{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "codex-astra-high-base-r04-2026-09-10T17-31-17", "taskChecksum": "3e4c2a40b9f9b3c731eb18882a73a8c8db2da47ef86663701987d39bc9a8c510", "source": { "format": "codex-rollout", "sha256": "87ce39ea16eb583c0eb8560392c502a9cf5406743286dfc60b31525028b6d11a", "records": 1060, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:AgentMessage": 27, "token_count": 133, "item_completed:Reasoning": 159, "task_complete": 1 } }, "sessionId": "01a08c73-1115-7c13-a695-541ce0eca25b", "agentVersion": "0.153.4", "model": "gpt-6-astra", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-10T17:52:26.417Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:26.417Z", "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": "2026-09-10T17:52:28.304Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:28.304Z", "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": "2026-09-10T17:52:28.304Z", "text": "\n /app\n sh\n 2026-09-10\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-10T17:52:28.306Z", "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": "2026-09-10", "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": "2026-09-10T17:52:28.307Z", "data": { "type": "turn_context", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-10", "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": "2026-09-10T17:52:28.322Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:28.322Z", "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": "2026-09-10T17:52:32.527Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-10T17:52:32.527Z", "text": "I’ll inspect the data and evaluation helpers, train a history-based two-tower model, and tune it on the public window before refitting for the hidden-test cutoff. I’ll save an initial eligible submission early and replace it as validation improves.\n" }, { "kind": "tool", "timestamp": "2026-09-10T17:52:34.410Z", "callId": "call_BTkicTDMxYxsN2cnAJoORXlI", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -la /app && ls -lh /task/data && cat /task/requirements.txt\",\"max_output_tokens\":6000}));\ntext(await tools.exec_command({cmd:\"cat /task/ranking_evaluation.py && cat /task/evaluate_public.py\",\"max_output_tokens\":12000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T17:52:34.515Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"6d2e09\",\"wall_time_seconds\":0.00000598,\"exit_code\":0,\"original_token_count\":627,\"output\":\"total 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\\ntotal 340M\\n-r--r--r-- 1 root root 342K Sep 10 17:35 candidate_catalog.parquet\\n-r--r--r-- 1 root root 83K Sep 10 17:35 target_users.parquet\\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 test_preference_state.parquet\\n-r--r--r-- 1 root root 838K Sep 10 17:35 train_dislikes.parquet\\n-r--r--r-- 1 root root 5.2M Sep 10 17:35 train_likes.parquet\\n-r--r--r-- 1 root root 305M Sep 10 17:35 train_listens.parquet\\n-r--r--r-- 1 root root 153K Sep 10 17:35 train_undislikes.parquet\\n-r--r--r-- 1 root root 1.8M Sep 10 17:35 train_unlikes.parquet\\n-r--r--r-- 1 root root 36K Sep 10 17:35 val_dislikes.parquet\\n-r--r--r-- 1 root root 292K Sep 10 17:35 val_likes.parquet\\n-r--r--r-- 1 root root 9.5M Sep 10 17:35 val_listens.parquet\\n-r--r--r-- 1 root root 7.7K Sep 10 17:35 val_undislikes.parquet\\n-r--r--r-- 1 root root 71K Sep 10 17:35 val_unlikes.parquet\\n-r--r--r-- 1 root root 7.3M Sep 10 17:35 validation_eligible_candidates.npz\\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 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\"}\n{\"chunk_id\":\"30ae78\",\"wall_time_seconds\":0.00000274,\"exit_code\":0,\"original_token_count\":3510,\"output\":\"\\\"\\\"\\\"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#!/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\"}\n{\"current_time\":\"2026-09-10 17:52:34 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:52:34.457Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ee5c84e887d1a0d9438e281e637b", "usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 0, "total_tokens": 16947 }, "turn_token_usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 0, "total_tokens": 16947 }, "thread_token_usage": { "input_tokens": 16805, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 0, "total_tokens": 16947 } } }, { "kind": "context", "timestamp": "2026-09-10T17:52:34.472Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-144ff877-5d49-4538-aee8-fdb674e99efb", "process_id": "86510", "command": [ "/bin/sh", "-lc", "ls -la /app && ls -lh /task/data && cat /task/requirements.txt" ], "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" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 10 17:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 10 17:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 10 17:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 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", "stderr": "", "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 10 17:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 10 17:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 10 17:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 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", "exit_code": 0, "duration": { "secs": 0, "nanos": 5980 }, "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 10 17:35 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 10 17:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 10 17:36 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 10 17:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 10 17:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 10 17:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 10 17:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 10 17:35 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 10 17:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 10 17:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 10 17:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 10 17:35 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 10 17:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 10 17:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 10 17:36 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" } } }, { "kind": "context", "timestamp": "2026-09-10T17:52:34.508Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27d16277-b2ea-4549-9ebd-0d983e3cae3c", "process_id": "27864", "command": [ "/bin/sh", "-lc", "cat /task/ranking_evaluation.py && cat /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "cat /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\"\"\"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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "stderr": "", "aggregated_output": "\"\"\"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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2740 }, "formatted_output": "\"\"\"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#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-10T17:52:37.288Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:52:37.288Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:52:44.684Z", "callId": "call_YhCLb4nY2oIvwRSdASmMN9sw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"ls -la /app/solution; python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\\nfor s in ['validation','test']:\\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\\nPY\",\"max_output_tokens\":5000}));\n" }, "result": { "timestamp": "2026-09-10T17:52:44.776Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"e02022\",\"wall_time_seconds\":0.00000399,\"exit_code\":1,\"original_token_count\":55,\"output\":\"total 8\\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 .\\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 ..\\nTraceback (most recent call last):\\n File \\\"\\\", line 1, in \\nModuleNotFoundError: No module named 'pandas'\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:52:44.735Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f325be79-2bd1-4a41-b376-0b255ecf9d6e", "process_id": "53497", "command": [ "/bin/sh", "-lc", "ls -la /app/solution; python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\nfor s in ['validation','test']:\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /app/solution; python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\nfor s in ['validation','test']:\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 ..\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 10 17:49 .\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 ..\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3990 }, "formatted_output": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 ..\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:52:44.775Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ee62db3087d1989554b9d03cb718", "usage": { "input_tokens": 21867, "cached_input_tokens": 16640, "cache_write_input_tokens": 0, "output_tokens": 271, "reasoning_output_tokens": 25, "total_tokens": 22138 }, "turn_token_usage": { "input_tokens": 38672, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 413, "reasoning_output_tokens": 25, "total_tokens": 39085 }, "thread_token_usage": { "input_tokens": 38672, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 413, "reasoning_output_tokens": 25, "total_tokens": 39085 } } }, { "kind": "tool", "timestamp": "2026-09-10T17:52:48.412Z", "callId": "call_3jwOq8oYmhYmdtV9nMOqBmBX", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"which python; ls -la /opt; ls -la /app; env | rg 'VIRTUAL|PATH'; ls /usr/local/bin | head\"}));\n" }, "result": { "timestamp": "2026-09-10T17:52:48.471Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"eb6eb3\",\"wall_time_seconds\":0.00000645,\"exit_code\":0,\"original_token_count\":148,\"output\":\"/usr/local/bin/python\\ntotal 16\\ndrwxr-xr-x 1 root root 4096 Sep 10 17:49 .\\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\\ndr-xr-xr-x 1 root root 4096 Sep 10 17:49 task-venv\\ntotal 12\\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\\nPATH=/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\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:52:48.455Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4426f451-3643-4d26-8e0d-d459852ee3cf", "process_id": "35147", "command": [ "/bin/sh", "-lc", "which python; ls -la /opt; ls -la /app; env | rg 'VIRTUAL|PATH'; ls /usr/local/bin | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; ls -la /opt; ls -la /app; env | rg 'VIRTUAL|PATH'; ls /usr/local/bin | head" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:49 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\nPATH=/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\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:49 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\nPATH=/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\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6450 }, "formatted_output": "/usr/local/bin/python\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 4 root root 4096 Sep 10 17:49 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 10 17:49 task-venv\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 10 17:49 .\ndrwxr-xr-x 1 root root 4096 Sep 10 17:52 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 10 17:49 solution\nPATH=/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\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:52:48.470Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ee6d2ea087d19222faa5eb85fa25", "usage": { "input_tokens": 22281, "cached_input_tokens": 21632, "cache_write_input_tokens": 0, "output_tokens": 51, "reasoning_output_tokens": 0, "total_tokens": 22332 }, "turn_token_usage": { "input_tokens": 60953, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 464, "reasoning_output_tokens": 25, "total_tokens": 61417 }, "thread_token_usage": { "input_tokens": 60953, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 464, "reasoning_output_tokens": 25, "total_tokens": 61417 } } }, { "kind": "tool", "timestamp": "2026-09-10T17:52:57.546Z", "callId": "call_EVXJSzV7PVB3mvhEzzVqDdet", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\\nfor s in ['validation','test']:\\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\\nPY\",\"max_output_tokens\":5000}));\n" }, "result": { "timestamp": "2026-09-10T17:52:59.933Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"18a284\",\"wall_time_seconds\":2.219135307,\"exit_code\":0,\"original_token_count\":1219,\"output\":\"target_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\\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\\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\\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\\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\\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\\n0 100 39420 8326270 0 100 170\\n1 100 39420 1441281 0 100 105\\n2 100 39625 286361 0 100 185\\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\\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\\nvalidation [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\\neligible 24657\\ntest [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\\neligible 24552\\ncpus 8\\nMemTotal: 15988972 kB\\nMemFree: 1537072 kB\\nMemAvailable: 14594920 kB\\nBuffers: 227732 kB\\nCached: 12547764 kB\\nSwapCached: 0 kB\\nActive: 723368 kB\\nInac\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:52:57.585Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ee70bcd487d1b1fcd9e5e8b58502", "usage": { "input_tokens": 22654, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 243, "reasoning_output_tokens": 0, "total_tokens": 22897 }, "turn_token_usage": { "input_tokens": 83607, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 707, "reasoning_output_tokens": 25, "total_tokens": 84314 }, "thread_token_usage": { "input_tokens": 83607, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 707, "reasoning_output_tokens": 25, "total_tokens": 84314 } } }, { "kind": "context", "timestamp": "2026-09-10T17:52:59.929Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5285b7ad-730d-4dfc-bea4-f3177423454f", "process_id": "10373", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\nfor s in ['validation','test']:\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, os\nfor f in ['target_users','candidate_catalog','validation_preference_state','test_preference_state','train_listens','train_likes','val_listens']:\n p='/task/data/'+f+'.parquet'; q=pq.ParquetFile(p); print(f,q.metadata.num_rows,q.schema); print(q.read_row_group(0).to_pandas().head(3))\nfor s in ['validation','test']:\n z=np.load('/task/data/'+s+'_eligible_candidates.npz');print(s,[(k,z[k].shape,z[k].dtype) for k in z.files]); print('eligible',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1).min())\nprint('cpus',os.cpu_count()); print(open('/proc/meminfo').read()[:200])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "target_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\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\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\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\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\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\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\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\nvalidation [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ntest [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24552\ncpus 8\nMemTotal: 15988972 kB\nMemFree: 1537072 kB\nMemAvailable: 14594920 kB\nBuffers: 227732 kB\nCached: 12547764 kB\nSwapCached: 0 kB\nActive: 723368 kB\nInac\n", "stderr": "", "aggregated_output": "target_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\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\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\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\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\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\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\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\nvalidation [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ntest [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24552\ncpus 8\nMemTotal: 15988972 kB\nMemFree: 1537072 kB\nMemAvailable: 14594920 kB\nBuffers: 227732 kB\nCached: 12547764 kB\nSwapCached: 0 kB\nActive: 723368 kB\nInac\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 219130507 }, "formatted_output": "target_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\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\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\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\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\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\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\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\nvalidation [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24657\ntest [('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\neligible 24552\ncpus 8\nMemTotal: 15988972 kB\nMemFree: 1537072 kB\nMemAvailable: 14594920 kB\nBuffers: 227732 kB\nCached: 12547764 kB\nSwapCached: 0 kB\nActive: 723368 kB\nInac\n" } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-10T17:53:20.599Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:53:20.599Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:53:36.415Z", "callId": "call_nU0SEmtcmiONdBtjdOvfDXEu", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/inspect_data.py <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,time\\nD='/task/data/'\\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids'];uids=z['uids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nii=pd.Index(items);ui=pd.Index(uids)\\nfor family in ['listens','likes','dislikes']:\\n df=pd.read_parquet(D+'val_'+family+'.parquet'); print(family,len(df),'users',df.uid.nunique(),'time',df.timestamp.min(),df.timestamp.max())\\n if family=='listens':df=df[(df.is_organic==1)&(df.played_ratio_pct>=80)]\\n else:df=df[df.is_organic==1]\\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);df=df[ok];r=r[ok];c=c[ok];df=df[mask[r,c]];print('eligible gold',len(df),df.uid.nunique());df[['uid','item_id']].drop_duplicates().to_parquet('/app/solution/val_'+family+'_gold.parquet')\\nstate=pd.read_parquet(D+'validation_preference_state.parquet');r=ui.get_indexer(state.uid);c=ii.get_indexer(state.item_id);ok=(r>=0)&(c>=0); print('state eligible',state[ok][mask[r[ok],c[ok]]].state.value_counts())\\nfor kind in ['listens','likes']:\\n counts={};npairs=0;yes=0;overlap=[0,0];gold=pd.read_parquet('/app/solution/val_'+kind+'_gold.parquet');gs=set(zip(gold.uid,gold.item_id));pairs=set();us=set()\\n for batch in pq.ParquetFile(D+'train_'+kind+'.parquet').iter_batches(batch_size=1000000):\\n df=batch.to_pandas(); us.update(df.uid.unique());\\n if kind=='listens':df=df[df.played_ratio_pct>=50]\\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);npairs+=ok.sum();yes+=mask[r[ok],c[ok]].sum(); pairs.update((int(u),int(i)) for u,i in zip(df.uid[ok],df.item_id[ok]) if (u,i) in gs)\\n print('train',kind,'users',len(us),'catalogevents',npairs,'eligible',yes,'goldseen',len(pairs),'goldtotal',len(gs),flush=True)\\nPY\\n/opt/task-venv/bin/python /app/solution/inspect_data.py\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-10T17:53:37.580Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"0f25e6\",\"wall_time_seconds\":1.001016851,\"session_id\":46870,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:53:36.467Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ee7c1d2087d1a65d7af076cc43d1", "usage": { "input_tokens": 24488, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 1088, "reasoning_output_tokens": 432, "total_tokens": 25576 }, "turn_token_usage": { "input_tokens": 108095, "cached_input_tokens": 84736, "cache_write_input_tokens": 0, "output_tokens": 1795, "reasoning_output_tokens": 457, "total_tokens": 109890 }, "thread_token_usage": { "input_tokens": 108095, "cached_input_tokens": 84736, "cache_write_input_tokens": 0, "output_tokens": 1795, "reasoning_output_tokens": 457, "total_tokens": 109890 } } }, { "kind": "context", "timestamp": "2026-09-10T17:53:44.631Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-91861bea-93b9-48fe-994a-405460b68667", "process_id": "46870", "command": [ "/bin/sh", "-lc", "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,time\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids'];uids=z['uids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nii=pd.Index(items);ui=pd.Index(uids)\nfor family in ['listens','likes','dislikes']:\n df=pd.read_parquet(D+'val_'+family+'.parquet'); print(family,len(df),'users',df.uid.nunique(),'time',df.timestamp.min(),df.timestamp.max())\n if family=='listens':df=df[(df.is_organic==1)&(df.played_ratio_pct>=80)]\n else:df=df[df.is_organic==1]\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);df=df[ok];r=r[ok];c=c[ok];df=df[mask[r,c]];print('eligible gold',len(df),df.uid.nunique());df[['uid','item_id']].drop_duplicates().to_parquet('/app/solution/val_'+family+'_gold.parquet')\nstate=pd.read_parquet(D+'validation_preference_state.parquet');r=ui.get_indexer(state.uid);c=ii.get_indexer(state.item_id);ok=(r>=0)&(c>=0); print('state eligible',state[ok][mask[r[ok],c[ok]]].state.value_counts())\nfor kind in ['listens','likes']:\n counts={};npairs=0;yes=0;overlap=[0,0];gold=pd.read_parquet('/app/solution/val_'+kind+'_gold.parquet');gs=set(zip(gold.uid,gold.item_id));pairs=set();us=set()\n for batch in pq.ParquetFile(D+'train_'+kind+'.parquet').iter_batches(batch_size=1000000):\n df=batch.to_pandas(); us.update(df.uid.unique());\n if kind=='listens':df=df[df.played_ratio_pct>=50]\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);npairs+=ok.sum();yes+=mask[r[ok],c[ok]].sum(); pairs.update((int(u),int(i)) for u,i in zip(df.uid[ok],df.item_id[ok]) if (u,i) in gs)\n print('train',kind,'users',len(us),'catalogevents',npairs,'eligible',yes,'goldseen',len(pairs),'goldtotal',len(gs),flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/inspect_data.py <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,time\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids'];uids=z['uids'];mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nii=pd.Index(items);ui=pd.Index(uids)\nfor family in ['listens','likes','dislikes']:\n df=pd.read_parquet(D+'val_'+family+'.parquet'); print(family,len(df),'users',df.uid.nunique(),'time',df.timestamp.min(),df.timestamp.max())\n if family=='listens':df=df[(df.is_organic==1)&(df.played_ratio_pct>=80)]\n else:df=df[df.is_organic==1]\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);df=df[ok];r=r[ok];c=c[ok];df=df[mask[r,c]];print('eligible gold',len(df),df.uid.nunique());df[['uid','item_id']].drop_duplicates().to_parquet('/app/solution/val_'+family+'_gold.parquet')\nstate=pd.read_parquet(D+'validation_preference_state.parquet');r=ui.get_indexer(state.uid);c=ii.get_indexer(state.item_id);ok=(r>=0)&(c>=0); print('state eligible',state[ok][mask[r[ok],c[ok]]].state.value_counts())\nfor kind in ['listens','likes']:\n counts={};npairs=0;yes=0;overlap=[0,0];gold=pd.read_parquet('/app/solution/val_'+kind+'_gold.parquet');gs=set(zip(gold.uid,gold.item_id));pairs=set();us=set()\n for batch in pq.ParquetFile(D+'train_'+kind+'.parquet').iter_batches(batch_size=1000000):\n df=batch.to_pandas(); us.update(df.uid.unique());\n if kind=='listens':df=df[df.played_ratio_pct>=50]\n r=ui.get_indexer(df.uid);c=ii.get_indexer(df.item_id);ok=(r>=0)&(c>=0);npairs+=ok.sum();yes+=mask[r[ok],c[ok]].sum(); pairs.update((int(u),int(i)) for u,i in zip(df.uid[ok],df.item_id[ok]) if (u,i) in gs)\n print('train',kind,'users',len(us),'catalogevents',npairs,'eligible',yes,'goldseen',len(pairs),'goldtotal',len(gs),flush=True)\nPY\n/opt/task-venv/bin/python /app/solution/inspect_data.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 1501697 users 8196 time 24788600 25393395\neligible gold 53536 4042\nlikes 37795 users 4114 time 24788815 25393390\neligible gold 4544 1431\ndislikes 3563 users 955 time 24789920 25393280\neligible gold 245 158\nstate eligible Series([], Name: count, dtype: int64)\ntrain listens users 9238 catalogevents 19959853 eligible 0 goldseen 0 goldtotal 35597\ntrain likes users 8144 catalogevents 472889 eligible 0 goldseen 0 goldtotal 4417\n", "stderr": "", "aggregated_output": "listens 1501697 users 8196 time 24788600 25393395\neligible gold 53536 4042\nlikes 37795 users 4114 time 24788815 25393390\neligible gold 4544 1431\ndislikes 3563 users 955 time 24789920 25393280\neligible gold 245 158\nstate eligible Series([], Name: count, dtype: int64)\ntrain listens users 9238 catalogevents 19959853 eligible 0 goldseen 0 goldtotal 35597\ntrain likes users 8144 catalogevents 472889 eligible 0 goldseen 0 goldtotal 4417\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 53170627 }, "formatted_output": "listens 1501697 users 8196 time 24788600 25393395\neligible gold 53536 4042\nlikes 37795 users 4114 time 24788815 25393390\neligible gold 4544 1431\ndislikes 3563 users 955 time 24789920 25393280\neligible gold 245 158\nstate eligible Series([], Name: count, dtype: int64)\ntrain listens users 9238 catalogevents 19959853 eligible 0 goldseen 0 goldtotal 35597\ntrain likes users 8144 catalogevents 472889 eligible 0 goldseen 0 goldtotal 4417\n" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-10T17:53:44.687Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:53:44.687Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:53:46.122Z", "callId": "call_k1pIU04UXzwPgbgL9xdxEXu2", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:46870,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}));\ntext(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; df -h /app\"}));\n" }, "result": { "timestamp": "2026-09-10T17:53:46.182Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"4191a7\",\"wall_time_seconds\":0.00000611,\"exit_code\":0,\"original_token_count\":109,\"output\":\"listens 1501697 users 8196 time 24788600 25393395\\neligible gold 53536 4042\\nlikes 37795 users 4114 time 24788815 25393390\\neligible gold 4544 1431\\ndislikes 3563 users 955 time 24789920 25393280\\neligible gold 245 158\\nstate eligible Series([], Name: count, dtype: int64)\\ntrain listens users 9238 catalogevents 19959853 eligible 0 goldseen 0 goldtotal 35597\\ntrain likes users 8144 catalogevents 472889 eligible 0 goldseen 0 goldtotal 4417\\n\"}\n{\"chunk_id\":\"210138\",\"wall_time_seconds\":0.0000046,\"exit_code\":0,\"original_token_count\":29,\"output\":\"8589934592\\n800000 100000\\nFilesystem Size Used Avail Use% Mounted on\\noverlay 96G 14G 83G 15% /\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:53:46.163Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1a0ce1cc-5fe1-4285-8298-ab7148b69bb2", "process_id": "83303", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; df -h /app" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.max; cat /sys/fs/cgroup/cpu.max; df -h /app" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "8589934592\n800000 100000\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\n", "stderr": "", "aggregated_output": "8589934592\n800000 100000\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4600 }, "formatted_output": "8589934592\n800000 100000\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:53:46.181Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2eea1c6f887d18c30e3d3e0e8380e", "usage": { "input_tokens": 25635, "cached_input_tokens": 22528, "cache_write_input_tokens": 0, "output_tokens": 196, "reasoning_output_tokens": 119, "total_tokens": 25831 }, "turn_token_usage": { "input_tokens": 133730, "cached_input_tokens": 107264, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 576, "total_tokens": 135721 }, "thread_token_usage": { "input_tokens": 133730, "cached_input_tokens": 107264, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 576, "total_tokens": 135721 } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-10T17:54:09.194Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:54:09.194Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T17:54:11.860Z", "text": "The eligibility checks exclude previously played tracks and active explicit preferences, so this is a new-track discovery task. I’ll use a denoising history encoder trained to predict held-out tracks, with a separate item network whose vectors can be computed offline. Training will use per-user event lists without constructing a global interaction matrix.\n" }, { "kind": "tool", "timestamp": "2026-09-10T17:54:28.422Z", "callId": "call_nNf9mMZkXkvrvEoWDQK4i5Ql", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/prepare.py <<'PY'\\n\\\"\\\"\\\"Prepare ragged per-user event lists; never builds a user-item matrix.\\\"\\\"\\\"\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, time\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution');CUTOFF=24786800\\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids']; idx=pd.Index(items)\\nfor stage in ['validation','test']:\\n out=O/f'{stage}_events.parquet'\\n if out.exists():continue\\n parts=[]\\n for split in (['train'] if stage=='validation' else ['train','val']):\\n for fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\\n f=b.to_pandas();i=idx.get_indexer(f.item_id);ok=i>=0\\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\\n f=f.loc[ok].copy();f['iid']=i[ok].astype('int32')\\n f['count']=np.ones(len(f),dtype='float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\\n f['organic']=((f.is_organic==1)&(f['played_ratio_pct']>=80)).astype('float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\\n f['likes']=np.ones(len(f),dtype='float32') if fam=='likes' else np.zeros(len(f),dtype='float32')\\n if fam not in ['listens','likes']:continue\\n cutoff=CUTOFF if stage=='validation' else 25393400\\n age=(cutoff-f.timestamp.astype('int64'))/86400\\n f['recent']=f['count']*np.exp(-age/30).astype('float32');f['org_recent']=f['organic']*np.exp(-age/30).astype('float32')\\n f=f.groupby(['uid','iid'],sort=False).