default=192);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--half',type=float,default=90);p.add_argument('--likes',type=float,default=3.);p.add_argument('--organic',type=float,default=1.);p.add_argument('--recent-days',type=int,default=0);p.add_argument('--lr',type=float,default=.001);p.add_argument('--batch',type=int,default=256);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--seed',type=int,default=123);p.add_argument('--resume');p.add_argument('--threads',type=int,default=4)\n args=p.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);t=time.time()\n ids=dict(np.load(ROOT/'ids.npz'));bags=make_bags('train' if args.stage=='validation' else 'test',args.half,args.likes,args.organic,args.recent_days)\n model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\n if args.resume:model.load_state_dict(torch.load(ROOT/args.resume,weights_only=False)['model'])\n opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\n gold=gold_data(None,ids) if args.stage=='validation' else None\n item_ids=torch.arange(len(ids['items'])); users=np.flatnonzero(np.diff(bags['offsets'])>1);best=-1\n for epoch in range(1,args.epochs+1):\n model.train(); losses=[];rng.shuffle(users)\n for st in range(0,len(users),args.batch):\n uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n print(args.name,args.stage,'epoch',epoch,'loss',round(np.mean(losses),5),'seconds',round(time.time()-t,1),flush=True)\n if epoch%args.eval_every==0 or epoch==1 or epoch==args.epochs:\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\n score=infer(model,bags,args.stage,out,ids,gold)\n print('SCORE',epoch,score,flush=True)\n if score>best or args.stage=='test':\n best=score;torch.save(dict(model=model.state_dict(),args=vars(args),epoch=epoch,score=score),ROOT/f'{args.name}_{args.stage}.pt')\n if args.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet')\n (ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':epoch,'score':score,'args':vars(args)},indent=2))\n print('DONE',best,flush=True)\nif __name__=='__main__':main()\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --epochs 80 --eval-every 5 > /app/solution/base.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/train.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nfrom pathlib import Path\nimport argparse,json,time,sys\nimport numpy as np,pandas as pd,torch\nfrom torch.nn import functional as F\nfrom model import TwoTower\nROOT=Path('/app/solution');DATA=Path('/task/data')\n\ndef make_bags(stage,half=90,like_weight=3.,organic_weight=1.,recent_days=0):\n cache=ROOT/f'bags_{stage}_{half}_{like_weight}_{organic_weight}_{recent_days}.npz'\n if cache.exists():return dict(np.load(cache))\n ids=np.load(ROOT/'ids.npz'); n=len(ids['users'])\n events=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':events.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n cutoff=24786800+(606600 if stage=='test' else 0)\n starts=[np.searchsorted(v['u'],np.arange(n+1)) for v in events]\n bagids=[];inw=[];outw=[];times=[];offsets=[0]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(events,starts)])\n pos=((v['k']==0)&(v['q']>=50))|(v['k']==1)\n if recent_days:pos&=v['t']>=cutoff-recent_days*86400\n v=v[pos]\n it,ix=np.unique(v['i'],return_inverse=True); k=len(it)\n age=(cutoff-v['t'])/86400\n decay=np.power(2.,-age/half) if half else np.ones(len(v))\n iw=np.where(v['k']==1,3.,1.)*np.where(v['o']==1,1.,.5)*decay\n ow=np.where(v['k']==1,like_weight,np.where((v['o']==1)&(v['q']>=80),organic_weight,.15))*decay\n a=np.bincount(ix,weights=iw,minlength=k); b=np.bincount(ix,weights=ow,minlength=k)\n a=np.sqrt(a);b=np.log1p(b)\n a=a/np.sqrt((a*a).sum()+1.e-9)\n bagids.append(it.astype('int32'));inw.append(a.astype('float32'));outw.append(b.astype('float32'))\n offsets.append(offsets[-1]+k)\n bags=dict(items=np.concatenate(bagids),weights=np.concatenate(inw),targets=np.concatenate(outw),offsets=np.array(offsets,dtype='int64'))\n np.savez(cache,**bags);print('bags',stage,len(bags['items']),flush=True);return bags\n\ndef batch(bags,users):\n off=bags['offsets']; lens=off[users+1]-off[users];offs=np.r_[0,np.cumsum(lens)[:-1]]\n ix=np.repeat(off[users]-offs,lens)+np.arange(lens.sum())\n return torch.from_numpy(bags['items'][ix].astype('int64')),torch.from_numpy(offs),torch.from_numpy(bags['weights'][ix]),ix,np.repeat(np.arange(len(users)),lens)\n\ndef gold_data(elig,ids):\n sys.path.insert(0,'/task');from evaluate_public import validation_truth\n from ranking_evaluation import CandidateSet\n truth=validation_truth(CandidateSet(DATA/'validation_eligible_candidates.npz'),set(ids['users']))\n itemidx={int(x):i for i,x in enumerate(ids['items'])}\n gold={u:{itemidx[i]:(2**g-1) for i,g in vals.items()} for u,vals in truth.items()}\n return gold\n\ndef infer(model,bags,stage,out,ids,gold=None,bs=256,return_scores=False):\n model.eval(); a=np.load(DATA/f'{stage}_eligible_candidates.npz'); userix=pd.Index(ids['users']).get_indexer(a['uids']); col=pd.Index(ids['items']).get_indexer(a['item_ids'])\n top=[];ndcg=[];discount=1/np.log2(np.arange(2,12));scoreparts=[]\n with torch.no_grad():\n iv=model.item(torch.arange(len(ids['items'])))[col].T.contiguous()\n for st in range(0,len(userix),bs):\n x,o,w,_,_=batch(bags,userix[st:st+bs]);scores=(model.user(x,o,w)@iv).numpy()\n mask=np.unpackbits(a['packed_eligible'][st:st+bs],axis=1,count=len(col),bitorder='little').astype(bool);scores[~mask]=-1.e9\n if return_scores:scoreparts.append(scores.copy())\n rank=torch.topk(torch.from_numpy(scores),100,dim=1).indices.numpy();top.append(a['item_ids'][rank])\n if gold:\n for j,rr in enumerate(rank):\n g=gold.get(int(a['uids'][st+j]))\n if g:\n gains=np.array([g.get(int(col[c]),0) for c in rr[:10]]);ideal=sorted(g.values(),reverse=True)[:10]\n ndcg.append(float(gains@discount/(np.array(ideal)@discount[:len(ideal)])))\n score=float(np.mean(ndcg)) if ndcg else 0.\n if out:\n frame=pd.DataFrame({'uid':np.repeat(a['uids'],100),'item_id':np.concatenate(top).ravel(),'rank':np.tile(np.arange(1,101),len(userix))})\n tmp=ROOT/(out+'.tmp.parquet');frame.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\n return (score,np.concatenate(scoreparts)) if return_scores else score\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation',choices=['validation','test']);p.add_argument('--name',default='base');p.add_argument('--epochs',type=int,default=60);p.add_argument('--hidden',type=int,default=384);p.add_argument('--dim',type=int,default=192);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--half',type=float,default=90);p.add_argument('--likes',type=float,default=3.);p.add_argument('--organic',type=float,default=1.);p.add_argument('--recent-days',type=int,default=0);p.add_argument('--lr',type=float,default=.001);p.add_argument('--batch',type=int,default=256);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--seed',type=int,default=123);p.add_argument('--resume');p.add_argument('--threads',type=int,default=4)\n args=p.parse_args();torch.set_num_threads(args.threads);torch.manual_seed(args.seed);rng=np.random.default_rng(args.seed);t=time.time()\n ids=dict(np.load(ROOT/'ids.npz'));bags=make_bags('train' if args.stage=='validation' else 'test',args.half,args.likes,args.organic,args.recent_days)\n model=TwoTower(len(ids['items']),args.hidden,args.dim,args.dropout)\n if args.resume:model.load_state_dict(torch.load(ROOT/args.resume,weights_only=False)['model'])\n opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)\n gold=gold_data(None,ids) if args.stage=='validation' else None\n item_ids=torch.arange(len(ids['items'])); users=np.flatnonzero(np.diff(bags['offsets'])>1);best=-1\n for epoch in range(1,args.epochs+1):\n model.train(); losses=[];rng.shuffle(users)\n for st in range(0,len(users),args.batch):\n uu=users[st:st+args.batch];x,o,w,ix,rows=batch(bags,uu)\n logits=model(x,o,w,item_ids);target=torch.zeros_like(logits)\n target[torch.from_numpy(rows),x]=torch.from_numpy(bags['targets'][ix]); target/=target.sum(1,keepdim=True).clamp_min(1e-8)\n loss=-(target*F.log_softmax(logits,dim=1)).sum(1).mean();opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n print(args.name,args.stage,'epoch',epoch,'loss',round(np.mean(losses),5),'seconds',round(time.time()-t,1),flush=True)\n if epoch%args.eval_every==0 or epoch==1 or epoch==args.epochs:\n out=f'{args.name}_validation.parquet' if args.stage=='validation' else 'submission.parquet'\n score=infer(model,bags,args.stage,out,ids,gold)\n print('SCORE',epoch,score,flush=True)\n if score>best or args.stage=='test':\n best=score;torch.save(dict(model=model.state_dict(),args=vars(args),epoch=epoch,score=score),ROOT/f'{args.name}_{args.stage}.pt')\n if args.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{args.name}_best_validation.parquet')\n (ROOT/f'{args.name}_metrics.json').write_text(json.dumps({'epoch':epoch,'score':score,'args':vars(args)},indent=2))\n print('DONE',best,flush=True)\nif __name__=='__main__':main()\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --epochs 80 --eval-every 5 > /app/solution/base.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 477, "nanos": 514425270 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:33:52.322Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-46d98a2a-96fe-4479-aea2-98db0627632b", "process_id": "1870", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name backup --epochs 30 --eval-every 30 > /app/solution/backup.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name backup --epochs 30 --eval-every 30 > /app/solution/backup.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 355, "nanos": 552398454 }, "formatted_output": "" } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-09T00:33:56.466Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:33:56.466Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:33:59.002Z", "text": "Temporal training raised public NDCG@10 from 0.0192 to 0.0308 after one epoch. The saved submission also passes all structural checks: 845,600 rows, with exactly 100 distinct eligible tracks per user. I’ll keep testing temporal training and recent-history weighting.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:34:01.115Z", "callId": "call_YIN99LQtHYTbePjuDshQqWjt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal1_validation.pt --half 14 --dest temporal_early_h14.npy --no-grid > /app/solution/temporal_h14.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:34:02.320Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"ae97a4\",\"wall_time_seconds\":1.001526255,\"session_id\":7937,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 00:34:02 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:34:01.181Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a96e075087d1b9d7fba9c8fa4df3", "usage": { "input_tokens": 46587, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 276, "reasoning_output_tokens": 108, "total_tokens": 46863 }, "turn_token_usage": { "input_tokens": 1012068, "cached_input_tokens": 972288, "cache_write_input_tokens": 0, "output_tokens": 18417, "reasoning_output_tokens": 7735, "total_tokens": 1030485 }, "thread_token_usage": { "input_tokens": 1012068, "cached_input_tokens": 972288, "cache_write_input_tokens": 0, "output_tokens": 18417, "reasoning_output_tokens": 7735, "total_tokens": 1030485 } } }, { "kind": "context", "timestamp": "2026-09-09T00:34:29.144Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d4e342a-4285-4f4f-9d33-56eda17af105", "process_id": "7937", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal1_validation.pt --half 14 --dest temporal_early_h14.npy --no-grid > /app/solution/temporal_h14.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal1_validation.pt --half 14 --dest temporal_early_h14.npy --no-grid > /app/solution/temporal_h14.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 27, "nanos": 850363816 }, "formatted_output": "" } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-09T00:34:30.913Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:34:30.913Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:34:32.108Z", "callId": "call_Ep13k7QmIN1oHUsBmUORnC3s", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/backup.log; cat /app/solution/temporal1.log; tail -5 /app/solution/vae.log; cat /app/solution/temporal_h14.log\",\"max_output_tokens\":1800}));\n" }, "result": { "timestamp": "2026-09-09T00:34:32.189Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"f807ed\",\"wall_time_seconds\":0.00000562,\"exit_code\":0,\"original_token_count\":152,\"output\":\"backup test epoch 27 loss 8.09714 seconds 297.9\\nbackup test epoch 28 loss 8.08969 seconds 310.5\\nbackup test epoch 29 loss 8.07145 seconds 324.6\\nbackup test epoch 30 loss 8.05624 seconds 341.9\\nSCORE 30 0.0\\nDONE 0.0\\ntemporal train samples 40369 entries 25813306\\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\\ntemporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\\nbags train 5618006\\nvae validation epoch 1 loss 9.93285 seconds 21.3\\nSCORE 1 0.015015606937384312\\nbags train 5618006\\ntemporal_early_h14.npy\\npop 0.0 score 0.032566433379436543\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:34:32.160Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a97a8ae087d1b6ee39699b7e19b0", "usage": { "input_tokens": 46939, "cached_input_tokens": 46464, "cache_write_input_tokens": 0, "output_tokens": 588, "reasoning_output_tokens": 516, "total_tokens": 47527 }, "turn_token_usage": { "input_tokens": 1059007, "cached_input_tokens": 1018752, "cache_write_input_tokens": 0, "output_tokens": 19005, "reasoning_output_tokens": 8251, "total_tokens": 1078012 }, "thread_token_usage": { "input_tokens": 1059007, "cached_input_tokens": 1018752, "cache_write_input_tokens": 0, "output_tokens": 19005, "reasoning_output_tokens": 8251, "total_tokens": 1078012 } } }, { "kind": "context", "timestamp": "2026-09-09T00:34:32.182Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2058329d-dc9a-4aa3-a455-453ce27daa63", "process_id": "10153", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/backup.log; cat /app/solution/temporal1.log; tail -5 /app/solution/vae.log; cat /app/solution/temporal_h14.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/backup.log; cat /app/solution/temporal1.log; tail -5 /app/solution/vae.log; cat /app/solution/temporal_h14.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "backup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\nbackup test epoch 30 loss 8.05624 seconds 341.9\nSCORE 30 0.0\nDONE 0.0\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\ntemporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\nbags train 5618006\nvae validation epoch 1 loss 9.93285 seconds 21.3\nSCORE 1 0.015015606937384312\nbags train 5618006\ntemporal_early_h14.npy\npop 0.0 score 0.032566433379436543\n", "stderr": "", "aggregated_output": "backup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\nbackup test epoch 30 loss 8.05624 seconds 341.9\nSCORE 30 0.0\nDONE 0.0\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\ntemporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\nbags train 5618006\nvae validation epoch 1 loss 9.93285 seconds 21.3\nSCORE 1 0.015015606937384312\nbags train 5618006\ntemporal_early_h14.npy\npop 0.0 score 0.032566433379436543\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5620 }, "formatted_output": "backup test epoch 27 loss 8.09714 seconds 297.9\nbackup test epoch 28 loss 8.08969 seconds 310.5\nbackup test epoch 29 loss 8.07145 seconds 324.6\nbackup test epoch 30 loss 8.05624 seconds 341.9\nSCORE 30 0.0\nDONE 0.0\ntemporal train samples 40369 entries 25813306\ntemporal1 1 loss 8.728095398673528 score 0.030802396609501334 time 124.85016894340515\ntemporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\nbags train 5618006\nvae validation epoch 1 loss 9.93285 seconds 21.3\nSCORE 1 0.015015606937384312\nbags train 5618006\ntemporal_early_h14.npy\npop 0.0 score 0.032566433379436543\n" } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-09T00:34:58.229Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:34:58.229Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:35:09.168Z", "callId": "call_3CvaRFGg1Am9by4stiBfZEST", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/trends.py <<'PY'\\n\\\"\\\"\\\"Offline, cutoff-correct item popularity and discovery trend features.\\\"\\\"\\\"\\nimport numpy as np\\nfrom train import ROOT\\n\\ndef features(stage):\\n p=ROOT/f'trends_{stage}.npz'\\n if p.exists():return dict(np.load(p))\\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['items']);out={}\\n for days in [7,14,30,60,90,180,360]:\\n cc=[];ll=[]\\n for e in ev:\\n good=(e['o']==1)&(e['t']>end-days*86400)&(((e['k']==0)&(e['q']>=80))|(e['k']==1))\\n v=e[good];pairs=np.unique(v['u'].astype('int64')*n+v['i']);cc.append(np.bincount(pairs%n,minlength=n))\\n v=v[v['k']==1];pairs=np.unique(v['u'].astype('int64')*n+v['i']);ll.append(np.bincount(pairs%n,minlength=n))\\n out[f'org{days}']=np.log1p(np.sum(cc,axis=0)).astype('float32');out[f'like{days}']=np.log1p(np.sum(ll,axis=0)).astype('float32')\\n np.savez(p,**out);return out\\nif __name__=='__main__':\\n import sys\\n features(sys.argv[1] if len(sys.argv)>1 else 'train')\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py > /app/solution/trends.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:35:10.375Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"23adac\",\"wall_time_seconds\":1.001409763,\"session_id\":37626,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:35:09.236Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a9988ce487d1a14b537af38fde7b", "usage": { "input_tokens": 47815, "cached_input_tokens": 46720, "cache_write_input_tokens": 0, "output_tokens": 995, "reasoning_output_tokens": 516, "total_tokens": 48810 }, "turn_token_usage": { "input_tokens": 1106822, "cached_input_tokens": 1065472, "cache_write_input_tokens": 0, "output_tokens": 20000, "reasoning_output_tokens": 8767, "total_tokens": 1126822 }, "thread_token_usage": { "input_tokens": 1106822, "cached_input_tokens": 1065472, "cache_write_input_tokens": 0, "output_tokens": 20000, "reasoning_output_tokens": 8767, "total_tokens": 1126822 } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-09T00:35:16.827Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:35:16.827Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T00:35:20.446Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3d6fd05c-7e7a-4a1a-80a8-b3ae9b159c31", "process_id": "37626", "command": [ "/bin/sh", "-lc", "cat > /app/solution/trends.py <<'PY'\n\"\"\"Offline, cutoff-correct item popularity and discovery trend features.\"\"\"\nimport numpy as np\nfrom train import ROOT\n\ndef features(stage):\n p=ROOT/f'trends_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['items']);out={}\n for days in [7,14,30,60,90,180,360]:\n cc=[];ll=[]\n for e in ev:\n good=(e['o']==1)&(e['t']>end-days*86400)&(((e['k']==0)&(e['q']>=80))|(e['k']==1))\n v=e[good];pairs=np.unique(v['u'].astype('int64')*n+v['i']);cc.append(np.bincount(pairs%n,minlength=n))\n v=v[v['k']==1];pairs=np.unique(v['u'].astype('int64')*n+v['i']);ll.append(np.bincount(pairs%n,minlength=n))\n out[f'org{days}']=np.log1p(np.sum(cc,axis=0)).astype('float32');out[f'like{days}']=np.log1p(np.sum(ll,axis=0)).astype('float32')\n np.savez(p,**out);return out\nif __name__=='__main__':\n import sys\n features(sys.argv[1] if len(sys.argv)>1 else 'train')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py > /app/solution/trends.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/trends.py <<'PY'\n\"\"\"Offline, cutoff-correct item popularity and discovery trend features.\"\"\"\nimport numpy as np\nfrom train import ROOT\n\ndef features(stage):\n p=ROOT/f'trends_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['items']);out={}\n for days in [7,14,30,60,90,180,360]:\n cc=[];ll=[]\n for e in ev:\n good=(e['o']==1)&(e['t']>end-days*86400)&(((e['k']==0)&(e['q']>=80))|(e['k']==1))\n v=e[good];pairs=np.unique(v['u'].astype('int64')*n+v['i']);cc.append(np.bincount(pairs%n,minlength=n))\n v=v[v['k']==1];pairs=np.unique(v['u'].astype('int64')*n+v['i']);ll.append(np.bincount(pairs%n,minlength=n))\n out[f'org{days}']=np.log1p(np.sum(cc,axis=0)).astype('float32');out[f'like{days}']=np.log1p(np.sum(ll,axis=0)).astype('float32')\n np.savez(p,**out);return out\nif __name__=='__main__':\n import sys\n features(sys.argv[1] if len(sys.argv)>1 else 'train')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/trends.py > /app/solution/trends.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 11, "nanos": 81421542 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-09T00:35:41.370Z", "callId": "call_h5eZhJsmkjrDVPZcxJHOBJS1", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/trends.py';s=open(p).read().replace(\\\"cc.append(np.bincount(pairs%n,minlength=n))\\\",\\\"cc.append(pairs)\\\").replace(\\\"ll.append(np.bincount(pairs%n,minlength=n))\\\",\\\"ll.append(pairs)\\\").replace(\\\"np.sum(cc,axis=0)\\\",\\\"np.bincount(np.unique(np.concatenate(cc))%n,minlength=n)\\\").replace(\\\"np.sum(ll,axis=0)\\\",\\\"np.bincount(np.unique(np.concatenate(ll))%n,minlength=n)\\\");open(p,'w').write(s)\\nPY\\ncat > /app/solution/calibrate.py <<'PY'\\n\\\"\\\"\\\"Validation-only tuning of item-tower bias features, never pairwise reranking.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport argparse,json\\nimport numpy as np,torch,pandas as pd\\nfrom train import ROOT,DATA,gold_data\\nfrom trends import features\\nclass Metric:\\n def __init__(self):\\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/'validation_eligible_candidates.npz');gold=gold_data(None,ids)\\n self.rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]);self.discount=1/np.log2(np.arange(2,12))\\n self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\\n self.idcg=[]\\n for j,r in enumerate(self.rows):\\n g=gold[int(a['uids'][r])];self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32);v=sorted(g.values(),reverse=True)[:10];self.idcg.append(np.array(v)@self.discount[:len(v)])\\n self.idcg=np.array(self.idcg)\\n def __call__(self,scores,bias=None):\\n dcg=[]\\n for st in range(0,len(self.rows),256):\\n ss=np.array(scores[self.rows[st:st+256]],copy=True)\\n if bias is not None:ss+=bias\\n top=torch.topk(torch.from_numpy(ss),10,dim=1).indices\\n gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\\n return float(np.mean(np.array(dcg)/self.idcg))\\nif __name__=='__main__':\\n p=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='calibration.json');a=p.parse_args();torch.set_num_threads(2)\\n m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');fs=features('train');best=(m(s),{});print('base',best,flush=True)\\n trials=[('org7','org360'),('org14','org360'),('org30','org360'),('org60','org360'),('like14','like360'),('like30','like360'),('org14',None),('org30',None),('like30',None)]\\n for recent,old in trials:\\n feat=fs[recent]-(fs[old] if old else 0)\\n for scale in [.2,.4,.7,1.]:\\n val=m(s,scale*feat);conf={recent:scale}\\n if old:conf[old]=-scale\\n print(conf,val,flush=True)\\n if val>best[0]:best=(val,conf);(ROOT/a.dest).write_text(json.dumps({'score':val,'bias':conf},indent=2))\\n print('BEST',best,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py temporal_early_h14.npy --dest temporal_calibration.json > /app/solution/calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:35:42.591Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d7cf54\",\"wall_time_seconds\":1.002317157,\"session_id\":8602,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:35:41.447Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a9be97c487d1aea895ab8096e0f5", "usage": { "input_tokens": 48867, "cached_input_tokens": 47616, "cache_write_input_tokens": 0, "output_tokens": 972, "reasoning_output_tokens": 68, "total_tokens": 49839 }, "turn_token_usage": { "input_tokens": 1155689, "cached_input_tokens": 1113088, "cache_write_input_tokens": 0, "output_tokens": 20972, "reasoning_output_tokens": 8835, "total_tokens": 1176661 }, "thread_token_usage": { "input_tokens": 1155689, "cached_input_tokens": 1113088, "cache_write_input_tokens": 0, "output_tokens": 20972, "reasoning_output_tokens": 8835, "total_tokens": 1176661 } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-09T00:35:52.174Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:35:52.174Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:35:57.064Z", "callId": "call_2GVs2viAjCl2NMWxuPCFt1LX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/calibrate.py';s=open(p).read().replace(\\\"self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\\\", \\\"self.truth=[]\\\").replace(\\\"self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32)\\\",\\\"self.truth.append(g)\\\").replace(\\\"gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\\\",\\\"gains=np.array([[g.get(int(i),0) for i in row] for g,row in zip(self.truth[st:st+len(ss)],top.numpy())]);dcg.extend(gains@self.discount)\\\");open(p,'w').write(s)\\nPY\\ntail -3 /app/solution/temporal1.log; tail -4 /app/solution/vae.log; cat /app/solution/calibration.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:35:57.258Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"a95466\",\"wall_time_seconds\":0.00000532,\"exit_code\":0,\"original_token_count\":321,\"output\":\"temporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\\ntemporal1 3 loss 8.272697315940373 score 0.027215628366107563 time 230.88239932060242\\ntemporal1 4 loss 8.13159872308562 score 0.02814246176966968 time 279.45438838005066\\nvae validation epoch 5 loss 8.41936 seconds 82.3\\nSCORE 5 0.024559822048936267\\nvae validation epoch 6 loss 8.27675 seconds 104.7\\nvae validation epoch 7 loss 8.17515 seconds 116.6\\nbase (0.032566433379436543, {})\\n{'org7': 0.2, 'org360': -0.2} 0.03714736735800626\\n{'org7': 0.4, 'org360': -0.4} 0.04109035739784981\\n{'org7': 0.7, 'org360': -0.7} 0.04466359925809984\\n{'org7': 1.0, 'org360': -1.0} 0.045541984000918044\\n{'org14': 0.2, 'org360': -0.2} 0.036543548634435076\\n{'org14': 0.4, 'org360': -0.4} 0.039455993782930854\\n{'org14': 0.7, 'org360': -0.7} 0.04348261006566588\\n{'org14': 1.0, 'org360': -1.0} 0.044648876896980445\\n{'org30': 0.2, 'org360': -0.2} 0.035064186798808746\\n{'org30': 0.4, 'org360': -0.4} 0.03745170993052696\\n{'org30': 0.7, 'org360': -0.7} 0.03950522686343062\\n{'org30': 1.0, 'org360': -1.0} 0.04096125979624791\\n{'org60': 0.2, 'org360': -0.2} 0.033874135751274084\\n{'org60': 0.4, 'org360': -0.4} 0.03465372626310339\\n{'org60': 0.7, 'org360': -0.7} 0.036132613104820986\\n{'org60': 1.0, 'org360': -1.0} 0.03674771991068549\\n\"}\n{\"current_time\":\"2026-09-09 00:35:57 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:35:57.154Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a9deed7887d1ad22d667c21dae04", "usage": { "input_tokens": 49897, "cached_input_tokens": 48640, "cache_write_input_tokens": 0, "output_tokens": 416, "reasoning_output_tokens": 174, "total_tokens": 50313 }, "turn_token_usage": { "input_tokens": 1205586, "cached_input_tokens": 1161728, "cache_write_input_tokens": 0, "output_tokens": 21388, "reasoning_output_tokens": 9009, "total_tokens": 1226974 }, "thread_token_usage": { "input_tokens": 1205586, "cached_input_tokens": 1161728, "cache_write_input_tokens": 0, "output_tokens": 21388, "reasoning_output_tokens": 9009, "total_tokens": 1226974 } } }, { "kind": "context", "timestamp": "2026-09-09T00:35:57.222Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8e18f168-4724-4dbe-81c3-e2426b4f4fc3", "process_id": "37002", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\", \"self.truth=[]\").replace(\"self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32)\",\"self.truth.append(g)\").replace(\"gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\",\"gains=np.array([[g.get(int(i),0) for i in row] for g,row in zip(self.truth[st:st+len(ss)],top.numpy())]);dcg.extend(gains@self.discount)\");open(p,'w').write(s)\nPY\ntail -3 /app/solution/temporal1.log; tail -4 /app/solution/vae.log; cat /app/solution/calibration.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\", \"self.truth=[]\").replace(\"self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32)\",\"self.truth.append(g)\").replace(\"gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\",\"gains=np.array([[g.get(int(i),0) for i in row] for g,row in zip(self.truth[st:st+len(ss)],top.numpy())]);dcg.extend(gains@self.discount)\");open(p,'w').write(s)\nPY\ntail -3 /app/solution/temporal1.log; tail -4 /app/solution/vae.log; cat /app/solution/calibration.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\ntemporal1 3 loss 8.272697315940373 score 0.027215628366107563 time 230.88239932060242\ntemporal1 4 loss 8.13159872308562 score 0.02814246176966968 time 279.45438838005066\nvae validation epoch 5 loss 8.41936 seconds 82.3\nSCORE 5 0.024559822048936267\nvae validation epoch 6 loss 8.27675 seconds 104.7\nvae validation epoch 7 loss 8.17515 seconds 116.6\nbase (0.032566433379436543, {})\n{'org7': 0.2, 'org360': -0.2} 0.03714736735800626\n{'org7': 0.4, 'org360': -0.4} 0.04109035739784981\n{'org7': 0.7, 'org360': -0.7} 0.04466359925809984\n{'org7': 1.0, 'org360': -1.0} 0.045541984000918044\n{'org14': 0.2, 'org360': -0.2} 0.036543548634435076\n{'org14': 0.4, 'org360': -0.4} 0.039455993782930854\n{'org14': 0.7, 'org360': -0.7} 0.04348261006566588\n{'org14': 1.0, 'org360': -1.0} 0.044648876896980445\n{'org30': 0.2, 'org360': -0.2} 0.035064186798808746\n{'org30': 0.4, 'org360': -0.4} 0.03745170993052696\n{'org30': 0.7, 'org360': -0.7} 0.03950522686343062\n{'org30': 1.0, 'org360': -1.0} 0.04096125979624791\n{'org60': 0.2, 'org360': -0.2} 0.033874135751274084\n{'org60': 0.4, 'org360': -0.4} 0.03465372626310339\n{'org60': 0.7, 'org360': -0.7} 0.036132613104820986\n{'org60': 1.0, 'org360': -1.0} 0.03674771991068549\n", "stderr": "", "aggregated_output": "temporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\ntemporal1 3 loss 8.272697315940373 score 0.027215628366107563 time 230.88239932060242\ntemporal1 4 loss 8.13159872308562 score 0.02814246176966968 time 279.45438838005066\nvae validation epoch 5 loss 8.41936 seconds 82.3\nSCORE 5 0.024559822048936267\nvae validation epoch 6 loss 8.27675 seconds 104.7\nvae validation epoch 7 loss 8.17515 seconds 116.6\nbase (0.032566433379436543, {})\n{'org7': 0.2, 'org360': -0.2} 0.03714736735800626\n{'org7': 0.4, 'org360': -0.4} 0.04109035739784981\n{'org7': 0.7, 'org360': -0.7} 0.04466359925809984\n{'org7': 1.0, 'org360': -1.0} 0.045541984000918044\n{'org14': 0.2, 'org360': -0.2} 0.036543548634435076\n{'org14': 0.4, 'org360': -0.4} 0.039455993782930854\n{'org14': 0.7, 'org360': -0.7} 0.04348261006566588\n{'org14': 1.0, 'org360': -1.0} 0.044648876896980445\n{'org30': 0.2, 'org360': -0.2} 0.035064186798808746\n{'org30': 0.4, 'org360': -0.4} 0.03745170993052696\n{'org30': 0.7, 'org360': -0.7} 0.03950522686343062\n{'org30': 1.0, 'org360': -1.0} 0.04096125979624791\n{'org60': 0.2, 'org360': -0.2} 0.033874135751274084\n{'org60': 0.4, 'org360': -0.4} 0.03465372626310339\n{'org60': 0.7, 'org360': -0.7} 0.036132613104820986\n{'org60': 1.0, 'org360': -1.0} 0.03674771991068549\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5320 }, "formatted_output": "temporal1 2 loss 8.438425383990324 score 0.028887132565382785 time 183.8128001689911\ntemporal1 3 loss 8.272697315940373 score 0.027215628366107563 time 230.88239932060242\ntemporal1 4 loss 8.13159872308562 score 0.02814246176966968 time 279.45438838005066\nvae validation epoch 5 loss 8.41936 seconds 82.3\nSCORE 5 0.024559822048936267\nvae validation epoch 6 loss 8.27675 seconds 104.7\nvae validation epoch 7 loss 8.17515 seconds 116.6\nbase (0.032566433379436543, {})\n{'org7': 0.2, 'org360': -0.2} 0.03714736735800626\n{'org7': 0.4, 'org360': -0.4} 0.04109035739784981\n{'org7': 0.7, 'org360': -0.7} 0.04466359925809984\n{'org7': 1.0, 'org360': -1.0} 0.045541984000918044\n{'org14': 0.2, 'org360': -0.2} 0.036543548634435076\n{'org14': 0.4, 'org360': -0.4} 0.039455993782930854\n{'org14': 0.7, 'org360': -0.7} 0.04348261006566588\n{'org14': 1.0, 'org360': -1.0} 0.044648876896980445\n{'org30': 0.2, 'org360': -0.2} 0.035064186798808746\n{'org30': 0.4, 'org360': -0.4} 0.03745170993052696\n{'org30': 0.7, 'org360': -0.7} 0.03950522686343062\n{'org30': 1.0, 'org360': -1.0} 0.04096125979624791\n{'org60': 0.2, 'org360': -0.2} 0.033874135751274084\n{'org60': 0.4, 'org360': -0.4} 0.03465372626310339\n{'org60': 0.7, 'org360': -0.7} 0.036132613104820986\n{'org60': 1.0, 'org360': -1.0} 0.03674771991068549\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:36:07.083Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-36766522-7a73-4cc0-bcbc-b0b126607d40", "process_id": "8602", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\"cc.append(np.bincount(pairs%n,minlength=n))\",\"cc.append(pairs)\").replace(\"ll.append(np.bincount(pairs%n,minlength=n))\",\"ll.append(pairs)\").replace(\"np.sum(cc,axis=0)\",\"np.bincount(np.unique(np.concatenate(cc))%n,minlength=n)\").replace(\"np.sum(ll,axis=0)\",\"np.bincount(np.unique(np.concatenate(ll))%n,minlength=n)\");open(p,'w').write(s)\nPY\ncat > /app/solution/calibrate.py <<'PY'\n\"\"\"Validation-only tuning of item-tower bias features, never pairwise reranking.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,json\nimport numpy as np,torch,pandas as pd\nfrom train import ROOT,DATA,gold_data\nfrom trends import features\nclass Metric:\n def __init__(self):\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/'validation_eligible_candidates.npz');gold=gold_data(None,ids)\n self.rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]);self.discount=1/np.log2(np.arange(2,12))\n self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\n self.idcg=[]\n for j,r in enumerate(self.rows):\n g=gold[int(a['uids'][r])];self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32);v=sorted(g.values(),reverse=True)[:10];self.idcg.append(np.array(v)@self.discount[:len(v)])\n self.idcg=np.array(self.idcg)\n def __call__(self,scores,bias=None):\n dcg=[]\n for st in range(0,len(self.rows),256):\n ss=np.array(scores[self.rows[st:st+256]],copy=True)\n if bias is not None:ss+=bias\n top=torch.topk(torch.from_numpy(ss),10,dim=1).indices\n gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\n return float(np.mean(np.array(dcg)/self.idcg))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='calibration.json');a=p.parse_args();torch.set_num_threads(2)\n m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');fs=features('train');best=(m(s),{});print('base',best,flush=True)\n trials=[('org7','org360'),('org14','org360'),('org30','org360'),('org60','org360'),('like14','like360'),('like30','like360'),('org14',None),('org30',None),('like30',None)]\n for recent,old in trials:\n feat=fs[recent]-(fs[old] if old else 0)\n for scale in [.2,.4,.7,1.]:\n val=m(s,scale*feat);conf={recent:scale}\n if old:conf[old]=-scale\n print(conf,val,flush=True)\n if val>best[0]:best=(val,conf);(ROOT/a.dest).write_text(json.dumps({'score':val,'bias':conf},indent=2))\n print('BEST',best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py temporal_early_h14.npy --dest temporal_calibration.json > /app/solution/calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/trends.py';s=open(p).read().replace(\"cc.append(np.bincount(pairs%n,minlength=n))\",\"cc.append(pairs)\").replace(\"ll.append(np.bincount(pairs%n,minlength=n))\",\"ll.append(pairs)\").replace(\"np.sum(cc,axis=0)\",\"np.bincount(np.unique(np.concatenate(cc))%n,minlength=n)\").replace(\"np.sum(ll,axis=0)\",\"np.bincount(np.unique(np.concatenate(ll))%n,minlength=n)\");open(p,'w').write(s)\nPY\ncat > /app/solution/calibrate.py <<'PY'\n\"\"\"Validation-only tuning of item-tower bias features, never pairwise reranking.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,json\nimport numpy as np,torch,pandas as pd\nfrom train import ROOT,DATA,gold_data\nfrom trends import features\nclass Metric:\n def __init__(self):\n ids=dict(np.load(ROOT/'ids.npz'));a=np.load(DATA/'validation_eligible_candidates.npz');gold=gold_data(None,ids)\n self.rows=np.array([r for r,u in enumerate(a['uids']) if int(u) in gold]);self.discount=1/np.log2(np.arange(2,12))\n self.truth=torch.zeros((len(self.rows),len(a['item_ids'])))\n self.idcg=[]\n for j,r in enumerate(self.rows):\n g=gold[int(a['uids'][r])];self.truth[j,list(g)]=torch.tensor(list(g.values()),dtype=torch.float32);v=sorted(g.values(),reverse=True)[:10];self.idcg.append(np.array(v)@self.discount[:len(v)])\n self.idcg=np.array(self.idcg)\n def __call__(self,scores,bias=None):\n dcg=[]\n for st in range(0,len(self.rows),256):\n ss=np.array(scores[self.rows[st:st+256]],copy=True)\n if bias is not None:ss+=bias\n top=torch.topk(torch.from_numpy(ss),10,dim=1).indices\n gains=self.truth[st:st+len(ss)].gather(1,top).numpy();dcg.extend(gains@self.discount)\n return float(np.mean(np.array(dcg)/self.idcg))\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='calibration.json');a=p.parse_args();torch.set_num_threads(2)\n m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');fs=features('train');best=(m(s),{});print('base',best,flush=True)\n trials=[('org7','org360'),('org14','org360'),('org30','org360'),('org60','org360'),('like14','like360'),('like30','like360'),('org14',None),('org30',None),('like30',None)]\n for recent,old in trials:\n feat=fs[recent]-(fs[old] if old else 0)\n for scale in [.2,.4,.7,1.]:\n val=m(s,scale*feat);conf={recent:scale}\n if old:conf[old]=-scale\n print(conf,val,flush=True)\n if val>best[0]:best=(val,conf);(ROOT/a.dest).write_text(json.dumps({'score':val,'bias':conf},indent=2))\n print('BEST',best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py temporal_early_h14.npy --dest temporal_calibration.json > /app/solution/calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 499707077 }, "formatted_output": "" } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-09T00:36:12.066Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:36:12.066Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:36:14.812Z", "text": "Recent listening trends help substantially: adding a cutoff-correct item bias raised the temporal model’s public score to 0.0455 NDCG@10. The bias can be included in the offline item vector, so serving still uses a single dot product. The variational model is also outperforming the original model in its early epochs.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:36:57.048Z", "callId": "call_VlNyfy06wZSn7Vs1kuqBxSAb", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/submit.py <<'PY'\\n\\\"\\\"\\\"Export independent tower vectors and perform blocked maximum-inner-product retrieval.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport argparse,json,time\\nimport torch,numpy as np,pandas as pd\\nfrom torch import nn\\nfrom model import TwoTower,VariationalTwoTower\\nfrom train import ROOT,DATA,make_bags,batch\\nfrom trends import features\\nclass EnsembleUserTower(nn.Module):\\n def __init__(self,towers,weights):\\n super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('coefficients',torch.tensor(weights))\\n def forward(self,histories):\\n return torch.cat([tower(*h)*coef for tower,h,coef in zip(self.towers,histories,self.coefficients)],dim=1)\\nclass EnsembleItemTower(nn.Module):\\n def __init__(self,towers):super().__init__();self.towers=nn.ModuleList(towers)\\n def forward(self,ids):return torch.cat([tower(ids) for tower in self.towers],dim=1)\\n\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--config',default='final_config.json');p.add_argument('--stage',choices=['validation','test'],default='test');p.add_argument('--output');a=p.parse_args();torch.set_num_threads(4);t=time.time();cfg=json.loads((ROOT/a.config).read_text())\\n ids=dict(np.load(ROOT/'ids.npz'));elig=np.load(DATA/f'{a.stage}_eligible_candidates.npz');ui=pd.Index(ids['users']).get_indexer(elig['uids']);col=pd.Index(ids['items']).get_indexer(elig['item_ids']);models=[];bags=[];weights=[]\\n for comp in cfg['components']:\\n ck=torch.load(ROOT/comp['checkpoint'],weights_only=False);c=ck['args'];model=(VariationalTwoTower if c.get('vae') else TwoTower)(len(ids['items']),c['hidden'],c['dim'],c['dropout']);model.load_state_dict(ck['model']);models.append(model);weights.append(comp.get('weight',1.))\\n bags.append(make_bags('train' if a.stage=='validation' else 'test',comp.get('half',c['half']),c['likes'],c['organic'],c.get('recent_days',0)))\\n # Absorb all calibrated item-only terms into the trainable item bias table.\\n if cfg.get('bias'):\\n fs=features('train' if a.stage=='validation' else 'test');bias=sum(float(w)*fs[k] for k,w in cfg['bias'].items())\\n with torch.no_grad():models[0].item.bias.weight[:,0].add_(torch.from_numpy(bias)/weights[0])\\n user=EnsembleUserTower([m.user for m in models],weights).eval();item=EnsembleItemTower([m.item for m in models]).eval()\\n with torch.no_grad():\\n vectors=item(torch.arange(len(ids['items']))).numpy();np.save(ROOT/f'{a.stage}_item_vectors.npy',vectors)\\n query=[]\\n for st in range(0,len(ui),256):\\n histories=[]\\n for b in bags:\\n x,o,w,_,_=batch(b,ui[st:st+256]);histories.append((x,o,w))\\n query.append(user(histories).numpy())\\n query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\\n assert query.shape[1]==vectors.shape[1]\\n # Item vectors are cached once, independently of every querying user.\\n iv=torch.from_numpy(vectors[col]).T.contiguous();ranks=[]\\n for st in range(0,len(ui),256):\\n scores=torch.from_numpy(query[st:st+256])@iv\\n mask=np.unpackbits(elig['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);scores.masked_fill_(~torch.from_numpy(mask),-torch.inf)\\n ranks.append(elig['item_ids'][scores.topk(100,dim=1).indices.numpy()])\\n f=pd.DataFrame({'uid':np.repeat(elig['uids'],100),'item_id':np.concatenate(ranks).ravel(),'rank':np.tile(np.arange(1,101),len(ui))})\\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet');tmp=ROOT/(out+'.tmp.parquet');f.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\\n print(json.dumps({'output':out,'rows':len(f),'dimension':query.shape[1],'seconds':time.time()-t}),flush=True)\\nif __name__=='__main__':main()\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport os,signal,shutil\\nshutil.copyfile('/app/solution/submission.parquet','/app/solution/backup_submission.parquet')\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/temporal.py' in c and b'temporal1' in c:os.kill(int(f),signal.SIGTERM);print('Stopped temporal experiment after validation declined.')\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\np='/app/solution/temporal.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\\\"model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\\\",\\\"model=(VariationalTwoTower if ck['args'].get('vae') else TwoTower)(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\\\");s=s.replace(\\\"ck=torch.load(ROOT/args.resume,weights_only=False);model=\\\", \\\"ck=torch.load(ROOT/args.resume,weights_only=False);torch.save(ck,ROOT/f'{args.name}_source.pt');model=\\\");s=s.replace(\\\"opt.zero_grad(set_to_none=True);loss.backward()\\\", \\\"loss=loss+(ck['args'].get('beta',.0002)*model.user.kl if ck['args'].get('vae') else 0.);opt.zero_grad(set_to_none=True);loss.backward()\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name temporal_backup --resume backup_test.pt --epochs 1 > /app/solution/temporal_backup.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:36:58.260Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"52c7d2\",\"wall_time_seconds\":1.001821995,\"session_id\":67442,\"original_token_count\":14,\"output\":\"Stopped temporal experiment after validation declined.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:36:57.105Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0a9ed78fc87d1b2dd59117580bff3", "usage": { "input_tokens": 51015, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 1937, "reasoning_output_tokens": 334, "total_tokens": 52952 }, "turn_token_usage": { "input_tokens": 1256601, "cached_input_tokens": 1211392, "cache_write_input_tokens": 0, "output_tokens": 23325, "reasoning_output_tokens": 9343, "total_tokens": 1279926 }, "thread_token_usage": { "input_tokens": 1256601, "cached_input_tokens": 1211392, "cache_write_input_tokens": 0, "output_tokens": 23325, "reasoning_output_tokens": 9343, "total_tokens": 1279926 } } }, { "kind": "context", "timestamp": "2026-09-09T00:36:57.395Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d16b6004-3476-47f1-b4dd-7ed275bb4777", "process_id": "23057", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal1 --epochs 12 > /app/solution/temporal1.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal1 --epochs 12 > /app/solution/temporal1.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 350, "nanos": 890003119 }, "formatted_output": "" } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-09T00:37:06.650Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:37:06.650Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:37:11.673Z", "callId": "call_a8RLvG0gvIcIb1YWUlKmBDnf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/temporal_validation_config.json <<'JSON'\\n{\\\"components\\\":[{\\\"checkpoint\\\":\\\"temporal1_validation.pt\\\",\\\"weight\\\":1.0,\\\"half\\\":14}],\\\"bias\\\":{\\\"org7\\\":1.0,\\\"org360\\\":-1.0}}\\nJSON\\ncat > /app/solution/final_config.json <<'JSON'\\n{\\\"components\\\":[{\\\"checkpoint\\\":\\\"temporal_backup_test.pt\\\",\\\"weight\\\":1.0,\\\"half\\\":14}],\\\"bias\\\":{\\\"org7\\\":1.0,\\\"org360\\\":-1.0}}\\nJSON\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config temporal_validation_config.json --stage validation > /app/solution/submit_validation.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -8 /app/solution/calibration.log; tail -7 /app/solution/vae.log; tail -4 /app/solution/temporal_backup.log\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:37:12.901Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5e3402\",\"wall_time_seconds\":1.000756701,\"session_id\":80839,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"971010\",\"wall_time_seconds\":0.00000501,\"exit_code\":0,\"original_token_count\":161,\"output\":\"{'org30': 0.4} 0.034947075417229044\\n{'org30': 0.7} 0.03473658741808412\\n{'org30': 1.0} 0.03389568291962476\\n{'like30': 0.2} 0.03608842573751488\\n{'like30': 0.4} 0.037381328042997666\\n{'like30': 0.7} 0.03869238740074736\\n{'like30': 1.0} 0.03910148871145745\\nBEST (0.045541984000918044, {'org7': 1.0, 'org360': -1.0})\\nvae validation epoch 7 loss 8.17515 seconds 116.6\\nvae validation epoch 8 loss 8.07641 seconds 133.3\\nvae validation epoch 9 loss 8.00328 seconds 146.7\\nvae validation epoch 10 loss 7.92087 seconds 160.2\\nSCORE 10 0.02409363462002259\\nvae validation epoch 11 loss 7.84645 seconds 181.1\\nvae validation epoch 12 loss 7.78698 seconds 191.2\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:37:11.752Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aa2a7d7887d19461fcc9985c5765", "usage": { "input_tokens": 53018, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 101, "total_tokens": 53368 }, "turn_token_usage": { "input_tokens": 1309619, "cached_input_tokens": 1262208, "cache_write_input_tokens": 0, "output_tokens": 23675, "reasoning_output_tokens": 9444, "total_tokens": 1333294 }, "thread_token_usage": { "input_tokens": 1309619, "cached_input_tokens": 1262208, "cache_write_input_tokens": 0, "output_tokens": 23675, "reasoning_output_tokens": 9444, "total_tokens": 1333294 } } }, { "kind": "context", "timestamp": "2026-09-09T00:37:12.895Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f5f32638-a89e-4fdf-ac70-498ec5590aa7", "process_id": "94900", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/calibration.log; tail -7 /app/solution/vae.log; tail -4 /app/solution/temporal_backup.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/calibration.log; tail -7 /app/solution/vae.log; tail -4 /app/solution/temporal_backup.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'org30': 0.4} 0.034947075417229044\n{'org30': 0.7} 0.03473658741808412\n{'org30': 1.0} 0.03389568291962476\n{'like30': 0.2} 0.03608842573751488\n{'like30': 0.4} 0.037381328042997666\n{'like30': 0.7} 0.03869238740074736\n{'like30': 1.0} 0.03910148871145745\nBEST (0.045541984000918044, {'org7': 1.0, 'org360': -1.0})\nvae validation epoch 7 loss 8.17515 seconds 116.6\nvae validation epoch 8 loss 8.07641 seconds 133.3\nvae validation epoch 9 loss 8.00328 seconds 146.7\nvae validation epoch 10 loss 7.92087 seconds 160.2\nSCORE 10 0.02409363462002259\nvae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\n", "stderr": "", "aggregated_output": "{'org30': 0.4} 0.034947075417229044\n{'org30': 0.7} 0.03473658741808412\n{'org30': 1.0} 0.03389568291962476\n{'like30': 0.2} 0.03608842573751488\n{'like30': 0.4} 0.037381328042997666\n{'like30': 0.7} 0.03869238740074736\n{'like30': 1.0} 0.03910148871145745\nBEST (0.045541984000918044, {'org7': 1.0, 'org360': -1.0})\nvae validation epoch 7 loss 8.17515 seconds 116.6\nvae validation epoch 8 loss 8.07641 seconds 133.3\nvae validation epoch 9 loss 8.00328 seconds 146.7\nvae validation epoch 10 loss 7.92087 seconds 160.2\nSCORE 10 0.02409363462002259\nvae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5010 }, "formatted_output": "{'org30': 0.4} 0.034947075417229044\n{'org30': 0.7} 0.03473658741808412\n{'org30': 1.0} 0.03389568291962476\n{'like30': 0.2} 0.03608842573751488\n{'like30': 0.4} 0.037381328042997666\n{'like30': 0.7} 0.03869238740074736\n{'like30': 1.0} 0.03910148871145745\nBEST (0.045541984000918044, {'org7': 1.0, 'org360': -1.0})\nvae validation epoch 7 loss 8.17515 seconds 116.6\nvae validation epoch 8 loss 8.07641 seconds 133.3\nvae validation epoch 9 loss 8.00328 seconds 146.7\nvae validation epoch 10 loss 7.92087 seconds 160.2\nSCORE 10 0.02409363462002259\nvae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:37:29.365Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50677789-373f-45f5-ad16-2f44ab696cb0", "process_id": "80839", "command": [ "/bin/sh", "-lc", "cat > /app/solution/temporal_validation_config.json <<'JSON'\n{\"components\":[{\"checkpoint\":\"temporal1_validation.pt\",\"weight\":1.0,\"half\":14}],\"bias\":{\"org7\":1.0,\"org360\":-1.0}}\nJSON\ncat > /app/solution/final_config.json <<'JSON'\n{\"components\":[{\"checkpoint\":\"temporal_backup_test.pt\",\"weight\":1.0,\"half\":14}],\"bias\":{\"org7\":1.0,\"org360\":-1.0}}\nJSON\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config temporal_validation_config.json --stage validation > /app/solution/submit_validation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/temporal_validation_config.json <<'JSON'\n{\"components\":[{\"checkpoint\":\"temporal1_validation.pt\",\"weight\":1.0,\"half\":14}],\"bias\":{\"org7\":1.0,\"org360\":-1.0}}\nJSON\ncat > /app/solution/final_config.json <<'JSON'\n{\"components\":[{\"checkpoint\":\"temporal_backup_test.pt\",\"weight\":1.0,\"half\":14}],\"bias\":{\"org7\":1.0,\"org360\":-1.0}}\nJSON\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config temporal_validation_config.json --stage validation > /app/solution/submit_validation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 508215982 }, "formatted_output": "" } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-09T00:37:39.280Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:37:39.280Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:37:45.772Z", "callId": "call_Z6GPt20Hv9LQd6fGy1MmcPdP", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/train.py';s=open(p).read().replace('return_scores=False):','return_scores=False,bias=None):');s=s.replace(\\\"mask=np.unpackbits(a['packed_eligible'][st:st+bs]\\\", \\\"if bias is not None:scores+=bias[None,:]\\\\n mask=np.unpackbits(a['packed_eligible'][st:st+bs]\\\");s=s.replace(\\\"p.add_argument('--beta',type=float,default=.0002);\\\", \\\"p.add_argument('--beta',type=float,default=.0002);p.add_argument('--trend',type=float,default=0.);\\\")\\ns=s.replace(\\\"score=infer(model,bags,args.stage,out,ids,gold)\\\", \\\"bias=None\\\\n if args.trend:\\\\n from trends import features\\\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias)\\\")\\nopen(p,'w').write(s)\\nPY\\ntail -6 /app/solution/vae.log; cat /app/solution/submit_validation.log; cat /app/solution/temporal_backup.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:37:45.968Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"05f72c\",\"wall_time_seconds\":0.000009181,\"exit_code\":0,\"original_token_count\":112,\"output\":\"vae validation epoch 11 loss 7.84645 seconds 181.1\\nvae validation epoch 12 loss 7.78698 seconds 191.2\\nvae validation epoch 13 loss 7.7116 seconds 200.9\\nvae validation epoch 14 loss 7.65301 seconds 211.1\\nvae validation epoch 15 loss 7.60445 seconds 222.4\\nSCORE 15 0.024164563799176836\\nbags train 5618006\\n{\\\"output\\\": \\\"validation.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 193, \\\"seconds\\\": 11.48898196220398}\\ntemporal test samples 41189 entries 26660861\\n\"}\n{\"current_time\":\"2026-09-09 00:37:45 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:37:45.864Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aa390f7087d1a99f0b6a7202fdd0", "usage": { "input_tokens": 53737, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 837, "reasoning_output_tokens": 516, "total_tokens": 54574 }, "turn_token_usage": { "input_tokens": 1363356, "cached_input_tokens": 1315072, "cache_write_input_tokens": 0, "output_tokens": 24512, "reasoning_output_tokens": 9960, "total_tokens": 1387868 }, "thread_token_usage": { "input_tokens": 1363356, "cached_input_tokens": 1315072, "cache_write_input_tokens": 0, "output_tokens": 24512, "reasoning_output_tokens": 9960, "total_tokens": 1387868 } } }, { "kind": "context", "timestamp": "2026-09-09T00:37:45.945Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-87d6a984-f3cf-4572-8d1b-34d40d1bdaae", "process_id": "17963", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace('return_scores=False):','return_scores=False,bias=None):');s=s.replace(\"mask=np.unpackbits(a['packed_eligible'][st:st+bs]\", \"if bias is not None:scores+=bias[None,:]\\n mask=np.unpackbits(a['packed_eligible'][st:st+bs]\");s=s.replace(\"p.add_argument('--beta',type=float,default=.0002);\", \"p.add_argument('--beta',type=float,default=.0002);p.add_argument('--trend',type=float,default=0.);\")\ns=s.replace(\"score=infer(model,bags,args.stage,out,ids,gold)\", \"bias=None\\n if args.trend:\\n from trends import features\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias)\")\nopen(p,'w').write(s)\nPY\ntail -6 /app/solution/vae.log; cat /app/solution/submit_validation.log; cat /app/solution/temporal_backup.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/train.py';s=open(p).read().replace('return_scores=False):','return_scores=False,bias=None):');s=s.replace(\"mask=np.unpackbits(a['packed_eligible'][st:st+bs]\", \"if bias is not None:scores+=bias[None,:]\\n mask=np.unpackbits(a['packed_eligible'][st:st+bs]\");s=s.replace(\"p.add_argument('--beta',type=float,default=.0002);\", \"p.add_argument('--beta',type=float,default=.0002);p.add_argument('--trend',type=float,default=0.);\")\ns=s.replace(\"score=infer(model,bags,args.stage,out,ids,gold)\", \"bias=None\\n if args.trend:\\n from trends import features\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias)\")\nopen(p,'w').write(s)\nPY\ntail -6 /app/solution/vae.log; cat /app/solution/submit_validation.log; cat /app/solution/temporal_backup.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\nvae validation epoch 13 loss 7.7116 seconds 200.9\nvae validation epoch 14 loss 7.65301 seconds 211.1\nvae validation epoch 15 loss 7.60445 seconds 222.4\nSCORE 15 0.024164563799176836\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 11.48898196220398}\ntemporal test samples 41189 entries 26660861\n", "stderr": "", "aggregated_output": "vae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\nvae validation epoch 13 loss 7.7116 seconds 200.9\nvae validation epoch 14 loss 7.65301 seconds 211.1\nvae validation epoch 15 loss 7.60445 seconds 222.4\nSCORE 15 0.024164563799176836\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 11.48898196220398}\ntemporal test samples 41189 entries 26660861\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9181 }, "formatted_output": "vae validation epoch 11 loss 7.84645 seconds 181.1\nvae validation epoch 12 loss 7.78698 seconds 191.2\nvae validation epoch 13 loss 7.7116 seconds 200.9\nvae validation epoch 14 loss 7.65301 seconds 211.1\nvae validation epoch 15 loss 7.60445 seconds 222.4\nSCORE 15 0.024164563799176836\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 11.48898196220398}\ntemporal test samples 41189 entries 26660861\n" } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-09T00:37:50.872Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:37:50.872Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:37:58.200Z", "callId": "call_aWNwzWFUk8dUYjTG5z43emmL", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal,shutil\\nshutil.copyfile('/app/solution/vae_validation.pt','/app/solution/vae_initial.pt')\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/train.py' in c and b'vae' in c:os.kill(int(f),signal.SIGTERM);print('Restarting with trend-aware checkpoint selection.')\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae_trend --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/vae_trend.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:37:59.425Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"6c089c\",\"wall_time_seconds\":1.001622398,\"session_id\":12140,\"original_token_count\":13,\"output\":\"Restarting with trend-aware checkpoint selection.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:37:58.262Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aa5a303887d1ab656fbe7a36b336", "usage": { "input_tokens": 54800, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 332, "reasoning_output_tokens": 57, "total_tokens": 55132 }, "turn_token_usage": { "input_tokens": 1418156, "cached_input_tokens": 1368576, "cache_write_input_tokens": 0, "output_tokens": 24844, "reasoning_output_tokens": 10017, "total_tokens": 1443000 }, "thread_token_usage": { "input_tokens": 1418156, "cached_input_tokens": 1368576, "cache_write_input_tokens": 0, "output_tokens": 24844, "reasoning_output_tokens": 10017, "total_tokens": 1443000 } } }, { "kind": "context", "timestamp": "2026-09-09T00:37:58.554Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eb494635-62bd-40fd-96d8-c6f3724bcf35", "process_id": "2801", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'80' in c:\n print('Stopping completed baseline experiment',f);os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 > /app/solution/vae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'80' in c:\n print('Stopping completed baseline experiment',f);os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 > /app/solution/vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 249, "nanos": 744434499 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:38:14.558Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-60146ec6-172b-4105-95fb-020bc01dfa0d", "process_id": "67442", "command": [ "/bin/sh", "-lc", "cat > /app/solution/submit.py <<'PY'\n\"\"\"Export independent tower vectors and perform blocked maximum-inner-product retrieval.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,json,time\nimport torch,numpy as np,pandas as pd\nfrom torch import nn\nfrom model import TwoTower,VariationalTwoTower\nfrom train import ROOT,DATA,make_bags,batch\nfrom trends import features\nclass EnsembleUserTower(nn.Module):\n def __init__(self,towers,weights):\n super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('coefficients',torch.tensor(weights))\n def forward(self,histories):\n return torch.cat([tower(*h)*coef for tower,h,coef in zip(self.towers,histories,self.coefficients)],dim=1)\nclass EnsembleItemTower(nn.Module):\n def __init__(self,towers):super().__init__();self.towers=nn.ModuleList(towers)\n def forward(self,ids):return torch.cat([tower(ids) for tower in self.towers],dim=1)\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--config',default='final_config.json');p.add_argument('--stage',choices=['validation','test'],default='test');p.add_argument('--output');a=p.parse_args();torch.set_num_threads(4);t=time.time();cfg=json.loads((ROOT/a.config).read_text())\n ids=dict(np.load(ROOT/'ids.npz'));elig=np.load(DATA/f'{a.stage}_eligible_candidates.npz');ui=pd.Index(ids['users']).get_indexer(elig['uids']);col=pd.Index(ids['items']).get_indexer(elig['item_ids']);models=[];bags=[];weights=[]\n for comp in cfg['components']:\n ck=torch.load(ROOT/comp['checkpoint'],weights_only=False);c=ck['args'];model=(VariationalTwoTower if c.get('vae') else TwoTower)(len(ids['items']),c['hidden'],c['dim'],c['dropout']);model.load_state_dict(ck['model']);models.append(model);weights.append(comp.get('weight',1.))\n bags.append(make_bags('train' if a.stage=='validation' else 'test',comp.get('half',c['half']),c['likes'],c['organic'],c.get('recent_days',0)))\n # Absorb all calibrated item-only terms into the trainable item bias table.\n if cfg.get('bias'):\n fs=features('train' if a.stage=='validation' else 'test');bias=sum(float(w)*fs[k] for k,w in cfg['bias'].items())\n with torch.no_grad():models[0].item.bias.weight[:,0].add_(torch.from_numpy(bias)/weights[0])\n user=EnsembleUserTower([m.user for m in models],weights).eval();item=EnsembleItemTower([m.item for m in models]).eval()\n with torch.no_grad():\n vectors=item(torch.arange(len(ids['items']))).numpy();np.save(ROOT/f'{a.stage}_item_vectors.npy',vectors)\n query=[]\n for st in range(0,len(ui),256):\n histories=[]\n for b in bags:\n x,o,w,_,_=batch(b,ui[st:st+256]);histories.append((x,o,w))\n query.append(user(histories).numpy())\n query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\n assert query.shape[1]==vectors.shape[1]\n # Item vectors are cached once, independently of every querying user.\n iv=torch.from_numpy(vectors[col]).T.contiguous();ranks=[]\n for st in range(0,len(ui),256):\n scores=torch.from_numpy(query[st:st+256])@iv\n mask=np.unpackbits(elig['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);scores.masked_fill_(~torch.from_numpy(mask),-torch.inf)\n ranks.append(elig['item_ids'][scores.topk(100,dim=1).indices.numpy()])\n f=pd.DataFrame({'uid':np.repeat(elig['uids'],100),'item_id':np.concatenate(ranks).ravel(),'rank':np.tile(np.arange(1,101),len(ui))})\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet');tmp=ROOT/(out+'.tmp.parquet');f.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\n print(json.dumps({'output':out,'rows':len(f),'dimension':query.shape[1],'seconds':time.time()-t}),flush=True)\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport os,signal,shutil\nshutil.copyfile('/app/solution/submission.parquet','/app/solution/backup_submission.parquet')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal1' in c:os.kill(int(f),signal.SIGTERM);print('Stopped temporal experiment after validation declined.')\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\np='/app/solution/temporal.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\"model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\",\"model=(VariationalTwoTower if ck['args'].get('vae') else TwoTower)(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\");s=s.replace(\"ck=torch.load(ROOT/args.resume,weights_only=False);model=\", \"ck=torch.load(ROOT/args.resume,weights_only=False);torch.save(ck,ROOT/f'{args.name}_source.pt');model=\");s=s.replace(\"opt.zero_grad(set_to_none=True);loss.backward()\", \"loss=loss+(ck['args'].get('beta',.0002)*model.user.kl if ck['args'].get('vae') else 0.);opt.zero_grad(set_to_none=True);loss.backward()\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name temporal_backup --resume backup_test.pt --epochs 1 > /app/solution/temporal_backup.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/submit.py <<'PY'\n\"\"\"Export independent tower vectors and perform blocked maximum-inner-product retrieval.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,json,time\nimport torch,numpy as np,pandas as pd\nfrom torch import nn\nfrom model import TwoTower,VariationalTwoTower\nfrom train import ROOT,DATA,make_bags,batch\nfrom trends import features\nclass EnsembleUserTower(nn.Module):\n def __init__(self,towers,weights):\n super().__init__();self.towers=nn.ModuleList(towers);self.register_buffer('coefficients',torch.tensor(weights))\n def forward(self,histories):\n return torch.cat([tower(*h)*coef for tower,h,coef in zip(self.towers,histories,self.coefficients)],dim=1)\nclass EnsembleItemTower(nn.Module):\n def __init__(self,towers):super().__init__();self.towers=nn.ModuleList(towers)\n def forward(self,ids):return torch.cat([tower(ids) for tower in self.towers],dim=1)\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--config',default='final_config.json');p.add_argument('--stage',choices=['validation','test'],default='test');p.add_argument('--output');a=p.parse_args();torch.set_num_threads(4);t=time.time();cfg=json.loads((ROOT/a.config).read_text())\n ids=dict(np.load(ROOT/'ids.npz'));elig=np.load(DATA/f'{a.stage}_eligible_candidates.npz');ui=pd.Index(ids['users']).get_indexer(elig['uids']);col=pd.Index(ids['items']).get_indexer(elig['item_ids']);models=[];bags=[];weights=[]\n for comp in cfg['components']:\n ck=torch.load(ROOT/comp['checkpoint'],weights_only=False);c=ck['args'];model=(VariationalTwoTower if c.get('vae') else TwoTower)(len(ids['items']),c['hidden'],c['dim'],c['dropout']);model.load_state_dict(ck['model']);models.append(model);weights.append(comp.get('weight',1.))\n bags.append(make_bags('train' if a.stage=='validation' else 'test',comp.get('half',c['half']),c['likes'],c['organic'],c.get('recent_days',0)))\n # Absorb all calibrated item-only terms into the trainable item bias table.\n if cfg.get('bias'):\n fs=features('train' if a.stage=='validation' else 'test');bias=sum(float(w)*fs[k] for k,w in cfg['bias'].items())\n with torch.no_grad():models[0].item.bias.weight[:,0].add_(torch.from_numpy(bias)/weights[0])\n user=EnsembleUserTower([m.user for m in models],weights).eval();item=EnsembleItemTower([m.item for m in models]).eval()\n with torch.no_grad():\n vectors=item(torch.arange(len(ids['items']))).numpy();np.save(ROOT/f'{a.stage}_item_vectors.npy',vectors)\n query=[]\n for st in range(0,len(ui),256):\n histories=[]\n for b in bags:\n x,o,w,_,_=batch(b,ui[st:st+256]);histories.append((x,o,w))\n query.append(user(histories).numpy())\n query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\n assert query.shape[1]==vectors.shape[1]\n # Item vectors are cached once, independently of every querying user.\n iv=torch.from_numpy(vectors[col]).T.contiguous();ranks=[]\n for st in range(0,len(ui),256):\n scores=torch.from_numpy(query[st:st+256])@iv\n mask=np.unpackbits(elig['packed_eligible'][st:st+256],axis=1,count=len(col),bitorder='little').astype(bool);scores.masked_fill_(~torch.from_numpy(mask),-torch.inf)\n ranks.append(elig['item_ids'][scores.topk(100,dim=1).indices.numpy()])\n f=pd.DataFrame({'uid':np.repeat(elig['uids'],100),'item_id':np.concatenate(ranks).ravel(),'rank':np.tile(np.arange(1,101),len(ui))})\n out=a.output or ('submission.parquet' if a.stage=='test' else 'validation.parquet');tmp=ROOT/(out+'.tmp.parquet');f.to_parquet(tmp,index=False);tmp.replace(ROOT/out)\n print(json.dumps({'output':out,'rows':len(f),'dimension':query.shape[1],'seconds':time.time()-t}),flush=True)\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport os,signal,shutil\nshutil.copyfile('/app/solution/submission.parquet','/app/solution/backup_submission.parquet')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal1' in c:os.kill(int(f),signal.SIGTERM);print('Stopped temporal experiment after validation declined.')\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\np='/app/solution/temporal.py';s=open(p).read().replace('from model import TwoTower','from model import TwoTower,VariationalTwoTower').replace(\"model=TwoTower(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\",\"model=(VariationalTwoTower if ck['args'].get('vae') else TwoTower)(len(ids['items']),ck['args']['hidden'],ck['args']['dim'],args.dropout)\");s=s.replace(\"ck=torch.load(ROOT/args.resume,weights_only=False);model=\", \"ck=torch.load(ROOT/args.resume,weights_only=False);torch.save(ck,ROOT/f'{args.name}_source.pt');model=\");s=s.replace(\"opt.zero_grad(set_to_none=True);loss.backward()\", \"loss=loss+(ck['args'].get('beta',.0002)*model.user.kl if ck['args'].get('vae') else 0.);opt.zero_grad(set_to_none=True);loss.backward()\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name temporal_backup --resume backup_test.pt --epochs 1 > /app/solution/temporal_backup.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Stopped temporal experiment after validation declined.\n", "stderr": "", "aggregated_output": "Stopped temporal experiment after validation declined.\n", "exit_code": 0, "duration": { "secs": 77, "nanos": 299824501 }, "formatted_output": "Stopped temporal experiment after validation declined.\n" } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-09T00:38:24.986Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:38:24.986Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:38:26.450Z", "text": "I’ve changed checkpoint selection to include the trend bias in validation. This will let me compare the rankings that would actually be served. The calibrated temporal model’s validation file is saved, and its refit on all observed history is underway.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:38:48.678Z", "callId": "call_NlEY72NCV7LkqRzUrn24E4Cp", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/temporal.py';s=open(p).read().replace(\\\"p.add_argument('--mix',type=float,default=0.);\\\", \\\"p.add_argument('--mix',type=float,default=0.);p.add_argument('--trend',type=float,default=1.);p.add_argument('--eval-half',type=float);\\\")\\ns=s.replace(\\\"bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\\\", \\\"bags=make_bags('train' if args.stage=='validation' else 'test',args.eval_half or args.half,3.,1.,0)\\\")\\ns=s.replace(\\\"score=infer(model,bags,args.stage,out,ids,gold);\\\", \\\"from trends import features\\\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias);\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/temporal_backup.log; tail -5 /app/solution/vae_trend.log; cat > /app/solution/README.md <<'MD'\\n# CPU two-tower discovery ranker\\n\\nThe model embeds a user's interaction history with a trainable PyTorch embedding-bag encoder and MLP. It does not use a user-ID embedding. A separate trainable item embedding and linear projection produce offline item vectors. The last vector coordinate represents an item bias, including cutoff-correct recent organic popularity calibration. Scores are exactly the dot product of the two equal-dimensional vectors.\\n\\nTraining consumes ragged event histories, never a global user-item matrix or its decomposition. Denoising pretraining predicts weighted positive history items. Temporal fine-tuning predicts first-time organic strong listens and organic likes in subsequent seven-day windows (like gain 7, listen gain 1). The small catalog uses minibatch exact softmax; `training_loss` implements a bounded negative-sampling path for large catalogs. Histories and labels are strictly separated at each cutoff.\\n\\nCurrent best public validation NDCG@10: **0.045542**, on 4,263 scored users. Validation features use training events only. The final model is refitted using train plus validation events; its eligibility mask and trend features use the test cutoff.\\n\\nFinal submission command (using the supplied environment):\\n\\n```sh\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\\n/opt/task-venv/bin/python /app/solution/verify.py\\n```\\n\\n`prepare.py` builds catalog-indexed event streams, `train.py` defines denoising training, `temporal.py` builds temporal examples and fine-tunes, and `model.py` defines the individual neural towers. `submit.py` exports independent item/user vectors, then performs blocked exact maximum-inner-product retrieval with the platform eligibility mask. Production can index the same offline item vectors in a MIPS index. No candidate-dependent neural scoring or post-retrieval reranking is used. More experiments are in progress; final configuration and reproduction details will be updated after selection.\\nMD\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:38:49.925Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"093b55\",\"wall_time_seconds\":1.001885103,\"session_id\":93649,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"4967e9\",\"wall_time_seconds\":0.000019411,\"exit_code\":0,\"original_token_count\":78,\"output\":\"temporal test samples 41189 entries 26660861\\ntemporal_backup 1 loss 8.742748219033945 score 0.0 time 71.62356805801392\\nvae_trend validation epoch 1 loss 8.24541 seconds 14.4\\nSCORE 1 0.03772340169065657\\nvae_trend validation epoch 2 loss 8.11365 seconds 30.2\\nvae_trend validation epoch 3 loss 8.03428 seconds 38.9\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:38:48.753Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aa679e0487d18e0a98b1a6bb7721", "usage": { "input_tokens": 55198, "cached_input_tokens": 54656, "cache_write_input_tokens": 0, "output_tokens": 1339, "reasoning_output_tokens": 487, "total_tokens": 56537 }, "turn_token_usage": { "input_tokens": 1473354, "cached_input_tokens": 1423232, "cache_write_input_tokens": 0, "output_tokens": 26183, "reasoning_output_tokens": 10504, "total_tokens": 1499537 }, "thread_token_usage": { "input_tokens": 1473354, "cached_input_tokens": 1423232, "cache_write_input_tokens": 0, "output_tokens": 26183, "reasoning_output_tokens": 10504, "total_tokens": 1499537 } } }, { "kind": "context", "timestamp": "2026-09-09T00:38:49.905Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b18739d4-2d90-454d-b546-308d1c633aad", "process_id": "85929", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/temporal_backup.log; tail -5 /app/solution/vae_trend.log; cat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\nThe model embeds a user's interaction history with a trainable PyTorch embedding-bag encoder and MLP. It does not use a user-ID embedding. A separate trainable item embedding and linear projection produce offline item vectors. The last vector coordinate represents an item bias, including cutoff-correct recent organic popularity calibration. Scores are exactly the dot product of the two equal-dimensional vectors.\n\nTraining consumes ragged event histories, never a global user-item matrix or its decomposition. Denoising pretraining predicts weighted positive history items. Temporal fine-tuning predicts first-time organic strong listens and organic likes in subsequent seven-day windows (like gain 7, listen gain 1). The small catalog uses minibatch exact softmax; `training_loss` implements a bounded negative-sampling path for large catalogs. Histories and labels are strictly separated at each cutoff.\n\nCurrent best public validation NDCG@10: **0.045542**, on 4,263 scored users. Validation features use training events only. The final model is refitted using train plus validation events; its eligibility mask and trend features use the test cutoff.\n\nFinal submission command (using the supplied environment):\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\n/opt/task-venv/bin/python /app/solution/verify.py\n```\n\n`prepare.py` builds catalog-indexed event streams, `train.py` defines denoising training, `temporal.py` builds temporal examples and fine-tunes, and `model.py` defines the individual neural towers. `submit.py` exports independent item/user vectors, then performs blocked exact maximum-inner-product retrieval with the platform eligibility mask. Production can index the same offline item vectors in a MIPS index. No candidate-dependent neural scoring or post-retrieval reranking is used. More experiments are in progress; final configuration and reproduction details will be updated after selection.\nMD" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/temporal_backup.log; tail -5 /app/solution/vae_trend.log; cat > /app/solution/README.md <<'MD'\n# CPU two-tower discovery ranker\n\nThe model embeds a user's interaction history with a trainable PyTorch embedding-bag encoder and MLP. It does not use a user-ID embedding. A separate trainable item embedding and linear projection produce offline item vectors. The last vector coordinate represents an item bias, including cutoff-correct recent organic popularity calibration. Scores are exactly the dot product of the two equal-dimensional vectors.\n\nTraining consumes ragged event histories, never a global user-item matrix or its decomposition. Denoising pretraining predicts weighted positive history items. Temporal fine-tuning predicts first-time organic strong listens and organic likes in subsequent seven-day windows (like gain 7, listen gain 1). The small catalog uses minibatch exact softmax; `training_loss` implements a bounded negative-sampling path for large catalogs. Histories and labels are strictly separated at each cutoff.\n\nCurrent best public validation NDCG@10: **0.045542**, on 4,263 scored users. Validation features use training events only. The final model is refitted using train plus validation events; its eligibility mask and trend features use the test cutoff.\n\nFinal submission command (using the supplied environment):\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\n/opt/task-venv/bin/python /app/solution/verify.py\n```\n\n`prepare.py` builds catalog-indexed event streams, `train.py` defines denoising training, `temporal.py` builds temporal examples and fine-tunes, and `model.py` defines the individual neural towers. `submit.py` exports independent item/user vectors, then performs blocked exact maximum-inner-product retrieval with the platform eligibility mask. Production can index the same offline item vectors in a MIPS index. No candidate-dependent neural scoring or post-retrieval reranking is used. More experiments are in progress; final configuration and reproduction details will be updated after selection.\nMD" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal test samples 41189 entries 26660861\ntemporal_backup 1 loss 8.742748219033945 score 0.0 time 71.62356805801392\nvae_trend validation epoch 1 loss 8.24541 seconds 14.4\nSCORE 1 0.03772340169065657\nvae_trend validation epoch 2 loss 8.11365 seconds 30.2\nvae_trend validation epoch 3 loss 8.03428 seconds 38.9\n", "stderr": "", "aggregated_output": "temporal test samples 41189 entries 26660861\ntemporal_backup 1 loss 8.742748219033945 score 0.0 time 71.62356805801392\nvae_trend validation epoch 1 loss 8.24541 seconds 14.4\nSCORE 1 0.03772340169065657\nvae_trend validation epoch 2 loss 8.11365 seconds 30.2\nvae_trend validation epoch 3 loss 8.03428 seconds 38.9\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 19411 }, "formatted_output": "temporal test samples 41189 entries 26660861\ntemporal_backup 1 loss 8.742748219033945 score 0.0 time 71.62356805801392\nvae_trend validation epoch 1 loss 8.24541 seconds 14.4\nSCORE 1 0.03772340169065657\nvae_trend validation epoch 2 loss 8.11365 seconds 30.2\nvae_trend validation epoch 3 loss 8.03428 seconds 38.9\n" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-09T00:38:52.663Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:38:52.663Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T00:38:52.718Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a4ae6ac0-e575-44a6-947b-b912b086a24b", "process_id": "93649", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace(\"p.add_argument('--mix',type=float,default=0.);\", \"p.add_argument('--mix',type=float,default=0.);p.add_argument('--trend',type=float,default=1.);p.add_argument('--eval-half',type=float);\")\ns=s.replace(\"bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\", \"bags=make_bags('train' if args.stage=='validation' else 'test',args.eval_half or args.half,3.,1.,0)\")\ns=s.replace(\"score=infer(model,bags,args.stage,out,ids,gold);\", \"from trends import features\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias);\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace(\"p.add_argument('--mix',type=float,default=0.);\", \"p.add_argument('--mix',type=float,default=0.);p.add_argument('--trend',type=float,default=1.);p.add_argument('--eval-half',type=float);\")\ns=s.replace(\"bags=make_bags('train' if args.stage=='validation' else 'test',args.half,3.,1.,0)\", \"bags=make_bags('train' if args.stage=='validation' else 'test',args.eval_half or args.half,3.,1.,0)\")\ns=s.replace(\"score=infer(model,bags,args.stage,out,ids,gold);\", \"from trends import features\\n fs=features('train' if args.stage=='validation' else 'test');bias=args.trend*(fs['org7']-fs['org360'])\\n score=infer(model,bags,args.stage,out,ids,gold,bias=bias);\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 3, "nanos": 863963170 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-09T00:38:57.170Z", "callId": "call_BK1eRH41LI3iSeLTHtjzjshi", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal2 --resume base_validation.pt --epochs 8 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/temporal2.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/submit_test.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:38:59.526Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"4c8258\",\"wall_time_seconds\":1.001251855,\"session_id\":97001,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"e5439b\",\"wall_time_seconds\":1.001853648,\"session_id\":64803,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:38:57.263Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aa9a30e887d195c0f9e04a41bd6f", "usage": { "input_tokens": 56740, "cached_input_tokens": 55040, "cache_write_input_tokens": 0, "output_tokens": 191, "reasoning_output_tokens": 23, "total_tokens": 56931 }, "turn_token_usage": { "input_tokens": 1530094, "cached_input_tokens": 1478272, "cache_write_input_tokens": 0, "output_tokens": 26374, "reasoning_output_tokens": 10527, "total_tokens": 1556468 }, "thread_token_usage": { "input_tokens": 1530094, "cached_input_tokens": 1478272, "cache_write_input_tokens": 0, "output_tokens": 26374, "reasoning_output_tokens": 10527, "total_tokens": 1556468 } } }, { "kind": "context", "timestamp": "2026-09-09T00:39:16.105Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6b5592f8-6fa7-41d7-9e1d-18494fd511f4", "process_id": "64803", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/submit_test.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/submit_test.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 582883135 }, "formatted_output": "" } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-09T00:39:27.989Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:39:27.989Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:39:29.629Z", "callId": "call_9ezGEo7nYEgTxzFKZDzUiJUI", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/vae_trend.log; tail -3 /app/solution/temporal2.log; cat /app/solution/public_score.json; cat /app/solution/submit_test.log\",\"max_output_tokens\":1300}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:39:29.706Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"4211a0\",\"wall_time_seconds\":0.00002404,\"exit_code\":0,\"original_token_count\":135,\"output\":\"vae_trend validation epoch 4 loss 7.95204 seconds 48.1\\nvae_trend validation epoch 5 loss 7.89433 seconds 57.3\\nSCORE 5 0.03799106961465909\\nvae_trend validation epoch 6 loss 7.87331 seconds 75.8\\nvae_trend validation epoch 7 loss 7.79802 seconds 85.1\\ntemporal train samples 43885 entries 29998200\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.045542,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nbags test 5778549\\n{\\\"output\\\": \\\"submission.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 193, \\\"seconds\\\": 12.697577476501465}\\n\"}\n{\"current_time\":\"2026-09-09 00:39:29 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:39:29.678Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-47cc70a1-743d-4b11-bd2e-7e34e358b529", "process_id": "63113", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/vae_trend.log; tail -3 /app/solution/temporal2.log; cat /app/solution/public_score.json; cat /app/solution/submit_test.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/vae_trend.log; tail -3 /app/solution/temporal2.log; cat /app/solution/public_score.json; cat /app/solution/submit_test.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae_trend validation epoch 4 loss 7.95204 seconds 48.1\nvae_trend validation epoch 5 loss 7.89433 seconds 57.3\nSCORE 5 0.03799106961465909\nvae_trend validation epoch 6 loss 7.87331 seconds 75.8\nvae_trend validation epoch 7 loss 7.79802 seconds 85.1\ntemporal train samples 43885 entries 29998200\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 12.697577476501465}\n", "stderr": "", "aggregated_output": "vae_trend validation epoch 4 loss 7.95204 seconds 48.1\nvae_trend validation epoch 5 loss 7.89433 seconds 57.3\nSCORE 5 0.03799106961465909\nvae_trend validation epoch 6 loss 7.87331 seconds 75.8\nvae_trend validation epoch 7 loss 7.79802 seconds 85.1\ntemporal train samples 43885 entries 29998200\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 12.697577476501465}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 24040 }, "formatted_output": "vae_trend validation epoch 4 loss 7.95204 seconds 48.1\nvae_trend validation epoch 5 loss 7.89433 seconds 57.3\nSCORE 5 0.03799106961465909\nvae_trend validation epoch 6 loss 7.87331 seconds 75.8\nvae_trend validation epoch 7 loss 7.79802 seconds 85.1\ntemporal train samples 43885 entries 29998200\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 12.697577476501465}\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:39:29.694Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aaa3b7d487d19922069452f13835", "usage": { "input_tokens": 57024, "cached_input_tokens": 56576, "cache_write_input_tokens": 0, "output_tokens": 597, "reasoning_output_tokens": 516, "total_tokens": 57621 }, "turn_token_usage": { "input_tokens": 1587118, "cached_input_tokens": 1534848, "cache_write_input_tokens": 0, "output_tokens": 26971, "reasoning_output_tokens": 11043, "total_tokens": 1614089 }, "thread_token_usage": { "input_tokens": 1587118, "cached_input_tokens": 1534848, "cache_write_input_tokens": 0, "output_tokens": 26971, "reasoning_output_tokens": 11043, "total_tokens": 1614089 } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-09T00:39:58.776Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:39:58.776Z", "text": "" } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-09T00:40:15.576Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:40:15.576Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:40:56.542Z", "callId": "call_Avc3CrZq2tZ5eBSsMrBF1f66", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat >> /app/solution/model.py <<'PY'\\n\\nclass ContextUserTower(nn.Module):\\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\\n super().__init__();self.history=nn.EmbeddingBag(n_items,dim,mode='sum');self.transform=nn.Linear(dim,dim);self.dropout=dropout\\n nn.init.normal_(self.history.weight,std=.05);nn.init.eye_(self.transform.weight);nn.init.zeros_(self.transform.bias)\\n def forward(self,ids,offsets,weights):\\n if self.training:weights=F.dropout(weights,p=self.dropout)\\n x=self.history(ids,offsets,per_sample_weights=weights);x=F.normalize(x,dim=-1)\\n x=F.normalize(x+.2*torch.tanh(self.transform(x)),dim=-1)*12.\\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\\nclass ContextItemTower(ItemTower):\\n def forward(self,ids):\\n return torch.cat([F.normalize(self.projection(self.embedding(ids)),dim=-1),self.bias(ids)],dim=-1)\\nclass ContextTwoTower(TwoTower):\\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\\n nn.Module.__init__(self);self.user=ContextUserTower(n_items,hidden,dim,dropout);self.item=ContextItemTower(n_items,dim)\\nPY\\ncat > /app/solution/context.py <<'PY'\\n\\\"\\\"\\\"Sampled-softmax temporal context pretraining; bounded histories and negatives.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport argparse,time,json\\nimport numpy as np,torch\\nfrom torch.nn import functional as F\\nfrom train import ROOT,DATA,make_bags,infer,gold_data\\nfrom model import ContextTwoTower\\nfrom trends import features\\n\\ndef sequences(stage):\\n p=ROOT/f'sequences_{stage}.npz'\\n if p.exists():return dict(np.load(p))\\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['users']);starts=[np.searchsorted(e['u'],np.arange(n+1)) for e in ev];pieces=[];offsets=[0]\\n for u in range(n):\\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);v=v[(v['t']>end-180*86400)&(((v['k']==0)&(v['q']>=80)&(v['o']==1))|(v['k']==1))];v=v[np.argsort(v['t'])]\\n # Collapse immediately repeated tracks to avoid identity reconstruction.\\n x=v['i'];x=x[np.r_[True,x[1:]!=x[:-1]]] if len(x) else x\\n pieces.append(x);offsets.append(offsets[-1]+len(x))\\n d=dict(items=np.concatenate(pieces),offsets=np.array(offsets));np.savez(p,**d);return d\\n\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='context');p.add_argument('--steps',type=int,default=4000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--dim',type=int,default=128);p.add_argument('--half',type=float,default=14);p.add_argument('--batch',type=int,default=512);p.add_argument('--context',type=int,default=16);p.add_argument('--window',type=int,default=64);p.add_argument('--lr',type=float,default=.002);a=p.parse_args();torch.set_num_threads(2);torch.manual_seed(731);rng=np.random.default_rng(731);t=time.time()\\n ids=dict(np.load(ROOT/'ids.npz'));n=len(ids['items']);d=sequences('train' if a.stage=='validation' else 'test');bags=make_bags('train' if a.stage=='validation' else 'test',a.half,3.,1.,0);fs=features('train' if a.stage=='validation' else 'test');bias=.5*fs['org7']\\n model=ContextTwoTower(n,dim=a.dim);opt=torch.optim.Adam(model.parameters(),lr=a.lr);valid=np.flatnonzero(np.diff(d['offsets'])>4);gold=gold_data(None,ids) if a.stage=='validation' else None;best=-1.;losses=[]\\n for step in range(1,a.steps+1):\\n model.train();uu=rng.choice(valid,a.batch);start=d['offsets'][uu];length=d['offsets'][uu+1]-start\\n pos=(rng.random(a.batch)*(length-1)).astype('int64')+1;target=d['items'][start+pos].astype('int64')\\n distance=(rng.random((a.batch,a.context))*np.minimum(pos,a.window)[:,None]).astype('int64')+1\\n x=d['items'][start[:,None]+pos[:,None]-distance].astype('int64');weight=(x!=target[:,None]).astype('float32');weight/=np.maximum(1.,weight.sum(1,keepdims=True))\\n uv=model.user(torch.from_numpy(x.ravel()),torch.arange(a.batch)*a.context,torch.from_numpy(weight.ravel()))\\n negatives=torch.from_numpy(rng.integers(0,n,size=2048));iv=model.item(negatives);pv=model.item(torch.from_numpy(target));positive=(uv*pv).sum(1,keepdim=True);negative=uv@iv.T\\n negative.masked_fill_(torch.from_numpy(target)[:,None]==negatives[None,:],-1e9)\\n loss=F.cross_entropy(torch.cat([positive,negative],1),torch.zeros(a.batch,dtype=torch.long));opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\\n if step%100==0:print(a.name,step,'loss',np.mean(losses[-100:]),'seconds',time.time()-t,flush=True)\\n if step%a.eval_every==0 or step==a.steps:\\n out=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet';score=infer(model,bags,a.stage,out,ids,gold,bias=bias);print('SCORE',step,score,flush=True)\\n if score>best or a.stage=='test':\\n best=score;args=vars(a).copy();args.update({'context_model':True,'hidden':a.dim,'dropout':.2,'likes':3.,'organic':1.});torch.save({'model':model.state_dict(),'args':args,'epoch':step,'score':score},ROOT/f'{a.name}_{a.stage}.pt')\\n if a.stage=='validation':\\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{a.name}_best_validation.parquet');(ROOT/f'{a.name}_metrics.json').write_text(json.dumps({'step':step,'score':score,'args':args},indent=2))\\nif __name__=='__main__':main()\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nfor p in ['/app/solution/analyze.py','/app/solution/submit.py']:\\n s=open(p).read().replace('from model import TwoTower,VariationalTwoTower','from model import TwoTower,VariationalTwoTower,ContextTwoTower')\\n s=s.replace(\\\"(VariationalTwoTower if args.get('vae') else TwoTower)\\\",\\\"(ContextTwoTower if args.get('context_model') else VariationalTwoTower if args.get('vae') else TwoTower)\\\")\\n s=s.replace(\\\"(VariationalTwoTower if c.get('vae') else TwoTower)\\\",\\\"(ContextTwoTower if c.get('context_model') else VariationalTwoTower if c.get('vae') else TwoTower)\\\")\\n open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py > /app/solution/context.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:40:57.738Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5d3716\",\"wall_time_seconds\":1.001053267,\"session_id\":64884,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:40:56.645Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aac1f4bc87d1836018acc315a2f2", "usage": { "input_tokens": 57891, "cached_input_tokens": 56832, "cache_write_input_tokens": 0, "output_tokens": 2811, "reasoning_output_tokens": 922, "total_tokens": 60702 }, "turn_token_usage": { "input_tokens": 1645009, "cached_input_tokens": 1591680, "cache_write_input_tokens": 0, "output_tokens": 29782, "reasoning_output_tokens": 11965, "total_tokens": 1674791 }, "thread_token_usage": { "input_tokens": 1645009, "cached_input_tokens": 1591680, "cache_write_input_tokens": 0, "output_tokens": 29782, "reasoning_output_tokens": 11965, "total_tokens": 1674791 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-09T00:41:10.760Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:41:10.760Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:41:12.170Z", "callId": "call_vnG1pRqMzZUJ99sdaTUjTLap", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -7 /app/solution/vae_trend.log; tail -6 /app/solution/temporal2.log; tail -4 /app/solution/context.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:41:12.276Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"8b3199\",\"wall_time_seconds\":0.00000434,\"exit_code\":0,\"original_token_count\":128,\"output\":\"vae_trend validation epoch 9 loss 7.66223 seconds 110.7\\nvae_trend validation epoch 10 loss 7.6035 seconds 124.3\\nSCORE 10 0.03790602630085159\\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\\ntemporal train samples 43885 entries 29998200\\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\\n8061935616\\n\"}\n{\"current_time\":\"2026-09-09 00:41:12 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:41:12.231Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8eb2dd49-70bb-407b-82bc-bb8765bd6a75", "process_id": "73636", "command": [ "/bin/sh", "-lc", "tail -7 /app/solution/vae_trend.log; tail -6 /app/solution/temporal2.log; tail -4 /app/solution/context.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -7 /app/solution/vae_trend.log; tail -6 /app/solution/temporal2.log; tail -4 /app/solution/context.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae_trend validation epoch 9 loss 7.66223 seconds 110.7\nvae_trend validation epoch 10 loss 7.6035 seconds 124.3\nSCORE 10 0.03790602630085159\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\n8061935616\n", "stderr": "", "aggregated_output": "vae_trend validation epoch 9 loss 7.66223 seconds 110.7\nvae_trend validation epoch 10 loss 7.6035 seconds 124.3\nSCORE 10 0.03790602630085159\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\n8061935616\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4340 }, "formatted_output": "vae_trend validation epoch 9 loss 7.66223 seconds 110.7\nvae_trend validation epoch 10 loss 7.6035 seconds 124.3\nSCORE 10 0.03790602630085159\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\n8061935616\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:41:12.245Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab19f46487d1a75943f6395d4513", "usage": { "input_tokens": 60760, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 300, "reasoning_output_tokens": 218, "total_tokens": 61060 }, "turn_token_usage": { "input_tokens": 1705769, "cached_input_tokens": 1649408, "cache_write_input_tokens": 0, "output_tokens": 30082, "reasoning_output_tokens": 12183, "total_tokens": 1735851 }, "thread_token_usage": { "input_tokens": 1705769, "cached_input_tokens": 1649408, "cache_write_input_tokens": 0, "output_tokens": 30082, "reasoning_output_tokens": 12183, "total_tokens": 1735851 } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-09T00:41:17.879Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:41:17.879Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:41:19.867Z", "text": "The second temporal variant reached 0.0464 NDCG@10 using more recent history and stronger dropout. I’m also testing a smaller model trained on nearby listening events to see whether local listening patterns add useful information. The best completed refit remains saved.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:41:21.311Z", "callId": "call_fjHXPQR7KngwitU2QThT7UG0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.stat; tail -4 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -4 /app/solution/vae_trend.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:41:21.452Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"3b2e41\",\"wall_time_seconds\":0.00002217,\"exit_code\":0,\"original_token_count\":419,\"output\":\"anon 3647938560\\nfile 4249067520\\nkernel 135897088\\nkernel_stack 1589248\\npagetables 11186176\\nsec_pagetables 0\\npercpu 1680\\nsock 0\\nvmalloc 24576\\nshmem 0\\nzswap 0\\nzswapped 0\\nfile_mapped 225280\\nfile_dirty 25055232\\nfile_writeback 0\\nswapcached 0\\nanon_thp 595591168\\nfile_thp 1742733312\\nshmem_thp 0\\ninactive_anon 0\\nactive_anon 3647614976\\ninactive_file 2199355392\\nactive_file 2049712128\\nunevictable 0\\nslab_reclaimable 120660952\\nslab_unreclaimable 2365784\\nslab 123026736\\nworkingset_refault_anon 0\\nworkingset_refault_file 48440\\nworkingset_activate_anon 0\\nworkingset_activate_file 4238\\nworkingset_restore_anon 0\\nworkingset_restore_file 0\\nworkingset_nodereclaim 0\\npgdemote_kswapd 0\\npgdemote_direct 0\\npgdemote_khugepaged 0\\npgdemote_proactive 0\\npgpromote_success 0\\npgscan 183174\\npgsteal 183170\\npswpin 0\\npswpout 0\\npgscan_kswapd 156089\\npgscan_direct 27085\\npgscan_khugepaged 0\\npgscan_proactive 0\\npgsteal_kswapd 156089\\npgsteal_direct 27081\\npgsteal_khugepaged 0\\npgsteal_proactive 0\\npgfault 114484546\\npgmajfault 71\\npgrefill 150407\\npgactivate 31\\npgdeactivate 0\\npglazyfree 0\\npglazyfreed 0\\nswpin_zero 0\\nswpout_zero 0\\nzswpin 0\\nzswpout 0\\nzswpwb 0\\nthp_fault_alloc 13575\\nthp_collapse_alloc 317\\nthp_swpout 0\\nthp_swpout_fallback 0\\nnuma_pages_migrated 0\\nnuma_pte_updates 0\\nnuma_hint_faults 0\\ncontext 100 loss 7.445383367538452 seconds 14.17275857925415\\ntemporal train samples 43885 entries 29998200\\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:41:21.397Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-15401d51-dd25-456f-b4bf-8461ced37549", "process_id": "56841", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.stat; tail -4 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -4 /app/solution/vae_trend.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.stat; tail -4 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -4 /app/solution/vae_trend.