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('timestamp','max')).reset_index()\\n parts.append(f)\\n print(stage,split,fam,'prepared',flush=True)\\n f=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=True).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('last','max')).reset_index()\\n f.to_parquet(out,index=False);print(stage,len(f),f.uid.nunique(),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": "2026-09-10T17:54:29.590Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"b3fdc6\",\"wall_time_seconds\":1.001153848,\"session_id\":25304,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:54:28.469Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2eeaa83f087d1bfe4199c11003cd3", "usage": { "input_tokens": 26106, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 1287, "reasoning_output_tokens": 516, "total_tokens": 27393 }, "turn_token_usage": { "input_tokens": 159836, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 3278, "reasoning_output_tokens": 1092, "total_tokens": 163114 }, "thread_token_usage": { "input_tokens": 159836, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 3278, "reasoning_output_tokens": 1092, "total_tokens": 163114 } } }, { "kind": "context", "timestamp": "2026-09-10T17:54:42.732Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-587d486a-fe2b-46b5-86f9-3b4226c3e8f7", "process_id": "25304", "command": [ "/bin/sh", "-lc", "cat > /app/solution/prepare.py <<'PY'\n\"\"\"Prepare ragged per-user event lists; never builds a user-item matrix.\"\"\"\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution');CUTOFF=24786800\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids']; idx=pd.Index(items)\nfor stage in ['validation','test']:\n out=O/f'{stage}_events.parquet'\n if out.exists():continue\n parts=[]\n for split in (['train'] if stage=='validation' else ['train','val']):\n for fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();i=idx.get_indexer(f.item_id);ok=i>=0\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\n f=f.loc[ok].copy();f['iid']=i[ok].astype('int32')\n f['count']=np.ones(len(f),dtype='float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\n f['organic']=((f.is_organic==1)&(f['played_ratio_pct']>=80)).astype('float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\n f['likes']=np.ones(len(f),dtype='float32') if fam=='likes' else np.zeros(len(f),dtype='float32')\n if fam not in ['listens','likes']:continue\n cutoff=CUTOFF if stage=='validation' else 25393400\n age=(cutoff-f.timestamp.astype('int64'))/86400\n f['recent']=f['count']*np.exp(-age/30).astype('float32');f['org_recent']=f['organic']*np.exp(-age/30).astype('float32')\n f=f.groupby(['uid','iid'],sort=False).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('timestamp','max')).reset_index()\n parts.append(f)\n print(stage,split,fam,'prepared',flush=True)\n f=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=True).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('last','max')).reset_index()\n f.to_parquet(out,index=False);print(stage,len(f),f.uid.nunique(),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'\n\"\"\"Prepare ragged per-user event lists; never builds a user-item matrix.\"\"\"\nimport pandas as pd, numpy as np, pyarrow.parquet as pq, time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution');CUTOFF=24786800\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids']; idx=pd.Index(items)\nfor stage in ['validation','test']:\n out=O/f'{stage}_events.parquet'\n if out.exists():continue\n parts=[]\n for split in (['train'] if stage=='validation' else ['train','val']):\n for fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();i=idx.get_indexer(f.item_id);ok=i>=0\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\n f=f.loc[ok].copy();f['iid']=i[ok].astype('int32')\n f['count']=np.ones(len(f),dtype='float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\n f['organic']=((f.is_organic==1)&(f['played_ratio_pct']>=80)).astype('float32') if fam=='listens' else np.zeros(len(f),dtype='float32')\n f['likes']=np.ones(len(f),dtype='float32') if fam=='likes' else np.zeros(len(f),dtype='float32')\n if fam not in ['listens','likes']:continue\n cutoff=CUTOFF if stage=='validation' else 25393400\n age=(cutoff-f.timestamp.astype('int64'))/86400\n f['recent']=f['count']*np.exp(-age/30).astype('float32');f['org_recent']=f['organic']*np.exp(-age/30).astype('float32')\n f=f.groupby(['uid','iid'],sort=False).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('timestamp','max')).reset_index()\n parts.append(f)\n print(stage,split,fam,'prepared',flush=True)\n f=pd.concat(parts,ignore_index=True).groupby(['uid','iid'],sort=True).agg(count=('count','sum'),organic=('organic','sum'),likes=('likes','sum'),recent=('recent','sum'),org_recent=('org_recent','sum'),last=('last','max')).reset_index()\n f.to_parquet(out,index=False);print(stage,len(f),f.uid.nunique(),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": 14, "nanos": 143884203 }, "formatted_output": "" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-10T17:54:56.163Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:54:56.163Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:56:05.979Z", "callId": "call_KWQYNy7sH4roroVZ5o6lyQfO", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\\n\\\"\\\"\\\"CPU neural retrieval with independent history and item towers.\\n\\nTraining reads ragged item/weight lists. Only minibatch scores are materialized.\\nThe --negatives option uses sampled softmax for large retrieval catalogs.\\n\\\"\\\"\\\"\\nimport argparse, json, time, sys, os\\nfrom pathlib import Path\\nimport numpy as np\\nimport pandas as pd\\nimport torch\\nfrom torch import nn\\nfrom torch.nn import functional as F\\nD=Path('/task/data'); O=Path('/app/solution'); N=30000\\n\\ndef seed(s):\\n np.random.seed(s);torch.manual_seed(s);torch.set_num_threads(8)\\n\\nclass UserTower(nn.Module):\\n def __init__(self,n_items,hidden,dim,dropout=.5,vae=False):\\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.project=nn.Linear(hidden,dim*(2 if vae else 1));self.dropout=dropout;self.vae=vae\\n nn.init.normal_(self.history.weight,std=.025)\\n def forward(self,ids,offsets,weights):\\n if self.training: weights=F.dropout(weights,p=self.dropout,training=True)\\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights));z=self.project(h)\\n if self.vae:\\n mu,lv=z.chunk(2,-1);lv=lv.clamp(-8,6); kl=.5*(mu.square()+lv.exp()-lv-1).sum(-1).mean()\\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\\n return z,kl\\n return torch.tanh(z),z.new_zeros(())\\n\\nclass ItemTower(nn.Module):\\n def __init__(self,n_items,dim):\\n super().__init__();self.identity=nn.Embedding(n_items,dim);self.project=nn.Linear(dim,dim,bias=False);self.bias=nn.Embedding(n_items,1)\\n nn.init.normal_(self.identity.weight,std=.05);nn.init.eye_(self.project.weight);nn.init.zeros_(self.bias.weight)\\n def forward(self,ids):\\n return torch.cat([self.project(torch.tanh(self.identity(ids))),self.bias(ids)],dim=-1)\\n\\nclass TwoTower(nn.Module):\\n def __init__(self,hidden=512,dim=256,dropout=.5,vae=False):\\n super().__init__();self.user_tower=UserTower(N,hidden,dim,dropout,vae);self.item_tower=ItemTower(N,dim)\\n def users(self,ids,offsets,weights):\\n z,kl=self.user_tower(ids,offsets,weights);return torch.cat([z,torch.ones_like(z[:,:1])],-1),kl\\n\\ndef load_data(stage,args):\\n f=pd.read_parquet(O/f'{stage}_events.parquet'); cutoff=24786800 if stage=='validation' else 25393400\\n age=(cutoff-f['last'].to_numpy())/86400\\n # Both play strength and explicit likes contribute to the history representation.\\n count=f['count'].to_numpy();organic=f['organic'].to_numpy();likes=f['likes'].to_numpy()\\n w=(np.maximum(count,1)**args.power)*(1+args.org*organic/np.maximum(count,1))+args.like*np.minimum(likes,1)\\n if args.decay>0:w*=np.exp(-age/args.decay)\\n w=w.astype('float32');groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\\n ids=f.iid.to_numpy(dtype='int64');\\n return uids,ptr,ids,w\\n\\ndef batch_data(rows,ptr,ids,w,device='cpu'):\\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\\n norm=np.sqrt(np.bincount(u,weights=bw*bw,minlength=len(rows)))\\n inp=bw/np.maximum(norm[u],1e-8);target=bw/np.bincount(u,weights=bw,minlength=len(rows))[u]\\n return tuple(torch.from_numpy(x) for x in [bi,offsets[:-1],inp.astype('float32'),u,target.astype('float32')])\\n\\ndef get_truth():\\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];ui=pd.Index(uids);ii=pd.Index(items)\\n gold={}\\n for fam,grade in [('listens',1),('likes',3)]:\\n f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\\n for u,i in zip(r,c): gold.setdefault(u,{})[i]=grade\\n discount=1/np.log2(np.arange(2,12));ideal=np.zeros(len(uids))\\n for u,g in gold.items():ideal[u]=np.dot(np.sort([2**v-1 for v in g.values()])[::-1][:10],discount[:min(10,len(g))])\\n return gold,ideal\\n\\ndef metrics(top,gold,ideal):\\n discount=1/np.log2(np.arange(2,12));s=0.\\n for u,g in gold.items():s+=sum((2**g.get(int(i),0)-1)*d for i,d in zip(top[u,:10],discount))/ideal[u]\\n return s/len(gold)\\n\\n@torch.no_grad()\\ndef rank(model,data,stage,output=None,adjust=0.,save_scores=None):\\n model.eval();uids,ptr,ids,w=data;z=np.load(D/f'{stage}_eligible_candidates.npz');rows=pd.Index(uids).get_indexer(z['uids']);items=z['item_ids'];emb=model.item_tower(torch.arange(N));alltop=[]; alls=[]\\n pop=torch.from_numpy(np.log1p(pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items].positive_listens.to_numpy()).astype('float32'))\\n for start in range(0,len(rows),128):\\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\\n if adjust:scores+=adjust*pop\\n if save_scores is not None:alls.append(scores.numpy().astype('float16'))\\n eligible=torch.from_numpy(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));scores.masked_fill_(~eligible,-1e9)\\n alltop.append(torch.topk(scores,100,dim=1).indices.numpy())\\n top=np.concatenate(alltop)\\n if save_scores is not None:np.save(save_scores,np.concatenate(alls))\\n if output:\\n f=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(rows))});tmp=str(output)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,output)\\n return top\\n\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='dae');p.add_argument('--epochs',type=int,default=50);p.add_argument('--hidden',type=int,default=512);p.add_argument('--dim',type=int,default=256);p.add_argument('--batch',type=int,default=128);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--lr',type=float,default=.001);p.add_argument('--wd',type=float,default=.01);p.add_argument('--power',type=float,default=.3);p.add_argument('--like',type=float,default=2.);p.add_argument('--org',type=float,default=1.);p.add_argument('--decay',type=float,default=180);p.add_argument('--vae',action='store_true');p.add_argument('--kl',type=float,default=.0005);p.add_argument('--seed',type=int,default=42);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--negatives',type=int,default=0);p.add_argument('--load');p.add_argument('--infer',action='store_true');p.add_argument('--adjust',type=float,default=0.);p.add_argument('--save-scores',action='store_true');a=p.parse_args();seed(a.seed)\\n data=load_data(a.stage,a);uids,ptr,ids,w=data;model=TwoTower(a.hidden,a.dim,a.dropout,a.vae)\\n if a.load:model.load_state_dict(torch.load(a.load,weights_only=False)['model'])\\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=a.wd);gold,ideal=get_truth();best=-1;start=time.time();path=O/(a.name+'.pt')\\n if a.infer:\\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.adjust,O/(a.name+'_scores.npy') if a.save_scores else None)\\n if a.stage=='validation':print('score',metrics(top,gold,ideal),flush=True)\\n return\\n allitems=torch.arange(N)\\n for epoch in range(1,a.epochs+1):\\n model.train();order=np.random.permutation(len(uids));losses=[]\\n for st in range(0,len(order),a.batch):\\n rows=order[st:st+a.batch];bi,off,bw,br,tw=batch_data(rows,ptr,ids,w);uv,kl=model.users(bi,off,bw)\\n if a.negatives and a.negativesbest:\\n best=score;torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch,'score':score},path);rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\\n else:\\n torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch},path);rank(model,data,a.stage,O/'submission.parquet')\\n print('BEST',best,flush=True)\\nif __name__=='__main__':main()\\nPY\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-10T17:56:06.099Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"cc643c\",\"wall_time_seconds\":0.00000318,\"exit_code\":0,\"original_token_count\":195,\"output\":\"test train likes prepared\\ntest train dislikes prepared\\ntest train unlikes prepared\\ntest train undislikes prepared\\ntest val listens prepared\\ntest val likes prepared\\ntest val dislikes prepared\\ntest val unlikes prepared\\ntest val undislikes prepared\\ntest 5850135 9924\\ntotal 165M\\n-rw-r--r-- 1 agent agent 1.8K Sep 10 17:53 inspect_data.py\\n-rw-r--r-- 1 agent agent 488 Sep 10 17:54 prepare.log\\n-rw-r--r-- 1 agent agent 2.0K Sep 10 17:54 prepare.py\\n-rw-r--r-- 1 agent agent 84M Sep 10 17:54 test_events.parquet\\n-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:06.021Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5cdf0e79-0007-47bc-8105-ab483d395470", "process_id": "23589", "command": [ "/bin/sh", "-lc", "tail /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural retrieval with independent history and item towers.\n\nTraining reads ragged item/weight lists. Only minibatch scores are materialized.\nThe --negatives option uses sampled softmax for large retrieval catalogs.\n\"\"\"\nimport argparse, json, time, sys, os\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nD=Path('/task/data'); O=Path('/app/solution'); N=30000\n\ndef seed(s):\n np.random.seed(s);torch.manual_seed(s);torch.set_num_threads(8)\n\nclass UserTower(nn.Module):\n def __init__(self,n_items,hidden,dim,dropout=.5,vae=False):\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.project=nn.Linear(hidden,dim*(2 if vae else 1));self.dropout=dropout;self.vae=vae\n nn.init.normal_(self.history.weight,std=.025)\n def forward(self,ids,offsets,weights):\n if self.training: weights=F.dropout(weights,p=self.dropout,training=True)\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights));z=self.project(h)\n if self.vae:\n mu,lv=z.chunk(2,-1);lv=lv.clamp(-8,6); kl=.5*(mu.square()+lv.exp()-lv-1).sum(-1).mean()\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\n return z,kl\n return torch.tanh(z),z.new_zeros(())\n\nclass ItemTower(nn.Module):\n def __init__(self,n_items,dim):\n super().__init__();self.identity=nn.Embedding(n_items,dim);self.project=nn.Linear(dim,dim,bias=False);self.bias=nn.Embedding(n_items,1)\n nn.init.normal_(self.identity.weight,std=.05);nn.init.eye_(self.project.weight);nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n return torch.cat([self.project(torch.tanh(self.identity(ids))),self.bias(ids)],dim=-1)\n\nclass TwoTower(nn.Module):\n def __init__(self,hidden=512,dim=256,dropout=.5,vae=False):\n super().