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "anon 3647938560\nfile 4249067520\nkernel 135897088\nkernel_stack 1589248\npagetables 11186176\nsec_pagetables 0\npercpu 1680\nsock 0\nvmalloc 24576\nshmem 0\nzswap 0\nzswapped 0\nfile_mapped 225280\nfile_dirty 25055232\nfile_writeback 0\nswapcached 0\nanon_thp 595591168\nfile_thp 1742733312\nshmem_thp 0\ninactive_anon 0\nactive_anon 3647614976\ninactive_file 2199355392\nactive_file 2049712128\nunevictable 0\nslab_reclaimable 120660952\nslab_unreclaimable 2365784\nslab 123026736\nworkingset_refault_anon 0\nworkingset_refault_file 48440\nworkingset_activate_anon 0\nworkingset_activate_file 4238\nworkingset_restore_anon 0\nworkingset_restore_file 0\nworkingset_nodereclaim 0\npgdemote_kswapd 0\npgdemote_direct 0\npgdemote_khugepaged 0\npgdemote_proactive 0\npgpromote_success 0\npgscan 183174\npgsteal 183170\npswpin 0\npswpout 0\npgscan_kswapd 156089\npgscan_direct 27085\npgscan_khugepaged 0\npgscan_proactive 0\npgsteal_kswapd 156089\npgsteal_direct 27081\npgsteal_khugepaged 0\npgsteal_proactive 0\npgfault 114484546\npgmajfault 71\npgrefill 150407\npgactivate 31\npgdeactivate 0\npglazyfree 0\npglazyfreed 0\nswpin_zero 0\nswpout_zero 0\nzswpin 0\nzswpout 0\nzswpwb 0\nthp_fault_alloc 13575\nthp_collapse_alloc 317\nthp_swpout 0\nthp_swpout_fallback 0\nnuma_pages_migrated 0\nnuma_pte_updates 0\nnuma_hint_faults 0\ncontext 100 loss 7.445383367538452 seconds 14.17275857925415\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\n", "stderr": "", "aggregated_output": "anon 3647938560\nfile 4249067520\nkernel 135897088\nkernel_stack 1589248\npagetables 11186176\nsec_pagetables 0\npercpu 1680\nsock 0\nvmalloc 24576\nshmem 0\nzswap 0\nzswapped 0\nfile_mapped 225280\nfile_dirty 25055232\nfile_writeback 0\nswapcached 0\nanon_thp 595591168\nfile_thp 1742733312\nshmem_thp 0\ninactive_anon 0\nactive_anon 3647614976\ninactive_file 2199355392\nactive_file 2049712128\nunevictable 0\nslab_reclaimable 120660952\nslab_unreclaimable 2365784\nslab 123026736\nworkingset_refault_anon 0\nworkingset_refault_file 48440\nworkingset_activate_anon 0\nworkingset_activate_file 4238\nworkingset_restore_anon 0\nworkingset_restore_file 0\nworkingset_nodereclaim 0\npgdemote_kswapd 0\npgdemote_direct 0\npgdemote_khugepaged 0\npgdemote_proactive 0\npgpromote_success 0\npgscan 183174\npgsteal 183170\npswpin 0\npswpout 0\npgscan_kswapd 156089\npgscan_direct 27085\npgscan_khugepaged 0\npgscan_proactive 0\npgsteal_kswapd 156089\npgsteal_direct 27081\npgsteal_khugepaged 0\npgsteal_proactive 0\npgfault 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0\nthp_fault_alloc 13575\nthp_collapse_alloc 317\nthp_swpout 0\nthp_swpout_fallback 0\nnuma_pages_migrated 0\nnuma_pte_updates 0\nnuma_hint_faults 0\ncontext 100 loss 7.445383367538452 seconds 14.17275857925415\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\nvae_trend validation epoch 11 loss 7.54659 seconds 143.0\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:41:21.438Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab28844487d1aae5a42f045c95f6", "usage": { "input_tokens": 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temporal2_validation.pt --dest temporal2_scores.npy --no-grid > /app/solution/temporal2_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:41:51.958Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"774f54\",\"wall_time_seconds\":1.002487643,\"session_id\":34570,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:41:50.838Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab31c4c087d185423089452ec12d", "usage": { "input_tokens": 62271, "cached_input_tokens": 61184, "cache_write_input_tokens": 0, "output_tokens": 602, "reasoning_output_tokens": 516, "total_tokens": 62873 }, 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seconds\nOutput:\n\n{\"chunk_id\":\"3fad7e\",\"wall_time_seconds\":0.00000604,\"exit_code\":0,\"original_token_count\":222,\"output\":\"context 600 loss 5.565176410675049 seconds 52.546571254730225\\ncontext 700 loss 5.421776976585388 seconds 58.99893307685852\\ncontext 800 loss 5.334733390808106 seconds 65.38134431838989\\ncontext 900 loss 5.230437984466553 seconds 71.72661256790161\\ntemporal train samples 43885 entries 29998200\\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\\nvae_trend validation epoch 15 loss 7.35697 seconds 199.1\\nSCORE 15 0.039875583423954995\\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\\ntemporal2_scores.npy\\npop 0.0 score 0.032202113493154015\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:42:19.592Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e7fa7ac-f2ac-48ea-8cec-a559e85342d4", "process_id": "85656", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -7 /app/solution/vae_trend.log; cat /app/solution/temporal2_analyze.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -7 /app/solution/vae_trend.log; cat /app/solution/temporal2_analyze.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "context 600 loss 5.565176410675049 seconds 52.546571254730225\ncontext 700 loss 5.421776976585388 seconds 58.99893307685852\ncontext 800 loss 5.334733390808106 seconds 65.38134431838989\ncontext 900 loss 5.230437984466553 seconds 71.72661256790161\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\nvae_trend validation epoch 15 loss 7.35697 seconds 199.1\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\ntemporal2_scores.npy\npop 0.0 score 0.032202113493154015\n", "stderr": "", "aggregated_output": "context 600 loss 5.565176410675049 seconds 52.546571254730225\ncontext 700 loss 5.421776976585388 seconds 58.99893307685852\ncontext 800 loss 5.334733390808106 seconds 65.38134431838989\ncontext 900 loss 5.230437984466553 seconds 71.72661256790161\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\nvae_trend validation epoch 15 loss 7.35697 seconds 199.1\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\ntemporal2_scores.npy\npop 0.0 score 0.032202113493154015\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6040 }, "formatted_output": "context 600 loss 5.565176410675049 seconds 52.546571254730225\ncontext 700 loss 5.421776976585388 seconds 58.99893307685852\ncontext 800 loss 5.334733390808106 seconds 65.38134431838989\ncontext 900 loss 5.230437984466553 seconds 71.72661256790161\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\nvae_trend validation epoch 12 loss 7.50998 seconds 156.4\nvae_trend validation epoch 13 loss 7.44622 seconds 169.8\nvae_trend validation epoch 14 loss 7.39477 seconds 184.6\nvae_trend validation epoch 15 loss 7.35697 seconds 199.1\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\ntemporal2_scores.npy\npop 0.0 score 0.032202113493154015\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:42:19.618Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": 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> /app/solution/fine_calibrate.py <<'PY'\\nimport argparse,json,numpy as np,torch\\nfrom calibrate import Metric\\nfrom train import ROOT\\nfrom trends import features\\np=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='fine_calibration.json');a=p.parse_args();torch.set_num_threads(2);m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');f=features('train');best=(-1,{})\\nfor trend in [.6,.9,1.2,1.5]:\\n for popularity in [-.3,0.,.3,.6]:\\n bias=trend*(f['org7']-f['org360'])+popularity*f['org360'];value=m(s,bias);config={'org7':trend,'org360':popularity-trend};print(config,value,flush=True)\\n if value>best[0]:best=(value,config)\\nbase=best[1];basebias=sum(w*f[k] for k,w in base.items())\\nfor feat in ['like14','like30','like90']:\\n for strength in [.2,.5,1.]:\\n bias=basebias+strength*(f[feat]-f['org'+feat[4:]]);value=m(s,bias);config=base.copy();config[feat]=strength;config['org'+feat[4:]]=config.get('org'+feat[4:],0)-strength;print(config,value,flush=True)\\n if value>best[0]:best=(value,config)\\n(ROOT/a.dest).write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2));print('BEST',best,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/fine_calibrate.py temporal2_scores.npy --dest temporal2_fine.json > /app/solution/fine_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:42:38.700Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"212ceb\",\"wall_time_seconds\":1.000290545,\"session_id\":64999,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:42:37.656Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab6bf62887d1917aae8b03fc36e6", "usage": { "input_tokens": 63896, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 538, "reasoning_output_tokens": 71, "total_tokens": 64434 }, "turn_token_usage": { "input_tokens": 1956179, "cached_input_tokens": 1895936, "cache_write_input_tokens": 0, "output_tokens": 32043, "reasoning_output_tokens": 13382, "total_tokens": 1988222 }, "thread_token_usage": { "input_tokens": 1956179, "cached_input_tokens": 1895936, "cache_write_input_tokens": 0, "output_tokens": 32043, "reasoning_output_tokens": 13382, "total_tokens": 1988222 } } }, { "kind": "context", "timestamp": "2026-09-09T00:43:03.358Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-565264f0-e2c7-419f-9f64-c9938720737d", "process_id": "64999", "command": [ "/bin/sh", "-lc", "cat > /app/solution/fine_calibrate.py <<'PY'\nimport argparse,json,numpy as np,torch\nfrom calibrate import Metric\nfrom train import ROOT\nfrom trends import features\np=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='fine_calibration.json');a=p.parse_args();torch.set_num_threads(2);m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');f=features('train');best=(-1,{})\nfor trend in [.6,.9,1.2,1.5]:\n for popularity in [-.3,0.,.3,.6]:\n bias=trend*(f['org7']-f['org360'])+popularity*f['org360'];value=m(s,bias);config={'org7':trend,'org360':popularity-trend};print(config,value,flush=True)\n if value>best[0]:best=(value,config)\nbase=best[1];basebias=sum(w*f[k] for k,w in base.items())\nfor feat in ['like14','like30','like90']:\n for strength in [.2,.5,1.]:\n bias=basebias+strength*(f[feat]-f['org'+feat[4:]]);value=m(s,bias);config=base.copy();config[feat]=strength;config['org'+feat[4:]]=config.get('org'+feat[4:],0)-strength;print(config,value,flush=True)\n if value>best[0]:best=(value,config)\n(ROOT/a.dest).write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2));print('BEST',best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/fine_calibrate.py temporal2_scores.npy --dest temporal2_fine.json > /app/solution/fine_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/fine_calibrate.py <<'PY'\nimport argparse,json,numpy as np,torch\nfrom calibrate import Metric\nfrom train import ROOT\nfrom trends import features\np=argparse.ArgumentParser();p.add_argument('scores');p.add_argument('--dest',default='fine_calibration.json');a=p.parse_args();torch.set_num_threads(2);m=Metric();s=np.load(ROOT/a.scores,mmap_mode='r');f=features('train');best=(-1,{})\nfor trend in [.6,.9,1.2,1.5]:\n for popularity in [-.3,0.,.3,.6]:\n bias=trend*(f['org7']-f['org360'])+popularity*f['org360'];value=m(s,bias);config={'org7':trend,'org360':popularity-trend};print(config,value,flush=True)\n if value>best[0]:best=(value,config)\nbase=best[1];basebias=sum(w*f[k] for k,w in base.items())\nfor feat in ['like14','like30','like90']:\n for strength in [.2,.5,1.]:\n bias=basebias+strength*(f[feat]-f['org'+feat[4:]]);value=m(s,bias);config=base.copy();config[feat]=strength;config['org'+feat[4:]]=config.get('org'+feat[4:],0)-strength;print(config,value,flush=True)\n if value>best[0]:best=(value,config)\n(ROOT/a.dest).write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2));print('BEST',best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/fine_calibrate.py temporal2_scores.npy --dest temporal2_fine.json > /app/solution/fine_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 663193371 }, "formatted_output": "" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-09T00:43:07.697Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:43:07.697Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:43:09.186Z", "callId": "call_RVlaJfyKdg0UUjlVW4OjjgA6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -7 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -5 /app/solution/vae_trend.log; tail -6 /app/solution/fine_calibration.log\",\"max_output_tokens\":1700}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:43:09.345Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"89ee06\",\"wall_time_seconds\":0.00000473,\"exit_code\":0,\"original_token_count\":363,\"output\":\"context 1000 loss 5.155974235534668 seconds 77.33192491531372\\nSCORE 1000 0.025592120514022206\\ncontext 1100 loss 5.106203508377075 seconds 92.40816450119019\\ncontext 1200 loss 5.025343761444092 seconds 97.80040216445923\\ncontext 1300 loss 5.00000768661499 seconds 104.07983255386353\\ncontext 1400 loss 4.925889387130737 seconds 111.2437105178833\\ncontext 1500 loss 4.906468720436096 seconds 118.26228594779968\\ntemporal train samples 43885 entries 29998200\\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\\ntemporal2 3 loss 8.386112091153167 score 0.044183496856321634 time 216.87577486038208\\nSCORE 15 0.039875583423954995\\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\\nvae_trend validation epoch 18 loss 7.24673 seconds 269.7\\nvae_trend validation epoch 19 loss 7.20655 seconds 291.1\\n{'org7': 1.5, 'org360': -1.2, 'like30': 0.5, 'org30': -0.5} 0.04725558170310427\\n{'org7': 1.5, 'org360': -1.2, 'like30': 1.0, 'org30': -1.0} 0.044467334126074745\\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.2, 'org90': -0.2} 0.04877335258489883\\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.5, 'org90': -0.5} 0.047160353658031826\\n{'org7': 1.5, 'org360': -1.2, 'like90': 1.0, 'org90': -1.0} 0.04470874109190847\\nBEST (0.04898825301004659, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2})\\n\"}\n{\"current_time\":\"2026-09-09 00:43:09 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:43:09.297Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab7f06a487d18a3c92111b2a8dea", "usage": { "input_tokens": 64490, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 601, "reasoning_output_tokens": 516, "total_tokens": 65091 }, "turn_token_usage": { "input_tokens": 2020669, "cached_input_tokens": 1959680, "cache_write_input_tokens": 0, "output_tokens": 32644, "reasoning_output_tokens": 13898, "total_tokens": 2053313 }, "thread_token_usage": { "input_tokens": 2020669, "cached_input_tokens": 1959680, "cache_write_input_tokens": 0, "output_tokens": 32644, "reasoning_output_tokens": 13898, "total_tokens": 2053313 } } }, { "kind": "context", "timestamp": "2026-09-09T00:43:09.310Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a2911383-908d-40dd-bb8e-e3dbb1fa7bb2", "process_id": "1410", "command": [ "/bin/sh", "-lc", "tail -7 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -5 /app/solution/vae_trend.log; tail -6 /app/solution/fine_calibration.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -7 /app/solution/context.log; tail -4 /app/solution/temporal2.log; tail -5 /app/solution/vae_trend.log; tail -6 /app/solution/fine_calibration.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "context 1000 loss 5.155974235534668 seconds 77.33192491531372\nSCORE 1000 0.025592120514022206\ncontext 1100 loss 5.106203508377075 seconds 92.40816450119019\ncontext 1200 loss 5.025343761444092 seconds 97.80040216445923\ncontext 1300 loss 5.00000768661499 seconds 104.07983255386353\ncontext 1400 loss 4.925889387130737 seconds 111.2437105178833\ncontext 1500 loss 4.906468720436096 seconds 118.26228594779968\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\ntemporal2 3 loss 8.386112091153167 score 0.044183496856321634 time 216.87577486038208\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\nvae_trend validation epoch 18 loss 7.24673 seconds 269.7\nvae_trend validation epoch 19 loss 7.20655 seconds 291.1\n{'org7': 1.5, 'org360': -1.2, 'like30': 0.5, 'org30': -0.5} 0.04725558170310427\n{'org7': 1.5, 'org360': -1.2, 'like30': 1.0, 'org30': -1.0} 0.044467334126074745\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.2, 'org90': -0.2} 0.04877335258489883\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.5, 'org90': -0.5} 0.047160353658031826\n{'org7': 1.5, 'org360': -1.2, 'like90': 1.0, 'org90': -1.0} 0.04470874109190847\nBEST (0.04898825301004659, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2})\n", "stderr": "", "aggregated_output": "context 1000 loss 5.155974235534668 seconds 77.33192491531372\nSCORE 1000 0.025592120514022206\ncontext 1100 loss 5.106203508377075 seconds 92.40816450119019\ncontext 1200 loss 5.025343761444092 seconds 97.80040216445923\ncontext 1300 loss 5.00000768661499 seconds 104.07983255386353\ncontext 1400 loss 4.925889387130737 seconds 111.2437105178833\ncontext 1500 loss 4.906468720436096 seconds 118.26228594779968\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\ntemporal2 3 loss 8.386112091153167 score 0.044183496856321634 time 216.87577486038208\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\nvae_trend validation epoch 18 loss 7.24673 seconds 269.7\nvae_trend validation epoch 19 loss 7.20655 seconds 291.1\n{'org7': 1.5, 'org360': -1.2, 'like30': 0.5, 'org30': -0.5} 0.04725558170310427\n{'org7': 1.5, 'org360': -1.2, 'like30': 1.0, 'org30': -1.0} 0.044467334126074745\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.2, 'org90': -0.2} 0.04877335258489883\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.5, 'org90': -0.5} 0.047160353658031826\n{'org7': 1.5, 'org360': -1.2, 'like90': 1.0, 'org90': -1.0} 0.04470874109190847\nBEST (0.04898825301004659, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2})\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4730 }, "formatted_output": "context 1000 loss 5.155974235534668 seconds 77.33192491531372\nSCORE 1000 0.025592120514022206\ncontext 1100 loss 5.106203508377075 seconds 92.40816450119019\ncontext 1200 loss 5.025343761444092 seconds 97.80040216445923\ncontext 1300 loss 5.00000768661499 seconds 104.07983255386353\ncontext 1400 loss 4.925889387130737 seconds 111.2437105178833\ncontext 1500 loss 4.906468720436096 seconds 118.26228594779968\ntemporal train samples 43885 entries 29998200\ntemporal2 1 loss 8.714205387026764 score 0.046387606287355665 time 81.007248878479\ntemporal2 2 loss 8.494318474170774 score 0.04575158716665283 time 140.36891388893127\ntemporal2 3 loss 8.386112091153167 score 0.044183496856321634 time 216.87577486038208\nSCORE 15 0.039875583423954995\nvae_trend validation epoch 16 loss 7.31726 seconds 228.9\nvae_trend validation epoch 17 loss 7.2677 seconds 253.9\nvae_trend validation epoch 18 loss 7.24673 seconds 269.7\nvae_trend validation epoch 19 loss 7.20655 seconds 291.1\n{'org7': 1.5, 'org360': -1.2, 'like30': 0.5, 'org30': -0.5} 0.04725558170310427\n{'org7': 1.5, 'org360': -1.2, 'like30': 1.0, 'org30': -1.0} 0.044467334126074745\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.2, 'org90': -0.2} 0.04877335258489883\n{'org7': 1.5, 'org360': -1.2, 'like90': 0.5, 'org90': -0.5} 0.047160353658031826\n{'org7': 1.5, 'org360': -1.2, 'like90': 1.0, 'org90': -1.0} 0.04470874109190847\nBEST (0.04898825301004659, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2})\n" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-09T00:43:23.245Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:43:23.245Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:43:29.714Z", "callId": "call_sov918X8XNjbY15xsrtINPgK", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/temporal.py' in c and b'temporal2' in c:os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nfor name in ['train','temporal','context']:\\n p='/app/solution/'+name+'.py';s=open(p).read();v='a' if name=='context' else 'args';s=s.replace(\\\"else 'submission.parquet'\\\", \\\"else f'{\\\"+v+\\\".name}_test.parquet'\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_base --epochs 35 --eval-every 35 > /app/solution/final_base.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:43:30.944Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5d9ad1\",\"wall_time_seconds\":1.001536682,\"session_id\":49267,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:43:29.839Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ab9e454087d185f5cc6b83fd3192", "usage": { "input_tokens": 65800, "cached_input_tokens": 64256, "cache_write_input_tokens": 0, "output_tokens": 517, "reasoning_output_tokens": 236, "total_tokens": 66317 }, "turn_token_usage": { "input_tokens": 2086469, "cached_input_tokens": 2023936, "cache_write_input_tokens": 0, "output_tokens": 33161, "reasoning_output_tokens": 14134, "total_tokens": 2119630 }, "thread_token_usage": { "input_tokens": 2086469, "cached_input_tokens": 2023936, "cache_write_input_tokens": 0, "output_tokens": 33161, "reasoning_output_tokens": 14134, "total_tokens": 2119630 } } }, { "kind": "context", "timestamp": "2026-09-09T00:43:30.097Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dab41948-1d78-4d0b-b026-53fa8aed610a", "process_id": "97001", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal2 --resume base_validation.pt --epochs 8 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/temporal2.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal2 --resume base_validation.pt --epochs 8 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/temporal2.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 272, "nanos": 744034807 }, "formatted_output": "" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-09T00:43:45.110Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:43:45.110Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:43:47.345Z", "text": "The latest item-bias calibration brought public NDCG@10 to 0.0490. I’ve started the matching final refit, including the selected pretraining schedule. I’ll finish checking history recency and model combinations before locking the final vectors.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:43:51.781Z", "callId": "call_drUf0rSzlF6JDYqt6c2ZYU21", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/history_sweep.py <<'PY'\\nimport json,torch\\nfrom analyze import score_cache\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom train import ROOT\\nm=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items())\\nfor half in [3.,7.,30.,60.]:\\n scores=score_cache('temporal2_validation.pt',half=half,dest=f'temporal2_h{half}_scores.npy');print('HALF',half,'SCORE',m(scores,bias),flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/history_sweep.py > /app/solution/history_sweep.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:43:53.004Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"1305e9\",\"wall_time_seconds\":1.001751806,\"session_id\":18706,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:43:51.860Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0abb323b087d1b59eb5df0a774c5f", "usage": { "input_tokens": 66376, "cached_input_tokens": 65664, "cache_write_input_tokens": 0, "output_tokens": 579, "reasoning_output_tokens": 300, "total_tokens": 66955 }, "turn_token_usage": { "input_tokens": 2152845, "cached_input_tokens": 2089600, "cache_write_input_tokens": 0, "output_tokens": 33740, "reasoning_output_tokens": 14434, "total_tokens": 2186585 }, "thread_token_usage": { "input_tokens": 2152845, "cached_input_tokens": 2089600, "cache_write_input_tokens": 0, "output_tokens": 33740, "reasoning_output_tokens": 14434, "total_tokens": 2186585 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-09T00:43:59.182Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:43:59.182Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:44:00.962Z", "callId": "call_GOUcKkFJXfdK08KGMoBPDsK1", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/vae_trend.log; tail -7 /app/solution/context.log; tail -4 /app/solution/final_base.log; cat /app/solution/history_sweep.log\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:44:01.102Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"88766c\",\"wall_time_seconds\":0.00000858,\"exit_code\":0,\"original_token_count\":179,\"output\":\"vae_trend validation epoch 19 loss 7.20655 seconds 291.1\\nvae_trend validation epoch 20 loss 7.18257 seconds 311.9\\nSCORE 20 0.03721306164400976\\nvae_trend validation epoch 21 loss 7.14251 seconds 337.8\\nvae_trend validation epoch 22 loss 7.11133 seconds 356.8\\ncontext 1700 loss 4.815499410629273 seconds 139.82620644569397\\ncontext 1800 loss 4.794606432914734 seconds 145.18110418319702\\ncontext 1900 loss 4.7551137590408326 seconds 150.5853397846222\\ncontext 2000 loss 4.751108732223511 seconds 154.95977473258972\\nSCORE 2000 0.02611081321532936\\ncontext 2100 loss 4.693783750534058 seconds 169.6291151046753\\ncontext 2200 loss 4.68234808921814 seconds 174.57176780700684\\nfinal_base test epoch 1 loss 9.76858 seconds 17.3\\n\"}\n{\"current_time\":\"2026-09-09 00:44:01 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:44:01.051Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b105f16-3c06-4708-99e5-5cb82842ebeb", "process_id": "95087", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/vae_trend.log; tail -7 /app/solution/context.log; tail -4 /app/solution/final_base.log; cat /app/solution/history_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/vae_trend.log; tail -7 /app/solution/context.log; tail -4 /app/solution/final_base.log; cat /app/solution/history_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae_trend validation epoch 19 loss 7.20655 seconds 291.1\nvae_trend validation epoch 20 loss 7.18257 seconds 311.9\nSCORE 20 0.03721306164400976\nvae_trend validation epoch 21 loss 7.14251 seconds 337.8\nvae_trend validation epoch 22 loss 7.11133 seconds 356.8\ncontext 1700 loss 4.815499410629273 seconds 139.82620644569397\ncontext 1800 loss 4.794606432914734 seconds 145.18110418319702\ncontext 1900 loss 4.7551137590408326 seconds 150.5853397846222\ncontext 2000 loss 4.751108732223511 seconds 154.95977473258972\nSCORE 2000 0.02611081321532936\ncontext 2100 loss 4.693783750534058 seconds 169.6291151046753\ncontext 2200 loss 4.68234808921814 seconds 174.57176780700684\nfinal_base test epoch 1 loss 9.76858 seconds 17.3\n", "stderr": "", "aggregated_output": "vae_trend validation epoch 19 loss 7.20655 seconds 291.1\nvae_trend validation epoch 20 loss 7.18257 seconds 311.9\nSCORE 20 0.03721306164400976\nvae_trend validation epoch 21 loss 7.14251 seconds 337.8\nvae_trend validation epoch 22 loss 7.11133 seconds 356.8\ncontext 1700 loss 4.815499410629273 seconds 139.82620644569397\ncontext 1800 loss 4.794606432914734 seconds 145.18110418319702\ncontext 1900 loss 4.7551137590408326 seconds 150.5853397846222\ncontext 2000 loss 4.751108732223511 seconds 154.95977473258972\nSCORE 2000 0.02611081321532936\ncontext 2100 loss 4.693783750534058 seconds 169.6291151046753\ncontext 2200 loss 4.68234808921814 seconds 174.57176780700684\nfinal_base test epoch 1 loss 9.76858 seconds 17.3\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8580 }, "formatted_output": "vae_trend validation epoch 19 loss 7.20655 seconds 291.1\nvae_trend validation epoch 20 loss 7.18257 seconds 311.9\nSCORE 20 0.03721306164400976\nvae_trend validation epoch 21 loss 7.14251 seconds 337.8\nvae_trend validation epoch 22 loss 7.11133 seconds 356.8\ncontext 1700 loss 4.815499410629273 seconds 139.82620644569397\ncontext 1800 loss 4.794606432914734 seconds 145.18110418319702\ncontext 1900 loss 4.7551137590408326 seconds 150.5853397846222\ncontext 2000 loss 4.751108732223511 seconds 154.95977473258972\nSCORE 2000 0.02611081321532936\ncontext 2100 loss 4.693783750534058 seconds 169.6291151046753\ncontext 2200 loss 4.68234808921814 seconds 174.57176780700684\nfinal_base test epoch 1 loss 9.76858 seconds 17.3\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:44:01.068Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0abc94f2887d1a4e67f040fab87af", "usage": { "input_tokens": 67013, "cached_input_tokens": 66176, "cache_write_input_tokens": 0, "output_tokens": 179, "reasoning_output_tokens": 98, "total_tokens": 67192 }, "turn_token_usage": { "input_tokens": 2219858, "cached_input_tokens": 2155776, "cache_write_input_tokens": 0, "output_tokens": 33919, "reasoning_output_tokens": 14532, "total_tokens": 2253777 }, "thread_token_usage": { "input_tokens": 2219858, "cached_input_tokens": 2155776, "cache_write_input_tokens": 0, "output_tokens": 33919, "reasoning_output_tokens": 14532, "total_tokens": 2253777 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-09T00:44:27.517Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:44:27.517Z", "text": "" } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-09T00:44:46.271Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:44:46.271Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:44:56.288Z", "callId": "call_hvHLKwstyCVNVeY6FabeOAhi", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/train.py';s=open(p).read().replace('def training_loss(model,bags,uu,rng,item_ids):','def training_loss(model,bags,uu,rng,item_ids,disjoint=0.):')\\ns=s.replace(\\\"x,o,w,ix,rows=batch(bags,uu)\\\\n if len(item_ids)\\\", \\\"x,o,w,ix,rows=batch(bags,uu)\\\\n heldout=None\\\\n if disjoint:\\\\n heldout=torch.from_numpy(rng.random(len(x)) /app/solution/history_sweep.py <<'PY'\nimport json,torch\nfrom analyze import score_cache\nfrom calibrate import Metric\nfrom trends import features\nfrom train import ROOT\nm=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items())\nfor half in [3.,7.,30.,60.]:\n scores=score_cache('temporal2_validation.pt',half=half,dest=f'temporal2_h{half}_scores.npy');print('HALF',half,'SCORE',m(scores,bias),flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/history_sweep.py > /app/solution/history_sweep.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/history_sweep.py <<'PY'\nimport json,torch\nfrom analyze import score_cache\nfrom calibrate import Metric\nfrom trends import features\nfrom train import ROOT\nm=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items())\nfor half in [3.,7.,30.,60.]:\n scores=score_cache('temporal2_validation.pt',half=half,dest=f'temporal2_h{half}_scores.npy');print('HALF',half,'SCORE',m(scores,bias),flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/history_sweep.py > /app/solution/history_sweep.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 91, "nanos": 224965516 }, "formatted_output": "" } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-09T00:45:47.150Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:45:47.150Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:45:48.830Z", "text": "Very short histories reduced validation quality; the model performs better with roughly two to four weeks of history decay. I’m keeping that broader history signal and checking whether the other encoders improve the final ranking when combined with it.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:45:59.874Z", "callId": "call_slyAKme8BjWKVupYavMARk0b", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/test_contract.py <<'PY'\\n\\\"\\\"\\\"Check the defining neural retrieval and large-catalog training contracts.\\\"\\\"\\\"\\nimport numpy as np,torch\\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\\nfrom train import training_loss\\ntorch.set_num_threads(1);torch.manual_seed(123)\\nx=torch.tensor([1,2,3,2,4]);o=torch.tensor([0,3]);w=torch.tensor([.5,.3,.2,.6,.4]);items=torch.tensor([6,7,8])\\nfor klass in [TwoTower,VariationalTwoTower,ContextTwoTower]:\\n m=klass(20,hidden=16,dim=8,dropout=.2).eval();uv=m.user(x,o,w);iv=m.item(items)\\n assert uv.shape==(2,9) and iv.shape==(3,9)\\n torch.testing.assert_close(m(x,o,w,items),uv@iv.T)\\n torch.testing.assert_close(iv,m.item(torch.arange(20))[items])\\n (uv@iv.T).sum().backward()\\n assert m.user.history.weight.grad.abs().sum()>0 and m.item.embedding.weight.grad.abs().sum()>0\\n print(klass.__name__,'independent vectors, exact dot product, and both tower gradients: PASS')\\nbags={'items':np.array([1,2,3,4]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2,'targets':np.ones(4,dtype='float32')}\\nm=TwoTower(50001,hidden=16,dim=8);loss=training_loss(m,bags,np.array([0,1]),np.random.default_rng(1),torch.arange(50001));loss.backward();assert torch.isfinite(loss)\\nprint('Large-catalog bounded sampled-softmax path: PASS')\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/vae_trend.log; tail -5 /app/solution/context.log; tail -3 /app/solution/history_sweep.log\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:46:01.225Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"306384\",\"wall_time_seconds\":1.001043577,\"session_id\":79699,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"ecacd6\",\"wall_time_seconds\":0.0000046,\"exit_code\":0,\"original_token_count\":142,\"output\":\"vae_trend validation epoch 24 loss 7.06101 seconds 403.7\\nvae_trend validation epoch 25 loss 7.02546 seconds 426.0\\nSCORE 25 0.03870824223183228\\nvae_trend validation epoch 26 loss 6.99819 seconds 459.2\\ncontext 3300 loss 4.496270174980164 seconds 264.10026359558105\\ncontext 3400 loss 4.483119516372681 seconds 270.04151487350464\\ncontext 3500 loss 4.462025966644287 seconds 275.9946541786194\\nSCORE 3500 0.027585720117468196\\ncontext 3600 loss 4.473261909484863 seconds 292.26651334762573\\nbags train 5618006\\ntemporal2_h60.0_scores.npy\\nHALF 60.0 SCORE 0.04865031568603182\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:45:59.969Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ac216c2087d1aa19bf1505b17bb1", "usage": { "input_tokens": 70039, "cached_input_tokens": 68992, "cache_write_input_tokens": 0, "output_tokens": 1067, "reasoning_output_tokens": 498, "total_tokens": 71106 }, "turn_token_usage": { "input_tokens": 2426595, "cached_input_tokens": 2358912, "cache_write_input_tokens": 0, "output_tokens": 36911, "reasoning_output_tokens": 16405, "total_tokens": 2463506 }, "thread_token_usage": { "input_tokens": 2426595, "cached_input_tokens": 2358912, "cache_write_input_tokens": 0, "output_tokens": 36911, "reasoning_output_tokens": 16405, "total_tokens": 2463506 } } }, { "kind": "context", "timestamp": "2026-09-09T00:46:01.194Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0b16cf27-8ddb-462c-9027-3e44b6c43cb0", "process_id": "89585", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/vae_trend.log; tail -5 /app/solution/context.log; tail -3 /app/solution/history_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/vae_trend.log; tail -5 /app/solution/context.log; tail -3 /app/solution/history_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae_trend validation epoch 24 loss 7.06101 seconds 403.7\nvae_trend validation epoch 25 loss 7.02546 seconds 426.0\nSCORE 25 0.03870824223183228\nvae_trend validation epoch 26 loss 6.99819 seconds 459.2\ncontext 3300 loss 4.496270174980164 seconds 264.10026359558105\ncontext 3400 loss 4.483119516372681 seconds 270.04151487350464\ncontext 3500 loss 4.462025966644287 seconds 275.9946541786194\nSCORE 3500 0.027585720117468196\ncontext 3600 loss 4.473261909484863 seconds 292.26651334762573\nbags train 5618006\ntemporal2_h60.0_scores.npy\nHALF 60.0 SCORE 0.04865031568603182\n", "stderr": "", "aggregated_output": "vae_trend validation epoch 24 loss 7.06101 seconds 403.7\nvae_trend validation epoch 25 loss 7.02546 seconds 426.0\nSCORE 25 0.03870824223183228\nvae_trend validation epoch 26 loss 6.99819 seconds 459.2\ncontext 3300 loss 4.496270174980164 seconds 264.10026359558105\ncontext 3400 loss 4.483119516372681 seconds 270.04151487350464\ncontext 3500 loss 4.462025966644287 seconds 275.9946541786194\nSCORE 3500 0.027585720117468196\ncontext 3600 loss 4.473261909484863 seconds 292.26651334762573\nbags train 5618006\ntemporal2_h60.0_scores.npy\nHALF 60.0 SCORE 0.04865031568603182\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4600 }, "formatted_output": "vae_trend validation epoch 24 loss 7.06101 seconds 403.7\nvae_trend validation epoch 25 loss 7.02546 seconds 426.0\nSCORE 25 0.03870824223183228\nvae_trend validation epoch 26 loss 6.99819 seconds 459.2\ncontext 3300 loss 4.496270174980164 seconds 264.10026359558105\ncontext 3400 loss 4.483119516372681 seconds 270.04151487350464\ncontext 3500 loss 4.462025966644287 seconds 275.9946541786194\nSCORE 3500 0.027585720117468196\ncontext 3600 loss 4.473261909484863 seconds 292.26651334762573\nbags train 5618006\ntemporal2_h60.0_scores.npy\nHALF 60.0 SCORE 0.04865031568603182\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:46:08.488Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7be07e12-0e87-487b-ab5c-520bbcde57c8", "process_id": "79699", "command": [ "/bin/sh", "-lc", "cat > /app/solution/test_contract.py <<'PY'\n\"\"\"Check the defining neural retrieval and large-catalog training contracts.\"\"\"\nimport numpy as np,torch\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\nfrom train import training_loss\ntorch.set_num_threads(1);torch.manual_seed(123)\nx=torch.tensor([1,2,3,2,4]);o=torch.tensor([0,3]);w=torch.tensor([.5,.3,.2,.6,.4]);items=torch.tensor([6,7,8])\nfor klass in [TwoTower,VariationalTwoTower,ContextTwoTower]:\n m=klass(20,hidden=16,dim=8,dropout=.2).eval();uv=m.user(x,o,w);iv=m.item(items)\n assert uv.shape==(2,9) and iv.shape==(3,9)\n torch.testing.assert_close(m(x,o,w,items),uv@iv.T)\n torch.testing.assert_close(iv,m.item(torch.arange(20))[items])\n (uv@iv.T).sum().backward()\n assert m.user.history.weight.grad.abs().sum()>0 and m.item.embedding.weight.grad.abs().sum()>0\n print(klass.__name__,'independent vectors, exact dot product, and both tower gradients: PASS')\nbags={'items':np.array([1,2,3,4]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2,'targets':np.ones(4,dtype='float32')}\nm=TwoTower(50001,hidden=16,dim=8);loss=training_loss(m,bags,np.array([0,1]),np.random.default_rng(1),torch.arange(50001));loss.backward();assert torch.isfinite(loss)\nprint('Large-catalog bounded sampled-softmax path: PASS')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/test_contract.py <<'PY'\n\"\"\"Check the defining neural retrieval and large-catalog training contracts.\"\"\"\nimport numpy as np,torch\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\nfrom train import training_loss\ntorch.set_num_threads(1);torch.manual_seed(123)\nx=torch.tensor([1,2,3,2,4]);o=torch.tensor([0,3]);w=torch.tensor([.5,.3,.2,.6,.4]);items=torch.tensor([6,7,8])\nfor klass in [TwoTower,VariationalTwoTower,ContextTwoTower]:\n m=klass(20,hidden=16,dim=8,dropout=.2).eval();uv=m.user(x,o,w);iv=m.item(items)\n assert uv.shape==(2,9) and iv.shape==(3,9)\n torch.testing.assert_close(m(x,o,w,items),uv@iv.T)\n torch.testing.assert_close(iv,m.item(torch.arange(20))[items])\n (uv@iv.T).sum().backward()\n assert m.user.history.weight.grad.abs().sum()>0 and m.item.embedding.weight.grad.abs().sum()>0\n print(klass.__name__,'independent vectors, exact dot product, and both tower gradients: PASS')\nbags={'items':np.array([1,2,3,4]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2,'targets':np.ones(4,dtype='float32')}\nm=TwoTower(50001,hidden=16,dim=8);loss=training_loss(m,bags,np.array([0,1]),np.random.default_rng(1),torch.arange(50001));loss.backward();assert torch.isfinite(loss)\nprint('Large-catalog bounded sampled-softmax path: PASS')\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n", "stderr": "", "aggregated_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 388089011 }, "formatted_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n" } } } ] }, { "id": 61, "role": "agent", "timestamp": "2026-09-09T00:46:10.515Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:46:10.515Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:46:16.950Z", "callId": "call_C05giIaiDHS8cPEP6cCTZjy4", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/train.py' in c and b'vae_trend' in c:os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name disjoint --vae --disjoint --hidden 600 --dim 256 --epochs 40 --half 30 --likes 7 --dropout .7 --beta .001 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/disjoint.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:79699,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1300}));\n" }, "result": { "timestamp": "2026-09-09T00:46:18.222Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"6fa97c\",\"wall_time_seconds\":1.001416946,\"session_id\":3740,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"d643c3\",\"wall_time_seconds\":0.00000365,\"exit_code\":0,\"original_token_count\":77,\"output\":\"TwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nLarge-catalog bounded sampled-softmax path: PASS\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:46:17.067Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ac4984c887d19177f5a9bcc25518", "usage": { "input_tokens": 71406, "cached_input_tokens": 69888, "cache_write_input_tokens": 0, "output_tokens": 410, "reasoning_output_tokens": 143, "total_tokens": 71816 }, "turn_token_usage": { "input_tokens": 2498001, "cached_input_tokens": 2428800, "cache_write_input_tokens": 0, "output_tokens": 37321, "reasoning_output_tokens": 16548, "total_tokens": 2535322 }, "thread_token_usage": { "input_tokens": 2498001, "cached_input_tokens": 2428800, "cache_write_input_tokens": 0, "output_tokens": 37321, "reasoning_output_tokens": 16548, "total_tokens": 2535322 } } }, { "kind": "context", "timestamp": "2026-09-09T00:46:17.356Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-23f0e6fa-0ce9-45a0-a0bd-735819070d84", "process_id": "12140", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal,shutil\nshutil.copyfile('/app/solution/vae_validation.pt','/app/solution/vae_initial.pt')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'vae' in c:os.kill(int(f),signal.SIGTERM);print('Restarting with trend-aware checkpoint selection.')\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae_trend --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/vae_trend.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal,shutil\nshutil.copyfile('/app/solution/vae_validation.pt','/app/solution/vae_initial.pt')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'vae' in c:os.kill(int(f),signal.SIGTERM);print('Restarting with trend-aware checkpoint selection.')\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name vae_trend --vae --hidden 600 --dim 256 --epochs 100 --half 30 --likes 7 --dropout .5 --beta .0002 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/vae_trend.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Restarting with trend-aware checkpoint selection.\n", "stderr": "", "aggregated_output": "Restarting with trend-aware checkpoint selection.\n", "exit_code": 143, "duration": { "secs": 498, "nanos": 934016600 }, "formatted_output": "Restarting with trend-aware checkpoint selection.\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:46:30.111Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e018e69b-ad0d-4f3b-8c49-936d72079e0b", "process_id": "64884", "command": [ "/bin/sh", "-lc", "cat >> /app/solution/model.py <<'PY'\n\nclass ContextUserTower(nn.Module):\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\n super().__init__();self.history=nn.EmbeddingBag(n_items,dim,mode='sum');self.transform=nn.Linear(dim,dim);self.dropout=dropout\n nn.init.normal_(self.history.weight,std=.05);nn.init.eye_(self.transform.weight);nn.init.zeros_(self.transform.bias)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n x=self.history(ids,offsets,per_sample_weights=weights);x=F.normalize(x,dim=-1)\n x=F.normalize(x+.2*torch.tanh(self.transform(x)),dim=-1)*12.\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\nclass ContextItemTower(ItemTower):\n def forward(self,ids):\n return torch.cat([F.normalize(self.projection(self.embedding(ids)),dim=-1),self.bias(ids)],dim=-1)\nclass ContextTwoTower(TwoTower):\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\n nn.Module.__init__(self);self.user=ContextUserTower(n_items,hidden,dim,dropout);self.item=ContextItemTower(n_items,dim)\nPY\ncat > /app/solution/context.py <<'PY'\n\"\"\"Sampled-softmax temporal context pretraining; bounded histories and negatives.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json\nimport numpy as np,torch\nfrom torch.nn import functional as F\nfrom train import ROOT,DATA,make_bags,infer,gold_data\nfrom model import ContextTwoTower\nfrom trends import features\n\ndef sequences(stage):\n p=ROOT/f'sequences_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['users']);starts=[np.searchsorted(e['u'],np.arange(n+1)) for e in ev];pieces=[];offsets=[0]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);v=v[(v['t']>end-180*86400)&(((v['k']==0)&(v['q']>=80)&(v['o']==1))|(v['k']==1))];v=v[np.argsort(v['t'])]\n # Collapse immediately repeated tracks to avoid identity reconstruction.\n x=v['i'];x=x[np.r_[True,x[1:]!=x[:-1]]] if len(x) else x\n pieces.append(x);offsets.append(offsets[-1]+len(x))\n d=dict(items=np.concatenate(pieces),offsets=np.array(offsets));np.savez(p,**d);return d\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='context');p.add_argument('--steps',type=int,default=4000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--dim',type=int,default=128);p.add_argument('--half',type=float,default=14);p.add_argument('--batch',type=int,default=512);p.add_argument('--context',type=int,default=16);p.add_argument('--window',type=int,default=64);p.add_argument('--lr',type=float,default=.002);a=p.parse_args();torch.set_num_threads(2);torch.manual_seed(731);rng=np.random.default_rng(731);t=time.time()\n ids=dict(np.load(ROOT/'ids.npz'));n=len(ids['items']);d=sequences('train' if a.stage=='validation' else 'test');bags=make_bags('train' if a.stage=='validation' else 'test',a.half,3.,1.,0);fs=features('train' if a.stage=='validation' else 'test');bias=.5*fs['org7']\n model=ContextTwoTower(n,dim=a.dim);opt=torch.optim.Adam(model.parameters(),lr=a.lr);valid=np.flatnonzero(np.diff(d['offsets'])>4);gold=gold_data(None,ids) if a.stage=='validation' else None;best=-1.;losses=[]\n for step in range(1,a.steps+1):\n model.train();uu=rng.choice(valid,a.batch);start=d['offsets'][uu];length=d['offsets'][uu+1]-start\n pos=(rng.random(a.batch)*(length-1)).astype('int64')+1;target=d['items'][start+pos].astype('int64')\n distance=(rng.random((a.batch,a.context))*np.minimum(pos,a.window)[:,None]).astype('int64')+1\n x=d['items'][start[:,None]+pos[:,None]-distance].astype('int64');weight=(x!=target[:,None]).astype('float32');weight/=np.maximum(1.,weight.sum(1,keepdims=True))\n uv=model.user(torch.from_numpy(x.ravel()),torch.arange(a.batch)*a.context,torch.from_numpy(weight.ravel()))\n negatives=torch.from_numpy(rng.integers(0,n,size=2048));iv=model.item(negatives);pv=model.item(torch.from_numpy(target));positive=(uv*pv).sum(1,keepdim=True);negative=uv@iv.T\n negative.masked_fill_(torch.from_numpy(target)[:,None]==negatives[None,:],-1e9)\n loss=F.cross_entropy(torch.cat([positive,negative],1),torch.zeros(a.batch,dtype=torch.long));opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n if step%100==0:print(a.name,step,'loss',np.mean(losses[-100:]),'seconds',time.time()-t,flush=True)\n if step%a.eval_every==0 or step==a.steps:\n out=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet';score=infer(model,bags,a.stage,out,ids,gold,bias=bias);print('SCORE',step,score,flush=True)\n if score>best or a.stage=='test':\n best=score;args=vars(a).copy();args.update({'context_model':True,'hidden':a.dim,'dropout':.2,'likes':3.,'organic':1.});torch.save({'model':model.state_dict(),'args':args,'epoch':step,'score':score},ROOT/f'{a.name}_{a.stage}.pt')\n if a.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{a.name}_best_validation.parquet');(ROOT/f'{a.name}_metrics.json').write_text(json.dumps({'step':step,'score':score,'args':args},indent=2))\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python - <<'PY'\nfor p in ['/app/solution/analyze.py','/app/solution/submit.py']:\n s=open(p).read().replace('from model import TwoTower,VariationalTwoTower','from model import TwoTower,VariationalTwoTower,ContextTwoTower')\n s=s.replace(\"(VariationalTwoTower if args.get('vae') else TwoTower)\",\"(ContextTwoTower if args.get('context_model') else VariationalTwoTower if args.get('vae') else TwoTower)\")\n s=s.replace(\"(VariationalTwoTower if c.get('vae') else TwoTower)\",\"(ContextTwoTower if c.get('context_model') else VariationalTwoTower if c.get('vae') else TwoTower)\")\n open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py > /app/solution/context.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat >> /app/solution/model.py <<'PY'\n\nclass ContextUserTower(nn.Module):\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\n super().__init__();self.history=nn.EmbeddingBag(n_items,dim,mode='sum');self.transform=nn.Linear(dim,dim);self.dropout=dropout\n nn.init.normal_(self.history.weight,std=.05);nn.init.eye_(self.transform.weight);nn.init.zeros_(self.transform.bias)\n def forward(self,ids,offsets,weights):\n if self.training:weights=F.dropout(weights,p=self.dropout)\n x=self.history(ids,offsets,per_sample_weights=weights);x=F.normalize(x,dim=-1)\n x=F.normalize(x+.2*torch.tanh(self.transform(x)),dim=-1)*12.\n return torch.cat([x,torch.ones_like(x[:,:1])],dim=1)\nclass ContextItemTower(ItemTower):\n def forward(self,ids):\n return torch.cat([F.normalize(self.projection(self.embedding(ids)),dim=-1),self.bias(ids)],dim=-1)\nclass ContextTwoTower(TwoTower):\n def __init__(self,n_items,hidden=128,dim=128,dropout=.2):\n nn.Module.__init__(self);self.user=ContextUserTower(n_items,hidden,dim,dropout);self.item=ContextItemTower(n_items,dim)\nPY\ncat > /app/solution/context.py <<'PY'\n\"\"\"Sampled-softmax temporal context pretraining; bounded histories and negatives.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport argparse,time,json\nimport numpy as np,torch\nfrom torch.nn import functional as F\nfrom train import ROOT,DATA,make_bags,infer,gold_data\nfrom model import ContextTwoTower\nfrom trends import features\n\ndef sequences(stage):\n p=ROOT/f'sequences_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n end=24786800+(606600 if stage=='test' else 0);n=len(np.load(ROOT/'ids.npz')['users']);starts=[np.searchsorted(e['u'],np.arange(n+1)) for e in ev];pieces=[];offsets=[0]\n for u in range(n):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);v=v[(v['t']>end-180*86400)&(((v['k']==0)&(v['q']>=80)&(v['o']==1))|(v['k']==1))];v=v[np.argsort(v['t'])]\n # Collapse immediately repeated tracks to avoid identity reconstruction.\n x=v['i'];x=x[np.r_[True,x[1:]!=x[:-1]]] if len(x) else x\n pieces.append(x);offsets.append(offsets[-1]+len(x))\n d=dict(items=np.concatenate(pieces),offsets=np.array(offsets));np.savez(p,**d);return d\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='context');p.add_argument('--steps',type=int,default=4000);p.add_argument('--eval-every',type=int,default=500);p.add_argument('--dim',type=int,default=128);p.add_argument('--half',type=float,default=14);p.add_argument('--batch',type=int,default=512);p.add_argument('--context',type=int,default=16);p.add_argument('--window',type=int,default=64);p.add_argument('--lr',type=float,default=.002);a=p.parse_args();torch.set_num_threads(2);torch.manual_seed(731);rng=np.random.default_rng(731);t=time.time()\n ids=dict(np.load(ROOT/'ids.npz'));n=len(ids['items']);d=sequences('train' if a.stage=='validation' else 'test');bags=make_bags('train' if a.stage=='validation' else 'test',a.half,3.,1.,0);fs=features('train' if a.stage=='validation' else 'test');bias=.5*fs['org7']\n model=ContextTwoTower(n,dim=a.dim);opt=torch.optim.Adam(model.parameters(),lr=a.lr);valid=np.flatnonzero(np.diff(d['offsets'])>4);gold=gold_data(None,ids) if a.stage=='validation' else None;best=-1.;losses=[]\n for step in range(1,a.steps+1):\n model.train();uu=rng.choice(valid,a.batch);start=d['offsets'][uu];length=d['offsets'][uu+1]-start\n pos=(rng.random(a.batch)*(length-1)).astype('int64')+1;target=d['items'][start+pos].astype('int64')\n distance=(rng.random((a.batch,a.context))*np.minimum(pos,a.window)[:,None]).astype('int64')+1\n x=d['items'][start[:,None]+pos[:,None]-distance].astype('int64');weight=(x!=target[:,None]).astype('float32');weight/=np.maximum(1.,weight.sum(1,keepdims=True))\n uv=model.user(torch.from_numpy(x.ravel()),torch.arange(a.batch)*a.context,torch.from_numpy(weight.ravel()))\n negatives=torch.from_numpy(rng.integers(0,n,size=2048));iv=model.item(negatives);pv=model.item(torch.from_numpy(target));positive=(uv*pv).sum(1,keepdim=True);negative=uv@iv.T\n negative.masked_fill_(torch.from_numpy(target)[:,None]==negatives[None,:],-1e9)\n loss=F.cross_entropy(torch.cat([positive,negative],1),torch.zeros(a.batch,dtype=torch.long));opt.zero_grad(set_to_none=True);loss.backward();opt.step();losses.append(loss.item())\n if step%100==0:print(a.name,step,'loss',np.mean(losses[-100:]),'seconds',time.time()-t,flush=True)\n if step%a.eval_every==0 or step==a.steps:\n out=f'{a.name}_validation.parquet' if a.stage=='validation' else 'submission.parquet';score=infer(model,bags,a.stage,out,ids,gold,bias=bias);print('SCORE',step,score,flush=True)\n if score>best or a.stage=='test':\n best=score;args=vars(a).copy();args.update({'context_model':True,'hidden':a.dim,'dropout':.2,'likes':3.,'organic':1.});torch.save({'model':model.state_dict(),'args':args,'epoch':step,'score':score},ROOT/f'{a.name}_{a.stage}.pt')\n if a.stage=='validation':\n import shutil;shutil.copyfile(ROOT/out,ROOT/f'{a.name}_best_validation.parquet');(ROOT/f'{a.name}_metrics.json').write_text(json.dumps({'step':step,'score':score,'args':args},indent=2))\nif __name__=='__main__':main()\nPY\n/opt/task-venv/bin/python - <<'PY'\nfor p in ['/app/solution/analyze.py','/app/solution/submit.py']:\n s=open(p).read().replace('from model import TwoTower,VariationalTwoTower','from model import TwoTower,VariationalTwoTower,ContextTwoTower')\n s=s.replace(\"(VariationalTwoTower if args.get('vae') else TwoTower)\",\"(ContextTwoTower if args.get('context_model') else VariationalTwoTower if args.get('vae') else TwoTower)\")\n s=s.replace(\"(VariationalTwoTower if c.get('vae') else TwoTower)\",\"(ContextTwoTower if c.get('context_model') else VariationalTwoTower if c.get('vae') else TwoTower)\")\n open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py > /app/solution/context.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 333, "nanos": 376028498 }, "formatted_output": "" } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-09T00:46:36.502Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:46:36.502Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:46:38.642Z", "callId": "call_BsZqU1CwjRpKorMH5mbIcWFu", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py vae_trend_validation.pt --dest vae_scores.npy --no-grid > /app/solution/vae_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/final_base.log; tail -4 /app/solution/context.log; tail -3 /app/solution/disjoint.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:46:39.954Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"452a59\",\"wall_time_seconds\":1.001260436,\"session_id\":3569,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"d5b895\",\"wall_time_seconds\":0.00000522,\"exit_code\":0,\"original_token_count\":106,\"output\":\"final_base test epoch 10 loss 8.6636 seconds 153.4\\nfinal_base test epoch 11 loss 8.62074 seconds 164.5\\nfinal_base test epoch 12 loss 8.5599 seconds 171.3\\nfinal_base test epoch 13 loss 8.49824 seconds 180.0\\ncontext 3800 loss 4.434284181594848 seconds 305.2355651855469\\ncontext 3900 loss 4.431431746482849 seconds 311.7789978981018\\ncontext 4000 loss 4.416066236495972 seconds 317.25573778152466\\nSCORE 4000 0.02943688173607104\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:46:38.751Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ac5a79b487d1acd7468953f744b5", "usage": { "input_tokens": 71969, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 497, "reasoning_output_tokens": 363, "total_tokens": 72466 }, "turn_token_usage": { "input_tokens": 2569970, "cached_input_tokens": 2499968, "cache_write_input_tokens": 0, "output_tokens": 37818, "reasoning_output_tokens": 16911, "total_tokens": 2607788 }, "thread_token_usage": { "input_tokens": 2569970, "cached_input_tokens": 2499968, "cache_write_input_tokens": 0, "output_tokens": 37818, "reasoning_output_tokens": 16911, "total_tokens": 2607788 } } }, { "kind": "context", "timestamp": "2026-09-09T00:46:39.904Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c2e45dc1-def1-4a89-84d1-853b964571d5", "process_id": "57813", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_base.log; tail -4 /app/solution/context.log; tail -3 /app/solution/disjoint.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_base.log; tail -4 /app/solution/context.log; tail -3 /app/solution/disjoint.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_base test epoch 10 loss 8.6636 seconds 153.4\nfinal_base test epoch 11 loss 8.62074 seconds 164.5\nfinal_base test epoch 12 loss 8.5599 seconds 171.3\nfinal_base test epoch 13 loss 8.49824 seconds 180.0\ncontext 3800 loss 4.434284181594848 seconds 305.2355651855469\ncontext 3900 loss 4.431431746482849 seconds 311.7789978981018\ncontext 4000 loss 4.416066236495972 seconds 317.25573778152466\nSCORE 4000 0.02943688173607104\n", "stderr": "", "aggregated_output": "final_base test epoch 10 loss 8.6636 seconds 153.4\nfinal_base test epoch 11 loss 8.62074 seconds 164.5\nfinal_base test epoch 12 loss 8.5599 seconds 171.3\nfinal_base test epoch 13 loss 8.49824 seconds 180.0\ncontext 3800 loss 4.434284181594848 seconds 305.2355651855469\ncontext 3900 loss 4.431431746482849 seconds 311.7789978981018\ncontext 4000 loss 4.416066236495972 seconds 317.25573778152466\nSCORE 4000 0.02943688173607104\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5220 }, "formatted_output": "final_base test epoch 10 loss 8.6636 seconds 153.4\nfinal_base test epoch 11 loss 8.62074 seconds 164.5\nfinal_base test epoch 12 loss 8.5599 seconds 171.3\nfinal_base test epoch 13 loss 8.49824 seconds 180.0\ncontext 3800 loss 4.434284181594848 seconds 305.2355651855469\ncontext 3900 loss 4.431431746482849 seconds 311.7789978981018\ncontext 4000 loss 4.416066236495972 seconds 317.25573778152466\nSCORE 4000 0.02943688173607104\n" } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-09T00:46:57.468Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:46:57.468Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:47:00.932Z", "callId": "call_bnr7V9K4z3I84alyXNGIdQcR", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_vae --resume vae_trend_validation.pt --epochs 3 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/temporal_vae.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_validation.pt --dest context_scores.npy --no-grid > /app/solution/context_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:47:03.380Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"8d4ba8\",\"wall_time_seconds\":1.003691067,\"session_id\":38106,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"f16506\",\"wall_time_seconds\":1.001137518,\"session_id\":94406,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:47:01.039Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ac702cf487d1822932c0835e133b", "usage": { "input_tokens": 72712, "cached_input_tokens": 71808, "cache_write_input_tokens": 0, "output_tokens": 558, "reasoning_output_tokens": 377, "total_tokens": 73270 }, "turn_token_usage": { "input_tokens": 2642682, "cached_input_tokens": 2571776, "cache_write_input_tokens": 0, "output_tokens": 38376, "reasoning_output_tokens": 17288, "total_tokens": 2681058 }, "thread_token_usage": { "input_tokens": 2642682, "cached_input_tokens": 2571776, "cache_write_input_tokens": 0, "output_tokens": 38376, "reasoning_output_tokens": 17288, "total_tokens": 2681058 } } }, { "kind": "context", "timestamp": "2026-09-09T00:47:01.530Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd597535-bd20-42bd-b5fe-86faf2729d8e", "process_id": "3569", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py vae_trend_validation.pt --dest vae_scores.npy --no-grid > /app/solution/vae_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py vae_trend_validation.pt --dest vae_scores.npy --no-grid > /app/solution/vae_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 22, "nanos": 700750278 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:47:24.714Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e3fa4ecf-53bb-4e01-8032-e8c06caf298d", "process_id": "94406", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_validation.pt --dest context_scores.npy --no-grid > /app/solution/context_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_validation.pt --dest context_scores.npy --no-grid > /app/solution/context_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 22, "nanos": 345256363 }, "formatted_output": "" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-09T00:47:31.692Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:47:31.692Z", "text": "" } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-09T00:47:43.213Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:47:43.213Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:47:55.903Z", "callId": "call_gDpqogMOU1eF4RharteNejBn", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/model.py';s=open(p).read().replace(\\\"nn.EmbeddingBag(n_items,hidden,mode='sum')\\\", \\\"nn.EmbeddingBag(n_items,hidden,mode='sum',sparse=n_items>50000)\\\").replace(\\\"nn.EmbeddingBag(n_items,dim,mode='sum')\\\", \\\"nn.EmbeddingBag(n_items,dim,mode='sum',sparse=n_items>50000)\\\").replace(\\\"nn.Embedding(n_items,dim)\\\", \\\"nn.Embedding(n_items,dim,sparse=n_items>50000)\\\").replace(\\\"nn.Embedding(n_items,1)\\\", \\\"nn.Embedding(n_items,1,sparse=n_items>50000)\\\");open(p,'w').write(s)\\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\\\ndef main():')\\nf='''\\\\nclass RetrievalOptimizer:\\n \\\"\\\"\\\"Use sparse embedding gradients for large catalogs and Adam for dense layers.\\\"\\\"\\\"\\n def __init__(self,model,lr,weight_decay=1e-5):\\n sparse=[m.weight for m in model.modules() if isinstance(m,(torch.nn.Embedding,torch.nn.EmbeddingBag)) and m.sparse]\\n sparse_ids={id(p) for p in sparse};dense=[p for p in model.parameters() if id(p) not in sparse_ids]\\n self.optimizers=[torch.optim.Adam(dense,lr=lr,weight_decay=weight_decay)]\\n if sparse:self.optimizers.append(torch.optim.SparseAdam(sparse,lr=lr))\\n def zero_grad(self,set_to_none=True):\\n for opt in self.optimizers:opt.zero_grad(set_to_none=set_to_none)\\n def step(self):\\n for opt in self.optimizers:opt.step()\\n'''\\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)');open(p,'w').write(s)\\np='/app/solution/test_contract.py';s=open(p).read().replace('from train import training_loss','from train import training_loss,RetrievalOptimizer').replace(\\\"loss.backward();assert torch.isfinite(loss)\\\",\\\"loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\\\");open(p,'w').write(s)\\nPY\\ntail -3 /app/solution/vae_analyze.log; tail -3 /app/solution/context_analyze.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/final_base.log; tail -4 /app/solution/temporal_vae.log\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:47:56.252Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n{\"chunk_id\":\"8bdba3\",\"wall_time_seconds\":0.00000578,\"exit_code\":0,\"original_token_count\":137,\"output\":\"vae_scores.npy\\npop 0.0 score 0.025588195710324398\\ncontext_scores.npy\\npop 0.0 score 0.023155714251069482\\nSCORE 1 0.039427328658754285\\ndisjoint validation epoch 2 loss 8.28832 seconds 48.9\\ndisjoint validation epoch 3 loss 8.22103 seconds 66.6\\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\\nfinal_base test epoch 17 loss 8.3274 seconds 220.2\\nfinal_base test epoch 18 loss 8.28625 seconds 231.2\\nfinal_base test epoch 19 loss 8.25189 seconds 241.0\\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\\ntemporal train samples 43885 entries 29998200\\n\"}\n{\"current_time\":\"2026-09-09 00:47:56 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:47:56.027Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ac87a6f087d1a80a22ef2c4ae459", "usage": { "input_tokens": 73363, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 1414, "reasoning_output_tokens": 776, "total_tokens": 74777 }, "turn_token_usage": { "input_tokens": 2716045, "cached_input_tokens": 2644352, "cache_write_input_tokens": 0, "output_tokens": 39790, "reasoning_output_tokens": 18064, "total_tokens": 2755835 }, "thread_token_usage": { "input_tokens": 2716045, "cached_input_tokens": 2644352, "cache_write_input_tokens": 0, "output_tokens": 39790, "reasoning_output_tokens": 18064, "total_tokens": 2755835 } } }, { "kind": "context", "timestamp": "2026-09-09T00:47:56.142Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f6c7ca5c-987b-44e5-944b-d1a2c8354563", "process_id": "69773", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"nn.EmbeddingBag(n_items,hidden,mode='sum')\", \"nn.EmbeddingBag(n_items,hidden,mode='sum',sparse=n_items>50000)\").replace(\"nn.EmbeddingBag(n_items,dim,mode='sum')\", \"nn.EmbeddingBag(n_items,dim,mode='sum',sparse=n_items>50000)\").replace(\"nn.Embedding(n_items,dim)\", \"nn.Embedding(n_items,dim,sparse=n_items>50000)\").replace(\"nn.Embedding(n_items,1)\", \"nn.Embedding(n_items,1,sparse=n_items>50000)\");open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\ndef main():')\nf='''\\nclass RetrievalOptimizer:\n \"\"\"Use sparse embedding gradients for large catalogs and Adam for dense layers.\"\"\"\n def __init__(self,model,lr,weight_decay=1e-5):\n sparse=[m.weight for m in model.modules() if isinstance(m,(torch.nn.Embedding,torch.nn.EmbeddingBag)) and m.sparse]\n sparse_ids={id(p) for p in sparse};dense=[p for p in model.parameters() if id(p) not in sparse_ids]\n self.optimizers=[torch.optim.Adam(dense,lr=lr,weight_decay=weight_decay)]\n if sparse:self.optimizers.append(torch.optim.SparseAdam(sparse,lr=lr))\n def zero_grad(self,set_to_none=True):\n for opt in self.optimizers:opt.zero_grad(set_to_none=set_to_none)\n def step(self):\n for opt in self.optimizers:opt.step()\n'''\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)');open(p,'w').write(s)\np='/app/solution/test_contract.py';s=open(p).read().replace('from train import training_loss','from train import training_loss,RetrievalOptimizer').replace(\"loss.backward();assert torch.isfinite(loss)\",\"loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\");open(p,'w').write(s)\nPY\ntail -3 /app/solution/vae_analyze.log; tail -3 /app/solution/context_analyze.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/final_base.log; tail -4 /app/solution/temporal_vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace(\"nn.EmbeddingBag(n_items,hidden,mode='sum')\", \"nn.EmbeddingBag(n_items,hidden,mode='sum',sparse=n_items>50000)\").replace(\"nn.EmbeddingBag(n_items,dim,mode='sum')\", \"nn.EmbeddingBag(n_items,dim,mode='sum',sparse=n_items>50000)\").replace(\"nn.Embedding(n_items,dim)\", \"nn.Embedding(n_items,dim,sparse=n_items>50000)\").replace(\"nn.Embedding(n_items,1)\", \"nn.Embedding(n_items,1,sparse=n_items>50000)\");open(p,'w').write(s)\np='/app/solution/train.py';s=open(p).read();pos=s.index('\\ndef main():')\nf='''\\nclass RetrievalOptimizer:\n \"\"\"Use sparse embedding gradients for large catalogs and Adam for dense layers.\"\"\"\n def __init__(self,model,lr,weight_decay=1e-5):\n sparse=[m.weight for m in model.modules() if isinstance(m,(torch.nn.Embedding,torch.nn.EmbeddingBag)) and m.sparse]\n sparse_ids={id(p) for p in sparse};dense=[p for p in model.parameters() if id(p) not in sparse_ids]\n self.optimizers=[torch.optim.Adam(dense,lr=lr,weight_decay=weight_decay)]\n if sparse:self.optimizers.append(torch.optim.SparseAdam(sparse,lr=lr))\n def zero_grad(self,set_to_none=True):\n for opt in self.optimizers:opt.zero_grad(set_to_none=set_to_none)\n def step(self):\n for opt in self.optimizers:opt.step()\n'''\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)');open(p,'w').write(s)\np='/app/solution/test_contract.py';s=open(p).read().replace('from train import training_loss','from train import training_loss,RetrievalOptimizer').replace(\"loss.backward();assert torch.isfinite(loss)\",\"loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\");open(p,'w').write(s)\nPY\ntail -3 /app/solution/vae_analyze.log; tail -3 /app/solution/context_analyze.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/final_base.log; tail -4 /app/solution/temporal_vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "vae_scores.npy\npop 0.0 score 0.025588195710324398\ncontext_scores.npy\npop 0.0 score 0.023155714251069482\nSCORE 1 0.039427328658754285\ndisjoint validation epoch 2 loss 8.28832 seconds 48.9\ndisjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\nfinal_base test epoch 17 loss 8.3274 seconds 220.2\nfinal_base test epoch 18 loss 8.28625 seconds 231.2\nfinal_base test epoch 19 loss 8.25189 seconds 241.0\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\ntemporal train samples 43885 entries 29998200\n", "stderr": "", "aggregated_output": "vae_scores.npy\npop 0.0 score 0.025588195710324398\ncontext_scores.npy\npop 0.0 score 0.023155714251069482\nSCORE 1 0.039427328658754285\ndisjoint validation epoch 2 loss 8.28832 seconds 48.9\ndisjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\nfinal_base test epoch 17 loss 8.3274 seconds 220.2\nfinal_base test epoch 18 loss 8.28625 seconds 231.2\nfinal_base test epoch 19 loss 8.25189 seconds 241.0\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\ntemporal train samples 43885 entries 29998200\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5780 }, "formatted_output": "vae_scores.npy\npop 0.0 score 0.025588195710324398\ncontext_scores.npy\npop 0.0 score 0.023155714251069482\nSCORE 1 0.039427328658754285\ndisjoint validation epoch 2 loss 8.28832 seconds 48.9\ndisjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\nfinal_base test epoch 17 loss 8.3274 seconds 220.2\nfinal_base test epoch 18 loss 8.28625 seconds 231.2\nfinal_base test epoch 19 loss 8.25189 seconds 241.0\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\ntemporal train samples 43885 entries 29998200\n" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-09T00:48:15.351Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:48:15.351Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:48:17.199Z", "text": "The contract checks pass: each model produces independent user and item vectors, the score equals their dot product, and both towers receive gradients. The bounded sampled-softmax path also passes a training check for a larger catalog. I’m now measuring whether combining the encoders improves ranking quality enough to justify the larger vector.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:48:25.716Z", "callId": "call_1K65dqGPuxTOdlXNzmEvPGW2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/ensemble_sweep.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nclass Blend:\\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\\ntorch.set_num_threads(2);m=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];best=(-1,None)\\nfor vae in [0.,.2,.4,.6]:\\n for context in [0.,.1,.2,.4]:\\n weights=[1-vae-context,vae,context]\\n if weights[0]<0:continue\\n val=m(Blend(arrays,weights),bias);print(weights,val,flush=True)\\n if val>best[0]:best=(val,weights)\\nprint('BEST',best,flush=True);(ROOT/'ensemble_best.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_sweep.py > /app/solution/ensemble.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:48:28.133Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"b822e6\",\"wall_time_seconds\":1.000603015,\"session_id\":56172,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"973695\",\"wall_time_seconds\":1.001355461,\"session_id\":21401,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:48:25.844Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0acbc922887d1b93d1a2330fdcd50", "usage": { "input_tokens": 75047, "cached_input_tokens": 73216, "cache_write_input_tokens": 0, "output_tokens": 907, "reasoning_output_tokens": 400, "total_tokens": 75954 }, "turn_token_usage": { "input_tokens": 2791092, "cached_input_tokens": 2717568, "cache_write_input_tokens": 0, "output_tokens": 40697, "reasoning_output_tokens": 18464, "total_tokens": 2831789 }, "thread_token_usage": { "input_tokens": 2791092, "cached_input_tokens": 2717568, "cache_write_input_tokens": 0, "output_tokens": 40697, "reasoning_output_tokens": 18464, "total_tokens": 2831789 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-09T00:48:41.994Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:48:41.994Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T00:48:42.187Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ea945045-1002-42ad-a518-c30cb80bcdd1", "process_id": "21401", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n", "stderr": "", "aggregated_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 57584096 }, "formatted_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\n" } } }, { "kind": "tool", "timestamp": "2026-09-09T00:48:43.981Z", "callId": "call_xYwsfIZFm8w1jHjLXQXVoCLh", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ensemble.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/temporal_vae.log; tail -4 /app/solution/final_base.log\",\"max_output_tokens\":1500}));\ntext(await tools.write_stdin({session_id:21401,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1300}));\n" }, "result": { "timestamp": "2026-09-09T00:48:44.146Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"54e4f4\",\"wall_time_seconds\":0.00000867,\"exit_code\":0,\"original_token_count\":111,\"output\":\"disjoint validation epoch 3 loss 8.22103 seconds 66.6\\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\\nSCORE 5 0.03901446565399594\\ntemporal train samples 43885 entries 29998200\\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\\nfinal_base test epoch 21 loss 8.1986 seconds 266.0\\nfinal_base test epoch 22 loss 8.17659 seconds 278.5\\nfinal_base test epoch 23 loss 8.15462 seconds 293.2\\n\"}\n{\"chunk_id\":\"65ca8d\",\"wall_time_seconds\":0.000002811,\"exit_code\":0,\"original_token_count\":77,\"output\":\"TwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nLarge-catalog bounded sampled-softmax path: PASS\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:48:44.083Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d3a4f115-4b8a-4665-8dfe-82f46d0c75b1", "process_id": "81108", "command": [ "/bin/sh", "-lc", "cat /app/solution/ensemble.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/temporal_vae.log; tail -4 /app/solution/final_base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ensemble.log; tail -4 /app/solution/disjoint.log; tail -4 /app/solution/temporal_vae.log; tail -4 /app/solution/final_base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "disjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ntemporal train samples 43885 entries 29998200\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\nfinal_base test epoch 21 loss 8.1986 seconds 266.0\nfinal_base test epoch 22 loss 8.17659 seconds 278.5\nfinal_base test epoch 23 loss 8.15462 seconds 293.2\n", "stderr": "", "aggregated_output": "disjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ntemporal train samples 43885 entries 29998200\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\nfinal_base test epoch 21 loss 8.1986 seconds 266.0\nfinal_base test epoch 22 loss 8.17659 seconds 278.5\nfinal_base test epoch 23 loss 8.15462 seconds 293.2\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8670 }, "formatted_output": "disjoint validation epoch 3 loss 8.22103 seconds 66.6\ndisjoint validation epoch 4 loss 8.18935 seconds 82.6\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ntemporal train samples 43885 entries 29998200\nfinal_base test epoch 20 loss 8.22079 seconds 251.3\nfinal_base test epoch 21 loss 8.1986 seconds 266.0\nfinal_base test epoch 22 loss 8.17659 seconds 278.5\nfinal_base test epoch 23 loss 8.15462 seconds 293.2\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:48:44.130Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0acdc758087d1a2dab7b91ef8c189", "usage": { "input_tokens": 76045, "cached_input_tokens": 74880, "cache_write_input_tokens": 0, "output_tokens": 349, "reasoning_output_tokens": 246, "total_tokens": 76394 }, "turn_token_usage": { "input_tokens": 2867137, "cached_input_tokens": 2792448, "cache_write_input_tokens": 0, "output_tokens": 41046, "reasoning_output_tokens": 18710, "total_tokens": 2908183 }, "thread_token_usage": { "input_tokens": 2867137, "cached_input_tokens": 2792448, "cache_write_input_tokens": 0, "output_tokens": 41046, "reasoning_output_tokens": 18710, "total_tokens": 2908183 } } }, { "kind": "context", "timestamp": "2026-09-09T00:49:07.066Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09eb3ff9-a837-4fe6-a038-393e18540d08", "process_id": "56172", "command": [ "/bin/sh", "-lc", "cat > /app/solution/ensemble_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nclass Blend:\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];best=(-1,None)\nfor vae in [0.,.2,.4,.6]:\n for context in [0.,.1,.2,.4]:\n weights=[1-vae-context,vae,context]\n if weights[0]<0:continue\n val=m(Blend(arrays,weights),bias);print(weights,val,flush=True)\n if val>best[0]:best=(val,weights)\nprint('BEST',best,flush=True);(ROOT/'ensemble_best.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_sweep.py > /app/solution/ensemble.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/ensemble_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nclass Blend:\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');c=json.loads((ROOT/'temporal2_fine.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];best=(-1,None)\nfor vae in [0.,.2,.4,.6]:\n for context in [0.,.1,.2,.4]:\n weights=[1-vae-context,vae,context]\n if weights[0]<0:continue\n val=m(Blend(arrays,weights),bias);print(weights,val,flush=True)\n if val>best[0]:best=(val,weights)\nprint('BEST',best,flush=True);(ROOT/'ensemble_best.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_sweep.py > /app/solution/ensemble.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 41, "nanos": 127999736 }, "formatted_output": "" } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-09T00:49:10.276Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:49:10.276Z", "text": "" } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-09T00:49:33.066Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:49:33.066Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:49:34.383Z", "callId": "call_UpVqmGT0H7AIDc8QQhUvDtPJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/ensemble.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/final_base.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:49:34.554Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"78e232\",\"wall_time_seconds\":0.00000548,\"exit_code\":0,\"original_token_count\":279,\"output\":\"[1.0, 0.0, 0.0] 0.04908713046941107\\n[0.9, 0.0, 0.1] 0.05022086361198549\\n[0.8, 0.0, 0.2] 0.05078186044602234\\n[0.6, 0.0, 0.4] 0.05043801220387186\\n[0.8, 0.2, 0.0] 0.051524229913681954\\n[0.7000000000000001, 0.2, 0.1] 0.053118039362038995\\n[0.6000000000000001, 0.2, 0.2] 0.05341630095200488\\n[0.4, 0.2, 0.4] 0.05129951238013273\\n[0.6, 0.4, 0.0] 0.052648461160421135\\n[0.5, 0.4, 0.1] 0.05332425357674849\\n[0.39999999999999997, 0.4, 0.2] 0.053711035557156485\\n[0.19999999999999996, 0.4, 0.4] 0.051828127783696554\\n[0.4, 0.6, 0.0] 0.05236589156803338\\n[0.30000000000000004, 0.6, 0.1] 0.05318365527274456\\n[0.2, 0.6, 0.2] 0.052812429627713736\\n[0.0, 0.6, 0.4] 0.04951526560549158\\nBEST (0.053711035557156485, [0.39999999999999997, 0.4, 0.2])\\ntemporal train samples 43885 entries 29998200\\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\\nSCORE 5 0.03901446565399594\\ndisjoint validation epoch 6 loss 8.10886 seconds 145.1\\ndisjoint validation epoch 7 loss 8.07829 seconds 173.2\\nfinal_base test epoch 24 loss 8.1351 seconds 314.5\\nfinal_base test epoch 25 loss 8.11729 seconds 334.1\\nfinal_base test epoch 26 loss 8.10617 seconds 350.5\\n\"}\n{\"current_time\":\"2026-09-09 00:49:34 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:49:34.494Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-619c5abd-5a94-4d12-92bd-3db4c9abfc82", "process_id": "82413", "command": [ "/bin/sh", "-lc", "cat /app/solution/ensemble.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/final_base.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ensemble.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/final_base.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[1.0, 0.0, 0.0] 0.04908713046941107\n[0.9, 0.0, 0.1] 0.05022086361198549\n[0.8, 0.0, 0.2] 0.05078186044602234\n[0.6, 0.0, 0.4] 0.05043801220387186\n[0.8, 0.2, 0.0] 0.051524229913681954\n[0.7000000000000001, 0.2, 0.1] 0.053118039362038995\n[0.6000000000000001, 0.2, 0.2] 0.05341630095200488\n[0.4, 0.2, 0.4] 0.05129951238013273\n[0.6, 0.4, 0.0] 0.052648461160421135\n[0.5, 0.4, 0.1] 0.05332425357674849\n[0.39999999999999997, 0.4, 0.2] 0.053711035557156485\n[0.19999999999999996, 0.4, 0.4] 0.051828127783696554\n[0.4, 0.6, 0.0] 0.05236589156803338\n[0.30000000000000004, 0.6, 0.1] 0.05318365527274456\n[0.2, 0.6, 0.2] 0.052812429627713736\n[0.0, 0.6, 0.4] 0.04951526560549158\nBEST (0.053711035557156485, [0.39999999999999997, 0.4, 0.2])\ntemporal train samples 43885 entries 29998200\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ndisjoint validation epoch 6 loss 8.10886 seconds 145.1\ndisjoint validation epoch 7 loss 8.07829 seconds 173.2\nfinal_base test epoch 24 loss 8.1351 seconds 314.5\nfinal_base test epoch 25 loss 8.11729 seconds 334.1\nfinal_base test epoch 26 loss 8.10617 seconds 350.5\n", "stderr": "", "aggregated_output": "[1.0, 0.0, 0.0] 0.04908713046941107\n[0.9, 0.0, 0.1] 0.05022086361198549\n[0.8, 0.0, 0.2] 0.05078186044602234\n[0.6, 0.0, 0.4] 0.05043801220387186\n[0.8, 0.2, 0.0] 0.051524229913681954\n[0.7000000000000001, 0.2, 0.1] 0.053118039362038995\n[0.6000000000000001, 0.2, 0.2] 0.05341630095200488\n[0.4, 0.2, 0.4] 0.05129951238013273\n[0.6, 0.4, 0.0] 0.052648461160421135\n[0.5, 0.4, 0.1] 0.05332425357674849\n[0.39999999999999997, 0.4, 0.2] 0.053711035557156485\n[0.19999999999999996, 0.4, 0.4] 0.051828127783696554\n[0.4, 0.6, 0.0] 0.05236589156803338\n[0.30000000000000004, 0.6, 0.1] 0.05318365527274456\n[0.2, 0.6, 0.2] 0.052812429627713736\n[0.0, 0.6, 0.4] 0.04951526560549158\nBEST (0.053711035557156485, [0.39999999999999997, 0.4, 0.2])\ntemporal train samples 43885 entries 29998200\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ndisjoint validation epoch 6 loss 8.10886 seconds 145.1\ndisjoint validation epoch 7 loss 8.07829 seconds 173.2\nfinal_base test epoch 24 loss 8.1351 seconds 314.5\nfinal_base test epoch 25 loss 8.11729 seconds 334.1\nfinal_base test epoch 26 loss 8.10617 seconds 350.5\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5480 }, "formatted_output": "[1.0, 0.0, 0.0] 0.04908713046941107\n[0.9, 0.0, 0.1] 0.05022086361198549\n[0.8, 0.0, 0.2] 0.05078186044602234\n[0.6, 0.0, 0.4] 0.05043801220387186\n[0.8, 0.2, 0.0] 0.051524229913681954\n[0.7000000000000001, 0.2, 0.1] 0.053118039362038995\n[0.6000000000000001, 0.2, 0.2] 0.05341630095200488\n[0.4, 0.2, 0.4] 0.05129951238013273\n[0.6, 0.4, 0.0] 0.052648461160421135\n[0.5, 0.4, 0.1] 0.05332425357674849\n[0.39999999999999997, 0.4, 0.2] 0.053711035557156485\n[0.19999999999999996, 0.4, 0.4] 0.051828127783696554\n[0.4, 0.6, 0.0] 0.05236589156803338\n[0.30000000000000004, 0.6, 0.1] 0.05318365527274456\n[0.2, 0.6, 0.2] 0.052812429627713736\n[0.0, 0.6, 0.4] 0.04951526560549158\nBEST (0.053711035557156485, [0.39999999999999997, 0.4, 0.2])\ntemporal train samples 43885 entries 29998200\ndisjoint validation epoch 5 loss 8.15434 seconds 104.1\nSCORE 5 0.03901446565399594\ndisjoint validation epoch 6 loss 8.10886 seconds 145.1\ndisjoint validation epoch 7 loss 8.07829 seconds 173.2\nfinal_base test epoch 24 loss 8.1351 seconds 314.5\nfinal_base test epoch 25 loss 8.11729 seconds 334.1\nfinal_base test epoch 26 loss 8.10617 seconds 350.5\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:49:34.524Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0acec5d6087d1a5f0128591fa5ddc", "usage": { "input_tokens": 76701, "cached_input_tokens": 75904, "cache_write_input_tokens": 0, "output_tokens": 1116, "reasoning_output_tokens": 1034, "total_tokens": 77817 }, "turn_token_usage": { "input_tokens": 2943838, "cached_input_tokens": 2868352, "cache_write_input_tokens": 0, "output_tokens": 42162, "reasoning_output_tokens": 19744, "total_tokens": 2986000 }, "thread_token_usage": { "input_tokens": 2943838, "cached_input_tokens": 2868352, "cache_write_input_tokens": 0, "output_tokens": 42162, "reasoning_output_tokens": 19744, "total_tokens": 2986000 } } } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-09T00:49:59.073Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:49:59.073Z", "text": "" } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-09T00:50:20.079Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:50:20.079Z", "text": "" } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-09T00:50:27.994Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:50:27.994Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:50:30.342Z", "text": "Combining the temporal, variational, and local-context encoders raised public NDCG@10 to 0.0537. Their vectors concatenate into a single 579-dimensional vector, preserving dot-product retrieval. I’m also checking whether recent first-time discoveries are a better item-popularity signal than repeat listening.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:50:45.711Z", "callId": "call_a92pJvHUXpdwZtab5NrMPKeP", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/discovery_trends.py <<'PY'\\n\\\"\\\"\\\"Count recent first-time organic adoption using only events at the cutoff.\\\"\\\"\\\"\\nimport numpy as np\\nfrom train import ROOT\\n\\ndef discovery_features(stage):\\n p=ROOT/f'discovery_trends_{stage}.npz'\\n if p.exists():return dict(np.load(p))\\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\\n ids=np.load(ROOT/'ids.npz');n=len(ids['items']);users=len(ids['users']);end=24786800+(606600 if stage=='test' else 0);starts=[np.searchsorted(e['u'],np.arange(users+1)) for e in ev]\\n out={f'{kind}{days}':np.zeros(n,dtype='float32') for kind in ['new','newlike'] for days in [3,7,14,30,60,180]}\\n for u in range(users):\\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);items,ix=np.unique(v['i'],return_inverse=True);first=np.full(len(items),2**31-1,dtype='int32');np.minimum.at(first,ix,v['t'])\\n good=(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))\\n for days in [3,7,14,30,60,180]:\\n keep=good&(first[ix]>end-days*86400);out[f'new{days}'][np.unique(v['i'][keep])]+=1;out[f'newlike{days}'][np.unique(v['i'][keep&(v['k']==1)])]+=1\\n out={k:np.log1p(v) for k,v in out.items()};np.savez(p,**out);return out\\nif __name__=='__main__':\\n import sys\\n discovery_features(sys.argv[1] if len(sys.argv)>1 else 'train')\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/context.py';s=open(p).read().replace(\\\"p.add_argument('--lr',type=float,default=.002);\\\", \\\"p.add_argument('--lr',type=float,default=.002);p.add_argument('--resume');\\\")\\ns=s.replace(\\\"model=ContextTwoTower(n,dim=a.dim);opt=\\\", \\\"model=ContextTwoTower(n,dim=a.dim)\\\\n if a.resume:model.load_state_dict(torch.load(ROOT/a.resume,weights_only=False)['model'])\\\\n opt=\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/discovery_trends.py > /app/solution/discovery_trends.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --name context_more --resume context_validation.pt --steps 4000 --lr .001 > /app/solution/context_more.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:50:48.143Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"14002f\",\"wall_time_seconds\":1.002298804,\"session_id\":78707,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"7564a8\",\"wall_time_seconds\":1.001905795,\"session_id\":29969,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:50:45.808Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ad1ec8ac87d1bfedbf93dee6a819", "usage": { "input_tokens": 78485, "cached_input_tokens": 76544, "cache_write_input_tokens": 0, "output_tokens": 2001, "reasoning_output_tokens": 1175, "total_tokens": 80486 }, "turn_token_usage": { "input_tokens": 3022323, "cached_input_tokens": 2944896, "cache_write_input_tokens": 0, "output_tokens": 44163, "reasoning_output_tokens": 20919, "total_tokens": 3066486 }, "thread_token_usage": { "input_tokens": 3022323, "cached_input_tokens": 2944896, "cache_write_input_tokens": 0, "output_tokens": 44163, "reasoning_output_tokens": 20919, "total_tokens": 3066486 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-09T00:50:53.480Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:50:53.480Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:50:55.527Z", "callId": "call_el6emOTRwO3y5QOuzSDreijN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:50:55.691Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"477b69\",\"wall_time_seconds\":0.0000041,\"exit_code\":0,\"original_token_count\":134,\"output\":\"final_base test epoch 29 loss 8.07145 seconds 391.6\\nfinal_base test epoch 30 loss 8.05624 seconds 406.9\\nfinal_base test epoch 31 loss 8.04232 seconds 419.0\\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\\ntemporal train samples 43885 entries 29998200\\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\\ndisjoint validation epoch 8 loss 8.04367 seconds 197.6\\ndisjoint validation epoch 9 loss 8.0096 seconds 218.5\\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\\nSCORE 10 0.04005708231345611\\n\"}\n{\"current_time\":\"2026-09-09 00:50:55 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:50:55.627Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b327dc8f-7581-461b-a3f8-ad100e4ef782", "process_id": "66204", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_base test epoch 29 loss 8.07145 seconds 391.6\nfinal_base test epoch 30 loss 8.05624 seconds 406.9\nfinal_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ndisjoint validation epoch 8 loss 8.04367 seconds 197.6\ndisjoint validation epoch 9 loss 8.0096 seconds 218.5\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\n", "stderr": "", "aggregated_output": "final_base test epoch 29 loss 8.07145 seconds 391.6\nfinal_base test epoch 30 loss 8.05624 seconds 406.9\nfinal_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ndisjoint validation epoch 8 loss 8.04367 seconds 197.6\ndisjoint validation epoch 9 loss 8.0096 seconds 218.5\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4100 }, "formatted_output": "final_base test epoch 29 loss 8.07145 seconds 391.6\nfinal_base test epoch 30 loss 8.05624 seconds 406.9\nfinal_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ndisjoint validation epoch 8 loss 8.04367 seconds 197.6\ndisjoint validation epoch 9 loss 8.0096 seconds 218.5\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:50:55.653Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ad68813487d18ab8b0b4f5d1a3bd", "usage": { "input_tokens": 80578, "cached_input_tokens": 78336, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 29, "total_tokens": 80691 }, "turn_token_usage": { "input_tokens": 3102901, "cached_input_tokens": 3023232, "cache_write_input_tokens": 0, "output_tokens": 44276, "reasoning_output_tokens": 20948, "total_tokens": 3147177 }, "thread_token_usage": { "input_tokens": 3102901, "cached_input_tokens": 3023232, "cache_write_input_tokens": 0, "output_tokens": 44276, "reasoning_output_tokens": 20948, "total_tokens": 3147177 } } }, { "kind": "context", "timestamp": "2026-09-09T00:51:03.680Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1be56b92-31dc-409e-a470-276cf7035069", "process_id": "78707", "command": [ "/bin/sh", "-lc", "cat > /app/solution/discovery_trends.py <<'PY'\n\"\"\"Count recent first-time organic adoption using only events at the cutoff.\"\"\"\nimport numpy as np\nfrom train import ROOT\n\ndef discovery_features(stage):\n p=ROOT/f'discovery_trends_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n ids=np.load(ROOT/'ids.npz');n=len(ids['items']);users=len(ids['users']);end=24786800+(606600 if stage=='test' else 0);starts=[np.searchsorted(e['u'],np.arange(users+1)) for e in ev]\n out={f'{kind}{days}':np.zeros(n,dtype='float32') for kind in ['new','newlike'] for days in [3,7,14,30,60,180]}\n for u in range(users):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);items,ix=np.unique(v['i'],return_inverse=True);first=np.full(len(items),2**31-1,dtype='int32');np.minimum.at(first,ix,v['t'])\n good=(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))\n for days in [3,7,14,30,60,180]:\n keep=good&(first[ix]>end-days*86400);out[f'new{days}'][np.unique(v['i'][keep])]+=1;out[f'newlike{days}'][np.unique(v['i'][keep&(v['k']==1)])]+=1\n out={k:np.log1p(v) for k,v in out.items()};np.savez(p,**out);return out\nif __name__=='__main__':\n import sys\n discovery_features(sys.argv[1] if len(sys.argv)>1 else 'train')\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/context.py';s=open(p).read().replace(\"p.add_argument('--lr',type=float,default=.002);\", \"p.add_argument('--lr',type=float,default=.002);p.add_argument('--resume');\")\ns=s.replace(\"model=ContextTwoTower(n,dim=a.dim);opt=\", \"model=ContextTwoTower(n,dim=a.dim)\\n if a.resume:model.load_state_dict(torch.load(ROOT/a.resume,weights_only=False)['model'])\\n opt=\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/discovery_trends.py > /app/solution/discovery_trends.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/discovery_trends.py <<'PY'\n\"\"\"Count recent first-time organic adoption using only events at the cutoff.\"\"\"\nimport numpy as np\nfrom train import ROOT\n\ndef discovery_features(stage):\n p=ROOT/f'discovery_trends_{stage}.npz'\n if p.exists():return dict(np.load(p))\n ev=[np.load(ROOT/'train_events.npy',mmap_mode='r')]\n if stage=='test':ev.append(np.load(ROOT/'val_events.npy',mmap_mode='r'))\n ids=np.load(ROOT/'ids.npz');n=len(ids['items']);users=len(ids['users']);end=24786800+(606600 if stage=='test' else 0);starts=[np.searchsorted(e['u'],np.arange(users+1)) for e in ev]\n out={f'{kind}{days}':np.zeros(n,dtype='float32') for kind in ['new','newlike'] for days in [3,7,14,30,60,180]}\n for u in range(users):\n v=np.concatenate([e[s[u]:s[u+1]] for e,s in zip(ev,starts)]);items,ix=np.unique(v['i'],return_inverse=True);first=np.full(len(items),2**31-1,dtype='int32');np.minimum.at(first,ix,v['t'])\n good=(v['o']==1)&(((v['k']==0)&(v['q']>=80))|(v['k']==1))\n for days in [3,7,14,30,60,180]:\n keep=good&(first[ix]>end-days*86400);out[f'new{days}'][np.unique(v['i'][keep])]+=1;out[f'newlike{days}'][np.unique(v['i'][keep&(v['k']==1)])]+=1\n out={k:np.log1p(v) for k,v in out.items()};np.savez(p,**out);return out\nif __name__=='__main__':\n import sys\n discovery_features(sys.argv[1] if len(sys.argv)>1 else 'train')\nPY\n/opt/task-venv/bin/python - <<'PY'\np='/app/solution/context.py';s=open(p).read().replace(\"p.add_argument('--lr',type=float,default=.002);\", \"p.add_argument('--lr',type=float,default=.002);p.add_argument('--resume');\")\ns=s.replace(\"model=ContextTwoTower(n,dim=a.dim);opt=\", \"model=ContextTwoTower(n,dim=a.dim)\\n if a.resume:model.load_state_dict(torch.load(ROOT/a.resume,weights_only=False)['model'])\\n opt=\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/discovery_trends.py > /app/solution/discovery_trends.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 743341580 }, "formatted_output": "" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-09T00:51:17.317Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:51:17.317Z", "text": "" } ] }, { "id": 75, "role": "agent", "timestamp": "2026-09-09T00:51:33.711Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:51:33.711Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:51:45.011Z", "callId": "call_pOgyU71vm7CUnrbP1Dv86VjS", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/adoption_calibrate.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom discovery_trends import discovery_features\\nm=Metric();torch.set_num_threads(2);f=features('train');f.update(discovery_features('train'))\\na=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'ensemble_scores.npy',mode='w+',dtype='float32',shape=a[0].shape)\\nfor st in range(0,len(s),128):s[st:st+128]=.4*a[0][st:st+128]+.4*a[1][st:st+128]+.2*a[2][st:st+128]\\ns.flush();base=json.loads((ROOT/'temporal2_fine.json').read_text())['bias'];bb=sum(w*f[k] for k,w in base.items());best=(m(s,bb),base);print('BASE',best,flush=True)\\nfor recent,other in [('new7','org7'),('new14','org14'),('new30','org30'),('new7','new180'),('new3','new30'),('newlike7','new7'),('newlike14','new14')]:\\n for strength in [.3,.6,1.,1.5]:\\n c=base.copy();c[recent]=c.get(recent,0)+strength;c[other]=c.get(other,0)-strength;score=m(s,bb+strength*(f[recent]-f[other]));print(c,score,flush=True)\\n if score>best[0]:best=(score,c)\\nprint('BEST',best,flush=True);(ROOT/'adoption_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/adoption_calibrate.py > /app/solution/adoption_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_vae_validation.pt --dest temporal_vae_scores.npy --no-grid > /app/solution/temporal_vae_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:51:47.458Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"959e9e\",\"wall_time_seconds\":1.001548034,\"session_id\":31468,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"1a1c1d\",\"wall_time_seconds\":1.00324248,\"session_id\":62363,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:51:45.144Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ad6fe82087d1a9dc1c517d40aaad", "usage": { "input_tokens": 80955, "cached_input_tokens": 80384, "cache_write_input_tokens": 0, "output_tokens": 1492, "reasoning_output_tokens": 906, "total_tokens": 82447 }, "turn_token_usage": { "input_tokens": 3183856, "cached_input_tokens": 3103616, "cache_write_input_tokens": 0, "output_tokens": 45768, "reasoning_output_tokens": 21854, "total_tokens": 3229624 }, "thread_token_usage": { "input_tokens": 3183856, "cached_input_tokens": 3103616, "cache_write_input_tokens": 0, "output_tokens": 45768, "reasoning_output_tokens": 21854, "total_tokens": 3229624 } } } ] }, { "id": 76, "role": "agent", "timestamp": "2026-09-09T00:51:50.639Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:51:50.639Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:51:52.306Z", "callId": "call_guh9hZwkWdYZJn7sBjrqaHXf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_base.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log; tail -3 /app/solution/temporal_vae.log\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:51:52.433Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"018b7e\",\"wall_time_seconds\":0.00000428,\"exit_code\":0,\"original_token_count\":184,\"output\":\"final_base test epoch 31 loss 8.04232 seconds 419.0\\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\\nfinal_base test epoch 33 loss 8.01608 seconds 461.5\\nfinal_base test epoch 34 loss 8.0101 seconds 488.2\\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\\nSCORE 10 0.04005708231345611\\ndisjoint validation epoch 11 loss 7.95976 seconds 285.1\\ndisjoint validation epoch 12 loss 7.92445 seconds 320.6\\ncontext_more 200 loss 4.11872505903244 seconds 27.994471788406372\\ncontext_more 300 loss 4.087611474990845 seconds 37.36386156082153\\ncontext_more 400 loss 4.07353821516037 seconds 46.6832914352417\\ntemporal train samples 43885 entries 29998200\\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:51:52.404Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e7378210-ebb8-4489-a122-b9b237892493", "process_id": "74239", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_base.