__init__();self.user_tower=UserTower(N,hidden,dim,dropout,vae);self.item_tower=ItemTower(N,dim)\n def users(self,ids,offsets,weights):\n z,kl=self.user_tower(ids,offsets,weights);return torch.cat([z,torch.ones_like(z[:,:1])],-1),kl\n\ndef load_data(stage,args):\n f=pd.read_parquet(O/f'{stage}_events.parquet'); cutoff=24786800 if stage=='validation' else 25393400\n age=(cutoff-f['last'].to_numpy())/86400\n # Both play strength and explicit likes contribute to the history representation.\n count=f['count'].to_numpy();organic=f['organic'].to_numpy();likes=f['likes'].to_numpy()\n w=(np.maximum(count,1)**args.power)*(1+args.org*organic/np.maximum(count,1))+args.like*np.minimum(likes,1)\n if args.decay>0:w*=np.exp(-age/args.decay)\n w=w.astype('float32');groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\n ids=f.iid.to_numpy(dtype='int64');\n return uids,ptr,ids,w\n\ndef batch_data(rows,ptr,ids,w,device='cpu'):\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\n norm=np.sqrt(np.bincount(u,weights=bw*bw,minlength=len(rows)))\n inp=bw/np.maximum(norm[u],1e-8);target=bw/np.bincount(u,weights=bw,minlength=len(rows))[u]\n return tuple(torch.from_numpy(x) for x in [bi,offsets[:-1],inp.astype('float32'),u,target.astype('float32')])\n\ndef get_truth():\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];ui=pd.Index(uids);ii=pd.Index(items)\n gold={}\n for fam,grade in [('listens',1),('likes',3)]:\n f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\n for u,i in zip(r,c): gold.setdefault(u,{})[i]=grade\n discount=1/np.log2(np.arange(2,12));ideal=np.zeros(len(uids))\n for u,g in gold.items():ideal[u]=np.dot(np.sort([2**v-1 for v in g.values()])[::-1][:10],discount[:min(10,len(g))])\n return gold,ideal\n\ndef metrics(top,gold,ideal):\n discount=1/np.log2(np.arange(2,12));s=0.\n for u,g in gold.items():s+=sum((2**g.get(int(i),0)-1)*d for i,d in zip(top[u,:10],discount))/ideal[u]\n return s/len(gold)\n\n@torch.no_grad()\ndef rank(model,data,stage,output=None,adjust=0.,save_scores=None):\n model.eval();uids,ptr,ids,w=data;z=np.load(D/f'{stage}_eligible_candidates.npz');rows=pd.Index(uids).get_indexer(z['uids']);items=z['item_ids'];emb=model.item_tower(torch.arange(N));alltop=[]; alls=[]\n pop=torch.from_numpy(np.log1p(pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items].positive_listens.to_numpy()).astype('float32'))\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\n if adjust:scores+=adjust*pop\n if save_scores is not None:alls.append(scores.numpy().astype('float16'))\n eligible=torch.from_numpy(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));scores.masked_fill_(~eligible,-1e9)\n alltop.append(torch.topk(scores,100,dim=1).indices.numpy())\n top=np.concatenate(alltop)\n if save_scores is not None:np.save(save_scores,np.concatenate(alls))\n if output:\n f=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(rows))});tmp=str(output)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,output)\n return top\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='dae');p.add_argument('--epochs',type=int,default=50);p.add_argument('--hidden',type=int,default=512);p.add_argument('--dim',type=int,default=256);p.add_argument('--batch',type=int,default=128);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--lr',type=float,default=.001);p.add_argument('--wd',type=float,default=.01);p.add_argument('--power',type=float,default=.3);p.add_argument('--like',type=float,default=2.);p.add_argument('--org',type=float,default=1.);p.add_argument('--decay',type=float,default=180);p.add_argument('--vae',action='store_true');p.add_argument('--kl',type=float,default=.0005);p.add_argument('--seed',type=int,default=42);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--negatives',type=int,default=0);p.add_argument('--load');p.add_argument('--infer',action='store_true');p.add_argument('--adjust',type=float,default=0.);p.add_argument('--save-scores',action='store_true');a=p.parse_args();seed(a.seed)\n data=load_data(a.stage,a);uids,ptr,ids,w=data;model=TwoTower(a.hidden,a.dim,a.dropout,a.vae)\n if a.load:model.load_state_dict(torch.load(a.load,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=a.wd);gold,ideal=get_truth();best=-1;start=time.time();path=O/(a.name+'.pt')\n if a.infer:\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.adjust,O/(a.name+'_scores.npy') if a.save_scores else None)\n if a.stage=='validation':print('score',metrics(top,gold,ideal),flush=True)\n return\n allitems=torch.arange(N)\n for epoch in range(1,a.epochs+1):\n model.train();order=np.random.permutation(len(uids));losses=[]\n for st in range(0,len(order),a.batch):\n rows=order[st:st+a.batch];bi,off,bw,br,tw=batch_data(rows,ptr,ids,w);uv,kl=model.users(bi,off,bw)\n if a.negatives and a.negativesbest:\n best=score;torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch,'score':score},path);rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch},path);rank(model,data,a.stage,O/'submission.parquet')\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail /app/solution/prepare.log; ls -lh /app/solution; cat > /app/solution/model.py <<'PY'\n\"\"\"CPU neural retrieval with independent history and item towers.\n\nTraining reads ragged item/weight lists. Only minibatch scores are materialized.\nThe --negatives option uses sampled softmax for large retrieval catalogs.\n\"\"\"\nimport argparse, json, time, sys, os\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nD=Path('/task/data'); O=Path('/app/solution'); N=30000\n\ndef seed(s):\n np.random.seed(s);torch.manual_seed(s);torch.set_num_threads(8)\n\nclass UserTower(nn.Module):\n def __init__(self,n_items,hidden,dim,dropout=.5,vae=False):\n super().__init__();self.history=nn.EmbeddingBag(n_items,hidden,mode='sum');self.project=nn.Linear(hidden,dim*(2 if vae else 1));self.dropout=dropout;self.vae=vae\n nn.init.normal_(self.history.weight,std=.025)\n def forward(self,ids,offsets,weights):\n if self.training: weights=F.dropout(weights,p=self.dropout,training=True)\n h=torch.tanh(self.history(ids,offsets,per_sample_weights=weights));z=self.project(h)\n if self.vae:\n mu,lv=z.chunk(2,-1);lv=lv.clamp(-8,6); kl=.5*(mu.square()+lv.exp()-lv-1).sum(-1).mean()\n z=mu+torch.randn_like(mu)*torch.exp(.5*lv) if self.training else mu\n return z,kl\n return torch.tanh(z),z.new_zeros(())\n\nclass ItemTower(nn.Module):\n def __init__(self,n_items,dim):\n super().__init__();self.identity=nn.Embedding(n_items,dim);self.project=nn.Linear(dim,dim,bias=False);self.bias=nn.Embedding(n_items,1)\n nn.init.normal_(self.identity.weight,std=.05);nn.init.eye_(self.project.weight);nn.init.zeros_(self.bias.weight)\n def forward(self,ids):\n return torch.cat([self.project(torch.tanh(self.identity(ids))),self.bias(ids)],dim=-1)\n\nclass TwoTower(nn.Module):\n def __init__(self,hidden=512,dim=256,dropout=.5,vae=False):\n super().__init__();self.user_tower=UserTower(N,hidden,dim,dropout,vae);self.item_tower=ItemTower(N,dim)\n def users(self,ids,offsets,weights):\n z,kl=self.user_tower(ids,offsets,weights);return torch.cat([z,torch.ones_like(z[:,:1])],-1),kl\n\ndef load_data(stage,args):\n f=pd.read_parquet(O/f'{stage}_events.parquet'); cutoff=24786800 if stage=='validation' else 25393400\n age=(cutoff-f['last'].to_numpy())/86400\n # Both play strength and explicit likes contribute to the history representation.\n count=f['count'].to_numpy();organic=f['organic'].to_numpy();likes=f['likes'].to_numpy()\n w=(np.maximum(count,1)**args.power)*(1+args.org*organic/np.maximum(count,1))+args.like*np.minimum(likes,1)\n if args.decay>0:w*=np.exp(-age/args.decay)\n w=w.astype('float32');groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\n ids=f.iid.to_numpy(dtype='int64');\n return uids,ptr,ids,w\n\ndef batch_data(rows,ptr,ids,w,device='cpu'):\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\n norm=np.sqrt(np.bincount(u,weights=bw*bw,minlength=len(rows)))\n inp=bw/np.maximum(norm[u],1e-8);target=bw/np.bincount(u,weights=bw,minlength=len(rows))[u]\n return tuple(torch.from_numpy(x) for x in [bi,offsets[:-1],inp.astype('float32'),u,target.astype('float32')])\n\ndef get_truth():\n z=np.load(D/'validation_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];ui=pd.Index(uids);ii=pd.Index(items)\n gold={}\n for fam,grade in [('listens',1),('likes',3)]:\n f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\n for u,i in zip(r,c): gold.setdefault(u,{})[i]=grade\n discount=1/np.log2(np.arange(2,12));ideal=np.zeros(len(uids))\n for u,g in gold.items():ideal[u]=np.dot(np.sort([2**v-1 for v in g.values()])[::-1][:10],discount[:min(10,len(g))])\n return gold,ideal\n\ndef metrics(top,gold,ideal):\n discount=1/np.log2(np.arange(2,12));s=0.\n for u,g in gold.items():s+=sum((2**g.get(int(i),0)-1)*d for i,d in zip(top[u,:10],discount))/ideal[u]\n return s/len(gold)\n\n@torch.no_grad()\ndef rank(model,data,stage,output=None,adjust=0.,save_scores=None):\n model.eval();uids,ptr,ids,w=data;z=np.load(D/f'{stage}_eligible_candidates.npz');rows=pd.Index(uids).get_indexer(z['uids']);items=z['item_ids'];emb=model.item_tower(torch.arange(N));alltop=[]; alls=[]\n pop=torch.from_numpy(np.log1p(pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items].positive_listens.to_numpy()).astype('float32'))\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\n if adjust:scores+=adjust*pop\n if save_scores is not None:alls.append(scores.numpy().astype('float16'))\n eligible=torch.from_numpy(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));scores.masked_fill_(~eligible,-1e9)\n alltop.append(torch.topk(scores,100,dim=1).indices.numpy())\n top=np.concatenate(alltop)\n if save_scores is not None:np.save(save_scores,np.concatenate(alls))\n if output:\n f=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(rows))});tmp=str(output)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,output)\n return top\n\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--name',default='dae');p.add_argument('--epochs',type=int,default=50);p.add_argument('--hidden',type=int,default=512);p.add_argument('--dim',type=int,default=256);p.add_argument('--batch',type=int,default=128);p.add_argument('--dropout',type=float,default=.5);p.add_argument('--lr',type=float,default=.001);p.add_argument('--wd',type=float,default=.01);p.add_argument('--power',type=float,default=.3);p.add_argument('--like',type=float,default=2.);p.add_argument('--org',type=float,default=1.);p.add_argument('--decay',type=float,default=180);p.add_argument('--vae',action='store_true');p.add_argument('--kl',type=float,default=.0005);p.add_argument('--seed',type=int,default=42);p.add_argument('--eval-every',type=int,default=5);p.add_argument('--negatives',type=int,default=0);p.add_argument('--load');p.add_argument('--infer',action='store_true');p.add_argument('--adjust',type=float,default=0.);p.add_argument('--save-scores',action='store_true');a=p.parse_args();seed(a.seed)\n data=load_data(a.stage,a);uids,ptr,ids,w=data;model=TwoTower(a.hidden,a.dim,a.dropout,a.vae)\n if a.load:model.load_state_dict(torch.load(a.load,weights_only=False)['model'])\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=a.wd);gold,ideal=get_truth();best=-1;start=time.time();path=O/(a.name+'.pt')\n if a.infer:\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else 'validation.parquet'),a.adjust,O/(a.name+'_scores.npy') if a.save_scores else None)\n if a.stage=='validation':print('score',metrics(top,gold,ideal),flush=True)\n return\n allitems=torch.arange(N)\n for epoch in range(1,a.epochs+1):\n model.train();order=np.random.permutation(len(uids));losses=[]\n for st in range(0,len(order),a.batch):\n rows=order[st:st+a.batch];bi,off,bw,br,tw=batch_data(rows,ptr,ids,w);uv,kl=model.users(bi,off,bw)\n if a.negatives and a.negativesbest:\n best=score;torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch,'score':score},path);rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(a),'epoch':epoch},path);rank(model,data,a.stage,O/'submission.parquet')\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test train likes prepared\ntest train dislikes prepared\ntest train unlikes prepared\ntest train undislikes prepared\ntest val listens prepared\ntest val likes prepared\ntest val dislikes prepared\ntest val unlikes prepared\ntest val undislikes prepared\ntest 5850135 9924\ntotal 165M\n-rw-r--r-- 1 agent agent 1.8K Sep 10 17:53 inspect_data.py\n-rw-r--r-- 1 agent agent 488 Sep 10 17:54 prepare.log\n-rw-r--r-- 1 agent agent 2.0K Sep 10 17:54 prepare.py\n-rw-r--r-- 1 agent agent 84M Sep 10 17:54 test_events.parquet\n-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n", "stderr": "", "aggregated_output": "test train likes prepared\ntest train dislikes prepared\ntest train unlikes prepared\ntest train undislikes prepared\ntest val listens prepared\ntest val likes prepared\ntest val dislikes prepared\ntest val unlikes prepared\ntest val undislikes prepared\ntest 5850135 9924\ntotal 165M\n-rw-r--r-- 1 agent agent 1.8K Sep 10 17:53 inspect_data.py\n-rw-r--r-- 1 agent agent 488 Sep 10 17:54 prepare.log\n-rw-r--r-- 1 agent agent 2.0K Sep 10 17:54 prepare.py\n-rw-r--r-- 1 agent agent 84M Sep 10 17:54 test_events.parquet\n-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3180 }, "formatted_output": "test train likes prepared\ntest train dislikes prepared\ntest train unlikes prepared\ntest train undislikes prepared\ntest val listens prepared\ntest val likes prepared\ntest val dislikes prepared\ntest val unlikes prepared\ntest val undislikes prepared\ntest 5850135 9924\ntotal 165M\n-rw-r--r-- 1 agent agent 1.8K Sep 10 17:53 inspect_data.py\n-rw-r--r-- 1 agent agent 488 Sep 10 17:54 prepare.log\n-rw-r--r-- 1 agent agent 2.0K Sep 10 17:54 prepare.py\n-rw-r--r-- 1 agent agent 84M Sep 10 17:54 test_events.parquet\n-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:56:06.098Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2eed5cbd887d1b852335719675790", "usage": { "input_tokens": 27452, "cached_input_tokens": 24320, "cache_write_input_tokens": 0, "output_tokens": 3152, "reasoning_output_tokens": 516, "total_tokens": 30604 }, "turn_token_usage": { "input_tokens": 187288, "cached_input_tokens": 157056, "cache_write_input_tokens": 0, "output_tokens": 6430, "reasoning_output_tokens": 1608, "total_tokens": 193718 }, "thread_token_usage": { "input_tokens": 187288, "cached_input_tokens": 157056, "cache_write_input_tokens": 0, "output_tokens": 6430, "reasoning_output_tokens": 1608, "total_tokens": 193718 } } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-10T17:56:11.363Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:56:11.363Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:56:13.085Z", "callId": "call_duzgx6eqWhQmqZndIANpAOft", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1\",\"yield_time_ms\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T17:56:14.254Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"333477\",\"wall_time_seconds\":1.000638811,\"session_id\":58808,\"original_token_count\":0,\"output\":\"\"}\n{\"current_time\":\"2026-09-10 17:56:14 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:13.132Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef36944087d1a9e2c2dcaf1594b7", "usage": { "input_tokens": 30942, "cached_input_tokens": 27264, "cache_write_input_tokens": 0, "output_tokens": 124, "reasoning_output_tokens": 35, "total_tokens": 31066 }, "turn_token_usage": { "input_tokens": 218230, "cached_input_tokens": 184320, "cache_write_input_tokens": 0, "output_tokens": 6554, "reasoning_output_tokens": 1643, "total_tokens": 224784 }, "thread_token_usage": { "input_tokens": 218230, "cached_input_tokens": 184320, "cache_write_input_tokens": 0, "output_tokens": 6554, "reasoning_output_tokens": 1643, "total_tokens": 224784 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-10T17:56:20.496Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:56:20.496Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:56:20.942Z", "callId": "call_ffMpNJzNIZPdOZmEzQyN75Vd", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-10T17:56:21.108Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"4b1970\",\"wall_time_seconds\":0.00000563,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:20.996Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef3e982487d192acb670d31d77e7", "usage": { "input_tokens": 31140, "cached_input_tokens": 30720, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 59, "total_tokens": 31237 }, "turn_token_usage": { "input_tokens": 249370, "cached_input_tokens": 215040, "cache_write_input_tokens": 0, "output_tokens": 6651, "reasoning_output_tokens": 1702, "total_tokens": 256021 }, "thread_token_usage": { "input_tokens": 249370, "cached_input_tokens": 215040, "cache_write_input_tokens": 0, "output_tokens": 6651, "reasoning_output_tokens": 1702, "total_tokens": 256021 } } }, { "kind": "context", "timestamp": "2026-09-10T17:56:21.076Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6c6affe2-5d84-46c2-8750-85b593004a7b", "process_id": "81735", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 5630 }, "formatted_output": "" } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-10T17:56:33.319Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:56:33.319Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T17:56:34.287Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5dafa3d0-6855-4bbc-9215-95ede1b9cb03", "process_id": "58808", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 1, "duration": { "secs": 21, "nanos": 37793266 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T17:56:34.633Z", "callId": "call_0eVVw9KynAAY3OsDXYP655gw", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log; ps -eo pid,etime,%cpu,rss,args | rg 'model.py|prepare.py'\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-10T17:56:34.713Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"913d2e\",\"wall_time_seconds\":0.00000359,\"exit_code\":1,\"original_token_count\":165,\"output\":\" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\\\", line 506, in repeat\\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\\\", line 57, in _wrapfunc\\n return bound(*args, **kwds)\\n ^^^^^^^^^^^^^^^^^^^^\\nValueError: repeats may not contain negative values.\\n/bin/sh: 1: ps: not found\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:34.672Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d1d963f-25d1-4de1-a23c-ed1b39a512ec", "process_id": "25006", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log; ps -eo pid,etime,%cpu,rss,args | rg 'model.py|prepare.py'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log; ps -eo pid,etime,%cpu,rss,args | rg 'model.py|prepare.py'" } ], "source": "unified_exec_startup", "status": "failed", "stdout": " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n/bin/sh: 1: ps: not found\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3590 }, "formatted_output": " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n/bin/sh: 1: ps: not found\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:56:34.712Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef454f6087d1a5ef36c152706c2b", "usage": { "input_tokens": 31294, "cached_input_tokens": 30976, "cache_write_input_tokens": 0, "output_tokens": 289, "reasoning_output_tokens": 230, "total_tokens": 31583 }, "turn_token_usage": { "input_tokens": 280664, "cached_input_tokens": 246016, "cache_write_input_tokens": 0, "output_tokens": 6940, "reasoning_output_tokens": 1932, "total_tokens": 287604 }, "thread_token_usage": { "input_tokens": 280664, "cached_input_tokens": 246016, "cache_write_input_tokens": 0, "output_tokens": 6940, "reasoning_output_tokens": 1932, "total_tokens": 287604 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-10T17:56:41.617Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:56:41.617Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:56:47.680Z", "callId": "call_NH4GD0xON6LeKym02rnD24yR", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/dae.log\",\"max_output_tokens\":3000}));\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/model.py'\\ns=open(p).read().replace(\\\"groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\\\", \\\"groups=f.groupby('uid',sort=True).size();groups=groups.reindex(np.union1d(groups.index.to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()),fill_value=0);uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:56:48.880Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"49d96c\",\"wall_time_seconds\":0.00000675,\"exit_code\":0,\"original_token_count\":422,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.619984149932861, \\\"seconds\\\": 12.9}\\nTraceback (most recent call last):\\n File \\\"/app/solution/model.py\\\", line 121, in \\n if __name__=='__main__':main()\\n ^^^^^^\\n File \\\"/app/solution/model.py\\\", line 115, in main\\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\\n ^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\\\", line 120, in decorate_context\\n return func(*args, **kwargs)\\n ^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/model.py\\\", line 81, in rank\\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/app/solution/model.py\\\", line 56, in batch_data\\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\\\", line 506, in repeat\\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\\\", line 57, in _wrapfunc\\n return bound(*args, **kwds)\\n ^^^^^^^^^^^^^^^^^^^^\\nValueError: repeats may not contain negative values.\\n\"}\n{\"chunk_id\":\"b603ba\",\"wall_time_seconds\":1.001119019,\"session_id\":81291,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:47.717Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da92fe88-53cb-4713-bad4-e6a3305bc8a4", "process_id": "51343", "command": [ "/bin/sh", "-lc", "cat /app/solution/dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/dae.log", "name": "dae.log", "path": "/app/solution/dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.619984149932861, \"seconds\": 12.9}\nTraceback (most recent call last):\n File \"/app/solution/model.py\", line 121, in \n if __name__=='__main__':main()\n ^^^^^^\n File \"/app/solution/model.py\", line 115, in main\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 81, in rank\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 56, in batch_data\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.619984149932861, \"seconds\": 12.9}\nTraceback (most recent call last):\n File \"/app/solution/model.py\", line 121, in \n if __name__=='__main__':main()\n ^^^^^^\n File \"/app/solution/model.py\", line 115, in main\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 81, in rank\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 56, in batch_data\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6750 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.619984149932861, \"seconds\": 12.9}\nTraceback (most recent call last):\n File \"/app/solution/model.py\", line 121, in \n if __name__=='__main__':main()\n ^^^^^^\n File \"/app/solution/model.py\", line 115, in main\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 81, in rank\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);scores=uv@emb.T\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/model.py\", line 56, in batch_data\n lengths=ptr[rows+1]-ptr[rows];offsets=np.r_[0,np.cumsum(lengths)];ix=np.concatenate([np.arange(ptr[r],ptr[r+1]) for r in rows]); bi=ids[ix];bw=w[ix].copy();u=np.repeat(np.arange(len(rows)),lengths)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 506, in repeat\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 57, in _wrapfunc\n return bound(*args, **kwds)\n ^^^^^^^^^^^^^^^^^^^^\nValueError: repeats may not contain negative values.\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:56:47.728Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef5320b487d193bb0de1fe408d5f", "usage": { "input_tokens": 31782, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 327, "reasoning_output_tokens": 93, "total_tokens": 32109 }, "turn_token_usage": { "input_tokens": 312446, "cached_input_tokens": 277120, "cache_write_input_tokens": 0, "output_tokens": 7267, "reasoning_output_tokens": 2025, "total_tokens": 319713 }, "thread_token_usage": { "input_tokens": 312446, "cached_input_tokens": 277120, "cache_write_input_tokens": 0, "output_tokens": 7267, "reasoning_output_tokens": 2025, "total_tokens": 319713 } } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-09-10T17:56:54.155Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:56:54.155Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T17:56:56.220Z", "text": "The data contains about 5.7 million distinct training user–track pairs across roughly 10,000 users. The first training epoch takes about 13 seconds on CPU. I also found a few target users with no history inside the retrieval catalog; the user tower will handle those with an empty-history representation.\n" }, { "kind": "tool", "timestamp": "2026-09-10T17:56:57.330Z", "callId": "call_ZTtS890onD5fDo5xeRb9wqvI", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:56:57.421Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"9f2be0\",\"wall_time_seconds\":0.00000487,\"exit_code\":2,\"original_token_count\":17,\"output\":\"ls: cannot access '/app/solution/dae.pt': No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:56:57.378Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef61133487d19d4ddb3942ead815", "usage": { "input_tokens": 32664, "cached_input_tokens": 31616, "cache_write_input_tokens": 0, "output_tokens": 158, "reasoning_output_tokens": 40, "total_tokens": 32822 }, "turn_token_usage": { "input_tokens": 345110, "cached_input_tokens": 308736, "cache_write_input_tokens": 0, "output_tokens": 7425, "reasoning_output_tokens": 2065, "total_tokens": 352535 }, "thread_token_usage": { "input_tokens": 345110, "cached_input_tokens": 308736, "cache_write_input_tokens": 0, "output_tokens": 7425, "reasoning_output_tokens": 2065, "total_tokens": 352535 } } }, { "kind": "context", "timestamp": "2026-09-10T17:56:57.406Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c22ff71e-dded-49af-89c3-d149458b52cf", "process_id": "12155", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n", "stderr": "", "aggregated_output": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 4870 }, "formatted_output": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n" } } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-09-10T17:57:05.943Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:57:05.943Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:57:06.689Z", "callId": "call_1rQP4S4ifvOTTHovWRmyCMCR", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:57:06.837Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"06de29\",\"wall_time_seconds\":0.00000344,\"exit_code\":2,\"original_token_count\":17,\"output\":\"ls: cannot access '/app/solution/dae.pt': No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:57:06.750Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef69b39887d1816acebe0705e153", "usage": { "input_tokens": 32897, "cached_input_tokens": 32512, "cache_write_input_tokens": 0, "output_tokens": 205, "reasoning_output_tokens": 156, "total_tokens": 33102 }, "turn_token_usage": { "input_tokens": 378007, "cached_input_tokens": 341248, "cache_write_input_tokens": 0, "output_tokens": 7630, "reasoning_output_tokens": 2221, "total_tokens": 385637 }, "thread_token_usage": { "input_tokens": 378007, "cached_input_tokens": 341248, "cache_write_input_tokens": 0, "output_tokens": 7630, "reasoning_output_tokens": 2221, "total_tokens": 385637 } } }, { "kind": "context", "timestamp": "2026-09-10T17:57:06.781Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d29cde24-c5e2-4a4a-9ee9-e56b9ab96de1", "process_id": "92473", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; ls -lh /app/solution/dae.pt" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n", "stderr": "", "aggregated_output": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n", "exit_code": 2, "duration": { "secs": 0, "nanos": 3440 }, "formatted_output": "ls: cannot access '/app/solution/dae.pt': No such file or directory\n" } } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-09-10T17:57:09.877Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:57:09.877Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:57:11.161Z", "callId": "call_6uGe5a0EO9EpeZFLky3hzncO", "name": "exec", "input": { "code": "text(await tools.clock__curr_time({}));\ntext(await tools.write_stdin({session_id:81291,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:57:16.171Z", "content": "Script completed\nWall time 5.0 seconds\nOutput:\n\n{\"current_time\":\"2026-09-10 17:57:11 UTC\"}\n{\"chunk_id\":\"749dba\",\"wall_time_seconds\":5.000781412,\"session_id\":81291,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:57:11.241Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef7331a487d1b74b64f269ae6711", "usage": { "input_tokens": 33175, "cached_input_tokens": 25984, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 14, "total_tokens": 33240 }, "turn_token_usage": { "input_tokens": 411182, "cached_input_tokens": 367232, "cache_write_input_tokens": 0, "output_tokens": 7695, "reasoning_output_tokens": 2235, "total_tokens": 418877 }, "thread_token_usage": { "input_tokens": 411182, "cached_input_tokens": 367232, "cache_write_input_tokens": 0, "output_tokens": 7695, "reasoning_output_tokens": 2235, "total_tokens": 418877 } } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-09-10T17:57:30.149Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:57:30.149Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:57:30.693Z", "callId": "call_z1mUnhPthcFOadGBKW3bgv1H", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log; ls -lh /app/solution/dae.pt\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:57:30.826Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"14871a\",\"wall_time_seconds\":0.00000291,\"exit_code\":0,\"original_token_count\":37,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.59323298625457, \\\"seconds\\\": 13.0}\\nVALID 1 0.015790644661616712\\n-rw-r--r-- 1 agent agent 89M Sep 10 17:57 /app/solution/dae.pt\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:57:30.739Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef7c586c87d1b1f9b9aded90dad3", "usage": { "input_tokens": 33315, "cached_input_tokens": 32768, "cache_write_input_tokens": 0, "output_tokens": 334, "reasoning_output_tokens": 285, "total_tokens": 33649 }, "turn_token_usage": { "input_tokens": 444497, "cached_input_tokens": 400000, "cache_write_input_tokens": 0, "output_tokens": 8029, "reasoning_output_tokens": 2520, "total_tokens": 452526 }, "thread_token_usage": { "input_tokens": 444497, "cached_input_tokens": 400000, "cache_write_input_tokens": 0, "output_tokens": 8029, "reasoning_output_tokens": 2520, "total_tokens": 452526 } } }, { "kind": "context", "timestamp": "2026-09-10T17:57:30.785Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1dd83de2-2d97-40e8-b918-4c7147fd0475", "process_id": "39802", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log; ls -lh /app/solution/dae.