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log; tail -3 /app/solution/temporal_vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_base.log; tail -4 /app/solution/disjoint.log; tail -3 /app/solution/context_more.log; tail -3 /app/solution/temporal_vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\nfinal_base test epoch 33 loss 8.01608 seconds 461.5\nfinal_base test epoch 34 loss 8.0101 seconds 488.2\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\ndisjoint validation epoch 11 loss 7.95976 seconds 285.1\ndisjoint validation epoch 12 loss 7.92445 seconds 320.6\ncontext_more 200 loss 4.11872505903244 seconds 27.994471788406372\ncontext_more 300 loss 4.087611474990845 seconds 37.36386156082153\ncontext_more 400 loss 4.07353821516037 seconds 46.6832914352417\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\n", "stderr": "", "aggregated_output": "final_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\nfinal_base test epoch 33 loss 8.01608 seconds 461.5\nfinal_base test epoch 34 loss 8.0101 seconds 488.2\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\ndisjoint validation epoch 11 loss 7.95976 seconds 285.1\ndisjoint validation epoch 12 loss 7.92445 seconds 320.6\ncontext_more 200 loss 4.11872505903244 seconds 27.994471788406372\ncontext_more 300 loss 4.087611474990845 seconds 37.36386156082153\ncontext_more 400 loss 4.07353821516037 seconds 46.6832914352417\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4280 }, "formatted_output": "final_base test epoch 31 loss 8.04232 seconds 419.0\nfinal_base test epoch 32 loss 8.03552 seconds 436.2\nfinal_base test epoch 33 loss 8.01608 seconds 461.5\nfinal_base test epoch 34 loss 8.0101 seconds 488.2\ndisjoint validation epoch 10 loss 7.98444 seconds 242.0\nSCORE 10 0.04005708231345611\ndisjoint validation epoch 11 loss 7.95976 seconds 285.1\ndisjoint validation epoch 12 loss 7.92445 seconds 320.6\ncontext_more 200 loss 4.11872505903244 seconds 27.994471788406372\ncontext_more 300 loss 4.087611474990845 seconds 37.36386156082153\ncontext_more 400 loss 4.07353821516037 seconds 46.6832914352417\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:51:52.419Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ada3b3e087d1b49df74505355bb4", "usage": { "input_tokens": 82542, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, "output_tokens": 105, "reasoning_output_tokens": 30, "total_tokens": 82647 }, "turn_token_usage": { "input_tokens": 3266398, "cached_input_tokens": 3184384, "cache_write_input_tokens": 0, "output_tokens": 45873, "reasoning_output_tokens": 21884, "total_tokens": 3312271 }, "thread_token_usage": { "input_tokens": 3266398, "cached_input_tokens": 3184384, "cache_write_input_tokens": 0, "output_tokens": 45873, "reasoning_output_tokens": 21884, "total_tokens": 3312271 } } } ] }, { "id": 77, "role": "agent", "timestamp": "2026-09-09T00:52:17.813Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:52:17.813Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:52:23.982Z", "callId": "call_iRNWPzgeMboM9jAid1BnWrJZ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/train.py' in c and b'disjoint' in c:os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\ncat > /app/solution/refit_vae.py <<'PY'\\nimport subprocess,sys\\nfrom pathlib import Path\\nroot=Path('/app/solution')\\nbase=[sys.executable,str(root/'train.py'),'--stage','test','--vae','--hidden','600','--dim','256','--half','30','--likes','7','--dropout','.5','--beta','.0002','--eval-every','15']\\nfor extra in [['--name','final_vae_initial','--epochs','5'],['--name','final_vae','--epochs','15','--trend','1','--resume','final_vae_initial_test.pt']]:\\n subprocess.run(base+extra,check=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/refit_vae.py > /app/solution/refit_vae.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:52:25.672Z", "content": "Script completed\nWall time 1.6 seconds\nOutput:\n\n{\"chunk_id\":\"24c035\",\"wall_time_seconds\":1.003549275,\"session_id\":46679,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 00:52:25 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:52:24.137Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ada8afa487d1ac824f9ffd905c58", "usage": { "input_tokens": 82963, "cached_input_tokens": 82304, "cache_write_input_tokens": 0, "output_tokens": 868, "reasoning_output_tokens": 516, "total_tokens": 83831 }, "turn_token_usage": { "input_tokens": 3349361, "cached_input_tokens": 3266688, "cache_write_input_tokens": 0, "output_tokens": 46741, "reasoning_output_tokens": 22400, "total_tokens": 3396102 }, "thread_token_usage": { "input_tokens": 3349361, "cached_input_tokens": 3266688, "cache_write_input_tokens": 0, "output_tokens": 46741, "reasoning_output_tokens": 22400, "total_tokens": 3396102 } } }, { "kind": "context", "timestamp": "2026-09-09T00:52:24.908Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-84152293-5d52-494e-a17c-87f131fed4fc", "process_id": "3740", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'vae_trend' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name disjoint --vae --disjoint --hidden 600 --dim 256 --epochs 40 --half 30 --likes 7 --dropout .7 --beta .001 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/disjoint.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'vae_trend' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --name disjoint --vae --disjoint --hidden 600 --dim 256 --epochs 40 --half 30 --likes 7 --dropout .7 --beta .001 --eval-every 5 --trend 1 --resume vae_initial.pt > /app/solution/disjoint.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 367, "nanos": 699426506 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:52:28.253Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34c2c449-3362-4e73-b16d-3542141d11bd", "process_id": "49267", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal2' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nfor name in ['train','temporal','context']:\n p='/app/solution/'+name+'.py';s=open(p).read();v='a' if name=='context' else 'args';s=s.replace(\"else 'submission.parquet'\", \"else f'{\"+v+\".name}_test.parquet'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_base --epochs 35 --eval-every 35 > /app/solution/final_base.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal2' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nfor name in ['train','temporal','context']:\n p='/app/solution/'+name+'.py';s=open(p).read();v='a' if name=='context' else 'args';s=s.replace(\"else 'submission.parquet'\", \"else f'{\"+v+\".name}_test.parquet'\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/train.py --stage test --name final_base --epochs 35 --eval-every 35 > /app/solution/final_base.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 538, "nanos": 314696292 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:52:30.847Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0132edbd-80d4-4802-965b-126518074949", "process_id": "62363", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_vae_validation.pt --dest temporal_vae_scores.npy --no-grid > /app/solution/temporal_vae_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_vae_validation.pt --dest temporal_vae_scores.npy --no-grid > /app/solution/temporal_vae_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 44, "nanos": 405866465 }, "formatted_output": "" } } } ] }, { "id": 78, "role": "agent", "timestamp": "2026-09-09T00:52:31.185Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:52:31.185Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:52:33.234Z", "text": "The 579-dimensional ensemble is now the leading configuration, and its component refits are underway on train plus validation. Experimental outputs use separate files so they cannot overwrite the saved submission. I’m finishing the popularity calibration and checking the temporally fine-tuned variational model.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:52:35.018Z", "callId": "call_GwjYCtPnIAejZM7TJzRgpzn5", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/adoption_calibration.log; cat /app/solution/temporal_vae_analyze.log; tail -3 /app/solution/context_more.log\",\"max_output_tokens\":1400}));\n" }, "result": { "timestamp": "2026-09-09T00:52:35.228Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"7fdbfa\",\"wall_time_seconds\":0.00000497,\"exit_code\":0,\"original_token_count\":129,\"output\":\"final_base test epoch 34 loss 8.0101 seconds 488.2\\nfinal_base test epoch 35 loss 8.00079 seconds 520.8\\nSCORE 35 0.0\\nDONE 0.0\\ntemporal train samples 43885 entries 29998200\\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\\ntemporal_vae_scores.npy\\npop 0.0 score 0.03509581468026776\\ncontext_more 700 loss 4.0247168898582455 seconds 87.61454486846924\\ncontext_more 800 loss 4.014897854328155 seconds 93.61907649040222\\ncontext_more 900 loss 3.997701895236969 seconds 97.46924710273743\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:52:35.146Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0adca2ee887d1a22eb0a90986d949", "usage": { "input_tokens": 83906, "cached_input_tokens": 82816, "cache_write_input_tokens": 0, "output_tokens": 243, "reasoning_output_tokens": 91, "total_tokens": 84149 }, "turn_token_usage": { "input_tokens": 3433267, "cached_input_tokens": 3349504, "cache_write_input_tokens": 0, "output_tokens": 46984, "reasoning_output_tokens": 22491, "total_tokens": 3480251 }, "thread_token_usage": { "input_tokens": 3433267, "cached_input_tokens": 3349504, "cache_write_input_tokens": 0, "output_tokens": 46984, "reasoning_output_tokens": 22491, "total_tokens": 3480251 } } }, { "kind": "context", "timestamp": "2026-09-09T00:52:35.148Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bf2b00de-da99-4064-b60e-223ef63a8089", "process_id": "29388", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/adoption_calibration.log; cat /app/solution/temporal_vae_analyze.log; tail -3 /app/solution/context_more.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_base.log; tail -3 /app/solution/temporal_vae.log; tail -4 /app/solution/adoption_calibration.log; cat /app/solution/temporal_vae_analyze.log; tail -3 /app/solution/context_more.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_base test epoch 34 loss 8.0101 seconds 488.2\nfinal_base test epoch 35 loss 8.00079 seconds 520.8\nSCORE 35 0.0\nDONE 0.0\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae_scores.npy\npop 0.0 score 0.03509581468026776\ncontext_more 700 loss 4.0247168898582455 seconds 87.61454486846924\ncontext_more 800 loss 4.014897854328155 seconds 93.61907649040222\ncontext_more 900 loss 3.997701895236969 seconds 97.46924710273743\n", "stderr": "", "aggregated_output": "final_base test epoch 34 loss 8.0101 seconds 488.2\nfinal_base test epoch 35 loss 8.00079 seconds 520.8\nSCORE 35 0.0\nDONE 0.0\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae_scores.npy\npop 0.0 score 0.03509581468026776\ncontext_more 700 loss 4.0247168898582455 seconds 87.61454486846924\ncontext_more 800 loss 4.014897854328155 seconds 93.61907649040222\ncontext_more 900 loss 3.997701895236969 seconds 97.46924710273743\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4970 }, "formatted_output": "final_base test epoch 34 loss 8.0101 seconds 488.2\nfinal_base test epoch 35 loss 8.00079 seconds 520.8\nSCORE 35 0.0\nDONE 0.0\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae_scores.npy\npop 0.0 score 0.03509581468026776\ncontext_more 700 loss 4.0247168898582455 seconds 87.61454486846924\ncontext_more 800 loss 4.014897854328155 seconds 93.61907649040222\ncontext_more 900 loss 3.997701895236969 seconds 97.46924710273743\n" } } } ] }, { "id": 79, "role": "agent", "timestamp": "2026-09-09T00:52:56.511Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:52:56.511Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:52:59.402Z", "callId": "call_qhFDLrTZtGIZtMAuuQGcxbl5", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -lh /app/solution/discovery_trends_train.npz /app/solution/ensemble_scores.npy; cat /app/solution/discovery_trends.log; head -7 /sys/fs/cgroup/memory.stat; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal --resume final_base_test.pt --epochs 1 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/final_temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:53:00.645Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"0ace48\",\"wall_time_seconds\":1.001648398,\"session_id\":94024,\"original_token_count\":70,\"output\":\"-rw-r--r-- 1 agent agent 1.4M Sep 9 00:51 /app/solution/discovery_trends_train.npz\\n-rw-r--r-- 1 agent agent 968M Sep 9 00:52 /app/solution/ensemble_scores.npy\\nanon 3880996864\\nfile 3683602432\\nkernel 72118272\\nkernel_stack 1310720\\npagetables 11591680\\nsec_pagetables 0\\npercpu 1680\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:52:59.451Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-58dbb688-d00c-4836-82b0-62e973ed72be", "process_id": "31468", "command": [ "/bin/sh", "-lc", "cat > /app/solution/adoption_calibrate.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nm=Metric();torch.set_num_threads(2);f=features('train');f.update(discovery_features('train'))\na=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'ensemble_scores.npy',mode='w+',dtype='float32',shape=a[0].shape)\nfor st in range(0,len(s),128):s[st:st+128]=.4*a[0][st:st+128]+.4*a[1][st:st+128]+.2*a[2][st:st+128]\ns.flush();base=json.loads((ROOT/'temporal2_fine.json').read_text())['bias'];bb=sum(w*f[k] for k,w in base.items());best=(m(s,bb),base);print('BASE',best,flush=True)\nfor recent,other in [('new7','org7'),('new14','org14'),('new30','org30'),('new7','new180'),('new3','new30'),('newlike7','new7'),('newlike14','new14')]:\n for strength in [.3,.6,1.,1.5]:\n c=base.copy();c[recent]=c.get(recent,0)+strength;c[other]=c.get(other,0)-strength;score=m(s,bb+strength*(f[recent]-f[other]));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nprint('BEST',best,flush=True);(ROOT/'adoption_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/adoption_calibrate.py > /app/solution/adoption_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/adoption_calibrate.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nm=Metric();torch.set_num_threads(2);f=features('train');f.update(discovery_features('train'))\na=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'ensemble_scores.npy',mode='w+',dtype='float32',shape=a[0].shape)\nfor st in range(0,len(s),128):s[st:st+128]=.4*a[0][st:st+128]+.4*a[1][st:st+128]+.2*a[2][st:st+128]\ns.flush();base=json.loads((ROOT/'temporal2_fine.json').read_text())['bias'];bb=sum(w*f[k] for k,w in base.items());best=(m(s,bb),base);print('BASE',best,flush=True)\nfor recent,other in [('new7','org7'),('new14','org14'),('new30','org30'),('new7','new180'),('new3','new30'),('newlike7','new7'),('newlike14','new14')]:\n for strength in [.3,.6,1.,1.5]:\n c=base.copy();c[recent]=c.get(recent,0)+strength;c[other]=c.get(other,0)-strength;score=m(s,bb+strength*(f[recent]-f[other]));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nprint('BEST',best,flush=True);(ROOT/'adoption_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/adoption_calibrate.py > /app/solution/adoption_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 74, "nanos": 216913707 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:52:59.533Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0add3824487d1b707a305ea7a7bd3", "usage": { "input_tokens": 84397, "cached_input_tokens": 83712, "cache_write_input_tokens": 0, "output_tokens": 553, "reasoning_output_tokens": 395, "total_tokens": 84950 }, "turn_token_usage": { "input_tokens": 3517664, "cached_input_tokens": 3433216, "cache_write_input_tokens": 0, "output_tokens": 47537, "reasoning_output_tokens": 22886, "total_tokens": 3565201 }, "thread_token_usage": { "input_tokens": 3517664, "cached_input_tokens": 3433216, "cache_write_input_tokens": 0, "output_tokens": 47537, "reasoning_output_tokens": 22886, "total_tokens": 3565201 } } } ] }, { "id": 80, "role": "agent", "timestamp": "2026-09-09T00:53:04.180Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:53:04.180Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:53:07.151Z", "callId": "call_sN41oIpXahPsjzyWt0wK3zTm", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:31468,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\ntext(await tools.exec_command({cmd:\"tail -5 /app/solution/refit_vae.log; tail -3 /app/solution/temporal_vae.log; tail -5 /app/solution/adoption_calibration.log; tail -3 /app/solution/context_more.log\",\"max_output_tokens\":1800}));\n" }, "result": { "timestamp": "2026-09-09T00:53:07.325Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"0da748\",\"wall_time_seconds\":0.00000308,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"b86fc9\",\"wall_time_seconds\":0.000005091,\"exit_code\":0,\"original_token_count\":253,\"output\":\"bags test 5778549\\ntemporal train samples 43885 entries 29998200\\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\\ntemporal_vae 2 loss 8.016883461974388 score 0.04562700657415584 time 328.64751529693604\\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.3, 'new14': -0.3} 0.05258850874699452\\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.6, 'new14': -0.6} 0.05136021147463867\\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.0, 'new14': -1.0} 0.04667202909338259\\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.5, 'new14': -1.5} 0.03601723838758403\\nBEST (0.05406063090921727, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\\ncontext_more 1100 loss 3.9943095374107362 seconds 115.73543858528137\\ncontext_more 1200 loss 3.96001211643219 seconds 122.55714344978333\\ncontext_more 1300 loss 3.9790855550765993 seconds 129.1217076778412\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:53:07.267Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c1924e1-d5b0-412a-a76b-3f6b11e4054a", "process_id": "50436", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/refit_vae.log; tail -3 /app/solution/temporal_vae.log; tail -5 /app/solution/adoption_calibration.log; tail -3 /app/solution/context_more.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/refit_vae.log; tail -3 /app/solution/temporal_vae.log; tail -5 /app/solution/adoption_calibration.log; tail -3 /app/solution/context_more.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags test 5778549\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae 2 loss 8.016883461974388 score 0.04562700657415584 time 328.64751529693604\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.3, 'new14': -0.3} 0.05258850874699452\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.6, 'new14': -0.6} 0.05136021147463867\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.0, 'new14': -1.0} 0.04667202909338259\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.5, 'new14': -1.5} 0.03601723838758403\nBEST (0.05406063090921727, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\ncontext_more 1100 loss 3.9943095374107362 seconds 115.73543858528137\ncontext_more 1200 loss 3.96001211643219 seconds 122.55714344978333\ncontext_more 1300 loss 3.9790855550765993 seconds 129.1217076778412\n", "stderr": "", "aggregated_output": "bags test 5778549\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae 2 loss 8.016883461974388 score 0.04562700657415584 time 328.64751529693604\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.3, 'new14': -0.3} 0.05258850874699452\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.6, 'new14': -0.6} 0.05136021147463867\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.0, 'new14': -1.0} 0.04667202909338259\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.5, 'new14': -1.5} 0.03601723838758403\nBEST (0.05406063090921727, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\ncontext_more 1100 loss 3.9943095374107362 seconds 115.73543858528137\ncontext_more 1200 loss 3.96001211643219 seconds 122.55714344978333\ncontext_more 1300 loss 3.9790855550765993 seconds 129.1217076778412\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5091 }, "formatted_output": "bags test 5778549\ntemporal train samples 43885 entries 29998200\ntemporal_vae 1 loss 8.311629955158677 score 0.04677739749054884 time 176.14703631401062\ntemporal_vae 2 loss 8.016883461974388 score 0.04562700657415584 time 328.64751529693604\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.3, 'new14': -0.3} 0.05258850874699452\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 0.6, 'new14': -0.6} 0.05136021147463867\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.0, 'new14': -1.0} 0.04667202909338259\n{'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'newlike14': 1.5, 'new14': -1.5} 0.03601723838758403\nBEST (0.05406063090921727, {'org7': 1.5, 'org360': -1.2, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\ncontext_more 1100 loss 3.9943095374107362 seconds 115.73543858528137\ncontext_more 1200 loss 3.96001211643219 seconds 122.55714344978333\ncontext_more 1300 loss 3.9790855550765993 seconds 129.1217076778412\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:53:07.313Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aded18fc87d1a3c23e317abfd5dc", "usage": { "input_tokens": 85119, "cached_input_tokens": 84224, "cache_write_input_tokens": 0, "output_tokens": 146, "reasoning_output_tokens": 38, "total_tokens": 85265 }, "turn_token_usage": { "input_tokens": 3602783, "cached_input_tokens": 3517440, "cache_write_input_tokens": 0, "output_tokens": 47683, "reasoning_output_tokens": 22924, "total_tokens": 3650466 }, "thread_token_usage": { "input_tokens": 3602783, "cached_input_tokens": 3517440, "cache_write_input_tokens": 0, "output_tokens": 47683, "reasoning_output_tokens": 22924, "total_tokens": 3650466 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-09T00:53:16.742Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:53:16.742Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:53:21.216Z", "callId": "call_nn0VSJbS0gZzGTrppLZeqNgN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/temporal.py' in c and b'temporal_vae' in c:os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\nrg SCORE /app/solution/context_more.log; tail -6 /app/solution/refit_vae.log; tail -3 /app/solution/final_temporal.log\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-09T00:53:21.428Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"6dda2b\",\"wall_time_seconds\":0.011820375,\"exit_code\":0,\"original_token_count\":35,\"output\":\"SCORE 500 0.02751714622646406\\nSCORE 1000 0.030146792813489653\\nbags test 5778549\\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:53:21.316Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0adf38c1087d1bec1c77bc41fedfd", "usage": { "input_tokens": 85845, "cached_input_tokens": 84992, "cache_write_input_tokens": 0, "output_tokens": 293, "reasoning_output_tokens": 113, "total_tokens": 86138 }, "turn_token_usage": { "input_tokens": 3688628, "cached_input_tokens": 3602432, "cache_write_input_tokens": 0, "output_tokens": 47976, "reasoning_output_tokens": 23037, "total_tokens": 3736604 }, "thread_token_usage": { "input_tokens": 3688628, "cached_input_tokens": 3602432, "cache_write_input_tokens": 0, "output_tokens": 47976, "reasoning_output_tokens": 23037, "total_tokens": 3736604 } } }, { "kind": "context", "timestamp": "2026-09-09T00:53:21.415Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f05033cd-1c5a-4911-bae5-d9ff445c0997", "process_id": "63906", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal_vae' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nrg SCORE /app/solution/context_more.log; tail -6 /app/solution/refit_vae.log; tail -3 /app/solution/final_temporal.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/temporal.py' in c and b'temporal_vae' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nrg SCORE /app/solution/context_more.log; tail -6 /app/solution/refit_vae.log; tail -3 /app/solution/final_temporal.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "SCORE 500 0.02751714622646406\nSCORE 1000 0.030146792813489653\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\n", "stderr": "", "aggregated_output": "SCORE 500 0.02751714622646406\nSCORE 1000 0.030146792813489653\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11807145 }, "formatted_output": "SCORE 500 0.02751714622646406\nSCORE 1000 0.030146792813489653\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:53:21.521Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a4ce8fa9-b95c-4a94-9430-317c6e3a5c3b", "process_id": "38106", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_vae --resume vae_trend_validation.pt --epochs 3 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/temporal_vae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_vae --resume vae_trend_validation.pt --epochs 3 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/temporal_vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 380, "nanos": 377036014 }, "formatted_output": "" } } } ] }, { "id": 82, "role": "agent", "timestamp": "2026-09-09T00:53:48.134Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:53:48.134Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:53:59.553Z", "callId": "call_arLNT5Kpc8o10dSiPlaehsCQ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/submit.py';s=open(p).read().replace(\\\"fs=features('train' if a.stage=='validation' else 'test');bias=sum\\\", \\\"fs=features('train' if a.stage=='validation' else 'test')\\\\n if any(k.startswith('new') for k in cfg['bias']):\\\\n from discovery_trends import discovery_features\\\\n fs.update(discovery_features('train' if a.stage=='validation' else 'test'))\\\\n bias=sum\\\");open(p,'w').write(s)\\nPY\\ncat > /app/solution/ensemble_refine.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom discovery_trends import discovery_features\\nclass Blend:\\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));c=json.loads((ROOT/'adoption_best.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy','temporal_vae_scores.npy']];best=(-1,None)\\nfor tv in [0.,.2,.4,.6,.8]:\\n for v in [0.,.2,.4]:\\n weights=[round(.8-tv-v,3),v,.2,tv]\\n if weights[0]<0:continue\\n score=m(Blend(arrays,weights),bias);print(weights,score,flush=True)\\n if score>best[0]:best=(score,weights)\\nprint('BEST',best,flush=True);(ROOT/'ensemble_refined.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_refine.py > /app/solution/ensemble_refined.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:54:00.812Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"f7cee6\",\"wall_time_seconds\":1.001844314,\"session_id\":15297,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 00:54:00 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:53:59.716Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ae019e1887d1bc5ff3ba1d8bc5d9", "usage": { "input_tokens": 86250, "cached_input_tokens": 85632, "cache_write_input_tokens": 0, "output_tokens": 1080, "reasoning_output_tokens": 516, "total_tokens": 87330 }, "turn_token_usage": { "input_tokens": 3774878, "cached_input_tokens": 3688064, "cache_write_input_tokens": 0, "output_tokens": 49056, "reasoning_output_tokens": 23553, "total_tokens": 3823934 }, "thread_token_usage": { "input_tokens": 3774878, "cached_input_tokens": 3688064, "cache_write_input_tokens": 0, "output_tokens": 49056, "reasoning_output_tokens": 23553, "total_tokens": 3823934 } } } ] }, { "id": 83, "role": "agent", "timestamp": "2026-09-09T00:54:22.975Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:54:22.975Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:54:24.264Z", "callId": "call_AtUHI1PqYRgy19zsDh51YUct", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/context_more.log; cat /app/solution/ensemble_refined.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T00:54:24.434Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"f584e3\",\"wall_time_seconds\":0.000053141,\"exit_code\":0,\"original_token_count\":145,\"output\":\"temporal test samples 44981 entries 31007155\\nbags test 5778549\\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\\nSCORE 1 0.0\\nfinal_vae_initial test epoch 2 loss 9.36782 seconds 65.0\\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\\nSCORE 2000 0.02883510037832524\\ncontext_more 2100 loss 3.885561237335205 seconds 193.45044589042664\\ncontext_more 2200 loss 3.8887176942825317 seconds 199.4681408405304\\ncontext_more 2300 loss 3.8948789191246034 seconds 205.38001084327698\\n[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:54:24.373Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ae29097c87d1be6c3eee8a36548b", "usage": { "input_tokens": 87406, "cached_input_tokens": 86016, "cache_write_input_tokens": 0, "output_tokens": 467, "reasoning_output_tokens": 392, "total_tokens": 87873 }, "turn_token_usage": { "input_tokens": 3862284, "cached_input_tokens": 3774080, "cache_write_input_tokens": 0, "output_tokens": 49523, "reasoning_output_tokens": 23945, "total_tokens": 3911807 }, "thread_token_usage": { "input_tokens": 3862284, "cached_input_tokens": 3774080, "cache_write_input_tokens": 0, "output_tokens": 49523, "reasoning_output_tokens": 23945, "total_tokens": 3911807 } } }, { "kind": "context", "timestamp": "2026-09-09T00:54:24.410Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76de6cb4-ca15-4893-acfc-bd2eb13080f9", "process_id": "86143", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/context_more.log; cat /app/solution/ensemble_refined.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/context_more.log; cat /app/solution/ensemble_refined.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\nSCORE 1 0.0\nfinal_vae_initial test epoch 2 loss 9.36782 seconds 65.0\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nSCORE 2000 0.02883510037832524\ncontext_more 2100 loss 3.885561237335205 seconds 193.45044589042664\ncontext_more 2200 loss 3.8887176942825317 seconds 199.4681408405304\ncontext_more 2300 loss 3.8948789191246034 seconds 205.38001084327698\n[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n", "stderr": "", "aggregated_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\nSCORE 1 0.0\nfinal_vae_initial test epoch 2 loss 9.36782 seconds 65.0\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nSCORE 2000 0.02883510037832524\ncontext_more 2100 loss 3.885561237335205 seconds 193.45044589042664\ncontext_more 2200 loss 3.8887176942825317 seconds 199.4681408405304\ncontext_more 2300 loss 3.8948789191246034 seconds 205.38001084327698\n[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 53141 }, "formatted_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 1 loss 9.93177 seconds 45.4\nSCORE 1 0.0\nfinal_vae_initial test epoch 2 loss 9.36782 seconds 65.0\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nSCORE 2000 0.02883510037832524\ncontext_more 2100 loss 3.885561237335205 seconds 193.45044589042664\ncontext_more 2200 loss 3.8887176942825317 seconds 199.4681408405304\ncontext_more 2300 loss 3.8948789191246034 seconds 205.38001084327698\n[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-09T00:54:37.476Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:54:37.476Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:54:39.407Z", "text": "Recent first-time discovery counts added a small gain, bringing the ensemble to 0.0541 NDCG@10. Temporal fine-tuning also improved the variational model individually; I’m checking whether that carries over to the ensemble or whether the original model contributes more useful variety.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:54:41.006Z", "callId": "call_TVufsYEINHP06JZbyu6pqixj", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/context_more_metrics.json; cat /app/solution/ensemble_refined.log; tail -3 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log\",\"max_output_tokens\":1800}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:54:41.174Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"4b4f55\",\"wall_time_seconds\":0.00000506,\"exit_code\":0,\"original_token_count\":270,\"output\":\"{\\n \\\"step\\\": 1000,\\n \\\"score\\\": 0.030146792813489653,\\n \\\"args\\\": {\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"name\\\": \\\"context_more\\\",\\n \\\"steps\\\": 4000,\\n \\\"eval_every\\\": 500,\\n \\\"dim\\\": 128,\\n \\\"half\\\": 14,\\n \\\"batch\\\": 512,\\n \\\"context\\\": 16,\\n \\\"window\\\": 64,\\n \\\"lr\\\": 0.001,\\n \\\"resume\\\": \\\"context_validation.pt\\\",\\n \\\"context_model\\\": true,\\n \\\"hidden\\\": 128,\\n \\\"dropout\\\": 0.2,\\n \\\"likes\\\": 3.0,\\n \\\"organic\\\": 1.0\\n }\\n}[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\\n[0.6, 0.2, 0.2, 0.0] 0.05308575923613933\\n[0.4, 0.4, 0.2, 0.0] 0.05406063090921727\\n[0.6, 0.0, 0.2, 0.2] 0.05295983919657089\\n[0.4, 0.2, 0.2, 0.2] 0.05413688963058208\\n[0.2, 0.4, 0.2, 0.2] 0.05381074684845005\\n[0.4, 0.0, 0.2, 0.4] 0.054770234799772305\\n[0.2, 0.2, 0.2, 0.4] 0.05426745777651496\\n[0.0, 0.4, 0.2, 0.4] 0.05251916715382253\\n[0.2, 0.0, 0.2, 0.6] 0.05417517446830508\\ntemporal test samples 44981 entries 31007155\\nbags test 5778549\\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\\nfinal_vae_initial test epoch 5 loss 8.41835 seconds 115.8\\nSCORE 5 0.0\\nDONE 0.0\\n\"}\n{\"current_time\":\"2026-09-09 00:54:41 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:54:41.080Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2190d359-a29e-4ad4-80fe-5edee41fb03d", "process_id": "18806", "command": [ "/bin/sh", "-lc", "cat /app/solution/context_more_metrics.json; cat /app/solution/ensemble_refined.log; tail -3 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/context_more_metrics.json; cat /app/solution/ensemble_refined.log; tail -3 /app/solution/final_temporal.log; tail -5 /app/solution/refit_vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"step\": 1000,\n \"score\": 0.030146792813489653,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_more\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 128,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 16,\n \"window\": 64,\n \"lr\": 0.001,\n \"resume\": \"context_validation.pt\",\n \"context_model\": true,\n \"hidden\": 128,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n[0.6, 0.2, 0.2, 0.0] 0.05308575923613933\n[0.4, 0.4, 0.2, 0.0] 0.05406063090921727\n[0.6, 0.0, 0.2, 0.2] 0.05295983919657089\n[0.4, 0.2, 0.2, 0.2] 0.05413688963058208\n[0.2, 0.4, 0.2, 0.2] 0.05381074684845005\n[0.4, 0.0, 0.2, 0.4] 0.054770234799772305\n[0.2, 0.2, 0.2, 0.4] 0.05426745777651496\n[0.0, 0.4, 0.2, 0.4] 0.05251916715382253\n[0.2, 0.0, 0.2, 0.6] 0.05417517446830508\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nfinal_vae_initial test epoch 5 loss 8.41835 seconds 115.8\nSCORE 5 0.0\nDONE 0.0\n", "stderr": "", "aggregated_output": "{\n \"step\": 1000,\n \"score\": 0.030146792813489653,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_more\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 128,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 16,\n \"window\": 64,\n \"lr\": 0.001,\n \"resume\": \"context_validation.pt\",\n \"context_model\": true,\n \"hidden\": 128,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n[0.6, 0.2, 0.2, 0.0] 0.05308575923613933\n[0.4, 0.4, 0.2, 0.0] 0.05406063090921727\n[0.6, 0.0, 0.2, 0.2] 0.05295983919657089\n[0.4, 0.2, 0.2, 0.2] 0.05413688963058208\n[0.2, 0.4, 0.2, 0.2] 0.05381074684845005\n[0.4, 0.0, 0.2, 0.4] 0.054770234799772305\n[0.2, 0.2, 0.2, 0.4] 0.05426745777651496\n[0.0, 0.4, 0.2, 0.4] 0.05251916715382253\n[0.2, 0.0, 0.2, 0.6] 0.05417517446830508\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nfinal_vae_initial test epoch 5 loss 8.41835 seconds 115.8\nSCORE 5 0.0\nDONE 0.0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5060 }, "formatted_output": "{\n \"step\": 1000,\n \"score\": 0.030146792813489653,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_more\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 128,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 16,\n \"window\": 64,\n \"lr\": 0.001,\n \"resume\": \"context_validation.pt\",\n \"context_model\": true,\n \"hidden\": 128,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}[0.8, 0.0, 0.2, 0.0] 0.05088634467136459\n[0.6, 0.2, 0.2, 0.0] 0.05308575923613933\n[0.4, 0.4, 0.2, 0.0] 0.05406063090921727\n[0.6, 0.0, 0.2, 0.2] 0.05295983919657089\n[0.4, 0.2, 0.2, 0.2] 0.05413688963058208\n[0.2, 0.4, 0.2, 0.2] 0.05381074684845005\n[0.4, 0.0, 0.2, 0.4] 0.054770234799772305\n[0.2, 0.2, 0.2, 0.4] 0.05426745777651496\n[0.0, 0.4, 0.2, 0.4] 0.05251916715382253\n[0.2, 0.0, 0.2, 0.6] 0.05417517446830508\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_vae_initial test epoch 3 loss 8.89618 seconds 77.8\nfinal_vae_initial test epoch 4 loss 8.59801 seconds 93.8\nfinal_vae_initial test epoch 5 loss 8.41835 seconds 115.8\nSCORE 5 0.0\nDONE 0.0\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:54:41.162Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0ae40e84887d19804f0851663c015", "usage": { "input_tokens": 88157, "cached_input_tokens": 87168, "cache_write_input_tokens": 0, "output_tokens": 431, "reasoning_output_tokens": 284, "total_tokens": 88588 }, "turn_token_usage": { "input_tokens": 3950441, "cached_input_tokens": 3861248, "cache_write_input_tokens": 0, "output_tokens": 49954, "reasoning_output_tokens": 24229, "total_tokens": 4000395 }, "thread_token_usage": { "input_tokens": 3950441, "cached_input_tokens": 3861248, "cache_write_input_tokens": 0, "output_tokens": 49954, "reasoning_output_tokens": 24229, "total_tokens": 4000395 } } }, { "kind": "context", "timestamp": "2026-09-09T00:54:43.499Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-68aa2803-5671-4230-a084-6be7023d8bf5", "process_id": "15297", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/submit.py';s=open(p).read().replace(\"fs=features('train' if a.stage=='validation' else 'test');bias=sum\", \"fs=features('train' if a.stage=='validation' else 'test')\\n if any(k.startswith('new') for k in cfg['bias']):\\n from discovery_trends import discovery_features\\n fs.update(discovery_features('train' if a.stage=='validation' else 'test'))\\n bias=sum\");open(p,'w').write(s)\nPY\ncat > /app/solution/ensemble_refine.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));c=json.loads((ROOT/'adoption_best.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy','temporal_vae_scores.npy']];best=(-1,None)\nfor tv in [0.,.2,.4,.6,.8]:\n for v in [0.,.2,.4]:\n weights=[round(.8-tv-v,3),v,.2,tv]\n if weights[0]<0:continue\n score=m(Blend(arrays,weights),bias);print(weights,score,flush=True)\n if score>best[0]:best=(score,weights)\nprint('BEST',best,flush=True);(ROOT/'ensemble_refined.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_refine.py > /app/solution/ensemble_refined.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/submit.py';s=open(p).read().replace(\"fs=features('train' if a.stage=='validation' else 'test');bias=sum\", \"fs=features('train' if a.stage=='validation' else 'test')\\n if any(k.startswith('new') for k in cfg['bias']):\\n from discovery_trends import discovery_features\\n fs.update(discovery_features('train' if a.stage=='validation' else 'test'))\\n bias=sum\");open(p,'w').write(s)\nPY\ncat > /app/solution/ensemble_refine.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,arrays,weights):self.arrays=arrays;self.weights=weights\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.weights,self.arrays) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));c=json.loads((ROOT/'adoption_best.json').read_text());bias=sum(w*f[k] for k,w in c['bias'].items());arrays=[np.load(ROOT/p,mmap_mode='r') for p in ['temporal2_h30.0_scores.npy','vae_scores.npy','context_scores.npy','temporal_vae_scores.npy']];best=(-1,None)\nfor tv in [0.,.2,.4,.6,.8]:\n for v in [0.,.2,.4]:\n weights=[round(.8-tv-v,3),v,.2,tv]\n if weights[0]<0:continue\n score=m(Blend(arrays,weights),bias);print(weights,score,flush=True)\n if score>best[0]:best=(score,weights)\nprint('BEST',best,flush=True);(ROOT/'ensemble_refined.json').write_text(json.dumps({'score':best[0],'weights':best[1],'bias':c['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/ensemble_refine.py > /app/solution/ensemble_refined.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 43, "nanos": 709338301 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:55:02.588Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3edc600e-e0b7-4c82-8d48-805fe17596cf", "process_id": "94024", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/discovery_trends_train.npz /app/solution/ensemble_scores.npy; cat /app/solution/discovery_trends.log; head -7 /sys/fs/cgroup/memory.stat; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal --resume final_base_test.pt --epochs 1 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/final_temporal.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/discovery_trends_train.npz /app/solution/ensemble_scores.npy; cat /app/solution/discovery_trends.log; head -7 /sys/fs/cgroup/memory.stat; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal --resume final_base_test.pt --epochs 1 --half 14 --days 84 --stride 7 --dropout .7 --lr .0001 > /app/solution/final_temporal.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 122, "nanos": 953784608 }, "formatted_output": "" } } } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-09T00:55:03.880Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:55:03.880Z", "text": "" } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-09T00:55:29.079Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:55:29.079Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:55:34.048Z", "callId": "call_Po70Wr9ohLodxkuXzTjm8P5x", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport os,signal\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\\\0')\\n if b'/app/solution/context.py' in c and b'context_more' in c:os.kill(int(f),signal.SIGTERM)\\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --stage test --name final_context --steps 4000 > /app/solution/final_context.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --name context_local --dim 256 --context 8 --window 16 --steps 4000 > 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51798, "reasoning_output_tokens": 25469, "total_tokens": 4182094 } } }, { "kind": "context", "timestamp": "2026-09-09T00:55:56.791Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2aa7e52c-3e76-4dfd-be0f-7a87a6557752", "process_id": "54120", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/refit_vae.log; cat /app/solution/ensemble_refined.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/refit_vae.log; cat /app/solution/ensemble_refined.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal 1 loss 8.715020087632267 score 0.0 time 114.53870844841003\nfinal_vae test epoch 2 loss 8.11656 seconds 40.4\nfinal_vae test epoch 3 loss 8.04117 seconds 52.6\nfinal_vae test epoch 4 loss 7.9603 seconds 63.0\n{\n \"score\": 0.054770234799772305,\n \"weights\": [\n 0.4,\n 0.0,\n 0.2,\n 0.4\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}", "stderr": "", "aggregated_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal 1 loss 8.715020087632267 score 0.0 time 114.53870844841003\nfinal_vae test epoch 2 loss 8.11656 seconds 40.4\nfinal_vae test epoch 3 loss 8.04117 seconds 52.6\nfinal_vae test epoch 4 loss 7.9603 seconds 63.0\n{\n \"score\": 0.054770234799772305,\n \"weights\": [\n 0.4,\n 0.0,\n 0.2,\n 0.4\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}", "exit_code": 0, "duration": { "secs": 0, "nanos": 4530 }, "formatted_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal 1 loss 8.715020087632267 score 0.0 time 114.53870844841003\nfinal_vae test epoch 2 loss 8.11656 seconds 40.4\nfinal_vae test epoch 3 loss 8.04117 seconds 52.6\nfinal_vae test epoch 4 loss 7.9603 seconds 63.0\n{\n \"score\": 0.054770234799772305,\n \"weights\": [\n 0.4,\n 0.0,\n 0.2,\n 0.4\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}" } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-09T00:56:02.563Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:56:02.563Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:56:04.970Z", "text": "The temporally fine-tuned variational model improves the ensemble to 0.0548 NDCG@10, while keeping its size at 579 dimensions. The temporal history model’s final refit has finished. The remaining component refits and one last local-context experiment are still running.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:56:10.061Z", "callId": "call_bql22CvIyCxf2ltewJ6z4wWP", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\nfrom pathlib import Path\\nr=Path('/app/solution');bias=json.loads((r/'temporal2_fine.json').read_text())['bias'];(r/'final_config.json').write_text(json.dumps({'components':[{'checkpoint':'final_temporal_test.pt','weight':1.,'half':30}],'bias':bias},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/current_best_submit.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > 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/app/solution/validation.parquet > /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 341782102 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:56:32.375Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e6e713ef-5d33-4307-a8ce-f5e5893b8910", "process_id": "56814", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');best=json.loads((r/'ensemble_refined.json').read_text());c={'components':[{'checkpoint':'temporal2_validation.pt','weight':.4,'half':30},{'checkpoint':'temporal_vae_validation.pt','weight':.4,'half':30},{'checkpoint':'context_validation.pt','weight':.2,'half':14}],'bias':best['bias']};(r/'selected_validation_config.json').write_text(json.dumps(c,indent=2));c['components'][0]['checkpoint']='final_temporal_test.pt';c['components'][1]['checkpoint']='final_temporal_vae_test.pt';c['components'][2]['checkpoint']='final_context_test.pt';(r/'planned_final_config.json').write_text(json.dumps(c,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config selected_validation_config.json --stage validation > /app/solution/selected_validation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');best=json.loads((r/'ensemble_refined.json').read_text());c={'components':[{'checkpoint':'temporal2_validation.pt','weight':.4,'half':30},{'checkpoint':'temporal_vae_validation.pt','weight':.4,'half':30},{'checkpoint':'context_validation.pt','weight':.2,'half':14}],'bias':best['bias']};(r/'selected_validation_config.json').write_text(json.dumps(c,indent=2));c['components'][0]['checkpoint']='final_temporal_test.pt';c['components'][1]['checkpoint']='final_temporal_vae_test.pt';c['components'][2]['checkpoint']='final_context_test.pt';(r/'planned_final_config.json').write_text(json.dumps(c,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config selected_validation_config.json --stage validation > /app/solution/selected_validation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 36, "nanos": 657818762 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T00:56:36.250Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2020d6e0-1fd8-4bfc-bfc3-c9b9db9f4445", "process_id": "89241", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');bias=json.loads((r/'temporal2_fine.json').read_text())['bias'];(r/'final_config.json').write_text(json.dumps({'components':[{'checkpoint':'final_temporal_test.pt','weight':1.,'half':30}],'bias':bias},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/current_best_submit.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');bias=json.loads((r/'temporal2_fine.json').read_text())['bias'];(r/'final_config.json').write_text(json.dumps({'components':[{'checkpoint':'final_temporal_test.pt','weight':1.,'half':30}],'bias':bias},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/current_best_submit.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 955253574 }, "formatted_output": "" } } } ] }, { "id": 89, "role": "agent", "timestamp": "2026-09-09T00:56:38.718Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:56:38.718Z", "text": "" } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-09T00:57:06.951Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:57:06.951Z", "text": "" } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-09T00:57:32.202Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:57:32.202Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:57:33.728Z", "callId": "call_6x5GKDWNAgOIVt8DToxutrFK", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/current_best_submit.log\",\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:57:33.865Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"a609f0\",\"wall_time_seconds\":0.00000697,\"exit_code\":0,\"original_token_count\":225,\"output\":\"final_vae test epoch 6 loss 7.88868 seconds 108.5\\nfinal_vae test epoch 7 loss 7.81825 seconds 124.7\\nfinal_vae test epoch 8 loss 7.7463 seconds 141.1\\nfinal_vae test epoch 9 loss 7.68476 seconds 155.2\\nSCORE 1000 0.0\\nfinal_context 1100 loss 5.136499872207642 seconds 97.47646760940552\\nfinal_context 1200 loss 5.05806893825531 seconds 103.46601009368896\\nfinal_context 1300 loss 4.992333950996399 seconds 109.38511228561401\\ncontext_local 400 loss 5.8598666095733645 seconds 70.91839671134949\\ncontext_local 500 loss 5.663832921981811 seconds 86.27546286582947\\nSCORE 500 0.02159773694674917\\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.045542,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nbags test 5778549\\n{\\\"output\\\": \\\"submission.