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log; ls -lh /app/solution/dae.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n-rw-r--r-- 1 agent agent 89M Sep 10 17:57 /app/solution/dae.pt\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n-rw-r--r-- 1 agent agent 89M Sep 10 17:57 /app/solution/dae.pt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 2910 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n-rw-r--r-- 1 agent agent 89M Sep 10 17:57 /app/solution/dae.pt\n" } } }, { "kind": "tool", "timestamp": "2026-09-10T17:57:35.090Z", "callId": "call_W6JNgps5DeSFWDhN3kyzQzDY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --stage test --name initial --load /app/solution/dae.pt --infer > /app/solution/initial.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:57:36.319Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"c1f68a\",\"wall_time_seconds\":1.003114378,\"session_id\":1073,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:57:35.154Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef8b18b887d1abc0a3854b6cb286", "usage": { "input_tokens": 33772, "cached_input_tokens": 33152, "cache_write_input_tokens": 0, "output_tokens": 82, "reasoning_output_tokens": 0, "total_tokens": 33854 }, "turn_token_usage": { "input_tokens": 478269, "cached_input_tokens": 433152, "cache_write_input_tokens": 0, "output_tokens": 8111, "reasoning_output_tokens": 2520, "total_tokens": 486380 }, "thread_token_usage": { "input_tokens": 478269, "cached_input_tokens": 433152, "cache_write_input_tokens": 0, "output_tokens": 8111, "reasoning_output_tokens": 2520, "total_tokens": 486380 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-10T17:57:50.042Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:57:50.042Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:57:51.352Z", "callId": "call_ugth2SZc90EYJd1rJldxZUpe", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:1073,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:57:56.502Z", "content": "Script completed\nWall time 5.1 seconds\nOutput:\n\n{\"chunk_id\":\"938619\",\"wall_time_seconds\":5.000876104,\"session_id\":1073,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"04fe90\",\"wall_time_seconds\":0.00000361,\"exit_code\":0,\"original_token_count\":56,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.59323298625457, \\\"seconds\\\": 13.0}\\nVALID 1 0.015790644661616712\\n{\\\"epoch\\\": 2, \\\"loss\\\": 8.878038601997572, \\\"seconds\\\": 43.8}\\nls: cannot access '/app/solution/submission.parquet': No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:57:51.390Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2ef909b2c87d19c84dc873a69ecda", "usage": { "input_tokens": 33913, "cached_input_tokens": 33536, "cache_write_input_tokens": 0, "output_tokens": 350, "reasoning_output_tokens": 261, "total_tokens": 34263 }, "turn_token_usage": { "input_tokens": 512182, "cached_input_tokens": 466688, "cache_write_input_tokens": 0, "output_tokens": 8461, "reasoning_output_tokens": 2781, "total_tokens": 520643 }, "thread_token_usage": { "input_tokens": 512182, "cached_input_tokens": 466688, "cache_write_input_tokens": 0, "output_tokens": 8461, "reasoning_output_tokens": 2781, "total_tokens": 520643 } } }, { "kind": "context", "timestamp": "2026-09-10T17:57:56.488Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8c803a47-6f9f-4679-a602-82c1dc4d704c", "process_id": "91382", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3610 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\nls: cannot access '/app/solution/submission.parquet': No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:58:00.737Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31255812-0c1b-488b-ba29-f135ac8189b8", "process_id": "1073", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --stage test --name initial --load /app/solution/dae.pt --infer > /app/solution/initial.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --stage test --name initial --load /app/solution/dae.pt --infer > /app/solution/initial.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 424186952 }, "formatted_output": "" } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-10T17:58:15.077Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:58:15.077Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T17:58:17.018Z", "text": "The first checkpoint scores 0.01579 on public NDCG@10. I’m continuing training and generating an initial submission using test-cutoff histories and eligibility. Later checkpoints will be judged on validation rankings built from training history only.\n" }, { "kind": "tool", "timestamp": "2026-09-10T17:58:17.623Z", "callId": "call_rtSBZbnBJ86RSv2eKRTE04Vo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T17:58:17.789Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"545fb1\",\"wall_time_seconds\":0.00000473,\"exit_code\":0,\"original_token_count\":69,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.59323298625457, \\\"seconds\\\": 13.0}\\nVALID 1 0.015790644661616712\\n{\\\"epoch\\\": 2, \\\"loss\\\": 8.878038601997572, \\\"seconds\\\": 43.8}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.542151463337433, \\\"seconds\\\": 72.7}\\n-rw-r--r-- 1 agent agent 1.5M Sep 10 17:58 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-10 17:58:17 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:58:17.662Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2efa4e08487d1970e3d626e87920a", "usage": { "input_tokens": 34437, "cached_input_tokens": 33024, "cache_write_input_tokens": 0, "output_tokens": 538, "reasoning_output_tokens": 415, "total_tokens": 34975 }, "turn_token_usage": { "input_tokens": 546619, "cached_input_tokens": 499712, "cache_write_input_tokens": 0, "output_tokens": 8999, "reasoning_output_tokens": 3196, "total_tokens": 555618 }, "thread_token_usage": { "input_tokens": 546619, "cached_input_tokens": 499712, "cache_write_input_tokens": 0, "output_tokens": 8999, "reasoning_output_tokens": 3196, "total_tokens": 555618 } } }, { "kind": "context", "timestamp": "2026-09-10T17:58:17.727Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f67ec570-ae21-46a7-9b68-1261731d467d", "process_id": "53775", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log; ls -lh /app/solution/submission.parquet; cat /app/solution/initial.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n-rw-r--r-- 1 agent agent 1.5M Sep 10 17:58 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n-rw-r--r-- 1 agent agent 1.5M Sep 10 17:58 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4730 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n-rw-r--r-- 1 agent agent 1.5M Sep 10 17:58 /app/solution/submission.parquet\n" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-10T17:58:23.533Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:58:23.533Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:58:31.586Z", "callId": "call_DFj5Y53YDpJsORNasfyM4HGN", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/check_submission.py <<'PY'\\n\\\"\\\"\\\"Check all public structural rules against the final cutoff.\\\"\\\"\\\"\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data');p=Path(sys.argv[1]) if len(sys.argv)>1 else Path('/app/solution/submission.parquet')\\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid))\\nz=np.load(D/'test_eligible_candidates.npz');u=pd.Index(z['uids']).get_indexer(f.uid);i=pd.Index(z['item_ids']).get_indexer(f.item_id)\\nassert (u>=0).all() and (i>=0).all()\\nassert (((z['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\\nprint(f'Valid: {len(f)} rows, {f.uid.nunique()} users, 100 distinct eligible items per user.')\\nPY\\n/opt/task-venv/bin/python /app/solution/check_submission.py\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:58:34.882Z", "content": "Script completed\nWall time 3.2 seconds\nOutput:\n\n{\"chunk_id\":\"80e023\",\"wall_time_seconds\":3.001859936,\"exit_code\":0,\"original_token_count\":18,\"output\":\"Valid: 845600 rows, 8456 users, 100 distinct eligible items per user.\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:58:31.644Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2efba626087d196058665d3489f1a", "usage": { "input_tokens": 35173, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 359, "reasoning_output_tokens": 75, "total_tokens": 35532 }, "turn_token_usage": { "input_tokens": 581792, "cached_input_tokens": 533504, "cache_write_input_tokens": 0, "output_tokens": 9358, "reasoning_output_tokens": 3271, "total_tokens": 591150 }, "thread_token_usage": { "input_tokens": 581792, "cached_input_tokens": 533504, "cache_write_input_tokens": 0, "output_tokens": 9358, "reasoning_output_tokens": 3271, "total_tokens": 591150 } } }, { "kind": "context", "timestamp": "2026-09-10T17:58:34.865Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-53139f7f-708f-4050-9ce5-fb04e3996743", "process_id": "92299", "command": [ "/bin/sh", "-lc", "cat > /app/solution/check_submission.py <<'PY'\n\"\"\"Check all public structural rules against the final cutoff.\"\"\"\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');p=Path(sys.argv[1]) if len(sys.argv)>1 else Path('/app/solution/submission.parquet')\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid))\nz=np.load(D/'test_eligible_candidates.npz');u=pd.Index(z['uids']).get_indexer(f.uid);i=pd.Index(z['item_ids']).get_indexer(f.item_id)\nassert (u>=0).all() and (i>=0).all()\nassert (((z['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\nprint(f'Valid: {len(f)} rows, {f.uid.nunique()} users, 100 distinct eligible items per user.')\nPY\n/opt/task-venv/bin/python /app/solution/check_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/check_submission.py <<'PY'\n\"\"\"Check all public structural rules against the final cutoff.\"\"\"\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');p=Path(sys.argv[1]) if len(sys.argv)>1 else Path('/app/solution/submission.parquet')\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid))\nz=np.load(D/'test_eligible_candidates.npz');u=pd.Index(z['uids']).get_indexer(f.uid);i=pd.Index(z['item_ids']).get_indexer(f.item_id)\nassert (u>=0).all() and (i>=0).all()\nassert (((z['packed_eligible'][u,i//8]>>(i%8))&1)==1).all()\nprint(f'Valid: {len(f)} rows, {f.uid.nunique()} users, 100 distinct eligible items per user.')\nPY\n/opt/task-venv/bin/python /app/solution/check_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Valid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "stderr": "", "aggregated_output": "Valid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 2865954 }, "formatted_output": "Valid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n" } } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-10T17:58:56.966Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:58:56.966Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:58:57.728Z", "callId": "call_uawDUeZ7iXFoDRicUSmAYZfE", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:58:57.861Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"3aeae0\",\"wall_time_seconds\":0.00000477,\"exit_code\":0,\"original_token_count\":86,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.59323298625457, \\\"seconds\\\": 13.0}\\nVALID 1 0.015790644661616712\\n{\\\"epoch\\\": 2, \\\"loss\\\": 8.878038601997572, \\\"seconds\\\": 43.8}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.542151463337433, \\\"seconds\\\": 72.7}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.312362634218657, \\\"seconds\\\": 85.3}\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.184120569473658, \\\"seconds\\\": 100.4}\\nVALID 5 0.016856033153077772\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:58:57.793Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-30028547-a18d-4108-8297-72bbb1ccb100", "process_id": "75402", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4770 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:58:57.859Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2efcb1e4887d18c1e882462cefae4", "usage": { "input_tokens": 35608, "cached_input_tokens": 34304, "cache_write_input_tokens": 0, "output_tokens": 458, "reasoning_output_tokens": 420, "total_tokens": 36066 }, "turn_token_usage": { "input_tokens": 617400, "cached_input_tokens": 567808, "cache_write_input_tokens": 0, "output_tokens": 9816, "reasoning_output_tokens": 3691, "total_tokens": 627216 }, "thread_token_usage": { "input_tokens": 617400, "cached_input_tokens": 567808, "cache_write_input_tokens": 0, "output_tokens": 9816, "reasoning_output_tokens": 3691, "total_tokens": 627216 } } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-10T17:59:10.778Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:59:10.778Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T17:59:28.401Z", "callId": "call_4tmcb8u2gkWdyo7yrz256Ya9", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/tune.py <<'PY'\\n\\\"\\\"\\\"Validation-only search of history aggregation and item prior calibration.\\\"\\\"\\\"\\nfrom model import *\\nimport itertools\\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\\nseed(42);torch.set_num_threads(a.threads)\\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\\nwith torch.no_grad():\\n emb=model.item_tower(torch.arange(N))\\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\\n cfg.decay=decay;cfg.power=power;data=load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\\n for start in range(0,len(rows),128):\\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\\n for (pop,tr),top in zip(params,tops):\\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\\n if score>best:best=score;print('BEST',r,flush=True)\\n print('DONE',decay,power,flush=True)\\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:59:28.595Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"f4cb39\",\"wall_time_seconds\":0.000025091,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:59:28.452Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2efe2b10c87d1a6406067306dc8d9", "usage": { "input_tokens": 36283, "cached_input_tokens": 34944, "cache_write_input_tokens": 0, "output_tokens": 935, "reasoning_output_tokens": 217, "total_tokens": 37218 }, "turn_token_usage": { "input_tokens": 653683, "cached_input_tokens": 602752, "cache_write_input_tokens": 0, "output_tokens": 10751, "reasoning_output_tokens": 3908, "total_tokens": 664434 }, "thread_token_usage": { "input_tokens": 653683, "cached_input_tokens": 602752, "cache_write_input_tokens": 0, "output_tokens": 10751, "reasoning_output_tokens": 3908, "total_tokens": 664434 } } }, { "kind": "context", "timestamp": "2026-09-10T17:59:28.524Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9e8548e7-36fb-4494-96fb-0e6254a1a59e", "process_id": "48956", "command": [ "/bin/sh", "-lc", "cat > /app/solution/tune.py <<'PY'\n\"\"\"Validation-only search of history aggregation and item prior calibration.