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 193, \\\"seconds\\\": 17.703742027282715}\\n\"}\n{\"current_time\":\"2026-09-09 00:57:33 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:57:33.834Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aeacb46887d1966659b7af6c7a02", "usage": { "input_tokens": 91989, "cached_input_tokens": 91392, "cache_write_input_tokens": 0, "output_tokens": 1643, "reasoning_output_tokens": 1552, "total_tokens": 93632 }, "turn_token_usage": { "input_tokens": 4313797, "cached_input_tokens": 4220160, "cache_write_input_tokens": 0, "output_tokens": 53826, "reasoning_output_tokens": 27113, "total_tokens": 4367623 }, "thread_token_usage": { "input_tokens": 4313797, "cached_input_tokens": 4220160, "cache_write_input_tokens": 0, "output_tokens": 53826, "reasoning_output_tokens": 27113, "total_tokens": 4367623 } } }, { "kind": "context", "timestamp": "2026-09-09T00:57:33.839Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d80ede71-c235-4bb0-a177-76bf1e494759", "process_id": "55766", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/current_best_submit.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/current_best_submit.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_vae test epoch 6 loss 7.88868 seconds 108.5\nfinal_vae test epoch 7 loss 7.81825 seconds 124.7\nfinal_vae test epoch 8 loss 7.7463 seconds 141.1\nfinal_vae test epoch 9 loss 7.68476 seconds 155.2\nSCORE 1000 0.0\nfinal_context 1100 loss 5.136499872207642 seconds 97.47646760940552\nfinal_context 1200 loss 5.05806893825531 seconds 103.46601009368896\nfinal_context 1300 loss 4.992333950996399 seconds 109.38511228561401\ncontext_local 400 loss 5.8598666095733645 seconds 70.91839671134949\ncontext_local 500 loss 5.663832921981811 seconds 86.27546286582947\nSCORE 500 0.02159773694674917\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 17.703742027282715}\n", "stderr": "", "aggregated_output": "final_vae test epoch 6 loss 7.88868 seconds 108.5\nfinal_vae test epoch 7 loss 7.81825 seconds 124.7\nfinal_vae test epoch 8 loss 7.7463 seconds 141.1\nfinal_vae test epoch 9 loss 7.68476 seconds 155.2\nSCORE 1000 0.0\nfinal_context 1100 loss 5.136499872207642 seconds 97.47646760940552\nfinal_context 1200 loss 5.05806893825531 seconds 103.46601009368896\nfinal_context 1300 loss 4.992333950996399 seconds 109.38511228561401\ncontext_local 400 loss 5.8598666095733645 seconds 70.91839671134949\ncontext_local 500 loss 5.663832921981811 seconds 86.27546286582947\nSCORE 500 0.02159773694674917\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 17.703742027282715}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6970 }, "formatted_output": "final_vae test epoch 6 loss 7.88868 seconds 108.5\nfinal_vae test epoch 7 loss 7.81825 seconds 124.7\nfinal_vae test epoch 8 loss 7.7463 seconds 141.1\nfinal_vae test epoch 9 loss 7.68476 seconds 155.2\nSCORE 1000 0.0\nfinal_context 1100 loss 5.136499872207642 seconds 97.47646760940552\nfinal_context 1200 loss 5.05806893825531 seconds 103.46601009368896\nfinal_context 1300 loss 4.992333950996399 seconds 109.38511228561401\ncontext_local 400 loss 5.8598666095733645 seconds 70.91839671134949\ncontext_local 500 loss 5.663832921981811 seconds 86.27546286582947\nSCORE 500 0.02159773694674917\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.045542,\n \"quality_users\": 4263\n }\n}\nbags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 193, \"seconds\": 17.703742027282715}\n" } } } ] }, { "id": 92, "role": "agent", "timestamp": "2026-09-09T00:57:36.658Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:57:36.658Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:57:38.185Z", "callId": "call_rV09nqx6CtWti9TxiUGEQ0Yq", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/selected_validation.log; cat /app/solution/selected_validation_config.json; ls -lh /app/solution/validation.parquet\",\"max_output_tokens\":1600}));\n" }, "result": { "timestamp": "2026-09-09T00:57:38.340Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"958ac7\",\"wall_time_seconds\":0.000007249,\"exit_code\":0,\"original_token_count\":165,\"output\":\"bags train 5618006\\nbags train 5618006\\n{\\\"output\\\": \\\"validation.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 579, \\\"seconds\\\": 28.212885856628418}\\n{\\n \\\"components\\\": [\\n {\\n \\\"checkpoint\\\": \\\"temporal2_validation.pt\\\",\\n \\\"weight\\\": 0.4,\\n \\\"half\\\": 30\\n },\\n {\\n \\\"checkpoint\\\": \\\"temporal_vae_validation.pt\\\",\\n \\\"weight\\\": 0.4,\\n \\\"half\\\": 30\\n },\\n {\\n \\\"checkpoint\\\": \\\"context_validation.pt\\\",\\n \\\"weight\\\": 0.2,\\n \\\"half\\\": 14\\n }\\n ],\\n \\\"bias\\\": {\\n \\\"org7\\\": 1.5,\\n \\\"org360\\\": -1.2,\\n \\\"like14\\\": 0.2,\\n \\\"org14\\\": -0.2,\\n \\\"new3\\\": 0.3,\\n \\\"new30\\\": -0.3\\n }\\n}-rw-r--r-- 1 agent agent 1.6M Sep 9 00:56 /app/solution/validation.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:57:38.287Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dc0afd47-d0e2-4382-ac80-a207715f56e3", "process_id": "84340", "command": [ "/bin/sh", "-lc", "cat /app/solution/selected_validation.log; cat /app/solution/selected_validation_config.json; ls -lh /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/selected_validation.log", "name": "selected_validation.log", "path": "/app/solution/selected_validation.log" }, { "type": "read", "cmd": "cat /app/solution/selected_validation_config.json", "name": "selected_validation_config.json", "path": "/app/solution/selected_validation_config.json" }, { "type": "list_files", "cmd": "ls -lh /app/solution/validation.parquet", "path": "validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags train 5618006\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 28.212885856628418}\n{\n \"components\": [\n {\n \"checkpoint\": \"temporal2_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"temporal_vae_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"context_validation.pt\",\n \"weight\": 0.2,\n \"half\": 14\n }\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}-rw-r--r-- 1 agent agent 1.6M Sep 9 00:56 /app/solution/validation.parquet\n", "stderr": "", "aggregated_output": "bags train 5618006\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 28.212885856628418}\n{\n \"components\": [\n {\n \"checkpoint\": \"temporal2_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"temporal_vae_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"context_validation.pt\",\n \"weight\": 0.2,\n \"half\": 14\n }\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}-rw-r--r-- 1 agent agent 1.6M Sep 9 00:56 /app/solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7249 }, "formatted_output": "bags train 5618006\nbags train 5618006\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 28.212885856628418}\n{\n \"components\": [\n {\n \"checkpoint\": \"temporal2_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"temporal_vae_validation.pt\",\n \"weight\": 0.4,\n \"half\": 30\n },\n {\n \"checkpoint\": \"context_validation.pt\",\n \"weight\": 0.2,\n \"half\": 14\n }\n ],\n \"bias\": {\n \"org7\": 1.5,\n \"org360\": -1.2,\n \"like14\": 0.2,\n \"org14\": -0.2,\n \"new3\": 0.3,\n \"new30\": -0.3\n }\n}-rw-r--r-- 1 agent agent 1.6M Sep 9 00:56 /app/solution/validation.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:57:38.335Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0aefe182c87d18ebc5353d06115c1", "usage": { "input_tokens": 94042, "cached_input_tokens": 91776, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 28, "total_tokens": 94130 }, "turn_token_usage": { "input_tokens": 4407839, "cached_input_tokens": 4311936, "cache_write_input_tokens": 0, "output_tokens": 53914, "reasoning_output_tokens": 27141, "total_tokens": 4461753 }, "thread_token_usage": { "input_tokens": 4407839, "cached_input_tokens": 4311936, "cache_write_input_tokens": 0, "output_tokens": 53914, "reasoning_output_tokens": 27141, "total_tokens": 4461753 } } } ] }, { "id": 93, "role": "agent", "timestamp": "2026-09-09T00:57:46.357Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:57:46.357Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:57:58.129Z", "callId": "call_mlfxozsTx9xXGLPDrefqfljf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/temporal.py';s=open(p).read().replace(\\\"p.add_argument('--eval-half',type=float);\\\", \\\"p.add_argument('--eval-half',type=float);p.add_argument('--ema',type=float,default=0.);\\\")\\ns=s.replace(\\\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\\\",\\\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\\\\n ema={k:v.detach().clone() for k,v in model.state_dict().items()} if args.ema else None\\\")\\ns=s.replace(\\\"opt.step();losses.append(loss.item())\\\", \\\"opt.step();losses.append(loss.item())\\\\n if ema is not None:\\\\n with torch.no_grad():\\\\n for k,v in model.state_dict().items():ema[k].mul_(args.ema).add_(v,alpha=1-args.ema)\\\")\\ns=s.replace(\\\"out=f'{args.name}_validation.parquet'\\\", \\\"if ema is not None:model.load_state_dict(ema)\\\\n out=f'{args.name}_validation.parquet'\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_ema --resume temporal2_source.pt --epochs 1 --half 14 --eval-half 30 --days 84 --stride 7 --dropout .7 --lr .0001 --ema .97 > /app/solution/temporal_ema.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T00:58:00.541Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"96658e\",\"wall_time_seconds\":1.001506023,\"session_id\":26353,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"3692f1\",\"wall_time_seconds\":1.001320189,\"session_id\":24879,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:57:58.271Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0af02900c87d1ada112de706db4a0", "usage": { "input_tokens": 94458, "cached_input_tokens": 93824, "cache_write_input_tokens": 0, "output_tokens": 598, "reasoning_output_tokens": 131, "total_tokens": 95056 }, "turn_token_usage": { "input_tokens": 4502297, "cached_input_tokens": 4405760, "cache_write_input_tokens": 0, "output_tokens": 54512, "reasoning_output_tokens": 27272, "total_tokens": 4556809 }, "thread_token_usage": { "input_tokens": 4502297, "cached_input_tokens": 4405760, "cache_write_input_tokens": 0, "output_tokens": 54512, "reasoning_output_tokens": 27272, "total_tokens": 4556809 } } }, { "kind": "context", "timestamp": "2026-09-09T00:58:05.360Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16f916ec-39a4-4132-aaa8-8821bc6d575c", "process_id": "26353", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 15232945 }, "formatted_output": "" } } } ] }, { "id": 94, "role": "agent", "timestamp": "2026-09-09T00:58:29.720Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:58:29.720Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T00:58:30.943Z", "text": "The selected ensemble ranking has been generated, and I’m checking that exact file with the public evaluator. I’m also testing weight averaging during temporal fine-tuning to reduce sensitivity to the final training batches.\n" }, { "kind": "tool", "timestamp": "2026-09-09T00:58:32.674Z", "callId": "call_hZzDaKFL0RUBWEIZ1Ywjb2bf", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/temporal_ema.log\",\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:58:32.857Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"75f492\",\"wall_time_seconds\":0.000017221,\"exit_code\":0,\"original_token_count\":207,\"output\":\"final_vae test epoch 9 loss 7.68476 seconds 155.2\\nfinal_vae test epoch 10 loss 7.63134 seconds 171.1\\nfinal_vae test epoch 11 loss 7.57821 seconds 186.7\\nfinal_vae test epoch 12 loss 7.53165 seconds 206.5\\nfinal_context 1900 loss 4.770923352241516 seconds 149.45180320739746\\nfinal_context 2000 loss 4.749095234870911 seconds 154.64522624015808\\nSCORE 2000 0.0\\nfinal_context 2100 loss 4.7447375440597535 seconds 168.34221529960632\\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\\ncontext_local 700 loss 5.388189172744751 seconds 125.60803651809692\\ncontext_local 800 loss 5.301666355133056 seconds 140.71042680740356\\ncontext_local 900 loss 5.191272597312928 seconds 156.9592514038086\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.05477,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}\n{\"current_time\":\"2026-09-09 00:58:32 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:58:32.766Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c9c9289c-9ab0-4e50-aa0e-b1dbc396d0a4", "process_id": "10242", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/temporal_ema.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/refit_vae.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/temporal_ema.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_vae test epoch 9 loss 7.68476 seconds 155.2\nfinal_vae test epoch 10 loss 7.63134 seconds 171.1\nfinal_vae test epoch 11 loss 7.57821 seconds 186.7\nfinal_vae test epoch 12 loss 7.53165 seconds 206.5\nfinal_context 1900 loss 4.770923352241516 seconds 149.45180320739746\nfinal_context 2000 loss 4.749095234870911 seconds 154.64522624015808\nSCORE 2000 0.0\nfinal_context 2100 loss 4.7447375440597535 seconds 168.34221529960632\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\ncontext_local 700 loss 5.388189172744751 seconds 125.60803651809692\ncontext_local 800 loss 5.301666355133056 seconds 140.71042680740356\ncontext_local 900 loss 5.191272597312928 seconds 156.9592514038086\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.05477,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "final_vae test epoch 9 loss 7.68476 seconds 155.2\nfinal_vae test epoch 10 loss 7.63134 seconds 171.1\nfinal_vae test epoch 11 loss 7.57821 seconds 186.7\nfinal_vae test epoch 12 loss 7.53165 seconds 206.5\nfinal_context 1900 loss 4.770923352241516 seconds 149.45180320739746\nfinal_context 2000 loss 4.749095234870911 seconds 154.64522624015808\nSCORE 2000 0.0\nfinal_context 2100 loss 4.7447375440597535 seconds 168.34221529960632\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\ncontext_local 700 loss 5.388189172744751 seconds 125.60803651809692\ncontext_local 800 loss 5.301666355133056 seconds 140.71042680740356\ncontext_local 900 loss 5.191272597312928 seconds 156.9592514038086\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.05477,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 17221 }, "formatted_output": "final_vae test epoch 9 loss 7.68476 seconds 155.2\nfinal_vae test epoch 10 loss 7.63134 seconds 171.1\nfinal_vae test epoch 11 loss 7.57821 seconds 186.7\nfinal_vae test epoch 12 loss 7.53165 seconds 206.5\nfinal_context 1900 loss 4.770923352241516 seconds 149.45180320739746\nfinal_context 2000 loss 4.749095234870911 seconds 154.64522624015808\nSCORE 2000 0.0\nfinal_context 2100 loss 4.7447375440597535 seconds 168.34221529960632\ncontext_local 600 loss 5.5073401689529415 seconds 111.53554272651672\ncontext_local 700 loss 5.388189172744751 seconds 125.60803651809692\ncontext_local 800 loss 5.301666355133056 seconds 140.71042680740356\ncontext_local 900 loss 5.191272597312928 seconds 156.9592514038086\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.05477,\n \"quality_users\": 4263\n }\n}\n" } } }, { "kind": "context", "timestamp": "2026-09-09T00:58:32.836Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0af18eb7c87d1a2a30012a2da3dde", "usage": { "input_tokens": 95148, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 656, "reasoning_output_tokens": 516, "total_tokens": 95804 }, "turn_token_usage": { "input_tokens": 4597445, "cached_input_tokens": 4500096, "cache_write_input_tokens": 0, "output_tokens": 55168, "reasoning_output_tokens": 27788, "total_tokens": 4652613 }, "thread_token_usage": { "input_tokens": 4597445, "cached_input_tokens": 4500096, "cache_write_input_tokens": 0, "output_tokens": 55168, "reasoning_output_tokens": 27788, "total_tokens": 4652613 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-09T00:59:00.702Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:59:00.702Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T00:59:11.822Z", "callId": "call_sPDU1iHOsvgsxRBR0yK5JMT6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/selected_bias_sweep.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom discovery_trends import discovery_features\\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));arr=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'selected_scores.npy',mode='w+',dtype='float32',shape=arr[0].shape)\\nfor st in range(0,len(s),128):s[st:st+128]=.4*arr[0][st:st+128]+.4*arr[1][st:st+128]+.2*arr[2][st:st+128]\\ns.flush();best=(-1,{})\\nfor trend in [1.,1.3,1.6,1.9,2.2]:\\n for popularity in [0.,.3,.6,.9]:\\n c={'org7':trend,'org360':popularity-trend,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3};score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\\n if score>best[0]:best=(score,c)\\nbase=best[1].copy()\\nfor like in [0.,.2,.4]:\\n for new in [0.,.3,.6]:\\n c=base.copy();c.update({'like14':like,'org14':-like,'new3':new,'new30':-new});score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\\n if score>best[0]:best=(score,c)\\nprint('BEST',best,flush=True);(ROOT/'selected_bias_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/selected_bias_sweep.py > /app/solution/selected_bias.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T00:59:13.084Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"76a938\",\"wall_time_seconds\":1.001595909,\"session_id\":36920,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 00:59:13 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T00:59:11.980Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0af39120c87d1b078cf1a50dcf0cd", "usage": { "input_tokens": 96181, "cached_input_tokens": 94976, "cache_write_input_tokens": 0, "output_tokens": 1074, "reasoning_output_tokens": 516, "total_tokens": 97255 }, "turn_token_usage": { "input_tokens": 4693626, "cached_input_tokens": 4595072, "cache_write_input_tokens": 0, "output_tokens": 56242, "reasoning_output_tokens": 28304, "total_tokens": 4749868 }, "thread_token_usage": { "input_tokens": 4693626, "cached_input_tokens": 4595072, "cache_write_input_tokens": 0, "output_tokens": 56242, "reasoning_output_tokens": 28304, "total_tokens": 4749868 } } } ] }, { "id": 96, "role": "agent", "timestamp": "2026-09-09T00:59:39.006Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T00:59:39.006Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T00:59:52.945Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-683c8259-86cb-414f-9316-9bdbc92a119a", "process_id": "46679", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'disjoint' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\ncat > /app/solution/refit_vae.py <<'PY'\nimport subprocess,sys\nfrom pathlib import Path\nroot=Path('/app/solution')\nbase=[sys.executable,str(root/'train.py'),'--stage','test','--vae','--hidden','600','--dim','256','--half','30','--likes','7','--dropout','.5','--beta','.0002','--eval-every','15']\nfor extra in [['--name','final_vae_initial','--epochs','5'],['--name','final_vae','--epochs','15','--trend','1','--resume','final_vae_initial_test.pt']]:\n subprocess.run(base+extra,check=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/refit_vae.py > /app/solution/refit_vae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/train.py' in c and b'disjoint' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\ncat > /app/solution/refit_vae.py <<'PY'\nimport subprocess,sys\nfrom pathlib import Path\nroot=Path('/app/solution')\nbase=[sys.executable,str(root/'train.py'),'--stage','test','--vae','--hidden','600','--dim','256','--half','30','--likes','7','--dropout','.5','--beta','.0002','--eval-every','15']\nfor extra in [['--name','final_vae_initial','--epochs','5'],['--name','final_vae','--epochs','15','--trend','1','--resume','final_vae_initial_test.pt']]:\n subprocess.run(base+extra,check=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/refit_vae.py > /app/solution/refit_vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 448, "nanos": 348526340 }, "formatted_output": "" } } } ] }, { "id": 97, "role": "agent", "timestamp": "2026-09-09T01:00:02.530Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:00:02.530Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T01:00:07.915Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d1562240-45e5-4110-8c9a-cb8010acee6e", "process_id": "36920", "command": [ "/bin/sh", "-lc", "cat > /app/solution/selected_bias_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));arr=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'selected_scores.npy',mode='w+',dtype='float32',shape=arr[0].shape)\nfor st in range(0,len(s),128):s[st:st+128]=.4*arr[0][st:st+128]+.4*arr[1][st:st+128]+.2*arr[2][st:st+128]\ns.flush();best=(-1,{})\nfor trend in [1.,1.3,1.6,1.9,2.2]:\n for popularity in [0.,.3,.6,.9]:\n c={'org7':trend,'org360':popularity-trend,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3};score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nbase=best[1].copy()\nfor like in [0.,.2,.4]:\n for new in [0.,.3,.6]:\n c=base.copy();c.update({'like14':like,'org14':-like,'new3':new,'new30':-new});score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nprint('BEST',best,flush=True);(ROOT/'selected_bias_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/selected_bias_sweep.py > /app/solution/selected_bias.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/selected_bias_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));arr=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy']];s=np.lib.format.open_memmap(ROOT/'selected_scores.npy',mode='w+',dtype='float32',shape=arr[0].shape)\nfor st in range(0,len(s),128):s[st:st+128]=.4*arr[0][st:st+128]+.4*arr[1][st:st+128]+.2*arr[2][st:st+128]\ns.flush();best=(-1,{})\nfor trend in [1.,1.3,1.6,1.9,2.2]:\n for popularity in [0.,.3,.6,.9]:\n c={'org7':trend,'org360':popularity-trend,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3};score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nbase=best[1].copy()\nfor like in [0.,.2,.4]:\n for new in [0.,.3,.6]:\n c=base.copy();c.update({'like14':like,'org14':-like,'new3':new,'new30':-new});score=m(s,sum(w*f[k] for k,w in c.items()));print(c,score,flush=True)\n if score>best[0]:best=(score,c)\nprint('BEST',best,flush=True);(ROOT/'selected_bias_best.json').write_text(json.dumps({'score':best[0],'bias':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/selected_bias_sweep.py > /app/solution/selected_bias.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 55, "nanos": 880071938 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T01:00:10.955Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a4f37a8f-a4ee-451d-ab7e-0982ccda9758", "process_id": "24879", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace(\"p.add_argument('--eval-half',type=float);\", \"p.add_argument('--eval-half',type=float);p.add_argument('--ema',type=float,default=0.);\")\ns=s.replace(\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\",\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\\n ema={k:v.detach().clone() for k,v in model.state_dict().items()} if args.ema else None\")\ns=s.replace(\"opt.step();losses.append(loss.item())\", \"opt.step();losses.append(loss.item())\\n if ema is not None:\\n with torch.no_grad():\\n for k,v in model.state_dict().items():ema[k].mul_(args.ema).add_(v,alpha=1-args.ema)\")\ns=s.replace(\"out=f'{args.name}_validation.parquet'\", \"if ema is not None:model.load_state_dict(ema)\\n out=f'{args.name}_validation.parquet'\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_ema --resume temporal2_source.pt --epochs 1 --half 14 --eval-half 30 --days 84 --stride 7 --dropout .7 --lr .0001 --ema .97 > /app/solution/temporal_ema.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace(\"p.add_argument('--eval-half',type=float);\", \"p.add_argument('--eval-half',type=float);p.add_argument('--ema',type=float,default=0.);\")\ns=s.replace(\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\",\"ybags={'offsets':d['yoffsets'],'items':d['yitems'],'weights':d['yweights']}\\n ema={k:v.detach().clone() for k,v in model.state_dict().items()} if args.ema else None\")\ns=s.replace(\"opt.step();losses.append(loss.item())\", \"opt.step();losses.append(loss.item())\\n if ema is not None:\\n with torch.no_grad():\\n for k,v in model.state_dict().items():ema[k].mul_(args.ema).add_(v,alpha=1-args.ema)\")\ns=s.replace(\"out=f'{args.name}_validation.parquet'\", \"if ema is not None:model.load_state_dict(ema)\\n out=f'{args.name}_validation.parquet'\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --name temporal_ema --resume temporal2_source.pt --epochs 1 --half 14 --eval-half 30 --days 84 --stride 7 --dropout .7 --lr .0001 --ema .97 > /app/solution/temporal_ema.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 131, "nanos": 417794063 }, "formatted_output": "" } } } ] }, { "id": 98, "role": "agent", "timestamp": "2026-09-09T01:00:28.157Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:00:28.157Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:00:29.927Z", "callId": "call_DKUhEba1cu93ZFPwjk9hEpEX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/temporal_ema.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; tail -5 /app/solution/selected_bias.log\",\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:00:30.092Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"92d78d\",\"wall_time_seconds\":0.00000948,\"exit_code\":0,\"original_token_count\":330,\"output\":\"final_vae test epoch 13 loss 7.47357 seconds 232.3\\nfinal_vae test epoch 14 loss 7.42671 seconds 261.9\\nfinal_vae test epoch 15 loss 7.39237 seconds 296.7\\nSCORE 15 0.0\\nDONE 0.0\\ntemporal_ema 1 loss 8.714205387026764 score 0.04686348406221016 time 123.0414400100708\\nfinal_context 3600 loss 4.494345765113831 seconds 279.7780570983887\\nfinal_context 3700 loss 4.477713394165039 seconds 282.91283202171326\\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\\ncontext_local 1300 loss 4.947619915008545 seconds 254.01325869560242\\ncontext_local 1400 loss 4.913752598762512 seconds 269.8294212818146\\ncontext_local 1500 loss 4.893044095039368 seconds 280.03832483291626\\nSCORE 1500 0.024294173501545602\\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.6, 'new30': -0.6} 0.05314497351016441\\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.0, 'new30': -0.0} 0.0531766445048606\\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.3, 'new30': -0.3} 0.05446249264132338\\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.6, 'new30': -0.6} 0.05283177095585283\\nBEST (0.055145050280062204, {'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\\n\"}\n{\"current_time\":\"2026-09-09 01:00:30 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:00:29.999Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb60a788-b75c-46df-8e69-59b83e0af300", "process_id": "54850", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/temporal_ema.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; tail -5 /app/solution/selected_bias.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/refit_vae.log; tail -4 /app/solution/temporal_ema.log; tail -4 /app/solution/final_context.log; tail -4 /app/solution/context_local.log; tail -5 /app/solution/selected_bias.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final_vae test epoch 13 loss 7.47357 seconds 232.3\nfinal_vae test epoch 14 loss 7.42671 seconds 261.9\nfinal_vae test epoch 15 loss 7.39237 seconds 296.7\nSCORE 15 0.0\nDONE 0.0\ntemporal_ema 1 loss 8.714205387026764 score 0.04686348406221016 time 123.0414400100708\nfinal_context 3600 loss 4.494345765113831 seconds 279.7780570983887\nfinal_context 3700 loss 4.477713394165039 seconds 282.91283202171326\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\ncontext_local 1300 loss 4.947619915008545 seconds 254.01325869560242\ncontext_local 1400 loss 4.913752598762512 seconds 269.8294212818146\ncontext_local 1500 loss 4.893044095039368 seconds 280.03832483291626\nSCORE 1500 0.024294173501545602\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.6, 'new30': -0.6} 0.05314497351016441\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.0, 'new30': -0.0} 0.0531766445048606\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.3, 'new30': -0.3} 0.05446249264132338\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.6, 'new30': -0.6} 0.05283177095585283\nBEST (0.055145050280062204, {'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\n", "stderr": "", "aggregated_output": "final_vae test epoch 13 loss 7.47357 seconds 232.3\nfinal_vae test epoch 14 loss 7.42671 seconds 261.9\nfinal_vae test epoch 15 loss 7.39237 seconds 296.7\nSCORE 15 0.0\nDONE 0.0\ntemporal_ema 1 loss 8.714205387026764 score 0.04686348406221016 time 123.0414400100708\nfinal_context 3600 loss 4.494345765113831 seconds 279.7780570983887\nfinal_context 3700 loss 4.477713394165039 seconds 282.91283202171326\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\ncontext_local 1300 loss 4.947619915008545 seconds 254.01325869560242\ncontext_local 1400 loss 4.913752598762512 seconds 269.8294212818146\ncontext_local 1500 loss 4.893044095039368 seconds 280.03832483291626\nSCORE 1500 0.024294173501545602\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.6, 'new30': -0.6} 0.05314497351016441\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.0, 'new30': -0.0} 0.0531766445048606\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.3, 'new30': -0.3} 0.05446249264132338\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.6, 'new30': -0.6} 0.05283177095585283\nBEST (0.055145050280062204, {'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9480 }, "formatted_output": "final_vae test epoch 13 loss 7.47357 seconds 232.3\nfinal_vae test epoch 14 loss 7.42671 seconds 261.9\nfinal_vae test epoch 15 loss 7.39237 seconds 296.7\nSCORE 15 0.0\nDONE 0.0\ntemporal_ema 1 loss 8.714205387026764 score 0.04686348406221016 time 123.0414400100708\nfinal_context 3600 loss 4.494345765113831 seconds 279.7780570983887\nfinal_context 3700 loss 4.477713394165039 seconds 282.91283202171326\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\ncontext_local 1300 loss 4.947619915008545 seconds 254.01325869560242\ncontext_local 1400 loss 4.913752598762512 seconds 269.8294212818146\ncontext_local 1500 loss 4.893044095039368 seconds 280.03832483291626\nSCORE 1500 0.024294173501545602\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.6, 'new30': -0.6} 0.05314497351016441\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.0, 'new30': -0.0} 0.0531766445048606\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.3, 'new30': -0.3} 0.05446249264132338\n{'org7': 1.3, 'org360': -1.0, 'like14': 0.4, 'org14': -0.4, 'new3': 0.6, 'new30': -0.6} 0.05283177095585283\nBEST (0.055145050280062204, {'org7': 1.3, 'org360': -1.0, 'like14': 0.2, 'org14': -0.2, 'new3': 0.3, 'new30': -0.3})\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:00:30.088Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0af61823c87d19f2ca8f426caf971", "usage": { "input_tokens": 97330, "cached_input_tokens": 96000, "cache_write_input_tokens": 0, "output_tokens": 1650, "reasoning_output_tokens": 1552, "total_tokens": 98980 }, "turn_token_usage": { "input_tokens": 4790956, "cached_input_tokens": 4691072, "cache_write_input_tokens": 0, "output_tokens": 57892, "reasoning_output_tokens": 29856, "total_tokens": 4848848 }, "thread_token_usage": { "input_tokens": 4790956, "cached_input_tokens": 4691072, "cache_write_input_tokens": 0, "output_tokens": 57892, "reasoning_output_tokens": 29856, "total_tokens": 4848848 } } }, { "kind": "context", "timestamp": "2026-09-09T01:00:38.456Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-afabe8d7-e178-4697-9572-3b775e201f40", "process_id": "95671", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/context.py' in c and b'context_more' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --stage test --name final_context --steps 4000 > /app/solution/final_context.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport os,signal\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n c=open('/proc/'+f+'/cmdline','rb').read().split(b'\\0')\n if b'/app/solution/context.py' in c and b'context_more' in c:os.kill(int(f),signal.SIGTERM)\n except (FileNotFoundError,ProcessLookupError,PermissionError):pass\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --stage test --name final_context --steps 4000 > /app/solution/final_context.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 304, "nanos": 224715310 }, "formatted_output": "" } } } ] }, { "id": 99, "role": "agent", "timestamp": "2026-09-09T01:00:51.222Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:00:51.222Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:00:53.356Z", "text": "The public evaluator confirms 0.054770 NDCG@10 for the ensemble file. A narrower bias sweep has reached 0.0551 internally, and weight averaging slightly improved the temporal component. The variational pretraining refit is complete; I’m running its final temporal fine-tuning pass.\n" }, { "kind": "tool", "timestamp": "2026-09-09T01:00:57.177Z", "callId": "call_yU3hX1dXMN7Zrc2hDrUUltQt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal_vae --resume final_vae_test.pt --epochs 1 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/final_temporal_vae.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_ema_validation.pt --half 30 --dest ema_scores.npy --no-grid > /app/solution/ema_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T01:00:59.527Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"341392\",\"wall_time_seconds\":1.00171185,\"session_id\":94268,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"22b777\",\"wall_time_seconds\":1.00121701,\"session_id\":50158,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:00:57.362Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0afae649087d1b9946a7097a476e9", "usage": { "input_tokens": 99667, "cached_input_tokens": 97152, "cache_write_input_tokens": 0, "output_tokens": 723, "reasoning_output_tokens": 461, "total_tokens": 100390 }, "turn_token_usage": { "input_tokens": 4890623, "cached_input_tokens": 4788224, "cache_write_input_tokens": 0, "output_tokens": 58615, "reasoning_output_tokens": 30317, "total_tokens": 4949238 }, "thread_token_usage": { "input_tokens": 4890623, "cached_input_tokens": 4788224, "cache_write_input_tokens": 0, "output_tokens": 58615, "reasoning_output_tokens": 30317, "total_tokens": 4949238 } } }, { "kind": "context", "timestamp": "2026-09-09T01:01:18.383Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-af539777-aeae-47e1-bcb9-8fa3f9f59ea4", "process_id": "50158", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_ema_validation.pt --half 30 --dest ema_scores.npy --no-grid > /app/solution/ema_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py temporal_ema_validation.pt --half 30 --dest ema_scores.npy --no-grid > /app/solution/ema_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 19, "nanos": 859835552 }, "formatted_output": "" } } } ] }, { "id": 100, "role": "agent", "timestamp": "2026-09-09T01:01:27.167Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:01:27.167Z", "text": "" } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-09T01:01:40.437Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:01:40.437Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:02:01.423Z", "callId": "call_Bs51dYU9ATopqKsnGhcWtOvO", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/temporal.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,batch,gold_data,infer','from train import ROOT,DATA,make_bags,batch,gold_data,infer,RetrievalOptimizer');pos=s.index('\\\\ndef main():')\\nf='''\\\\ndef temporal_loss(model,d,ybags,uu,rng,items):\\n x,o,w,_,rows=batch(d,uu)\\n if len(items)<=50000:\\n y,_,yw,_,yr=batch(ybags,uu);logits=model(x,o,w,items)\\n logits[torch.from_numpy(rows),x]=-1e9\\n logp=F.log_softmax(logits,dim=1)\\n return -(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\\n positive=[]\\n for u in uu:\\n start,end=ybags['offsets'][u:u+2];weight=ybags['weights'][start:end]\\n positive.append(rng.choice(ybags['items'][start:end],p=weight/weight.sum()))\\n sampled=rng.integers(0,len(items),size=(len(uu),257),dtype=np.int64);sampled[:,0]=positive\\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled));logits=torch.einsum('bd,bkd->bk',uv,iv)\\n for row,u in enumerate(uu):\\n start,end=d['offsets'][u:u+2]\\n blocked=np.isin(sampled[row,1:],d['items'][start:end])|(sampled[row,1:]==sampled[row,0])\\n logits[row,1:].masked_fill_(torch.from_numpy(blocked),-1e9)\\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\\n'''\\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)')\\nold=\\\"\\\"\\\"uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\\n logits=model(x,o,w,items)\\n # Previously observed positives cannot be discovery outcomes.\\n logits[torch.from_numpy(rows),x]=-1e9\\n logp=F.log_softmax(logits,dim=1)\\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\\\"\\\"\\\"\\nassert old in s;s=s.replace(old,\\\"uu=examples[st:st+256];loss=temporal_loss(model,d,ybags,uu,rng,items)\\\");open(p,'w').write(s)\\np='/app/solution/context.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,infer,gold_data','from train import ROOT,DATA,make_bags,infer,gold_data,RetrievalOptimizer').replace('opt=torch.optim.Adam(model.parameters(),lr=a.lr)','opt=RetrievalOptimizer(model,lr=a.lr,weight_decay=0.)');open(p,'w').write(s)\\np='/app/solution/test_contract.py';s=open(p).read()+'''\\\\nfrom temporal import temporal_loss\\nm=TwoTower(50001,hidden=16,dim=8)\\nybags={'items':np.array([8,9,10,11]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2}\\nloss=temporal_loss(m,bags,ybags,np.array([0,1]),np.random.default_rng(2),torch.arange(50001));loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\\nprint('Large-catalog temporal sampled-softmax and sparse optimizer: PASS')\\n''';open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/ema_analyze.log; tail -4 /app/solution/context_local.log; tail -4 /app/solution/final_temporal_vae.log; tail -4 /app/solution/final_context.log\",\"max_output_tokens\":1300}));\n" }, "result": { "timestamp": "2026-09-09T01:02:02.692Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"102dfd\",\"wall_time_seconds\":1.001096269,\"session_id\":12190,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"5fbb75\",\"wall_time_seconds\":0.00000739,\"exit_code\":0,\"original_token_count\":151,\"output\":\"ema_scores.npy\\npop 0.0 score 0.03132997016463674\\ncontext_local 2100 loss 4.708982176780701 seconds 349.1501319408417\\ncontext_local 2200 loss 4.681242337226868 seconds 358.5571846961975\\ncontext_local 2300 loss 4.6654860353469845 seconds 370.41118931770325\\ncontext_local 2400 loss 4.64445249080658 seconds 381.7298574447632\\ntemporal test samples 44981 entries 31007155\\nbags test 5778549\\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\\nfinal_context 4000 loss 4.475131950378418 seconds 293.23172903060913\\nSCORE 4000 0.0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:02:01.566Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0afcbbe9887d195a0d9482269966c", "usage": { "input_tokens": 100480, "cached_input_tokens": 99456, "cache_write_input_tokens": 0, "output_tokens": 1800, "reasoning_output_tokens": 779, "total_tokens": 102280 }, "turn_token_usage": { "input_tokens": 4991103, "cached_input_tokens": 4887680, "cache_write_input_tokens": 0, "output_tokens": 60415, "reasoning_output_tokens": 31096, "total_tokens": 5051518 }, "thread_token_usage": { "input_tokens": 4991103, "cached_input_tokens": 4887680, "cache_write_input_tokens": 0, "output_tokens": 60415, "reasoning_output_tokens": 31096, "total_tokens": 5051518 } } }, { "kind": "context", "timestamp": "2026-09-09T01:02:02.665Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-607cb01c-766c-4619-a817-18a09c18748f", "process_id": "6944", "command": [ "/bin/sh", "-lc", "cat /app/solution/ema_analyze.log; tail -4 /app/solution/context_local.log; tail -4 /app/solution/final_temporal_vae.log; tail -4 /app/solution/final_context.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/ema_analyze.log; tail -4 /app/solution/context_local.log; tail -4 /app/solution/final_temporal_vae.log; tail -4 /app/solution/final_context.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "ema_scores.npy\npop 0.0 score 0.03132997016463674\ncontext_local 2100 loss 4.708982176780701 seconds 349.1501319408417\ncontext_local 2200 loss 4.681242337226868 seconds 358.5571846961975\ncontext_local 2300 loss 4.6654860353469845 seconds 370.41118931770325\ncontext_local 2400 loss 4.64445249080658 seconds 381.7298574447632\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\nfinal_context 4000 loss 4.475131950378418 seconds 293.23172903060913\nSCORE 4000 0.0\n", "stderr": "", "aggregated_output": "ema_scores.npy\npop 0.0 score 0.03132997016463674\ncontext_local 2100 loss 4.708982176780701 seconds 349.1501319408417\ncontext_local 2200 loss 4.681242337226868 seconds 358.5571846961975\ncontext_local 2300 loss 4.6654860353469845 seconds 370.41118931770325\ncontext_local 2400 loss 4.64445249080658 seconds 381.7298574447632\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\nfinal_context 4000 loss 4.475131950378418 seconds 293.23172903060913\nSCORE 4000 0.0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7390 }, "formatted_output": "ema_scores.npy\npop 0.0 score 0.03132997016463674\ncontext_local 2100 loss 4.708982176780701 seconds 349.1501319408417\ncontext_local 2200 loss 4.681242337226868 seconds 358.5571846961975\ncontext_local 2300 loss 4.6654860353469845 seconds 370.41118931770325\ncontext_local 2400 loss 4.64445249080658 seconds 381.7298574447632\ntemporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_context 3800 loss 4.479671578407288 seconds 286.0311472415924\nfinal_context 3900 loss 4.47359103679657 seconds 289.4370903968811\nfinal_context 4000 loss 4.475131950378418 seconds 293.23172903060913\nSCORE 4000 0.0\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:02:12.664Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3e31dfd4-230b-45d5-894b-391e5e72b49a", "process_id": "12190", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,batch,gold_data,infer','from train import ROOT,DATA,make_bags,batch,gold_data,infer,RetrievalOptimizer');pos=s.index('\\ndef main():')\nf='''\\ndef temporal_loss(model,d,ybags,uu,rng,items):\n x,o,w,_,rows=batch(d,uu)\n if len(items)<=50000:\n y,_,yw,_,yr=batch(ybags,uu);logits=model(x,o,w,items)\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n return -(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\n positive=[]\n for u in uu:\n start,end=ybags['offsets'][u:u+2];weight=ybags['weights'][start:end]\n positive.append(rng.choice(ybags['items'][start:end],p=weight/weight.sum()))\n sampled=rng.integers(0,len(items),size=(len(uu),257),dtype=np.int64);sampled[:,0]=positive\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled));logits=torch.einsum('bd,bkd->bk',uv,iv)\n for row,u in enumerate(uu):\n start,end=d['offsets'][u:u+2]\n blocked=np.isin(sampled[row,1:],d['items'][start:end])|(sampled[row,1:]==sampled[row,0])\n logits[row,1:].masked_fill_(torch.from_numpy(blocked),-1e9)\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\n'''\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)')\nold=\"\"\"uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\n logits=model(x,o,w,items)\n # Previously observed positives cannot be discovery outcomes.\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\"\"\"\nassert old in s;s=s.replace(old,\"uu=examples[st:st+256];loss=temporal_loss(model,d,ybags,uu,rng,items)\");open(p,'w').write(s)\np='/app/solution/context.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,infer,gold_data','from train import ROOT,DATA,make_bags,infer,gold_data,RetrievalOptimizer').replace('opt=torch.optim.Adam(model.parameters(),lr=a.lr)','opt=RetrievalOptimizer(model,lr=a.lr,weight_decay=0.)');open(p,'w').write(s)\np='/app/solution/test_contract.py';s=open(p).read()+'''\\nfrom temporal import temporal_loss\nm=TwoTower(50001,hidden=16,dim=8)\nybags={'items':np.array([8,9,10,11]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2}\nloss=temporal_loss(m,bags,ybags,np.array([0,1]),np.random.default_rng(2),torch.arange(50001));loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\nprint('Large-catalog temporal sampled-softmax and sparse optimizer: PASS')\n''';open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/temporal.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,batch,gold_data,infer','from train import ROOT,DATA,make_bags,batch,gold_data,infer,RetrievalOptimizer');pos=s.index('\\ndef main():')\nf='''\\ndef temporal_loss(model,d,ybags,uu,rng,items):\n x,o,w,_,rows=batch(d,uu)\n if len(items)<=50000:\n y,_,yw,_,yr=batch(ybags,uu);logits=model(x,o,w,items)\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n return -(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\n positive=[]\n for u in uu:\n start,end=ybags['offsets'][u:u+2];weight=ybags['weights'][start:end]\n positive.append(rng.choice(ybags['items'][start:end],p=weight/weight.sum()))\n sampled=rng.integers(0,len(items),size=(len(uu),257),dtype=np.int64);sampled[:,0]=positive\n uv=model.user(x,o,w);iv=model.item(torch.from_numpy(sampled));logits=torch.einsum('bd,bkd->bk',uv,iv)\n for row,u in enumerate(uu):\n start,end=d['offsets'][u:u+2]\n blocked=np.isin(sampled[row,1:],d['items'][start:end])|(sampled[row,1:]==sampled[row,0])\n logits[row,1:].masked_fill_(torch.from_numpy(blocked),-1e9)\n return F.cross_entropy(logits,torch.zeros(len(uu),dtype=torch.long))\n'''\ns=s[:pos]+f+s[pos:];s=s.replace('opt=torch.optim.Adam(model.parameters(),lr=args.lr,weight_decay=1e-5)','opt=RetrievalOptimizer(model,lr=args.lr,weight_decay=1e-5)')\nold=\"\"\"uu=examples[st:st+256];x,o,w,_,rows=batch(d,uu);y,_,yw,_,yr=batch(ybags,uu)\n logits=model(x,o,w,items)\n # Previously observed positives cannot be discovery outcomes.\n logits[torch.from_numpy(rows),x]=-1e9\n logp=F.log_softmax(logits,dim=1)\n loss=-(logp[torch.from_numpy(yr),y]*yw).sum()/len(uu)\"\"\"\nassert old in s;s=s.replace(old,\"uu=examples[st:st+256];loss=temporal_loss(model,d,ybags,uu,rng,items)\");open(p,'w').write(s)\np='/app/solution/context.py';s=open(p).read().replace('from train import ROOT,DATA,make_bags,infer,gold_data','from train import ROOT,DATA,make_bags,infer,gold_data,RetrievalOptimizer').replace('opt=torch.optim.Adam(model.parameters(),lr=a.lr)','opt=RetrievalOptimizer(model,lr=a.lr,weight_decay=0.)');open(p,'w').write(s)\np='/app/solution/test_contract.py';s=open(p).read()+'''\\nfrom temporal import temporal_loss\nm=TwoTower(50001,hidden=16,dim=8)\nybags={'items':np.array([8,9,10,11]),'offsets':np.array([0,2,4]),'weights':np.ones(4,dtype='float32')/2}\nloss=temporal_loss(m,bags,ybags,np.array([0,1]),np.random.default_rng(2),torch.arange(50001));loss.backward();RetrievalOptimizer(m,.001).step();assert torch.isfinite(loss)\nprint('Large-catalog temporal sampled-softmax and sparse optimizer: PASS')\n''';open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n", "stderr": "", "aggregated_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 55226369 }, "formatted_output": "TwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n" } } } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-09T01:02:24.551Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:02:24.551Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:02:27.458Z", "callId": "call_YVTP0lSqBRM1B5IZPrltUKdt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"rg SCORE /app/solution/context_local.log; cat /app/solution/selected_bias_best.json; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_more_validation.pt --dest context_more_scores.npy --no-grid > /app/solution/context_more_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.write_stdin({session_id:12190,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:02:28.652Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b92072\",\"wall_time_seconds\":1.001101919,\"session_id\":83183,\"original_token_count\":80,\"output\":\"SCORE 500 0.02159773694674917\\nSCORE 1000 0.021237031161707994\\nSCORE 1500 0.024294173501545602\\nSCORE 2000 0.02454483413034205\\nSCORE 2500 0.02638343515658139\\n{\\n \\\"score\\\": 0.055145050280062204,\\n \\\"bias\\\": {\\n \\\"org7\\\": 1.3,\\n \\\"org360\\\": -1.0,\\n \\\"like14\\\": 0.2,\\n \\\"org14\\\": -0.2,\\n \\\"new3\\\": 0.3,\\n \\\"new30\\\": -0.3\\n }\\n}\"}\n{\"chunk_id\":\"7fecc8\",\"wall_time_seconds\":0.00000201,\"exit_code\":0,\"original_token_count\":94,\"output\":\"TwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nLarge-catalog bounded sampled-softmax path: PASS\\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\\n\"}\n{\"current_time\":\"2026-09-09 01:02:28 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:02:27.627Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b00aef6887d1adda2f09337ec72c", "usage": { "input_tokens": 102589, "cached_input_tokens": 100352, "cache_write_input_tokens": 0, "output_tokens": 606, "reasoning_output_tokens": 462, "total_tokens": 103195 }, "turn_token_usage": { "input_tokens": 5093692, "cached_input_tokens": 4988032, "cache_write_input_tokens": 0, "output_tokens": 61021, "reasoning_output_tokens": 31558, "total_tokens": 5154713 }, "thread_token_usage": { "input_tokens": 5093692, "cached_input_tokens": 4988032, "cache_write_input_tokens": 0, "output_tokens": 61021, "reasoning_output_tokens": 31558, "total_tokens": 5154713 } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-09T01:02:36.470Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:02:36.470Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:02:38.405Z", "text": "Both pretraining and temporal fine-tuning now have checked, bounded negative-sampling paths with sparse embedding updates for large catalogs. The original context model’s final refit is complete. I’m comparing two small model variants before the final export.