\"\"\"\nfrom model import *\nimport itertools\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\nseed(42);torch.set_num_threads(a.threads)\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\nwith torch.no_grad():\n emb=model.item_tower(torch.arange(N))\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\n cfg.decay=decay;cfg.power=power;data=load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\n for (pop,tr),top in zip(params,tops):\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\n if score>best:best=score;print('BEST',r,flush=True)\n print('DONE',decay,power,flush=True)\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/tune.py <<'PY'\n\"\"\"Validation-only search of history aggregation and item prior calibration.\"\"\"\nfrom model import *\nimport itertools\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\nseed(42);torch.set_num_threads(a.threads)\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\nwith torch.no_grad():\n emb=model.item_tower(torch.arange(N))\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\n cfg.decay=decay;cfg.power=power;data=load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\n for (pop,tr),top in zip(params,tops):\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\n if score>best:best=score;print('BEST',r,flush=True)\n print('DONE',decay,power,flush=True)\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 25091 }, "formatted_output": "" } } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-10T17:59:30.856Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T17:59:30.856Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T17:59:33.262Z", "text": "The initial submission passes all structural checks: 845,600 rows, 8,456 users, and 100 distinct eligible tracks per user. Validation quality has risen to 0.01686 after five epochs. I’m also preparing comparisons of history recency and item popularity adjustments, all expressible within the towers’ dot-product score.\n" }, { "kind": "tool", "timestamp": "2026-09-10T17:59:34.393Z", "callId": "call_X6i3ePOSJtLWLMoTw0etyzZe", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -10 /app/solution/dae.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T17:59:34.535Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"70cf40\",\"wall_time_seconds\":0.000004471,\"exit_code\":0,\"original_token_count\":130,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.59323298625457, \\\"seconds\\\": 13.0}\\nVALID 1 0.015790644661616712\\n{\\\"epoch\\\": 2, \\\"loss\\\": 8.878038601997572, \\\"seconds\\\": 43.8}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.542151463337433, \\\"seconds\\\": 72.7}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.312362634218657, \\\"seconds\\\": 85.3}\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.184120569473658, \\\"seconds\\\": 100.4}\\nVALID 5 0.016856033153077772\\n{\\\"epoch\\\": 6, \\\"loss\\\": 8.091072278145033, \\\"seconds\\\": 128.7}\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.005893291571201, \\\"seconds\\\": 141.3}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 7.917067503317808, \\\"seconds\\\": 153.7}\\n\"}\n{\"current_time\":\"2026-09-10 17:59:34 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:59:34.474Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb1b1bad-c31c-4b03-83e3-2291f048b21e", "process_id": "69849", "command": [ "/bin/sh", "-lc", "tail -10 /app/solution/dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -10 /app/solution/dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4471 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.59323298625457, \"seconds\": 13.0}\nVALID 1 0.015790644661616712\n{\"epoch\": 2, \"loss\": 8.878038601997572, \"seconds\": 43.8}\n{\"epoch\": 3, \"loss\": 8.542151463337433, \"seconds\": 72.7}\n{\"epoch\": 4, \"loss\": 8.312362634218657, \"seconds\": 85.3}\n{\"epoch\": 5, \"loss\": 8.184120569473658, \"seconds\": 100.4}\nVALID 5 0.016856033153077772\n{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T17:59:34.509Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", 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"exec", "input": { "code": "text(await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/dae_validation.parquet > /app/solution/public_initial.json\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-10T17:59:41.197Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d340e7\",\"wall_time_seconds\":1.003768276,\"session_id\":79567,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T17:59:40.125Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f006c16c87d19483022e8d94d7d3", "usage": { "input_tokens": 37724, "cached_input_tokens": 37120, "cache_write_input_tokens": 0, "output_tokens": 108, 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/app/solution/public_initial.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 5, "nanos": 96362129 }, "formatted_output": "" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-10T18:00:05.758Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:00:05.758Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:00:06.466Z", "callId": "call_KKrHOPfMz204J0urslGdOER2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; cat /app/solution/public_initial.json\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:00:06.520Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"ed2954\",\"wall_time_seconds\":0.00000438,\"exit_code\":0,\"original_token_count\":113,\"output\":\"{\\\"epoch\\\": 6, \\\"loss\\\": 8.091072278145033, \\\"seconds\\\": 128.7}\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.005893291571201, \\\"seconds\\\": 141.3}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 7.917067503317808, \\\"seconds\\\": 153.7}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 7.854101639527541, \\\"seconds\\\": 169.3}\\n{\\\"epoch\\\": 10, \\\"loss\\\": 7.794070940751296, \\\"seconds\\\": 183.0}\\nVALID 10 0.01776232071124077\\n{\\n \\\"model\\\": \\\"/app/solution/dae_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.016856,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:00:06.503Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e45ee600-b33b-4bc4-a38f-61c4123b0653", "process_id": "37193", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; cat /app/solution/public_initial.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; cat /app/solution/public_initial.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\n \"model\": \"/app/solution/dae_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016856,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\n \"model\": \"/app/solution/dae_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016856,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4380 }, "formatted_output": "{\"epoch\": 6, \"loss\": 8.091072278145033, \"seconds\": 128.7}\n{\"epoch\": 7, \"loss\": 8.005893291571201, \"seconds\": 141.3}\n{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\n \"model\": \"/app/solution/dae_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016856,\n \"quality_users\": 4263\n }\n}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:00:06.519Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f00d61e887d187bed8c27cae695f", "usage": { "input_tokens": 37890, "cached_input_tokens": 37504, "cache_write_input_tokens": 0, "output_tokens": 563, "reasoning_output_tokens": 516, "total_tokens": 38453 }, "turn_token_usage": { "input_tokens": 766572, "cached_input_tokens": 712832, "cache_write_input_tokens": 0, "output_tokens": 11556, "reasoning_output_tokens": 4485, "total_tokens": 778128 }, "thread_token_usage": { "input_tokens": 766572, "cached_input_tokens": 712832, "cache_write_input_tokens": 0, "output_tokens": 11556, "reasoning_output_tokens": 4485, "total_tokens": 778128 } } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-10T18:00:31.826Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:00:31.826Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:00:57.020Z", "callId": "call_WXtFixVUsYlw1HHyPlYQ5vF8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/temporal_prepare.py <<'PY'\\n\\\"\\\"\\\"Past-only rolling weekly training tasks with novel organic outcomes.\\\"\\\"\\\"\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\\nD=Path('/task/data');O=Path('/app/solution');idx=pd.Index(np.load(D/'validation_eligible_candidates.npz')['item_ids']);start=time.time()\\np=O/'raw_positive.parquet'\\nif not p.exists():\\n parts=[]\\n for split in ['train','val']:\\n for fam in ['listens','likes']:\\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\\n f=b.to_pandas();ii=idx.get_indexer(f.item_id);ok=ii>=0\\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\\n f=f.loc[ok].copy();f['iid']=ii[ok].astype('int32');f['like']=np.uint8(fam=='likes');f['quality']=((f.is_organic==1)&(f.played_ratio_pct>=80)).astype('uint8') if fam=='listens' else (7*(f.is_organic==1)).astype('uint8');f['organic']=f.is_organic.astype('uint8');parts.append(f[['uid','iid','timestamp','like','quality','organic']])\\n print(split,fam,time.time()-start,flush=True)\\n f=pd.concat(parts,ignore_index=True).sort_values(['uid','timestamp']);f.to_parquet(p,index=False)\\nelse:f=pd.read_parquet(p)\\nfor stage,cutoff in [('validation',24786800),('test',25393400)]:\\n out=O/f'{stage}_temporal.npz'\\n if out.exists():continue\\n hids=[];hweights=[];tids=[];tweights=[];hp=[0];tp=[0];ru=[];rt=[]\\n cuts=np.arange(cutoff-12*604800,cutoff,604800)\\n for uid,g in f.groupby('uid',sort=False):\\n times=g.timestamp.to_numpy();items=g.iid.to_numpy();likes=g.like.to_numpy();quality=g.quality.to_numpy();organic=g.organic.to_numpy()\\n for cut in cuts:\\n end=cut+604800;lo=np.searchsorted(times,cut);hi=np.searchsorted(times,end)\\n if lo<20 or hi==lo:continue\\n hs=items[:lo];unique,inv=np.unique(hs,return_inverse=True)\\n ts=items[lo:hi];q=quality[lo:hi];ok=(q>0)&(~np.isin(ts,unique))\\n if not ok.any():continue\\n ti,iv=np.unique(ts[ok],return_inverse=True);tw=np.zeros(len(ti),dtype='float32');np.maximum.at(tw,iv,q[ok])\\n # Log-compressed positive counts plus likes, with a 90-day event decay.\\n age=(cut-times[:lo].astype('int64'))/86400;dec=np.exp(-age/90)\\n cnt=np.bincount(inv,weights=(1-likes[:lo])*dec,minlength=len(unique));org=np.bincount(inv,weights=(1-likes[:lo])*organic[:lo]*dec,minlength=len(unique));lk=np.bincount(inv,weights=likes[:lo]*dec,minlength=len(unique))\\n hw=((np.maximum(cnt,1e-5)**.3)*(1+org/np.maximum(cnt,1e-5))+2*np.minimum(lk,1)).astype('float32')\\n hids.append(unique);hweights.append(hw);tids.append(ti);tweights.append(tw);hp.append(hp[-1]+len(unique));tp.append(tp[-1]+len(ti));ru.append(uid);rt.append(cut)\\n if len(ru) and len(ru)%10000<12:print(stage,'examples',len(ru),'seconds',time.time()-start,flush=True)\\n np.savez(out,hids=np.concatenate(hids),hweights=np.concatenate(hweights),hptr=np.array(hp),tids=np.concatenate(tids),tweights=np.concatenate(tweights),tptr=np.array(tp),uids=np.array(ru),cutoffs=np.array(rt));print(stage,'saved',len(ru),time.time()-start,flush=True)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py > /app/solution/temporal_prepare.log 2>&1\",\"yield_time_ms\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:00:58.274Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"ea5a70\",\"wall_time_seconds\":1.000763914,\"session_id\":47830,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:00:57.066Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f026ec4087d1b8f33dbca77febb9", "usage": { "input_tokens": 38709, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 1556, "reasoning_output_tokens": 516, "total_tokens": 40265 }, "turn_token_usage": { "input_tokens": 805281, "cached_input_tokens": 750592, "cache_write_input_tokens": 0, "output_tokens": 13112, "reasoning_output_tokens": 5001, "total_tokens": 818393 }, "thread_token_usage": { "input_tokens": 805281, "cached_input_tokens": 750592, "cache_write_input_tokens": 0, "output_tokens": 13112, "reasoning_output_tokens": 5001, "total_tokens": 818393 } } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-10T18:01:18.782Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:01:18.782Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:01:20.885Z", "text": "The baseline’s gains are modest so far. I’m preparing a second training objective using past weekly cutoffs: the model will see only earlier history and predict new tracks with strong organic plays or organic likes in the following week. This more closely matches the discovery objective; validation outcomes will remain separate from validation-stage training.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:01:21.631Z", "callId": "call_Iyd3N1iAuC7FAvWmexKHiokH", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/dae.log; tail -5 /app/solution/temporal_prepare.