\n" }, { "kind": "context", "timestamp": "2026-09-09T01:02:44.794Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73b434d1-0cd3-475c-be87-6c9d31c0c1b9", "process_id": "94268", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal_vae --resume final_vae_test.pt --epochs 1 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/final_temporal_vae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal.py --stage test --name final_temporal_vae --resume final_vae_test.pt --epochs 1 --half 30 --days 84 --stride 7 --dropout .5 --lr .0001 > /app/solution/final_temporal_vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 107, "nanos": 441774558 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T01:02:49.015Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-af21f17c-e43e-49b3-888c-b9b2c16b5f7b", "process_id": "83183", "command": [ "/bin/sh", "-lc", "rg SCORE /app/solution/context_local.log; cat /app/solution/selected_bias_best.json; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_more_validation.pt --dest context_more_scores.npy --no-grid > /app/solution/context_more_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "rg SCORE /app/solution/context_local.log; cat /app/solution/selected_bias_best.json; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_more_validation.pt --dest context_more_scores.npy --no-grid > /app/solution/context_more_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 21, "nanos": 376418075 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-09T01:02:49.235Z", "callId": "call_0Sc4uAFAxK3rLbPPyYXHA3Cd", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/variation_sweep.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom discovery_trends import discovery_features\\nclass Blend:\\n def __init__(self,arrays):self.arrays=arrays\\n def __getitem__(self,ix):return .4*self.arrays[0][ix]+.4*self.arrays[1][ix]+.2*self.arrays[2][ix]\\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());tv=np.load(ROOT/'temporal_vae_scores.npy',mmap_mode='r');best=(-1,None)\\nfor e in ['temporal2_h30.0_scores.npy','ema_scores.npy']:\\n for c in ['context_scores.npy','context_more_scores.npy']:\\n s=Blend([np.load(ROOT/e,mmap_mode='r'),tv,np.load(ROOT/c,mmap_mode='r')]);score=m(s,bias);print(e,c,score,flush=True)\\n if score>best[0]:best=(score,[e,c])\\nprint('BEST',best,flush=True);(ROOT/'variation_best.json').write_text(json.dumps({'score':best[0],'files':best[1],'bias':cfg['bias']},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/variation_sweep.py > /app/solution/variation.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/context_local.log; cat /app/solution/context_more_analyze.log\",\"max_output_tokens\":1100}));\n" }, "result": { "timestamp": "2026-09-09T01:02:50.471Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"083496\",\"wall_time_seconds\":1.001028612,\"session_id\":35737,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"49a3b1\",\"wall_time_seconds\":0.00000598,\"exit_code\":0,\"original_token_count\":93,\"output\":\"temporal test samples 44981 entries 31007155\\nbags test 5778549\\nfinal_temporal_vae 1 loss 8.31404873457822 score 0.0 time 102.07786130905151\\nSCORE 2500 0.02638343515658139\\ncontext_local 2600 loss 4.6201707553863525 seconds 414.9176766872406\\ncontext_local 2700 loss 4.6111252498626705 seconds 425.24651741981506\\ncontext_more_scores.npy\\npop 0.0 score 0.024599625271847426\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:02:49.389Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b024f56887d193d054a3830ee818", "usage": { "input_tokens": 103543, "cached_input_tokens": 102400, "cache_write_input_tokens": 0, "output_tokens": 623, "reasoning_output_tokens": 116, "total_tokens": 104166 }, "turn_token_usage": { "input_tokens": 5197235, "cached_input_tokens": 5090432, "cache_write_input_tokens": 0, "output_tokens": 61644, "reasoning_output_tokens": 31674, "total_tokens": 5258879 }, "thread_token_usage": { "input_tokens": 5197235, "cached_input_tokens": 5090432, "cache_write_input_tokens": 0, "output_tokens": 61644, "reasoning_output_tokens": 31674, "total_tokens": 5258879 } } }, { "kind": "context", "timestamp": "2026-09-09T01:02:50.456Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8b757dbf-66a5-4697-b348-0e31941801d9", "process_id": "99741", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/context_local.log; cat /app/solution/context_more_analyze.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/context_local.log; cat /app/solution/context_more_analyze.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal_vae 1 loss 8.31404873457822 score 0.0 time 102.07786130905151\nSCORE 2500 0.02638343515658139\ncontext_local 2600 loss 4.6201707553863525 seconds 414.9176766872406\ncontext_local 2700 loss 4.6111252498626705 seconds 425.24651741981506\ncontext_more_scores.npy\npop 0.0 score 0.024599625271847426\n", "stderr": "", "aggregated_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal_vae 1 loss 8.31404873457822 score 0.0 time 102.07786130905151\nSCORE 2500 0.02638343515658139\ncontext_local 2600 loss 4.6201707553863525 seconds 414.9176766872406\ncontext_local 2700 loss 4.6111252498626705 seconds 425.24651741981506\ncontext_more_scores.npy\npop 0.0 score 0.024599625271847426\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5980 }, "formatted_output": "temporal test samples 44981 entries 31007155\nbags test 5778549\nfinal_temporal_vae 1 loss 8.31404873457822 score 0.0 time 102.07786130905151\nSCORE 2500 0.02638343515658139\ncontext_local 2600 loss 4.6201707553863525 seconds 414.9176766872406\ncontext_local 2700 loss 4.6111252498626705 seconds 425.24651741981506\ncontext_more_scores.npy\npop 0.0 score 0.024599625271847426\n" } } } ] }, { "id": 104, "role": "agent", "timestamp": "2026-09-09T01:03:02.306Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:03:02.306Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-09T01:03:06.742Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d9055c1-8b35-4e8c-87c3-91f6c257349f", "process_id": "35737", "command": [ "/bin/sh", "-lc", "cat > /app/solution/variation_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,arrays):self.arrays=arrays\n def __getitem__(self,ix):return .4*self.arrays[0][ix]+.4*self.arrays[1][ix]+.2*self.arrays[2][ix]\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());tv=np.load(ROOT/'temporal_vae_scores.npy',mmap_mode='r');best=(-1,None)\nfor e in ['temporal2_h30.0_scores.npy','ema_scores.npy']:\n for c in ['context_scores.npy','context_more_scores.npy']:\n s=Blend([np.load(ROOT/e,mmap_mode='r'),tv,np.load(ROOT/c,mmap_mode='r')]);score=m(s,bias);print(e,c,score,flush=True)\n if score>best[0]:best=(score,[e,c])\nprint('BEST',best,flush=True);(ROOT/'variation_best.json').write_text(json.dumps({'score':best[0],'files':best[1],'bias':cfg['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/variation_sweep.py > /app/solution/variation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/variation_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,arrays):self.arrays=arrays\n def __getitem__(self,ix):return .4*self.arrays[0][ix]+.4*self.arrays[1][ix]+.2*self.arrays[2][ix]\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());tv=np.load(ROOT/'temporal_vae_scores.npy',mmap_mode='r');best=(-1,None)\nfor e in ['temporal2_h30.0_scores.npy','ema_scores.npy']:\n for c in ['context_scores.npy','context_more_scores.npy']:\n s=Blend([np.load(ROOT/e,mmap_mode='r'),tv,np.load(ROOT/c,mmap_mode='r')]);score=m(s,bias);print(e,c,score,flush=True)\n if score>best[0]:best=(score,[e,c])\nprint('BEST',best,flush=True);(ROOT/'variation_best.json').write_text(json.dumps({'score':best[0],'files':best[1],'bias':cfg['bias']},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/variation_sweep.py > /app/solution/variation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 330908403 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-09T01:03:08.357Z", "callId": "call_nRw6kcWjnOYCyxBMY04GLEs8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport json\\nfrom pathlib import Path\\nr=Path('/app/solution');bias=json.loads((r/'selected_bias_best.json').read_text())['bias'];c=json.loads((r/'planned_final_config.json').read_text());c['bias']=bias;(r/'final_config.json').write_text(json.dumps(c,indent=2));c=json.loads((r/'selected_validation_config.json').read_text());c['bias']=bias;(r/'selected_validation_config.json').write_text(json.dumps(c,indent=2))\\nPY\",\"max_output_tokens\":1000}));\nconst results=await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/ensemble_submission.log 2>&1\",yield_time_ms:1000,max_output_tokens:1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config selected_validation_config.json --stage validation > /app/solution/ensemble_validation.log 2>&1\",yield_time_ms:1000,max_output_tokens:1000})]); results.forEach(text);\n" }, "result": { "timestamp": "2026-09-09T01:03:09.705Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"a3545b\",\"wall_time_seconds\":0.00000524,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"e65bce\",\"wall_time_seconds\":1.000966927,\"session_id\":28595,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"34b62d\",\"wall_time_seconds\":1.000919776,\"session_id\":30587,\"original_token_count\":0,\"output\":\"\"}}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:03:08.479Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b03ab04c87d19ce80b5c82168baf", "usage": { "input_tokens": 104393, "cached_input_tokens": 103424, "cache_write_input_tokens": 0, "output_tokens": 519, "reasoning_output_tokens": 231, "total_tokens": 104912 }, "turn_token_usage": { "input_tokens": 5301628, "cached_input_tokens": 5193856, "cache_write_input_tokens": 0, "output_tokens": 62163, "reasoning_output_tokens": 31905, "total_tokens": 5363791 }, "thread_token_usage": { "input_tokens": 5301628, "cached_input_tokens": 5193856, "cache_write_input_tokens": 0, "output_tokens": 62163, "reasoning_output_tokens": 31905, "total_tokens": 5363791 } } }, { "kind": "context", "timestamp": "2026-09-09T01:03:08.514Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b484ed1b-5a57-47f4-b463-455849d1a193", "process_id": "4279", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');bias=json.loads((r/'selected_bias_best.json').read_text())['bias'];c=json.loads((r/'planned_final_config.json').read_text());c['bias']=bias;(r/'final_config.json').write_text(json.dumps(c,indent=2));c=json.loads((r/'selected_validation_config.json').read_text());c['bias']=bias;(r/'selected_validation_config.json').write_text(json.dumps(c,indent=2))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\nr=Path('/app/solution');bias=json.loads((r/'selected_bias_best.json').read_text())['bias'];c=json.loads((r/'planned_final_config.json').read_text());c['bias']=bias;(r/'final_config.json').write_text(json.dumps(c,indent=2));c=json.loads((r/'selected_validation_config.json').read_text());c['bias']=bias;(r/'selected_validation_config.json').write_text(json.dumps(c,indent=2))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 5240 }, "formatted_output": "" } } } ] }, { "id": 105, "role": "agent", "timestamp": "2026-09-09T01:03:17.738Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:03:17.738Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:03:19.382Z", "callId": "call_Jz2MaMAdGCX2jKfoPjebyQMJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/variation.log; tail -4 /app/solution/context_local.log; cat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; ls -lh /app/solution/*vectors.npy\",\"max_output_tokens\":1600}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:03:19.522Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"a7f120\",\"wall_time_seconds\":0.000005701,\"exit_code\":0,\"original_token_count\":228,\"output\":\"temporal2_h30.0_scores.npy context_scores.npy 0.055145050280062204\\ntemporal2_h30.0_scores.npy context_more_scores.npy 0.054538404791002\\nema_scores.npy context_scores.npy 0.05495065632223683\\nema_scores.npy context_more_scores.npy 0.05472103189687083\\nBEST (0.055145050280062204, ['temporal2_h30.0_scores.npy', 'context_scores.npy'])\\ncontext_local 2800 loss 4.56063865184784 seconds 434.0722539424896\\ncontext_local 2900 loss 4.575375413894653 seconds 442.6692626476288\\ncontext_local 3000 loss 4.551926240921021 seconds 451.57685232162476\\nSCORE 3000 0.02596453090204024\\nbags test 5778549\\n-rw-r--r-- 1 agent agent 23M Sep 9 00:56 /app/solution/test_item_vectors.npy\\n-rw-r--r-- 1 agent agent 6.3M Sep 9 00:56 /app/solution/test_user_vectors.npy\\n-rw-r--r-- 1 agent agent 67M Sep 9 01:03 /app/solution/validation_item_vectors.npy\\n-rw-r--r-- 1 agent agent 19M Sep 9 01:03 /app/solution/validation_user_vectors.npy\\n\"}\n{\"current_time\":\"2026-09-09 01:03:19 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:03:19.473Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-24e0a2e3-c735-414d-a2b0-04abd407ab86", "process_id": "5056", "command": [ "/bin/sh", "-lc", "cat /app/solution/variation.log; tail -4 /app/solution/context_local.log; cat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; ls -lh /app/solution/*vectors.npy" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/variation.log; tail -4 /app/solution/context_local.log; cat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; ls -lh /app/solution/*vectors.npy" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "temporal2_h30.0_scores.npy context_scores.npy 0.055145050280062204\ntemporal2_h30.0_scores.npy context_more_scores.npy 0.054538404791002\nema_scores.npy context_scores.npy 0.05495065632223683\nema_scores.npy context_more_scores.npy 0.05472103189687083\nBEST (0.055145050280062204, ['temporal2_h30.0_scores.npy', 'context_scores.npy'])\ncontext_local 2800 loss 4.56063865184784 seconds 434.0722539424896\ncontext_local 2900 loss 4.575375413894653 seconds 442.6692626476288\ncontext_local 3000 loss 4.551926240921021 seconds 451.57685232162476\nSCORE 3000 0.02596453090204024\nbags test 5778549\n-rw-r--r-- 1 agent agent 23M Sep 9 00:56 /app/solution/test_item_vectors.npy\n-rw-r--r-- 1 agent agent 6.3M Sep 9 00:56 /app/solution/test_user_vectors.npy\n-rw-r--r-- 1 agent agent 67M Sep 9 01:03 /app/solution/validation_item_vectors.npy\n-rw-r--r-- 1 agent agent 19M Sep 9 01:03 /app/solution/validation_user_vectors.npy\n", "stderr": "", "aggregated_output": "temporal2_h30.0_scores.npy context_scores.npy 0.055145050280062204\ntemporal2_h30.0_scores.npy context_more_scores.npy 0.054538404791002\nema_scores.npy context_scores.npy 0.05495065632223683\nema_scores.npy context_more_scores.npy 0.05472103189687083\nBEST (0.055145050280062204, ['temporal2_h30.0_scores.npy', 'context_scores.npy'])\ncontext_local 2800 loss 4.56063865184784 seconds 434.0722539424896\ncontext_local 2900 loss 4.575375413894653 seconds 442.6692626476288\ncontext_local 3000 loss 4.551926240921021 seconds 451.57685232162476\nSCORE 3000 0.02596453090204024\nbags test 5778549\n-rw-r--r-- 1 agent agent 23M Sep 9 00:56 /app/solution/test_item_vectors.npy\n-rw-r--r-- 1 agent agent 6.3M Sep 9 00:56 /app/solution/test_user_vectors.npy\n-rw-r--r-- 1 agent agent 67M Sep 9 01:03 /app/solution/validation_item_vectors.npy\n-rw-r--r-- 1 agent agent 19M Sep 9 01:03 /app/solution/validation_user_vectors.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5701 }, "formatted_output": "temporal2_h30.0_scores.npy context_scores.npy 0.055145050280062204\ntemporal2_h30.0_scores.npy context_more_scores.npy 0.054538404791002\nema_scores.npy context_scores.npy 0.05495065632223683\nema_scores.npy context_more_scores.npy 0.05472103189687083\nBEST (0.055145050280062204, ['temporal2_h30.0_scores.npy', 'context_scores.npy'])\ncontext_local 2800 loss 4.56063865184784 seconds 434.0722539424896\ncontext_local 2900 loss 4.575375413894653 seconds 442.6692626476288\ncontext_local 3000 loss 4.551926240921021 seconds 451.57685232162476\nSCORE 3000 0.02596453090204024\nbags test 5778549\n-rw-r--r-- 1 agent agent 23M Sep 9 00:56 /app/solution/test_item_vectors.npy\n-rw-r--r-- 1 agent agent 6.3M Sep 9 00:56 /app/solution/test_user_vectors.npy\n-rw-r--r-- 1 agent agent 67M Sep 9 01:03 /app/solution/validation_item_vectors.npy\n-rw-r--r-- 1 agent agent 19M Sep 9 01:03 /app/solution/validation_user_vectors.npy\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:03:19.520Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b04de83887d192dc73bfbf094c71", "usage": { "input_tokens": 105051, "cached_input_tokens": 104192, "cache_write_input_tokens": 0, "output_tokens": 221, "reasoning_output_tokens": 134, "total_tokens": 105272 }, "turn_token_usage": { "input_tokens": 5406679, "cached_input_tokens": 5298048, "cache_write_input_tokens": 0, "output_tokens": 62384, "reasoning_output_tokens": 32039, "total_tokens": 5469063 }, "thread_token_usage": { "input_tokens": 5406679, "cached_input_tokens": 5298048, "cache_write_input_tokens": 0, "output_tokens": 62384, "reasoning_output_tokens": 32039, "total_tokens": 5469063 } } }, { "kind": "context", "timestamp": "2026-09-09T01:03:24.271Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e940c730-b62a-44a3-8ad7-c069b17db52a", "process_id": "30587", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config selected_validation_config.json --stage validation > /app/solution/ensemble_validation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config selected_validation_config.json --stage validation > /app/solution/ensemble_validation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 15, "nanos": 569367926 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T01:03:31.632Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07db9f80-0041-43d2-998e-1a4f6ac17e88", "process_id": "28595", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/ensemble_submission.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/ensemble_submission.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 22, "nanos": 933866107 }, "formatted_output": "" } } } ] }, { "id": 106, "role": "agent", "timestamp": "2026-09-09T01:03:46.479Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:03:46.479Z", "text": "" } ] }, { "id": 107, "role": "agent", "timestamp": "2026-09-09T01:04:11.613Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:04:11.613Z", "text": "" } ] }, { "id": 108, "role": "agent", "timestamp": "2026-09-09T01:04:35.892Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:04:35.892Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:04:51.915Z", "callId": "call_18NvlyIh4NjmTyuzDceyG8LC", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/reproduce.py <<'PY'\\n\\\"\\\"\\\"Rebuild the selected three-component neural ranker from supplied event logs.\\\"\\\"\\\"\\nimport argparse,json,os,subprocess,sys\\nfrom pathlib import Path\\nROOT=Path('/app/solution')\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='test');a=p.parse_args();os.environ['OPENBLAS_NUM_THREADS']='1'\\n def run(script,*args):subprocess.run([sys.executable,str(ROOT/script),*map(str,args)],check=True,cwd=ROOT)\\n run('prepare.py')\\n stage=['--stage',a.stage]\\n run('train.py',*stage,'--name','final_base','--epochs',35,'--eval-every',35)\\n run('temporal.py',*stage,'--name','final_temporal','--resume',f'final_base_{a.stage}.pt','--epochs',1,'--half',14,'--days',84,'--stride',7,'--dropout',.7,'--lr',.0001)\\n vae=['--vae','--hidden',600,'--dim',256,'--half',30,'--likes',7,'--dropout',.5,'--beta',.0002]\\n run('train.py',*stage,*vae,'--name','final_vae_initial','--epochs',5,'--eval-every',15)\\n run('train.py',*stage,*vae,'--name','final_vae','--epochs',15,'--eval-every',15,'--trend',1,'--resume',f'final_vae_initial_{a.stage}.pt')\\n run('temporal.py',*stage,'--name','final_temporal_vae','--resume',f'final_vae_{a.stage}.pt','--epochs',1,'--half',30,'--days',84,'--stride',7,'--dropout',.5,'--lr',.0001)\\n run('context.py',*stage,'--name','final_context','--steps',4000,'--eval-every',4000)\\n config={'components':[{'checkpoint':f'final_temporal_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_temporal_vae_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_context_{a.stage}.pt','weight':.2,'half':14}],'bias':{'org7':1.3,'org360':-1.,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3}}\\n out='final_config.json' if a.stage=='test' else 'reproduced_validation_config.json';(ROOT/out).write_text(json.dumps(config,indent=2))\\n run('submit.py','--config',out,*stage)\\n run('verify.py',ROOT/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.stage)\\nif __name__=='__main__':main()\\nPY\\ncat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; tail -3 /app/solution/context_local.log\",\"max_output_tokens\":1200}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:04:52.051Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5cf3fe\",\"wall_time_seconds\":0.0000045,\"exit_code\":0,\"original_token_count\":105,\"output\":\"bags test 5778549\\n{\\\"output\\\": \\\"submission.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 579, \\\"seconds\\\": 18.379935026168823}\\n{\\\"output\\\": \\\"validation.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 579, \\\"seconds\\\": 10.883377313613892}\\ncontext_local 3700 loss 4.470458402633667 seconds 528.073858499527\\ncontext_local 3800 loss 4.466076021194458 seconds 536.6796321868896\\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\\n\"}\n{\"current_time\":\"2026-09-09 01:04:51 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:04:51.963Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d37f98d6-63bc-4187-b711-307061eab09f", "process_id": "25674", "command": [ "/bin/sh", "-lc", "cat > /app/solution/reproduce.py <<'PY'\n\"\"\"Rebuild the selected three-component neural ranker from supplied event logs.\"\"\"\nimport argparse,json,os,subprocess,sys\nfrom pathlib import Path\nROOT=Path('/app/solution')\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='test');a=p.parse_args();os.environ['OPENBLAS_NUM_THREADS']='1'\n def run(script,*args):subprocess.run([sys.executable,str(ROOT/script),*map(str,args)],check=True,cwd=ROOT)\n run('prepare.py')\n stage=['--stage',a.stage]\n run('train.py',*stage,'--name','final_base','--epochs',35,'--eval-every',35)\n run('temporal.py',*stage,'--name','final_temporal','--resume',f'final_base_{a.stage}.pt','--epochs',1,'--half',14,'--days',84,'--stride',7,'--dropout',.7,'--lr',.0001)\n vae=['--vae','--hidden',600,'--dim',256,'--half',30,'--likes',7,'--dropout',.5,'--beta',.0002]\n run('train.py',*stage,*vae,'--name','final_vae_initial','--epochs',5,'--eval-every',15)\n run('train.py',*stage,*vae,'--name','final_vae','--epochs',15,'--eval-every',15,'--trend',1,'--resume',f'final_vae_initial_{a.stage}.pt')\n run('temporal.py',*stage,'--name','final_temporal_vae','--resume',f'final_vae_{a.stage}.pt','--epochs',1,'--half',30,'--days',84,'--stride',7,'--dropout',.5,'--lr',.0001)\n run('context.py',*stage,'--name','final_context','--steps',4000,'--eval-every',4000)\n config={'components':[{'checkpoint':f'final_temporal_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_temporal_vae_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_context_{a.stage}.pt','weight':.2,'half':14}],'bias':{'org7':1.3,'org360':-1.,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3}}\n out='final_config.json' if a.stage=='test' else 'reproduced_validation_config.json';(ROOT/out).write_text(json.dumps(config,indent=2))\n run('submit.py','--config',out,*stage)\n run('verify.py',ROOT/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.stage)\nif __name__=='__main__':main()\nPY\ncat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; tail -3 /app/solution/context_local.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/reproduce.py <<'PY'\n\"\"\"Rebuild the selected three-component neural ranker from supplied event logs.\"\"\"\nimport argparse,json,os,subprocess,sys\nfrom pathlib import Path\nROOT=Path('/app/solution')\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],default='test');a=p.parse_args();os.environ['OPENBLAS_NUM_THREADS']='1'\n def run(script,*args):subprocess.run([sys.executable,str(ROOT/script),*map(str,args)],check=True,cwd=ROOT)\n run('prepare.py')\n stage=['--stage',a.stage]\n run('train.py',*stage,'--name','final_base','--epochs',35,'--eval-every',35)\n run('temporal.py',*stage,'--name','final_temporal','--resume',f'final_base_{a.stage}.pt','--epochs',1,'--half',14,'--days',84,'--stride',7,'--dropout',.7,'--lr',.0001)\n vae=['--vae','--hidden',600,'--dim',256,'--half',30,'--likes',7,'--dropout',.5,'--beta',.0002]\n run('train.py',*stage,*vae,'--name','final_vae_initial','--epochs',5,'--eval-every',15)\n run('train.py',*stage,*vae,'--name','final_vae','--epochs',15,'--eval-every',15,'--trend',1,'--resume',f'final_vae_initial_{a.stage}.pt')\n run('temporal.py',*stage,'--name','final_temporal_vae','--resume',f'final_vae_{a.stage}.pt','--epochs',1,'--half',30,'--days',84,'--stride',7,'--dropout',.5,'--lr',.0001)\n run('context.py',*stage,'--name','final_context','--steps',4000,'--eval-every',4000)\n config={'components':[{'checkpoint':f'final_temporal_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_temporal_vae_{a.stage}.pt','weight':.4,'half':30},{'checkpoint':f'final_context_{a.stage}.pt','weight':.2,'half':14}],'bias':{'org7':1.3,'org360':-1.,'like14':.2,'org14':-.2,'new3':.3,'new30':-.3}}\n out='final_config.json' if a.stage=='test' else 'reproduced_validation_config.json';(ROOT/out).write_text(json.dumps(config,indent=2))\n run('submit.py','--config',out,*stage)\n run('verify.py',ROOT/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.stage)\nif __name__=='__main__':main()\nPY\ncat /app/solution/ensemble_submission.log; cat /app/solution/ensemble_validation.log; tail -3 /app/solution/context_local.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "bags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 18.379935026168823}\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 10.883377313613892}\ncontext_local 3700 loss 4.470458402633667 seconds 528.073858499527\ncontext_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\n", "stderr": "", "aggregated_output": "bags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 18.379935026168823}\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 10.883377313613892}\ncontext_local 3700 loss 4.470458402633667 seconds 528.073858499527\ncontext_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4500 }, "formatted_output": "bags test 5778549\n{\"output\": \"submission.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 18.379935026168823}\n{\"output\": \"validation.parquet\", \"rows\": 845600, \"dimension\": 579, \"seconds\": 10.883377313613892}\ncontext_local 3700 loss 4.470458402633667 seconds 528.073858499527\ncontext_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:04:52.048Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b057d38887d18ef8629d5a208cca", "usage": { "input_tokens": 105673, "cached_input_tokens": 104832, "cache_write_input_tokens": 0, "output_tokens": 2243, "reasoning_output_tokens": 1507, "total_tokens": 107916 }, "turn_token_usage": { "input_tokens": 5512352, "cached_input_tokens": 5402880, "cache_write_input_tokens": 0, "output_tokens": 64627, "reasoning_output_tokens": 33546, "total_tokens": 5576979 }, "thread_token_usage": { "input_tokens": 5512352, "cached_input_tokens": 5402880, "cache_write_input_tokens": 0, "output_tokens": 64627, "reasoning_output_tokens": 33546, "total_tokens": 5576979 } } } ] }, { "id": 109, "role": "agent", "timestamp": "2026-09-09T01:04:57.742Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:04:57.742Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:05:00.397Z", "text": "The refitted 579-dimensional ensemble is now saved as the final submission. Weight averaging and extra context-training steps did not improve the ensemble, so I kept the stronger configuration. I’m validating the saved files and finishing reproducibility documentation while the last local-context experiment completes.\n" }, { "kind": "context", "timestamp": "2026-09-09T01:05:01.531Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-36b2259a-52ee-497b-87ae-252a684975fb", "process_id": "5644", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --name context_local --dim 256 --context 8 --window 16 --steps 4000 > /app/solution/context_local.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/context.py --name context_local --dim 256 --context 8 --window 16 --steps 4000 > /app/solution/context_local.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 566, "nanos": 124336610 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-09T01:05:03.126Z", "callId": "call_MxfGaG3UJ2o8qpoR0ceUTYEB", "name": "exec", "input": { "code": "const results=await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json\",yield_time_ms:1000,max_output_tokens:1000}),\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/submission_validation.txt\",yield_time_ms:1000,max_output_tokens:1000})\n]);results.forEach(text);\n" }, "result": { "timestamp": "2026-09-09T01:05:04.302Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"ba25ef\",\"wall_time_seconds\":1.001409112,\"session_id\":47064,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"fe5eaa\",\"wall_time_seconds\":1.001501523,\"session_id\":47437,\"original_token_count\":0,\"output\":\"\"}}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:05:03.270Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b0b458a487d1b30b435b15b45aee", "usage": { "input_tokens": 108136, "cached_input_tokens": 105472, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 85, "total_tokens": 108417 }, "turn_token_usage": { "input_tokens": 5620488, "cached_input_tokens": 5508352, "cache_write_input_tokens": 0, "output_tokens": 64908, "reasoning_output_tokens": 33631, "total_tokens": 5685396 }, "thread_token_usage": { "input_tokens": 5620488, "cached_input_tokens": 5508352, "cache_write_input_tokens": 0, "output_tokens": 64908, "reasoning_output_tokens": 33631, "total_tokens": 5685396 } } }, { "kind": "context", "timestamp": "2026-09-09T01:05:05.210Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74fdf53c-8d90-491a-8b5e-0141a9082d8d", "process_id": "47437", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/submission_validation.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py > /app/solution/submission_validation.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 1, "nanos": 910242753 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-09T01:05:06.499Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-82a6cb38-059e-4b96-894c-add468f027bb", "process_id": "47064", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet > /app/solution/public_score.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 3, "nanos": 203376804 }, "formatted_output": "" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-09T01:05:08.469Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:05:08.469Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:05:09.685Z", "callId": "call_dRKd8GfI5mo3ytq0bLeRVj0j", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/submission_validation.txt; ls -lh /app/solution/*_test.pt\",\"max_output_tokens\":1600}));\n" }, "result": { "timestamp": "2026-09-09T01:05:09.823Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"7a79dc\",\"wall_time_seconds\":0.00000343,\"exit_code\":0,\"original_token_count\":270,\"output\":\"context_local 3800 loss 4.466076021194458 seconds 536.6796321868896\\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\\ncontext_local 4000 loss 4.44937497138977 seconds 554.0953226089478\\nSCORE 4000 0.024811973138014216\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.055145,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nVALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\\n-rw-r--r-- 1 agent agent 68M Sep 9 00:33 /app/solution/backup_test.pt\\n-rw-r--r-- 1 agent agent 68M Sep 9 00:52 /app/solution/final_base_test.pt\\n-rw-r--r-- 1 agent agent 30M Sep 9 01:00 /app/solution/final_context_test.pt\\n-rw-r--r-- 1 agent agent 68M Sep 9 00:55 /app/solution/final_temporal_test.pt\\n-rw-r--r-- 1 agent agent 100M Sep 9 01:02 /app/solution/final_temporal_vae_test.pt\\n-rw-r--r-- 1 agent agent 100M Sep 9 00:54 /app/solution/final_vae_initial_test.pt\\n-rw-r--r-- 1 agent agent 100M Sep 9 00:59 /app/solution/final_vae_test.pt\\n-rw-r--r-- 1 agent agent 68M Sep 9 00:38 /app/solution/temporal_backup_test.pt\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:05:09.731Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-05bc4e2f-981c-4b54-bd44-1d6405da4cab", "process_id": "90295", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/submission_validation.txt; ls -lh /app/solution/*_test.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/context_local.log; cat /app/solution/public_score.json; cat /app/solution/submission_validation.txt; ls -lh /app/solution/*_test.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "context_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\ncontext_local 4000 loss 4.44937497138977 seconds 554.0953226089478\nSCORE 4000 0.024811973138014216\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055145,\n \"quality_users\": 4263\n }\n}\nVALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n-rw-r--r-- 1 agent agent 68M Sep 9 00:33 /app/solution/backup_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:52 /app/solution/final_base_test.pt\n-rw-r--r-- 1 agent agent 30M Sep 9 01:00 /app/solution/final_context_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:55 /app/solution/final_temporal_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 01:02 /app/solution/final_temporal_vae_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:54 /app/solution/final_vae_initial_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:59 /app/solution/final_vae_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:38 /app/solution/temporal_backup_test.pt\n", "stderr": "", "aggregated_output": "context_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\ncontext_local 4000 loss 4.44937497138977 seconds 554.0953226089478\nSCORE 4000 0.024811973138014216\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055145,\n \"quality_users\": 4263\n }\n}\nVALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n-rw-r--r-- 1 agent agent 68M Sep 9 00:33 /app/solution/backup_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:52 /app/solution/final_base_test.pt\n-rw-r--r-- 1 agent agent 30M Sep 9 01:00 /app/solution/final_context_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:55 /app/solution/final_temporal_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 01:02 /app/solution/final_temporal_vae_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:54 /app/solution/final_vae_initial_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:59 /app/solution/final_vae_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:38 /app/solution/temporal_backup_test.pt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3430 }, "formatted_output": "context_local 3800 loss 4.466076021194458 seconds 536.6796321868896\ncontext_local 3900 loss 4.444174160957337 seconds 545.3537857532501\ncontext_local 4000 loss 4.44937497138977 seconds 554.0953226089478\nSCORE 4000 0.024811973138014216\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.055145,\n \"quality_users\": 4263\n }\n}\nVALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\n-rw-r--r-- 1 agent agent 68M Sep 9 00:33 /app/solution/backup_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:52 /app/solution/final_base_test.pt\n-rw-r--r-- 1 agent agent 30M Sep 9 01:00 /app/solution/final_context_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:55 /app/solution/final_temporal_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 01:02 /app/solution/final_temporal_vae_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:54 /app/solution/final_vae_initial_test.pt\n-rw-r--r-- 1 agent agent 100M Sep 9 00:59 /app/solution/final_vae_test.pt\n-rw-r--r-- 1 agent agent 68M Sep 9 00:38 /app/solution/temporal_backup_test.pt\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:05:09.821Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b0c085f887d188d963789f75c5b4", "usage": { "input_tokens": 108521, "cached_input_tokens": 107904, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 60, "total_tokens": 108650 }, "turn_token_usage": { "input_tokens": 5729009, "cached_input_tokens": 5616256, "cache_write_input_tokens": 0, "output_tokens": 65037, "reasoning_output_tokens": 33691, "total_tokens": 5794046 }, "thread_token_usage": { "input_tokens": 5729009, "cached_input_tokens": 5616256, "cache_write_input_tokens": 0, "output_tokens": 65037, "reasoning_output_tokens": 33691, "total_tokens": 5794046 } } } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-09T01:05:38.633Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:05:38.633Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:05:52.238Z", "callId": "call_X5diUWucedtrNIpe6hzskPXX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_local_validation.pt --dest local_scores.npy --no-grid > /app/solution/local_analyze.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/benchmark.py <<'PY'\\n\\\"\\\"\\\"Measure history-to-query encoding, excluding the external ANN index.\\\"\\\"\\\"\\nimport os\\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\\nimport json,time,numpy as np,torch\\nfrom train import ROOT,make_bags,batch\\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\\nfrom submit import EnsembleUserTower\\ntorch.set_num_threads(1);cfg=json.loads((ROOT/'final_config.json').read_text());n=len(np.load(ROOT/'ids.npz')['items']);models=[];bags=[]\\nfor c in cfg['components']:\\n ck=torch.load(ROOT/c['checkpoint'],weights_only=False);a=ck['args'];m=(ContextTwoTower if a.get('context_model') else VariationalTwoTower if a.get('vae') else TwoTower)(n,a['hidden'],a['dim'],a['dropout']);m.load_state_dict(ck['model']);models.append(m.user)\\n bags.append(make_bags('test',c.get('half',a['half']),a['likes'],a['organic'],a.get('recent_days',0)))\\nuser=EnsembleUserTower(models,[c['weight'] for c in cfg['components']]).eval();rng=np.random.default_rng(789);times=[];users=rng.integers(0,len(bags[0]['offsets'])-1,230)\\nwith torch.inference_mode():\\n for j,u in enumerate(users):\\n start=time.perf_counter();history=[]\\n for b in bags:\\n x,o,w,_,_=batch(b,np.array([u]));history.append((x,o,w))\\n vec=user(history)\\n if j>=30:times.append(1000*(time.perf_counter()-start))\\nresult={'cpu_threads':1,'queries':len(times),'dimension':int(vec.shape[1]),'median_query_encoding_ms':float(np.median(times)),'p95_query_encoding_ms':float(np.percentile(times,95)),'includes':'Ragged-history tensor assembly and all neural user towers','excludes':'Event-history storage access and external ANN retrieval'}\\n(ROOT/'latency.json').write_text(json.dumps(result,indent=2));print(json.dumps(result,indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/benchmark.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T01:05:54.576Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"cb634f\",\"wall_time_seconds\":1.000862863,\"session_id\":51439,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"23d368\",\"wall_time_seconds\":1.00121707,\"session_id\":27576,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:05:52.432Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b0c617e087d1aca11a3861dfda40", "usage": { "input_tokens": 109133, "cached_input_tokens": 108288, "cache_write_input_tokens": 0, "output_tokens": 1140, "reasoning_output_tokens": 516, "total_tokens": 110273 }, "turn_token_usage": { "input_tokens": 5838142, "cached_input_tokens": 5724544, "cache_write_input_tokens": 0, "output_tokens": 66177, "reasoning_output_tokens": 34207, "total_tokens": 5904319 }, "thread_token_usage": { "input_tokens": 5838142, "cached_input_tokens": 5724544, "cache_write_input_tokens": 0, "output_tokens": 66177, "reasoning_output_tokens": 34207, "total_tokens": 5904319 } } }, { "kind": "context", "timestamp": "2026-09-09T01:05:59.217Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0ac3da47-791b-4341-a4f7-2e4a6c15b357", "process_id": "27576", "command": [ "/bin/sh", "-lc", "cat > /app/solution/benchmark.py <<'PY'\n\"\"\"Measure history-to-query encoding, excluding the external ANN index.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport json,time,numpy as np,torch\nfrom train import ROOT,make_bags,batch\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\nfrom submit import EnsembleUserTower\ntorch.set_num_threads(1);cfg=json.loads((ROOT/'final_config.json').read_text());n=len(np.load(ROOT/'ids.npz')['items']);models=[];bags=[]\nfor c in cfg['components']:\n ck=torch.load(ROOT/c['checkpoint'],weights_only=False);a=ck['args'];m=(ContextTwoTower if a.get('context_model') else VariationalTwoTower if a.get('vae') else TwoTower)(n,a['hidden'],a['dim'],a['dropout']);m.load_state_dict(ck['model']);models.append(m.user)\n bags.append(make_bags('test',c.get('half',a['half']),a['likes'],a['organic'],a.get('recent_days',0)))\nuser=EnsembleUserTower(models,[c['weight'] for c in cfg['components']]).eval();rng=np.random.default_rng(789);times=[];users=rng.integers(0,len(bags[0]['offsets'])-1,230)\nwith torch.inference_mode():\n for j,u in enumerate(users):\n start=time.perf_counter();history=[]\n for b in bags:\n x,o,w,_,_=batch(b,np.array([u]));history.append((x,o,w))\n vec=user(history)\n if j>=30:times.append(1000*(time.perf_counter()-start))\nresult={'cpu_threads':1,'queries':len(times),'dimension':int(vec.shape[1]),'median_query_encoding_ms':float(np.median(times)),'p95_query_encoding_ms':float(np.percentile(times,95)),'includes':'Ragged-history tensor assembly and all neural user towers','excludes':'Event-history storage access and external ANN retrieval'}\n(ROOT/'latency.json').write_text(json.dumps(result,indent=2));print(json.dumps(result,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/benchmark.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/benchmark.py <<'PY'\n\"\"\"Measure history-to-query encoding, excluding the external ANN index.\"\"\"\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','1')\nimport json,time,numpy as np,torch\nfrom train import ROOT,make_bags,batch\nfrom model import TwoTower,VariationalTwoTower,ContextTwoTower\nfrom submit import EnsembleUserTower\ntorch.set_num_threads(1);cfg=json.loads((ROOT/'final_config.json').read_text());n=len(np.load(ROOT/'ids.npz')['items']);models=[];bags=[]\nfor c in cfg['components']:\n ck=torch.load(ROOT/c['checkpoint'],weights_only=False);a=ck['args'];m=(ContextTwoTower if a.get('context_model') else VariationalTwoTower if a.get('vae') else TwoTower)(n,a['hidden'],a['dim'],a['dropout']);m.load_state_dict(ck['model']);models.append(m.user)\n bags.append(make_bags('test',c.get('half',a['half']),a['likes'],a['organic'],a.get('recent_days',0)))\nuser=EnsembleUserTower(models,[c['weight'] for c in cfg['components']]).eval();rng=np.random.default_rng(789);times=[];users=rng.integers(0,len(bags[0]['offsets'])-1,230)\nwith torch.inference_mode():\n for j,u in enumerate(users):\n start=time.perf_counter();history=[]\n for b in bags:\n x,o,w,_,_=batch(b,np.array([u]));history.append((x,o,w))\n vec=user(history)\n if j>=30:times.append(1000*(time.perf_counter()-start))\nresult={'cpu_threads':1,'queries':len(times),'dimension':int(vec.shape[1]),'median_query_encoding_ms':float(np.median(times)),'p95_query_encoding_ms':float(np.percentile(times,95)),'includes':'Ragged-history tensor assembly and all neural user towers','excludes':'Event-history storage access and external ANN retrieval'}\n(ROOT/'latency.json').write_text(json.dumps(result,indent=2));print(json.dumps(result,indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/benchmark.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}\n", "stderr": "", "aggregated_output": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 643654088 }, "formatted_output": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}\n" } } }, { "kind": "context", "timestamp": "2026-09-09T01:06:10.066Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-111e062d-b98f-4ab8-ad23-070d15b48dd6", "process_id": "51439", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_local_validation.pt --dest local_scores.npy --no-grid > /app/solution/local_analyze.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/analyze.py context_local_validation.pt --dest local_scores.npy --no-grid > /app/solution/local_analyze.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 658205820 }, "formatted_output": "" } } } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-09T01:06:21.598Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:06:21.598Z", "text": "" } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-09T01:06:52.065Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:06:52.065Z", "text": "" } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-09T01:07:19.013Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:07:19.013Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:07:20.522Z", "callId": "call_KL4x2S7FSdSn4TKACzafLYeo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/latency.json; cat /app/solution/local_analyze.log; cat /app/solution/context_local_metrics.json\",\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:07:20.696Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"a3f7e0\",\"wall_time_seconds\":0.000002871,\"exit_code\":0,\"original_token_count\":187,\"output\":\"{\\n \\\"cpu_threads\\\": 1,\\n \\\"queries\\\": 200,\\n \\\"dimension\\\": 579,\\n \\\"median_query_encoding_ms\\\": 0.7098749999840948,\\n \\\"p95_query_encoding_ms\\\": 1.161876600076538,\\n \\\"includes\\\": \\\"Ragged-history tensor assembly and all neural user towers\\\",\\n \\\"excludes\\\": \\\"Event-history storage access and external ANN retrieval\\\"\\n}local_scores.npy\\npop 0.0 score 0.020479397969250645\\n{\\n \\\"step\\\": 3500,\\n \\\"score\\\": 0.027660326353761356,\\n \\\"args\\\": {\\n \\\"stage\\\": \\\"validation\\\",\\n \\\"name\\\": \\\"context_local\\\",\\n \\\"steps\\\": 4000,\\n \\\"eval_every\\\": 500,\\n \\\"dim\\\": 256,\\n \\\"half\\\": 14,\\n \\\"batch\\\": 512,\\n \\\"context\\\": 8,\\n \\\"window\\\": 16,\\n \\\"lr\\\": 0.002,\\n \\\"resume\\\": null,\\n \\\"context_model\\\": true,\\n \\\"hidden\\\": 256,\\n \\\"dropout\\\": 0.2,\\n \\\"likes\\\": 3.0,\\n \\\"organic\\\": 1.0\\n }\\n}\"}\n{\"current_time\":\"2026-09-09 01:07:20 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:07:20.572Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9103e428-25ae-4a82-8583-aea064ed2e11", "process_id": "16797", "command": [ "/bin/sh", "-lc", "cat /app/solution/latency.json; cat /app/solution/local_analyze.log; cat /app/solution/context_local_metrics.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/latency.json", "name": "latency.json", "path": "/app/solution/latency.json" }, { "type": "read", "cmd": "cat /app/solution/local_analyze.log", "name": "local_analyze.log", "path": "/app/solution/local_analyze.log" }, { "type": "read", "cmd": "cat /app/solution/context_local_metrics.json", "name": "context_local_metrics.json", "path": "/app/solution/context_local_metrics.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}local_scores.npy\npop 0.0 score 0.020479397969250645\n{\n \"step\": 3500,\n \"score\": 0.027660326353761356,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_local\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 256,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 8,\n \"window\": 16,\n \"lr\": 0.002,\n \"resume\": null,\n \"context_model\": true,\n \"hidden\": 256,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}", "stderr": "", "aggregated_output": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}local_scores.npy\npop 0.0 score 0.020479397969250645\n{\n \"step\": 3500,\n \"score\": 0.027660326353761356,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_local\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 256,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 8,\n \"window\": 16,\n \"lr\": 0.002,\n \"resume\": null,\n \"context_model\": true,\n \"hidden\": 256,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}", "exit_code": 0, "duration": { "secs": 0, "nanos": 2871 }, "formatted_output": "{\n \"cpu_threads\": 1,\n \"queries\": 200,\n \"dimension\": 579,\n \"median_query_encoding_ms\": 0.7098749999840948,\n \"p95_query_encoding_ms\": 1.161876600076538,\n \"includes\": \"Ragged-history tensor assembly and all neural user towers\",\n \"excludes\": \"Event-history storage access and external ANN retrieval\"\n}local_scores.npy\npop 0.0 score 0.020479397969250645\n{\n \"step\": 3500,\n \"score\": 0.027660326353761356,\n \"args\": {\n \"stage\": \"validation\",\n \"name\": \"context_local\",\n \"steps\": 4000,\n \"eval_every\": 500,\n \"dim\": 256,\n \"half\": 14,\n \"batch\": 512,\n \"context\": 8,\n \"window\": 16,\n \"lr\": 0.002,\n \"resume\": null,\n \"context_model\": true,\n \"hidden\": 256,\n \"dropout\": 0.2,\n \"likes\": 3.0,\n \"organic\": 1.0\n }\n}" } } }, { "kind": "context", "timestamp": "2026-09-09T01:07:20.693Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b0f2db5087d1a66e5b4f8f995739", "usage": { "input_tokens": 110364, "cached_input_tokens": 108928, "cache_write_input_tokens": 0, "output_tokens": 1602, "reasoning_output_tokens": 1536, "total_tokens": 111966 }, "turn_token_usage": { "input_tokens": 5948506, "cached_input_tokens": 5833472, "cache_write_input_tokens": 0, "output_tokens": 67779, "reasoning_output_tokens": 35743, "total_tokens": 6016285 }, "thread_token_usage": { "input_tokens": 5948506, "cached_input_tokens": 5833472, "cache_write_input_tokens": 0, "output_tokens": 67779, "reasoning_output_tokens": 35743, "total_tokens": 6016285 } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-09T01:07:38.423Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:07:38.423Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:07:40.839Z", "text": "The saved submission passes all schema and eligibility checks, and its matching validation ranking scores 0.055145 NDCG@10. On one CPU thread, history tensor assembly plus user encoding measured 0.71 ms median and 1.16 ms p95; this excludes history storage access and ANN retrieval.\n" }, { "kind": "tool", "timestamp": "2026-09-09T01:07:49.178Z", "callId": "call_mT69mChuuXgylXqZNQFWSS97", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/local_sweep.py <<'PY'\\nimport json,numpy as np,torch\\nfrom train import ROOT\\nfrom calibrate import Metric\\nfrom trends import features\\nfrom discovery_trends import discovery_features\\nclass Blend:\\n def __init__(self,a,w):self.a=a;self.w=w\\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.w,self.a) if w)\\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());a=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy','local_scores.npy']];best=(-1,None)\\nfor old,local in [(.2,0.),(0.,.1),(0.,.2),(0.,.3),(.1,.1),(.15,.1),(.2,.05)]:\\n w=[(1-old-local)/2,(1-old-local)/2,old,local];score=m(Blend(a,w),bias);print(w,score,flush=True)\\n if score>best[0]:best=(score,w)\\nprint('BEST',best,flush=True);(ROOT/'local_sweep_best.json').write_text(json.dumps({'score':best[0],'weights':best[1]},indent=2))\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/local_sweep.py > /app/solution/local_sweep.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T01:07:50.354Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"518641\",\"wall_time_seconds\":1.001167034,\"session_id\":77720,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:07:49.344Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b1490b0c87d18e93cac73fb709c0", "usage": { "input_tokens": 112334, "cached_input_tokens": 110208, "cache_write_input_tokens": 0, "output_tokens": 823, "reasoning_output_tokens": 345, "total_tokens": 113157 }, "turn_token_usage": { "input_tokens": 6060840, "cached_input_tokens": 5943680, "cache_write_input_tokens": 0, "output_tokens": 68602, "reasoning_output_tokens": 36088, "total_tokens": 6129442 }, "thread_token_usage": { "input_tokens": 6060840, "cached_input_tokens": 5943680, "cache_write_input_tokens": 0, "output_tokens": 68602, "reasoning_output_tokens": 36088, "total_tokens": 6129442 } } }, { "kind": "context", "timestamp": "2026-09-09T01:08:02.707Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27dbfba9-f892-420f-82ae-47bc97ccd10f", "process_id": "77720", "command": [ "/bin/sh", "-lc", "cat > /app/solution/local_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,a,w):self.a=a;self.w=w\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.w,self.a) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());a=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy','local_scores.npy']];best=(-1,None)\nfor old,local in [(.2,0.),(0.,.1),(0.,.2),(0.,.3),(.1,.1),(.15,.1),(.2,.05)]:\n w=[(1-old-local)/2,(1-old-local)/2,old,local];score=m(Blend(a,w),bias);print(w,score,flush=True)\n if score>best[0]:best=(score,w)\nprint('BEST',best,flush=True);(ROOT/'local_sweep_best.json').write_text(json.dumps({'score':best[0],'weights':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/local_sweep.py > /app/solution/local_sweep.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/local_sweep.py <<'PY'\nimport json,numpy as np,torch\nfrom train import ROOT\nfrom calibrate import Metric\nfrom trends import features\nfrom discovery_trends import discovery_features\nclass Blend:\n def __init__(self,a,w):self.a=a;self.w=w\n def __getitem__(self,ix):return sum(w*a[ix] for w,a in zip(self.w,self.a) if w)\ntorch.set_num_threads(2);m=Metric();f=features('train');f.update(discovery_features('train'));cfg=json.loads((ROOT/'selected_bias_best.json').read_text());bias=sum(w*f[k] for k,w in cfg['bias'].items());a=[np.load(ROOT/x,mmap_mode='r') for x in ['temporal2_h30.0_scores.npy','temporal_vae_scores.npy','context_scores.npy','local_scores.npy']];best=(-1,None)\nfor old,local in [(.2,0.),(0.,.1),(0.,.2),(0.,.3),(.1,.1),(.15,.1),(.2,.05)]:\n w=[(1-old-local)/2,(1-old-local)/2,old,local];score=m(Blend(a,w),bias);print(w,score,flush=True)\n if score>best[0]:best=(score,w)\nprint('BEST',best,flush=True);(ROOT/'local_sweep_best.json').write_text(json.dumps({'score':best[0],'weights':best[1]},indent=2))\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/local_sweep.py > /app/solution/local_sweep.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 13, "nanos": 355395854 }, "formatted_output": "" } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-09T01:08:19.217Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:08:19.217Z", "text": "" } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-09T01:08:35.770Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:08:35.770Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:09:03.256Z", "callId": "call_VWq4oXpuNo9oyn1EMs31ZchV", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\np='/app/solution/submit.py';s=open(p).read().replace(\\\"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\\\", \\\"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\\\\n np.savez(ROOT/f'{a.stage}_vector_ids.npz',item_ids=ids['items'],uids=elig['uids'])\\\");open(p,'w').write(s)\\nimport numpy as np\\nfor stage in ['validation','test']:\\n ids=np.load('/app/solution/ids.npz');a=np.load('/task/data/'+stage+'_eligible_candidates.npz');np.savez('/app/solution/'+stage+'_vector_ids.npz',item_ids=ids['items'],uids=a['uids'])\\nPY\\ncat > /app/solution/README.md <<'MD'\\n# CPU neural two-tower discovery ranker\\n\\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The matching public validation ranking scores **0.055145 graded NDCG@10** over 4,263 users, confirmed by `/task/evaluate_public.py`.\\n\\nThe composite user and item towers produce **579-dimensional vectors**. The score is exactly their dot product. Each component has its own trainable history encoder and independently trainable item embedding/projection network; there is no user-ID embedding.\\n\\n| Component | User encoder | Latent dimensions | Mixture weight |\\n|---|---|---:|---:|\\n| Temporal denoising model | Weighted embedding bag, normalization, 384 → 384 → 192 MLP with tanh | 192 + bias | 0.4 |\\n| Temporal variational model | 600-dimensional embedding bag, tanh, Gaussian posterior; posterior mean at inference | 256 + bias | 0.4 |\\n| Listening-context model | 128-dimensional embedding bag, residual tanh projection and normalization | 128 + bias | 0.2 |\\n\\nUser vectors concatenate weighted component outputs; item vectors concatenate the corresponding item outputs. Cutoff-correct organic popularity, like-rate, and recent first-discovery trends are absorbed into the item tower’s bias coordinate **offline**. Retrieval applies only the platform eligibility mask and maximum inner product; it has no candidate-dependent neural scoring or reranking.\\n\\nDenoising pretraining uses weighted multinomial softmax reconstruction with history dropout. Temporal fine-tuning predicts previously unseen organic outcomes in later seven-day windows, assigning weight 7 to likes and 1 to listens completed to at least 80%. The context model uses sampled softmax to predict organic listening/like events from nearby historical tracks. Histories are ragged event-derived arrays: no global user-item matrix is constructed or decomposed. For catalogs above 50,000 items, pretraining and temporal training automatically use bounded sampled softmax and sparse embedding updates. The context objective always uses sampled negatives.\\n\\nValidation features and temporal training examples use only `train_*`. After selection, every final component was refitted on `train_* + val_*`; final trend features and eligibility use the test cutoff. These interaction-based offline results are not a causal estimate of online lift.\\n\\nGenerate the final ranking from the fitted checkpoints:\\n\\n```sh\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\\n/opt/task-venv/bin/python /app/solution/verify.py\\n```\\n\\nRebuild all selected components from the supplied logs with `reproduce.py --stage test`; use `--stage validation` for the public backtest. `model.py`, `train.py`, `temporal.py`, and `context.py` define and train the towers. `test_contract.py` checks independent vectors, exact dot products, both towers’ gradients, and the large-catalog training paths.\\n\\n`test_item_vectors.npy` is the offline MIPS-index input; `test_vector_ids.npz` supplies its item IDs and the user-vector row IDs. The benchmark uses blocked exact retrieval; production can index these same vectors in a filtered MIPS service. On one CPU thread, `benchmark.py` measured **0.71 ms median / 1.16 ms p95** for history tensor assembly plus user encoding over 200 queries, excluding history storage access and ANN retrieval.\\nMD\\ncat /app/solution/local_sweep.log\",\"max_output_tokens\":1400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:09:03.691Z", "content": "Script completed\nWall time 0.4 seconds\nOutput:\n\n{\"chunk_id\":\"4695c2\",\"wall_time_seconds\":0.234108628,\"exit_code\":0,\"original_token_count\":89,\"output\":\"[0.4, 0.4, 0.2, 0.0] 0.055145050280062204\\n[0.45, 0.45, 0.0, 0.1] 0.054398959425752354\\n[0.4, 0.4, 0.0, 0.2] 0.05431410784602384\\n[0.35, 0.35, 0.0, 0.3] 0.05424406578827831\\n[0.4, 0.4, 0.1, 0.1] 0.05466859764599578\\n[0.375, 0.375, 0.15, 0.1] 0.05503452091621575\\n[0.375, 0.375, 0.2, 0.05] 0.055127459497968274\\nBEST (0.055145050280062204, [0.4, 0.4, 0.2, 0.0])\\n\"}\n{\"current_time\":\"2026-09-09 01:09:03 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:09:03.448Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b166a0dc87d1a617b06434578379", "usage": { "input_tokens": 113213, "cached_input_tokens": 112128, "cache_write_input_tokens": 0, "output_tokens": 1831, "reasoning_output_tokens": 840, "total_tokens": 115044 }, "turn_token_usage": { "input_tokens": 6174053, "cached_input_tokens": 6055808, "cache_write_input_tokens": 0, "output_tokens": 70433, "reasoning_output_tokens": 36928, "total_tokens": 6244486 }, "thread_token_usage": { "input_tokens": 6174053, "cached_input_tokens": 6055808, "cache_write_input_tokens": 0, "output_tokens": 70433, "reasoning_output_tokens": 36928, "total_tokens": 6244486 } } }, { "kind": "context", "timestamp": "2026-09-09T01:09:03.667Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4fb59a98-a82f-4c80-9ea4-b6359777d8ae", "process_id": "24603", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/submit.py';s=open(p).read().replace(\"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\", \"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\\n np.savez(ROOT/f'{a.stage}_vector_ids.npz',item_ids=ids['items'],uids=elig['uids'])\");open(p,'w').write(s)\nimport numpy as np\nfor stage in ['validation','test']:\n ids=np.load('/app/solution/ids.npz');a=np.load('/task/data/'+stage+'_eligible_candidates.npz');np.savez('/app/solution/'+stage+'_vector_ids.npz',item_ids=ids['items'],uids=a['uids'])\nPY\ncat > /app/solution/README.md <<'MD'\n# CPU neural two-tower discovery ranker\n\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The matching public validation ranking scores **0.055145 graded NDCG@10** over 4,263 users, confirmed by `/task/evaluate_public.py`.\n\nThe composite user and item towers produce **579-dimensional vectors**. The score is exactly their dot product. Each component has its own trainable history encoder and independently trainable item embedding/projection network; there is no user-ID embedding.\n\n| Component | User encoder | Latent dimensions | Mixture weight |\n|---|---|---:|---:|\n| Temporal denoising model | Weighted embedding bag, normalization, 384 → 384 → 192 MLP with tanh | 192 + bias | 0.4 |\n| Temporal variational model | 600-dimensional embedding bag, tanh, Gaussian posterior; posterior mean at inference | 256 + bias | 0.4 |\n| Listening-context model | 128-dimensional embedding bag, residual tanh projection and normalization | 128 + bias | 0.2 |\n\nUser vectors concatenate weighted component outputs; item vectors concatenate the corresponding item outputs. Cutoff-correct organic popularity, like-rate, and recent first-discovery trends are absorbed into the item tower’s bias coordinate **offline**. Retrieval applies only the platform eligibility mask and maximum inner product; it has no candidate-dependent neural scoring or reranking.\n\nDenoising pretraining uses weighted multinomial softmax reconstruction with history dropout. Temporal fine-tuning predicts previously unseen organic outcomes in later seven-day windows, assigning weight 7 to likes and 1 to listens completed to at least 80%. The context model uses sampled softmax to predict organic listening/like events from nearby historical tracks. Histories are ragged event-derived arrays: no global user-item matrix is constructed or decomposed. For catalogs above 50,000 items, pretraining and temporal training automatically use bounded sampled softmax and sparse embedding updates. The context objective always uses sampled negatives.\n\nValidation features and temporal training examples use only `train_*`. After selection, every final component was refitted on `train_* + val_*`; final trend features and eligibility use the test cutoff. These interaction-based offline results are not a causal estimate of online lift.\n\nGenerate the final ranking from the fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\n/opt/task-venv/bin/python /app/solution/verify.py\n```\n\nRebuild all selected components from the supplied logs with `reproduce.py --stage test`; use `--stage validation` for the public backtest. `model.py`, `train.py`, `temporal.py`, and `context.py` define and train the towers. `test_contract.py` checks independent vectors, exact dot products, both towers’ gradients, and the large-catalog training paths.\n\n`test_item_vectors.npy` is the offline MIPS-index input; `test_vector_ids.npz` supplies its item IDs and the user-vector row IDs. The benchmark uses blocked exact retrieval; production can index these same vectors in a filtered MIPS service. On one CPU thread, `benchmark.py` measured **0.71 ms median / 1.16 ms p95** for history tensor assembly plus user encoding over 200 queries, excluding history storage access and ANN retrieval.\nMD\ncat /app/solution/local_sweep.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\np='/app/solution/submit.py';s=open(p).read().replace(\"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\", \"query=np.concatenate(query);np.save(ROOT/f'{a.stage}_user_vectors.npy',query)\\n np.savez(ROOT/f'{a.stage}_vector_ids.npz',item_ids=ids['items'],uids=elig['uids'])\");open(p,'w').write(s)\nimport numpy as np\nfor stage in ['validation','test']:\n ids=np.load('/app/solution/ids.npz');a=np.load('/task/data/'+stage+'_eligible_candidates.npz');np.savez('/app/solution/'+stage+'_vector_ids.npz',item_ids=ids['items'],uids=a['uids'])\nPY\ncat > /app/solution/README.md <<'MD'\n# CPU neural two-tower discovery ranker\n\n`submission.parquet` contains **845,600 rows: 100 distinct eligible tracks for each of 8,456 target users**. The matching public validation ranking scores **0.055145 graded NDCG@10** over 4,263 users, confirmed by `/task/evaluate_public.py`.\n\nThe composite user and item towers produce **579-dimensional vectors**. The score is exactly their dot product. Each component has its own trainable history encoder and independently trainable item embedding/projection network; there is no user-ID embedding.\n\n| Component | User encoder | Latent dimensions | Mixture weight |\n|---|---|---:|---:|\n| Temporal denoising model | Weighted embedding bag, normalization, 384 → 384 → 192 MLP with tanh | 192 + bias | 0.4 |\n| Temporal variational model | 600-dimensional embedding bag, tanh, Gaussian posterior; posterior mean at inference | 256 + bias | 0.4 |\n| Listening-context model | 128-dimensional embedding bag, residual tanh projection and normalization | 128 + bias | 0.2 |\n\nUser vectors concatenate weighted component outputs; item vectors concatenate the corresponding item outputs. Cutoff-correct organic popularity, like-rate, and recent first-discovery trends are absorbed into the item tower’s bias coordinate **offline**. Retrieval applies only the platform eligibility mask and maximum inner product; it has no candidate-dependent neural scoring or reranking.\n\nDenoising pretraining uses weighted multinomial softmax reconstruction with history dropout. Temporal fine-tuning predicts previously unseen organic outcomes in later seven-day windows, assigning weight 7 to likes and 1 to listens completed to at least 80%. The context model uses sampled softmax to predict organic listening/like events from nearby historical tracks. Histories are ragged event-derived arrays: no global user-item matrix is constructed or decomposed. For catalogs above 50,000 items, pretraining and temporal training automatically use bounded sampled softmax and sparse embedding updates. The context objective always uses sampled negatives.\n\nValidation features and temporal training examples use only `train_*`. After selection, every final component was refitted on `train_* + val_*`; final trend features and eligibility use the test cutoff. These interaction-based offline results are not a causal estimate of online lift.\n\nGenerate the final ranking from the fitted checkpoints:\n\n```sh\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --config final_config.json --stage test\n/opt/task-venv/bin/python /app/solution/verify.py\n```\n\nRebuild all selected components from the supplied logs with `reproduce.py --stage test`; use `--stage validation` for the public backtest. `model.py`, `train.py`, `temporal.py`, and `context.py` define and train the towers. `test_contract.py` checks independent vectors, exact dot products, both towers’ gradients, and the large-catalog training paths.\n\n`test_item_vectors.npy` is the offline MIPS-index input; `test_vector_ids.npz` supplies its item IDs and the user-vector row IDs. The benchmark uses blocked exact retrieval; production can index these same vectors in a filtered MIPS service. On one CPU thread, `benchmark.py` measured **0.71 ms median / 1.16 ms p95** for history tensor assembly plus user encoding over 200 queries, excluding history storage access and ANN retrieval.\nMD\ncat /app/solution/local_sweep.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[0.4, 0.4, 0.2, 0.0] 0.055145050280062204\n[0.45, 0.45, 0.0, 0.1] 0.054398959425752354\n[0.4, 0.4, 0.0, 0.2] 0.05431410784602384\n[0.35, 0.35, 0.0, 0.3] 0.05424406578827831\n[0.4, 0.4, 0.1, 0.1] 0.05466859764599578\n[0.375, 0.375, 0.15, 0.1] 0.05503452091621575\n[0.375, 0.375, 0.2, 0.05] 0.055127459497968274\nBEST (0.055145050280062204, [0.4, 0.4, 0.2, 0.0])\n", "stderr": "", "aggregated_output": "[0.4, 0.4, 0.2, 0.0] 0.055145050280062204\n[0.45, 0.45, 0.0, 0.1] 0.054398959425752354\n[0.4, 0.4, 0.0, 0.2] 0.05431410784602384\n[0.35, 0.35, 0.0, 0.3] 0.05424406578827831\n[0.4, 0.4, 0.1, 0.1] 0.05466859764599578\n[0.375, 0.375, 0.15, 0.1] 0.05503452091621575\n[0.375, 0.375, 0.2, 0.05] 0.055127459497968274\nBEST (0.055145050280062204, [0.4, 0.4, 0.2, 0.0])\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 234111359 }, "formatted_output": "[0.4, 0.4, 0.2, 0.0] 0.055145050280062204\n[0.45, 0.45, 0.0, 0.1] 0.054398959425752354\n[0.4, 0.4, 0.0, 0.2] 0.05431410784602384\n[0.35, 0.35, 0.0, 0.3] 0.05424406578827831\n[0.4, 0.4, 0.1, 0.1] 0.05466859764599578\n[0.375, 0.375, 0.15, 0.1] 0.05503452091621575\n[0.375, 0.375, 0.2, 0.05] 0.055127459497968274\nBEST (0.055145050280062204, [0.4, 0.4, 0.2, 0.0])\n" } } } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-09T01:09:35.387Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:09:35.387Z", "text": "" } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-09T01:10:05.251Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:10:05.251Z", "text": "" } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-09T01:10:23.732Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:10:23.732Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:10:25.531Z", "text": "The last local-context experiment did not improve the ensemble, so the final model remains unchanged. The README and rebuild command are in place. I’m now checking the final vector exports and removing unused experiment caches.\n" }, { "kind": "tool", "timestamp": "2026-09-09T01:10:40.728Z", "callId": "call_0u57KWU9H3RzuLJANZNArNtQ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport os,numpy as np,pandas as pd,json\\nr=Path('/app/solution');files=list(r.iterdir());print('Total artifacts GB',round(sum(p.stat().st_size for p in files if p.is_file())/1e9,2));print('Largest files:')\\nfor p in sorted([p for p in files if p.is_file()],key=lambda p:p.stat().st_size,reverse=True)[:15]:print(p.name,round(p.stat().st_size/1e6,1),'MB')\\nfor f in os.listdir('/proc'):\\n if f.isdigit():\\n try:\\n a=Path('/proc/'+f+'/cmdline').read_bytes().split(b'\\\\0')\\n if len(a)>1 and a[0].endswith(b'python') and a[1].startswith(b'/app/solution/'):print('Active task process',f,a[1].decode())\\n except (FileNotFoundError,PermissionError,ProcessLookupError):pass\\nv=np.load(r/'test_item_vectors.npy',mmap_mode='r');u=np.load(r/'test_user_vectors.npy',mmap_mode='r');idx=np.load(r/'test_vector_ids.npz');assert np.isfinite(v).all() and np.isfinite(u).all();assert v.shape==(len(idx['item_ids']),579);assert u.shape==(len(idx['uids']),579)\\nf=pd.read_parquet(r/'submission.parquet');a=np.load('/task/data/test_eligible_candidates.npz');assert np.array_equal(idx['uids'],a['uids']);assert set(f.uid)==set(idx['uids']);print('Final exported vectors:',u.shape,v.shape,'finite, aligned IDs')\\n# Check actual exported dot-product top-100 order on 32 distributed users.\\ncol=pd.Index(idx['item_ids']).get_indexer(a['item_ids']);rows=np.linspace(0,len(u)-1,32,dtype=int)\\nfor row in rows:\\n scores=u[row]@v[col].T;mask=np.unpackbits(a['packed_eligible'][row],count=len(col),bitorder='little').astype(bool);scores[~mask]=-np.inf\\n got=f[f.uid==int(idx['uids'][row])].sort_values('rank').item_id.to_numpy();gotcols=pd.Index(a['item_ids']).get_indexer(got)\\n assert np.all(np.diff(scores[gotcols])<=2e-4)\\n best=np.partition(scores,-100)[-100];assert scores[gotcols].min()>=best-2e-4\\nprint('Exported dot products reproduce the eligible top 100 for 32 checked users.')\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":2200}));\n" }, "result": { "timestamp": "2026-09-09T01:10:41.905Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"9de15d\",\"wall_time_seconds\":1.002020834,\"session_id\":3793,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:10:40.860Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b1afe85087d1a60c99091bb6bb44", "usage": { "input_tokens": 115361, "cached_input_tokens": 113024, "cache_write_input_tokens": 0, "output_tokens": 2141, "reasoning_output_tokens": 1473, "total_tokens": 117502 }, "turn_token_usage": { "input_tokens": 6289414, "cached_input_tokens": 6168832, "cache_write_input_tokens": 0, "output_tokens": 72574, "reasoning_output_tokens": 38401, "total_tokens": 6361988 }, "thread_token_usage": { "input_tokens": 6289414, "cached_input_tokens": 6168832, "cache_write_input_tokens": 0, "output_tokens": 72574, "reasoning_output_tokens": 38401, "total_tokens": 6361988 } } }, { "kind": "context", "timestamp": "2026-09-09T01:10:46.469Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6bf5b6a1-afe1-4cc0-9ed9-6face6b048c8", "process_id": "3793", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,numpy as np,pandas as pd,json\nr=Path('/app/solution');files=list(r.iterdir());print('Total artifacts GB',round(sum(p.stat().st_size for p in files if p.is_file())/1e9,2));print('Largest files:')\nfor p in sorted([p for p in files if p.is_file()],key=lambda p:p.stat().st_size,reverse=True)[:15]:print(p.name,round(p.stat().st_size/1e6,1),'MB')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n a=Path('/proc/'+f+'/cmdline').read_bytes().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and a[1].startswith(b'/app/solution/'):print('Active task process',f,a[1].decode())\n except (FileNotFoundError,PermissionError,ProcessLookupError):pass\nv=np.load(r/'test_item_vectors.npy',mmap_mode='r');u=np.load(r/'test_user_vectors.npy',mmap_mode='r');idx=np.load(r/'test_vector_ids.npz');assert np.isfinite(v).all() and np.isfinite(u).all();assert v.shape==(len(idx['item_ids']),579);assert u.shape==(len(idx['uids']),579)\nf=pd.read_parquet(r/'submission.parquet');a=np.load('/task/data/test_eligible_candidates.npz');assert np.array_equal(idx['uids'],a['uids']);assert set(f.uid)==set(idx['uids']);print('Final exported vectors:',u.shape,v.shape,'finite, aligned IDs')\n# Check actual exported dot-product top-100 order on 32 distributed users.\ncol=pd.Index(idx['item_ids']).get_indexer(a['item_ids']);rows=np.linspace(0,len(u)-1,32,dtype=int)\nfor row in rows:\n scores=u[row]@v[col].T;mask=np.unpackbits(a['packed_eligible'][row],count=len(col),bitorder='little').astype(bool);scores[~mask]=-np.inf\n got=f[f.uid==int(idx['uids'][row])].sort_values('rank').item_id.to_numpy();gotcols=pd.Index(a['item_ids']).get_indexer(got)\n assert np.all(np.diff(scores[gotcols])<=2e-4)\n best=np.partition(scores,-100)[-100];assert scores[gotcols].min()>=best-2e-4\nprint('Exported dot products reproduce the eligible top 100 for 32 checked users.')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,numpy as np,pandas as pd,json\nr=Path('/app/solution');files=list(r.iterdir());print('Total artifacts GB',round(sum(p.stat().st_size for p in files if p.is_file())/1e9,2));print('Largest files:')\nfor p in sorted([p for p in files if p.is_file()],key=lambda p:p.stat().st_size,reverse=True)[:15]:print(p.name,round(p.stat().st_size/1e6,1),'MB')\nfor f in os.listdir('/proc'):\n if f.isdigit():\n try:\n a=Path('/proc/'+f+'/cmdline').read_bytes().split(b'\\0')\n if len(a)>1 and a[0].endswith(b'python') and a[1].startswith(b'/app/solution/'):print('Active task process',f,a[1].decode())\n except (FileNotFoundError,PermissionError,ProcessLookupError):pass\nv=np.load(r/'test_item_vectors.npy',mmap_mode='r');u=np.load(r/'test_user_vectors.npy',mmap_mode='r');idx=np.load(r/'test_vector_ids.npz');assert np.isfinite(v).all() and np.isfinite(u).all();assert v.shape==(len(idx['item_ids']),579);assert u.shape==(len(idx['uids']),579)\nf=pd.read_parquet(r/'submission.parquet');a=np.load('/task/data/test_eligible_candidates.npz');assert np.array_equal(idx['uids'],a['uids']);assert set(f.uid)==set(idx['uids']);print('Final exported vectors:',u.shape,v.shape,'finite, aligned IDs')\n# Check actual exported dot-product top-100 order on 32 distributed users.\ncol=pd.Index(idx['item_ids']).get_indexer(a['item_ids']);rows=np.linspace(0,len(u)-1,32,dtype=int)\nfor row in rows:\n scores=u[row]@v[col].T;mask=np.unpackbits(a['packed_eligible'][row],count=len(col),bitorder='little').astype(bool);scores[~mask]=-np.inf\n got=f[f.uid==int(idx['uids'][row])].sort_values('rank').item_id.to_numpy();gotcols=pd.Index(a['item_ids']).get_indexer(got)\n assert np.all(np.diff(scores[gotcols])<=2e-4)\n best=np.partition(scores,-100)[-100];assert scores[gotcols].min()>=best-2e-4\nprint('Exported dot products reproduce the eligible top 100 for 32 checked users.')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Total artifacts GB 20.87\nLargest files:\ntemporal2_h30.0_scores.npy 1014.7 MB\ntemporal_vae_scores.npy 1014.7 MB\ncontext_more_scores.npy 1014.7 MB\nensemble_scores.npy 1014.7 MB\nselected_scores.npy 1014.7 MB\ncontext_scores.npy 1014.7 MB\nlocal_scores.npy 1014.7 MB\nema_scores.npy 1014.7 MB\ntemporal_early_h14.npy 1014.7 MB\ntemporal2_h60.0_scores.npy 1014.7 MB\nvae_scores.npy 1014.7 MB\ntemporal2_h7.0_scores.npy 1014.7 MB\nearly_scores.npy 1014.7 MB\ntemporal2_scores.npy 1014.7 MB\ntemporal2_h3.0_scores.npy 1014.7 MB\nFinal exported vectors: (8456, 579) (30000, 579) finite, aligned IDs\nExported dot products reproduce the eligible top 100 for 32 checked users.\n", "stderr": "", "aggregated_output": "Total artifacts GB 20.87\nLargest files:\ntemporal2_h30.0_scores.npy 1014.7 MB\ntemporal_vae_scores.npy 1014.7 MB\ncontext_more_scores.npy 1014.7 MB\nensemble_scores.npy 1014.7 MB\nselected_scores.npy 1014.7 MB\ncontext_scores.npy 1014.7 MB\nlocal_scores.npy 1014.7 MB\nema_scores.npy 1014.7 MB\ntemporal_early_h14.npy 1014.7 MB\ntemporal2_h60.0_scores.npy 1014.7 MB\nvae_scores.npy 1014.7 MB\ntemporal2_h7.0_scores.npy 1014.7 MB\nearly_scores.npy 1014.7 MB\ntemporal2_scores.npy 1014.7 MB\ntemporal2_h3.0_scores.npy 1014.7 MB\nFinal exported vectors: (8456, 579) (30000, 579) finite, aligned IDs\nExported dot products reproduce the eligible top 100 for 32 checked users.\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 566267793 }, "formatted_output": "Total artifacts GB 20.87\nLargest files:\ntemporal2_h30.0_scores.npy 1014.7 MB\ntemporal_vae_scores.npy 1014.7 MB\ncontext_more_scores.npy 1014.7 MB\nensemble_scores.npy 1014.7 MB\nselected_scores.npy 1014.7 MB\ncontext_scores.npy 1014.7 MB\nlocal_scores.npy 1014.7 MB\nema_scores.npy 1014.7 MB\ntemporal_early_h14.npy 1014.7 MB\ntemporal2_h60.0_scores.npy 1014.7 MB\nvae_scores.npy 1014.7 MB\ntemporal2_h7.0_scores.npy 1014.7 MB\nearly_scores.npy 1014.7 MB\ntemporal2_scores.npy 1014.7 MB\ntemporal2_h3.0_scores.npy 1014.7 MB\nFinal exported vectors: (8456, 579) (30000, 579) finite, aligned IDs\nExported dot products reproduce the eligible top 100 for 32 checked users.\n" } } } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-09T01:11:07.303Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:11:07.303Z", "text": "" } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-09T01:11:33.983Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:11:33.983Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:11:34.863Z", "callId": "call_TU1IkS1TGXaVo2LtY9IrFbbD", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:3793,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":2400}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:11:34.999Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"809a13\",\"wall_time_seconds\":0.0000015,\"exit_code\":0,\"original_token_count\":164,\"output\":\"Total artifacts GB 20.87\\nLargest files:\\ntemporal2_h30.0_scores.npy 1014.7 MB\\ntemporal_vae_scores.npy 1014.7 MB\\ncontext_more_scores.npy 1014.7 MB\\nensemble_scores.npy 1014.7 MB\\nselected_scores.npy 1014.7 MB\\ncontext_scores.npy 1014.7 MB\\nlocal_scores.npy 1014.7 MB\\nema_scores.npy 1014.7 MB\\ntemporal_early_h14.npy 1014.7 MB\\ntemporal2_h60.0_scores.npy 1014.7 MB\\nvae_scores.npy 1014.7 MB\\ntemporal2_h7.0_scores.npy 1014.7 MB\\nearly_scores.npy 1014.7 MB\\ntemporal2_scores.npy 1014.7 MB\\ntemporal2_h3.0_scores.npy 1014.7 MB\\nFinal exported vectors: (8456, 579) (30000, 579) finite, aligned IDs\\nExported dot products reproduce the eligible top 100 for 32 checked users.\\n\"}\n{\"current_time\":\"2026-09-09 01:11:34 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:11:34.996Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b2121b1c87d1b1ce49a5cfe7e3fe", "usage": { "input_tokens": 117560, "cached_input_tokens": 115200, "cache_write_input_tokens": 0, "output_tokens": 1085, "reasoning_output_tokens": 1034, "total_tokens": 118645 }, "turn_token_usage": { "input_tokens": 6406974, "cached_input_tokens": 6284032, "cache_write_input_tokens": 0, "output_tokens": 73659, "reasoning_output_tokens": 39435, "total_tokens": 6480633 }, "thread_token_usage": { "input_tokens": 6406974, "cached_input_tokens": 6284032, "cache_write_input_tokens": 0, "output_tokens": 73659, "reasoning_output_tokens": 39435, "total_tokens": 6480633 } } } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-09T01:12:01.234Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:12:01.234Z", "text": "" } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-09T01:12:13.024Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:12:13.024Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:12:23.648Z", "callId": "call_stCd7K0YCUNG3DCydRt0kFfn", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport json,torch\\nr=Path('/app/solution');keep_ck=set();keep_bags=set()\\nfor config,stage in [('final_config.json','test'),('selected_validation_config.json','train')]:\\n for c in json.loads((r/config).read_text())['components']:\\n keep_ck.add(c['checkpoint']);a=torch.load(r/c['checkpoint'],weights_only=False)['args'];h=c.get('half',a['half']);name=f\\\"bags_{stage}_{h}_{a['likes']}_{a['organic']}_{a.get('recent_days',0)}.npz\\\";assert (r/name).exists(),name;keep_bags.add(name)\\nkeep_ck.update(['final_base_test.pt','final_vae_initial_test.pt','final_vae_test.pt','temporal2_source.pt','temporal_vae_source.pt'])\\nremoved=[]\\nfor p in list(r.iterdir()):\\n if not p.is_file():continue\\n remove=(p.name.endswith('_scores.npy') or p.name=='temporal_early_h14.npy' or (p.name.startswith('temporal_') and p.suffix=='.npz') or p.name.startswith('sequences_') or (p.name.startswith('bags_') and p.name not in keep_bags) or (p.suffix=='.pt' and p.name not in keep_ck) or (p.suffix=='.parquet' and p.name not in ['submission.parquet','validation.parquet']))\\n if remove:removed.append((p.name,p.stat().st_size));p.unlink()\\nprint('Removed',len(removed),'unused artifacts;',round(sum(s for _,s in removed)/1e9,2),'GB')\\nprint('Retained',round(sum(p.stat().st_size for p in r.iterdir() if p.is_file())/1e9,2),'GB')\\nprint('Serving and validation history caches:',sorted(keep_bags))\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1400}));\n" }, "result": { "timestamp": "2026-09-09T01:12:24.823Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"ebd2a6\",\"wall_time_seconds\":1.001067209,\"session_id\":51699,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:12:23.814Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b24767c487d1abd95564eb0e9b90", "usage": { "input_tokens": 118954, "cached_input_tokens": 117376, "cache_write_input_tokens": 0, "output_tokens": 1228, "reasoning_output_tokens": 761, "total_tokens": 120182 }, "turn_token_usage": { "input_tokens": 6525928, "cached_input_tokens": 6401408, "cache_write_input_tokens": 0, "output_tokens": 74887, "reasoning_output_tokens": 40196, "total_tokens": 6600815 }, "thread_token_usage": { "input_tokens": 6525928, "cached_input_tokens": 6401408, "cache_write_input_tokens": 0, "output_tokens": 74887, "reasoning_output_tokens": 40196, "total_tokens": 6600815 } } }, { "kind": "context", "timestamp": "2026-09-09T01:12:27.768Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1e7aedc6-dc56-4a09-8eca-9c5b01474d11", "process_id": "51699", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport json,torch\nr=Path('/app/solution');keep_ck=set();keep_bags=set()\nfor config,stage in [('final_config.json','test'),('selected_validation_config.json','train')]:\n for c in json.loads((r/config).read_text())['components']:\n keep_ck.add(c['checkpoint']);a=torch.load(r/c['checkpoint'],weights_only=False)['args'];h=c.get('half',a['half']);name=f\"bags_{stage}_{h}_{a['likes']}_{a['organic']}_{a.get('recent_days',0)}.npz\";assert (r/name).exists(),name;keep_bags.add(name)\nkeep_ck.update(['final_base_test.pt','final_vae_initial_test.pt','final_vae_test.pt','temporal2_source.pt','temporal_vae_source.pt'])\nremoved=[]\nfor p in list(r.iterdir()):\n if not p.is_file():continue\n remove=(p.name.endswith('_scores.npy') or p.name=='temporal_early_h14.npy' or (p.name.startswith('temporal_') and p.suffix=='.npz') or p.name.startswith('sequences_') or (p.name.startswith('bags_') and p.name not in keep_bags) or (p.suffix=='.pt' and p.name not in keep_ck) or (p.suffix=='.parquet' and p.name not in ['submission.parquet','validation.parquet']))\n if remove:removed.append((p.name,p.stat().st_size));p.unlink()\nprint('Removed',len(removed),'unused artifacts;',round(sum(s for _,s in removed)/1e9,2),'GB')\nprint('Retained',round(sum(p.stat().st_size for p in r.iterdir() if p.is_file())/1e9,2),'GB')\nprint('Serving and validation history caches:',sorted(keep_bags))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport json,torch\nr=Path('/app/solution');keep_ck=set();keep_bags=set()\nfor config,stage in [('final_config.json','test'),('selected_validation_config.json','train')]:\n for c in json.loads((r/config).read_text())['components']:\n keep_ck.add(c['checkpoint']);a=torch.load(r/c['checkpoint'],weights_only=False)['args'];h=c.get('half',a['half']);name=f\"bags_{stage}_{h}_{a['likes']}_{a['organic']}_{a.get('recent_days',0)}.npz\";assert (r/name).exists(),name;keep_bags.add(name)\nkeep_ck.update(['final_base_test.pt','final_vae_initial_test.pt','final_vae_test.pt','temporal2_source.pt','temporal_vae_source.pt'])\nremoved=[]\nfor p in list(r.iterdir()):\n if not p.is_file():continue\n remove=(p.name.endswith('_scores.npy') or p.name=='temporal_early_h14.npy' or (p.name.startswith('temporal_') and p.suffix=='.npz') or p.name.startswith('sequences_') or (p.name.startswith('bags_') and p.name not in keep_bags) or (p.suffix=='.pt' and p.name not in keep_ck) or (p.suffix=='.parquet' and p.name not in ['submission.parquet','validation.parquet']))\n if remove:removed.append((p.name,p.stat().st_size));p.unlink()\nprint('Removed',len(removed),'unused artifacts;',round(sum(s for _,s in removed)/1e9,2),'GB')\nprint('Retained',round(sum(p.stat().st_size for p in r.iterdir() if p.is_file())/1e9,2),'GB')\nprint('Serving and validation history caches:',sorted(keep_bags))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Removed 79 unused artifacts; 18.88 GB\nRetained 1.99 GB\nServing and validation history caches: ['bags_test_14_3.0_1.0_0.npz', 'bags_test_30_3.0_1.0_0.npz', 'bags_test_30_7.0_1.0_0.npz', 'bags_train_14_3.0_1.0_0.npz', 'bags_train_30_3.0_1.0_0.npz', 'bags_train_30_7.0_1.0_0.npz']\n", "stderr": "", "aggregated_output": "Removed 79 unused artifacts; 18.88 GB\nRetained 1.99 GB\nServing and validation history caches: ['bags_test_14_3.0_1.0_0.npz', 'bags_test_30_3.0_1.0_0.npz', 'bags_test_30_7.0_1.0_0.npz', 'bags_train_14_3.0_1.0_0.npz', 'bags_train_30_3.0_1.0_0.npz', 'bags_train_30_7.0_1.0_0.npz']\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 947545029 }, "formatted_output": "Removed 79 unused artifacts; 18.88 GB\nRetained 1.99 GB\nServing and validation history caches: ['bags_test_14_3.0_1.0_0.npz', 'bags_test_30_3.0_1.0_0.npz', 'bags_test_30_7.0_1.0_0.npz', 'bags_train_14_3.0_1.0_0.npz', 'bags_train_30_3.0_1.0_0.npz', 'bags_train_30_7.0_1.0_0.npz']\n" } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-09T01:12:50.663Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:12:50.663Z", "text": "" } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-09T01:13:01.877Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:13:01.877Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-09T01:13:04.496Z", "text": "The exported vectors are finite and aligned with their IDs, and their dot products reproduce the submitted eligible top 100 for the sampled users checked. Cleanup is complete; I’m doing one final run from the retained checkpoints and caches.\n" }, { "kind": "tool", "timestamp": "2026-09-09T01:13:06.497Z", "callId": "call_kMRDn3sF0D8nNodjucypuj3C", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:51699,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1600}));\ntext(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/final_run.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-09T01:13:07.684Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"172179\",\"wall_time_seconds\":0.00000175,\"exit_code\":0,\"original_token_count\":70,\"output\":\"Removed 79 unused artifacts; 18.88 GB\\nRetained 1.99 GB\\nServing and validation history caches: ['bags_test_14_3.0_1.0_0.npz', 'bags_test_30_3.0_1.0_0.npz', 'bags_test_30_7.0_1.0_0.npz', 'bags_train_14_3.0_1.0_0.npz', 'bags_train_30_3.0_1.0_0.npz', 'bags_train_30_7.0_1.0_0.npz']\\n\"}\n{\"chunk_id\":\"5a2a8f\",\"wall_time_seconds\":1.001221353,\"session_id\":14083,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-09 01:13:07 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:13:06.619Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b279164487d1b7a9cf4427b089f4", "usage": { "input_tokens": 120241, "cached_input_tokens": 118784, "cache_write_input_tokens": 0, "output_tokens": 940, "reasoning_output_tokens": 776, "total_tokens": 121181 }, "turn_token_usage": { "input_tokens": 6646169, "cached_input_tokens": 6520192, "cache_write_input_tokens": 0, "output_tokens": 75827, "reasoning_output_tokens": 40972, "total_tokens": 6721996 }, "thread_token_usage": { "input_tokens": 6646169, "cached_input_tokens": 6520192, "cache_write_input_tokens": 0, "output_tokens": 75827, "reasoning_output_tokens": 40972, "total_tokens": 6721996 } } }, { "kind": "context", "timestamp": "2026-09-09T01:13:16.510Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f74b07d0-2e63-4cd8-9165-483d4f5d2237", "process_id": "14083", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/final_run.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/submit.py --stage test > /app/solution/final_run.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 9, "nanos": 839609839 }, "formatted_output": "" } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-09T01:13:36.234Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:13:36.234Z", "text": "" } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-09T01:14:07.524Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:14:07.524Z", "text": "" } ] }, { "id": 129, "role": "agent", "timestamp": "2026-09-09T01:14:34.886Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:14:34.886Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:14:36.629Z", "callId": "call_b0oRD4h2ycXc783d30fbBnhx", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/final_run.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py\",\"yield_time_ms\":1000,\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-09T01:14:37.805Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"3b6c5a\",\"wall_time_seconds\":1.001089036,\"session_id\":76197,\"original_token_count\":25,\"output\":\"{\\\"output\\\": \\\"submission.parquet\\\", \\\"rows\\\": 845600, \\\"dimension\\\": 579, \\\"seconds\\\": 6.066086292266846}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:14:36.766Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b2a3e06c87d1ba10a2664f31dbcc", "usage": { "input_tokens": 121426, "cached_input_tokens": 120064, "cache_write_input_tokens": 0, "output_tokens": 1642, "reasoning_output_tokens": 1552, "total_tokens": 123068 }, "turn_token_usage": { "input_tokens": 6767595, "cached_input_tokens": 6640256, "cache_write_input_tokens": 0, "output_tokens": 77469, "reasoning_output_tokens": 42524, "total_tokens": 6845064 }, "thread_token_usage": { "input_tokens": 6767595, "cached_input_tokens": 6640256, "cache_write_input_tokens": 0, "output_tokens": 77469, "reasoning_output_tokens": 42524, "total_tokens": 6845064 } } }, { "kind": "context", "timestamp": "2026-09-09T01:14:45.378Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-52c81357-088d-45b5-ac26-fb02af13e2c0", "process_id": "76197", "command": [ "/bin/sh", "-lc", "cat /app/solution/final_run.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/final_run.log; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify.py; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/test_contract.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\nTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n", "stderr": "", "aggregated_output": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\nTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 574434469 }, "formatted_output": "VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\nTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\nLarge-catalog bounded sampled-softmax path: PASS\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\n" } } } ] }, { "id": 130, "role": "agent", "timestamp": "2026-09-09T01:14:58.996Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:14:58.996Z", "text": "" } ] }, { "id": 131, "role": "agent", "timestamp": "2026-09-09T01:15:03.428Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-09T01:15:03.428Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-09T01:15:08.868Z", "callId": "call_C3oXSad7oiLoODV0Of91fk1A", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:76197,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1700}));\ntext(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport hashlib,json\\nr=Path('/app/solution');paths=['submission.parquet','validation.parquet','final_config.json','final_temporal_test.pt','final_temporal_vae_test.pt','final_context_test.pt'];m={'public_ndcg_at_10':.055145,'users':8456,'rows':845600,'embedding_dimension':579,'files':{}}\\nfor name in paths:\\n h=hashlib.sha256()\\n with (r/name).open('rb') as f:\\n for block in iter(lambda:f.read(1024*1024),b''):h.update(block)\\n m['files'][name]={'bytes':(r/name).stat().st_size,'sha256':h.hexdigest()}\\n(r/'manifest.json').write_text(json.dumps(m,indent=2));print('Final artifact manifest saved.')\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-09T01:15:09.127Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n{\"chunk_id\":\"f7c626\",\"wall_time_seconds\":0.00000183,\"exit_code\":0,\"original_token_count\":115,\"output\":\"VALID: 845,600 rows, 8,456 users, 100 distinct eligible items and ranks 1–100 each.\\nTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nVariationalTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nContextTwoTower independent vectors, exact dot product, and both tower gradients: PASS\\nLarge-catalog bounded sampled-softmax path: PASS\\nLarge-catalog temporal sampled-softmax and sparse optimizer: PASS\\n\"}\n{\"chunk_id\":\"629cf5\",\"wall_time_seconds\":0.088397399,\"exit_code\":0,\"original_token_count\":8,\"output\":\"Final artifact manifest saved.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-09T01:15:09.027Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b2fdfc1087d18f5a87c22d0cd2f8", "usage": { "input_tokens": 123161, "cached_input_tokens": 121216, "cache_write_input_tokens": 0, "output_tokens": 915, "reasoning_output_tokens": 651, "total_tokens": 124076 }, "turn_token_usage": { "input_tokens": 6890756, "cached_input_tokens": 6761472, "cache_write_input_tokens": 0, "output_tokens": 78384, "reasoning_output_tokens": 43175, "total_tokens": 6969140 }, "thread_token_usage": { "input_tokens": 6890756, "cached_input_tokens": 6761472, "cache_write_input_tokens": 0, "output_tokens": 78384, "reasoning_output_tokens": 43175, "total_tokens": 6969140 } } }, { "kind": "context", "timestamp": "2026-09-09T01:15:09.120Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-53128399-c39a-439a-94f6-dceadf651d8a", "process_id": "84368", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\nr=Path('/app/solution');paths=['submission.parquet','validation.parquet','final_config.json','final_temporal_test.pt','final_temporal_vae_test.pt','final_context_test.pt'];m={'public_ndcg_at_10':.055145,'users':8456,'rows':845600,'embedding_dimension':579,'files':{}}\nfor name in paths:\n h=hashlib.sha256()\n with (r/name).open('rb') as f:\n for block in iter(lambda:f.read(1024*1024),b''):h.update(block)\n m['files'][name]={'bytes':(r/name).stat().st_size,'sha256':h.hexdigest()}\n(r/'manifest.json').write_text(json.dumps(m,indent=2));print('Final artifact manifest saved.')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json\nr=Path('/app/solution');paths=['submission.parquet','validation.parquet','final_config.json','final_temporal_test.pt','final_temporal_vae_test.pt','final_context_test.pt'];m={'public_ndcg_at_10':.055145,'users':8456,'rows':845600,'embedding_dimension':579,'files':{}}\nfor name in paths:\n h=hashlib.sha256()\n with (r/name).open('rb') as f:\n for block in iter(lambda:f.read(1024*1024),b''):h.update(block)\n m['files'][name]={'bytes':(r/name).stat().st_size,'sha256':h.hexdigest()}\n(r/'manifest.json').write_text(json.dumps(m,indent=2));print('Final artifact manifest saved.')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Final artifact manifest saved.\n", "stderr": "", "aggregated_output": "Final artifact manifest saved.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 88399009 }, "formatted_output": "Final artifact manifest saved.\n" } } } ] }, { "id": 132, "role": "agent", "timestamp": "2026-09-09T01:15:14.103Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-09T01:15:14.103Z", "text": "Saved [submission.parquet](/app/solution/submission.parquet) with 845,600 validated rows—100 eligible tracks per user.\n\nPublic NDCG@10: **0.055145**.\n\nBoth towers, training code, and reproduction commands are documented in [README.md](/app/solution/README.md)." }, { "kind": "context", "timestamp": "2026-09-09T01:15:14.178Z", "data": { "type": "token_usage_record", "thread_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "session_id": "01a0838b-5647-7e93-a0b3-c13238bc7211", "root_turn_id": "01a0838b-5657-71b0-9a1c-0bd5ddd46ae5", "response_id": "resp_0cf7a7753b420d83016aa0b31e768487d1a5fba0af97abe9d3", "usage": { "input_tokens": 124273, "cached_input_tokens": 123008, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 0, "total_tokens": 124344 }, "turn_token_usage": { "input_tokens": 7015029, "cached_input_tokens": 6884480, "cache_write_input_tokens": 0, "output_tokens": 78455, "reasoning_output_tokens": 43175, "total_tokens": 7093484 }, "thread_token_usage": { "input_tokens": 7015029, "cached_input_tokens": 6884480, "cache_write_input_tokens": 0, "output_tokens": 78455, "reasoning_output_tokens": 43175, "total_tokens": 7093484 } } } ] } ] }