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:01:21.789Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"48f8e9\",\"wall_time_seconds\":0.00000194,\"exit_code\":0,\"original_token_count\":174,\"output\":\"{\\\"epoch\\\": 8, \\\"loss\\\": 7.917067503317808, \\\"seconds\\\": 153.7}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 7.854101639527541, \\\"seconds\\\": 169.3}\\n{\\\"epoch\\\": 10, \\\"loss\\\": 7.794070940751296, \\\"seconds\\\": 183.0}\\nVALID 10 0.01776232071124077\\n{\\\"epoch\\\": 11, \\\"loss\\\": 7.73982126896198, \\\"seconds\\\": 210.5}\\n{\\\"epoch\\\": 12, \\\"loss\\\": 7.681554256341396, \\\"seconds\\\": 222.8}\\n{\\\"epoch\\\": 13, \\\"loss\\\": 7.638425924839118, \\\"seconds\\\": 235.5}\\n{\\\"epoch\\\": 14, \\\"loss\\\": 7.595434989684668, \\\"seconds\\\": 257.1}\\nvalidation examples 11 seconds 12.280136585235596\\nvalidation examples 11 seconds 12.280382633209229\\nvalidation examples 11 seconds 12.281773090362549\\nvalidation examples 10004 seconds 18.198708534240723\\nvalidation examples 10008 seconds 18.20182466506958\\n\"}\n{\"current_time\":\"2026-09-10 18:01:21 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:01:21.695Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f05a83fc87d1b05185b72cb05ce2", "usage": { "input_tokens": 40323, "cached_input_tokens": 38528, "cache_write_input_tokens": 0, "output_tokens": 610, "reasoning_output_tokens": 480, "total_tokens": 40933 }, "turn_token_usage": { "input_tokens": 845604, "cached_input_tokens": 789120, "cache_write_input_tokens": 0, "output_tokens": 13722, "reasoning_output_tokens": 5481, "total_tokens": 859326 }, "thread_token_usage": { "input_tokens": 845604, "cached_input_tokens": 789120, "cache_write_input_tokens": 0, "output_tokens": 13722, "reasoning_output_tokens": 5481, "total_tokens": 859326 } } }, { "kind": "context", "timestamp": "2026-09-10T18:01:21.728Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-223aafcd-7592-4321-917a-f1ceaa15fef5", "process_id": "4755", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/dae.log; tail -5 /app/solution/temporal_prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/dae.log; tail -5 /app/solution/temporal_prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\"epoch\": 11, \"loss\": 7.73982126896198, \"seconds\": 210.5}\n{\"epoch\": 12, \"loss\": 7.681554256341396, \"seconds\": 222.8}\n{\"epoch\": 13, \"loss\": 7.638425924839118, \"seconds\": 235.5}\n{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\nvalidation examples 11 seconds 12.280136585235596\nvalidation examples 11 seconds 12.280382633209229\nvalidation examples 11 seconds 12.281773090362549\nvalidation examples 10004 seconds 18.198708534240723\nvalidation examples 10008 seconds 18.20182466506958\n", "stderr": "", "aggregated_output": "{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\"epoch\": 11, \"loss\": 7.73982126896198, \"seconds\": 210.5}\n{\"epoch\": 12, \"loss\": 7.681554256341396, \"seconds\": 222.8}\n{\"epoch\": 13, \"loss\": 7.638425924839118, \"seconds\": 235.5}\n{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\nvalidation examples 11 seconds 12.280136585235596\nvalidation examples 11 seconds 12.280382633209229\nvalidation examples 11 seconds 12.281773090362549\nvalidation examples 10004 seconds 18.198708534240723\nvalidation examples 10008 seconds 18.20182466506958\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1940 }, "formatted_output": "{\"epoch\": 8, \"loss\": 7.917067503317808, \"seconds\": 153.7}\n{\"epoch\": 9, \"loss\": 7.854101639527541, \"seconds\": 169.3}\n{\"epoch\": 10, \"loss\": 7.794070940751296, \"seconds\": 183.0}\nVALID 10 0.01776232071124077\n{\"epoch\": 11, \"loss\": 7.73982126896198, \"seconds\": 210.5}\n{\"epoch\": 12, \"loss\": 7.681554256341396, \"seconds\": 222.8}\n{\"epoch\": 13, \"loss\": 7.638425924839118, \"seconds\": 235.5}\n{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\nvalidation examples 11 seconds 12.280136585235596\nvalidation examples 11 seconds 12.280382633209229\nvalidation examples 11 seconds 12.281773090362549\nvalidation examples 10004 seconds 18.198708534240723\nvalidation examples 10008 seconds 18.20182466506958\n" } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-10T18:01:38.654Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:01:38.654Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T18:02:10.284Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1cb8c16e-6c89-45a5-8e71-f75dc779d145", "process_id": "47830", "command": [ "/bin/sh", "-lc", "cat > /app/solution/temporal_prepare.py <<'PY'\n\"\"\"Past-only rolling weekly training tasks with novel organic outcomes.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nD=Path('/task/data');O=Path('/app/solution');idx=pd.Index(np.load(D/'validation_eligible_candidates.npz')['item_ids']);start=time.time()\np=O/'raw_positive.parquet'\nif not p.exists():\n parts=[]\n for split in ['train','val']:\n for fam in ['listens','likes']:\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();ii=idx.get_indexer(f.item_id);ok=ii>=0\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\n f=f.loc[ok].copy();f['iid']=ii[ok].astype('int32');f['like']=np.uint8(fam=='likes');f['quality']=((f.is_organic==1)&(f.played_ratio_pct>=80)).astype('uint8') if fam=='listens' else (7*(f.is_organic==1)).astype('uint8');f['organic']=f.is_organic.astype('uint8');parts.append(f[['uid','iid','timestamp','like','quality','organic']])\n print(split,fam,time.time()-start,flush=True)\n f=pd.concat(parts,ignore_index=True).sort_values(['uid','timestamp']);f.to_parquet(p,index=False)\nelse:f=pd.read_parquet(p)\nfor stage,cutoff in [('validation',24786800),('test',25393400)]:\n out=O/f'{stage}_temporal.npz'\n if out.exists():continue\n hids=[];hweights=[];tids=[];tweights=[];hp=[0];tp=[0];ru=[];rt=[]\n cuts=np.arange(cutoff-12*604800,cutoff,604800)\n for uid,g in f.groupby('uid',sort=False):\n times=g.timestamp.to_numpy();items=g.iid.to_numpy();likes=g.like.to_numpy();quality=g.quality.to_numpy();organic=g.organic.to_numpy()\n for cut in cuts:\n end=cut+604800;lo=np.searchsorted(times,cut);hi=np.searchsorted(times,end)\n if lo<20 or hi==lo:continue\n hs=items[:lo];unique,inv=np.unique(hs,return_inverse=True)\n ts=items[lo:hi];q=quality[lo:hi];ok=(q>0)&(~np.isin(ts,unique))\n if not ok.any():continue\n ti,iv=np.unique(ts[ok],return_inverse=True);tw=np.zeros(len(ti),dtype='float32');np.maximum.at(tw,iv,q[ok])\n # Log-compressed positive counts plus likes, with a 90-day event decay.\n age=(cut-times[:lo].astype('int64'))/86400;dec=np.exp(-age/90)\n cnt=np.bincount(inv,weights=(1-likes[:lo])*dec,minlength=len(unique));org=np.bincount(inv,weights=(1-likes[:lo])*organic[:lo]*dec,minlength=len(unique));lk=np.bincount(inv,weights=likes[:lo]*dec,minlength=len(unique))\n hw=((np.maximum(cnt,1e-5)**.3)*(1+org/np.maximum(cnt,1e-5))+2*np.minimum(lk,1)).astype('float32')\n hids.append(unique);hweights.append(hw);tids.append(ti);tweights.append(tw);hp.append(hp[-1]+len(unique));tp.append(tp[-1]+len(ti));ru.append(uid);rt.append(cut)\n if len(ru) and len(ru)%10000<12:print(stage,'examples',len(ru),'seconds',time.time()-start,flush=True)\n np.savez(out,hids=np.concatenate(hids),hweights=np.concatenate(hweights),hptr=np.array(hp),tids=np.concatenate(tids),tweights=np.concatenate(tweights),tptr=np.array(tp),uids=np.array(ru),cutoffs=np.array(rt));print(stage,'saved',len(ru),time.time()-start,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py > /app/solution/temporal_prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/temporal_prepare.py <<'PY'\n\"\"\"Past-only rolling weekly training tasks with novel organic outcomes.\"\"\"\nfrom pathlib import Path\nimport numpy as np,pandas as pd,pyarrow.parquet as pq,time\nD=Path('/task/data');O=Path('/app/solution');idx=pd.Index(np.load(D/'validation_eligible_candidates.npz')['item_ids']);start=time.time()\np=O/'raw_positive.parquet'\nif not p.exists():\n parts=[]\n for split in ['train','val']:\n for fam in ['listens','likes']:\n for b in pq.ParquetFile(D/f'{split}_{fam}.parquet').iter_batches(batch_size=1000000):\n f=b.to_pandas();ii=idx.get_indexer(f.item_id);ok=ii>=0\n if fam=='listens':ok &= f.played_ratio_pct.to_numpy()>=50\n f=f.loc[ok].copy();f['iid']=ii[ok].astype('int32');f['like']=np.uint8(fam=='likes');f['quality']=((f.is_organic==1)&(f.played_ratio_pct>=80)).astype('uint8') if fam=='listens' else (7*(f.is_organic==1)).astype('uint8');f['organic']=f.is_organic.astype('uint8');parts.append(f[['uid','iid','timestamp','like','quality','organic']])\n print(split,fam,time.time()-start,flush=True)\n f=pd.concat(parts,ignore_index=True).sort_values(['uid','timestamp']);f.to_parquet(p,index=False)\nelse:f=pd.read_parquet(p)\nfor stage,cutoff in [('validation',24786800),('test',25393400)]:\n out=O/f'{stage}_temporal.npz'\n if out.exists():continue\n hids=[];hweights=[];tids=[];tweights=[];hp=[0];tp=[0];ru=[];rt=[]\n cuts=np.arange(cutoff-12*604800,cutoff,604800)\n for uid,g in f.groupby('uid',sort=False):\n times=g.timestamp.to_numpy();items=g.iid.to_numpy();likes=g.like.to_numpy();quality=g.quality.to_numpy();organic=g.organic.to_numpy()\n for cut in cuts:\n end=cut+604800;lo=np.searchsorted(times,cut);hi=np.searchsorted(times,end)\n if lo<20 or hi==lo:continue\n hs=items[:lo];unique,inv=np.unique(hs,return_inverse=True)\n ts=items[lo:hi];q=quality[lo:hi];ok=(q>0)&(~np.isin(ts,unique))\n if not ok.any():continue\n ti,iv=np.unique(ts[ok],return_inverse=True);tw=np.zeros(len(ti),dtype='float32');np.maximum.at(tw,iv,q[ok])\n # Log-compressed positive counts plus likes, with a 90-day event decay.\n age=(cut-times[:lo].astype('int64'))/86400;dec=np.exp(-age/90)\n cnt=np.bincount(inv,weights=(1-likes[:lo])*dec,minlength=len(unique));org=np.bincount(inv,weights=(1-likes[:lo])*organic[:lo]*dec,minlength=len(unique));lk=np.bincount(inv,weights=likes[:lo]*dec,minlength=len(unique))\n hw=((np.maximum(cnt,1e-5)**.3)*(1+org/np.maximum(cnt,1e-5))+2*np.minimum(lk,1)).astype('float32')\n hids.append(unique);hweights.append(hw);tids.append(ti);tweights.append(tw);hp.append(hp[-1]+len(unique));tp.append(tp[-1]+len(ti));ru.append(uid);rt.append(cut)\n if len(ru) and len(ru)%10000<12:print(stage,'examples',len(ru),'seconds',time.time()-start,flush=True)\n np.savez(out,hids=np.concatenate(hids),hweights=np.concatenate(hweights),hptr=np.array(hp),tids=np.concatenate(tids),tweights=np.concatenate(tweights),tptr=np.array(tp),uids=np.array(ru),cutoffs=np.array(rt));print(stage,'saved',len(ru),time.time()-start,flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py > /app/solution/temporal_prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 73, "nanos": 21096969 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:02:49.765Z", "callId": "call_GqKrnCUzuVR4CdIa4nQMjAwE", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/temporal_train.py <<'PY'\\n\\\"\\\"\\\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\\\"\\\"\\\"\\nfrom model import *\\n\\ndef temporal_data(stage):\\n p=O/f'{stage}_temporal_history.npz'\\n if not p.exists():\\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestampbest:\\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\\n else:\\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\\n print('BEST',best,flush=True)\\nif __name__=='__main__':main()\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:02:49.923Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"f31460\",\"wall_time_seconds\":0.0000028,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:02:49.864Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0a3a81487d18148a88594a29e45", "usage": { "input_tokens": 41627, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 1148, "reasoning_output_tokens": 0, "total_tokens": 42775 }, "turn_token_usage": { "input_tokens": 887231, "cached_input_tokens": 830208, "cache_write_input_tokens": 0, "output_tokens": 14870, "reasoning_output_tokens": 5481, "total_tokens": 902101 }, "thread_token_usage": { "input_tokens": 887231, "cached_input_tokens": 830208, "cache_write_input_tokens": 0, "output_tokens": 14870, "reasoning_output_tokens": 5481, "total_tokens": 902101 } } }, { "kind": "context", "timestamp": "2026-09-10T18:02:49.890Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-207cdef9-e250-4b93-8481-c1c30b514d57", "process_id": "45422", "command": [ "/bin/sh", "-lc", "cat > /app/solution/temporal_train.py <<'PY'\n\"\"\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\"\"\"\nfrom model import *\n\ndef temporal_data(stage):\n p=O/f'{stage}_temporal_history.npz'\n if not p.exists():\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestampbest:\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/temporal_train.py <<'PY'\n\"\"\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\"\"\"\nfrom model import *\n\ndef temporal_data(stage):\n p=O/f'{stage}_temporal_history.npz'\n if not p.exists():\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestampbest:\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 2800 }, "formatted_output": "" } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-10T18:02:55.984Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:02:55.984Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:02:57.420Z", "callId": "call_osqgqjJrDHLGRFCEDD7NKjdo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal_prepare.log; ls -lh /app/solution/*temporal* /app/solution/raw_positive.parquet\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-10T18:02:57.555Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"125d77\",\"wall_time_seconds\":0.00000415,\"exit_code\":0,\"original_token_count\":263,\"output\":\"{\\\"epoch\\\": 14, \\\"loss\\\": 7.595434989684668, \\\"seconds\\\": 257.1}\\n{\\\"epoch\\\": 15, \\\"loss\\\": 7.5414970043378, \\\"seconds\\\": 293.8}\\nVALID 15 0.01945074695797453\\n{\\\"epoch\\\": 16, \\\"loss\\\": 7.502994879698142, \\\"seconds\\\": 328.0}\\n{\\\"epoch\\\": 17, \\\"loss\\\": 7.4713649810888825, \\\"seconds\\\": 339.7}\\n{\\\"epoch\\\": 18, \\\"loss\\\": 7.428267032672197, \\\"seconds\\\": 351.5}\\ntest examples 40000 seconds 64.73566842079163\\ntest examples 40009 seconds 64.74145293235779\\ntest examples 40009 seconds 64.74506378173828\\ntest examples 50001 seconds 69.24412679672241\\ntest examples 50009 seconds 69.24641823768616\\ntest saved 51855 70.40952014923096\\n-rw-r--r-- 1 agent agent 136M Sep 10 18:01 /app/solution/raw_positive.parquet\\n-rw-r--r-- 1 agent agent 1.7K Sep 10 18:02 /app/solution/temporal_prepare.log\\n-rw-r--r-- 1 agent agent 2.9K Sep 10 18:00 /app/solution/temporal_prepare.py\\n-rw-r--r-- 1 agent agent 3.4K Sep 10 18:02 /app/solution/temporal_train.py\\n-rw-r--r-- 1 agent agent 259M Sep 10 18:02 /app/solution/test_temporal.npz\\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 /app/solution/validation_temporal.npz\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:02:57.533Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0ca301087d1a277a3616086d98f", "usage": { "input_tokens": 42831, "cached_input_tokens": 41472, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 67, "total_tokens": 42969 }, "turn_token_usage": { "input_tokens": 930062, "cached_input_tokens": 871680, "cache_write_input_tokens": 0, "output_tokens": 15008, "reasoning_output_tokens": 5548, "total_tokens": 945070 }, "thread_token_usage": { "input_tokens": 930062, "cached_input_tokens": 871680, "cache_write_input_tokens": 0, "output_tokens": 15008, "reasoning_output_tokens": 5548, "total_tokens": 945070 } } }, { "kind": "context", "timestamp": "2026-09-10T18:02:57.541Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c4ceaa16-c658-4ab7-97b7-fb37a8446ed2", "process_id": "8494", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal_prepare.log; ls -lh /app/solution/*temporal* /app/solution/raw_positive.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal_prepare.log; ls -lh /app/solution/*temporal* /app/solution/raw_positive.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\n{\"epoch\": 15, \"loss\": 7.5414970043378, \"seconds\": 293.8}\nVALID 15 0.01945074695797453\n{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\ntest examples 40000 seconds 64.73566842079163\ntest examples 40009 seconds 64.74145293235779\ntest examples 40009 seconds 64.74506378173828\ntest examples 50001 seconds 69.24412679672241\ntest examples 50009 seconds 69.24641823768616\ntest saved 51855 70.40952014923096\n-rw-r--r-- 1 agent agent 136M Sep 10 18:01 /app/solution/raw_positive.parquet\n-rw-r--r-- 1 agent agent 1.7K Sep 10 18:02 /app/solution/temporal_prepare.log\n-rw-r--r-- 1 agent agent 2.9K Sep 10 18:00 /app/solution/temporal_prepare.py\n-rw-r--r-- 1 agent agent 3.4K Sep 10 18:02 /app/solution/temporal_train.py\n-rw-r--r-- 1 agent agent 259M Sep 10 18:02 /app/solution/test_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 /app/solution/validation_temporal.npz\n", "stderr": "", "aggregated_output": "{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\n{\"epoch\": 15, \"loss\": 7.5414970043378, \"seconds\": 293.8}\nVALID 15 0.01945074695797453\n{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\ntest examples 40000 seconds 64.73566842079163\ntest examples 40009 seconds 64.74145293235779\ntest examples 40009 seconds 64.74506378173828\ntest examples 50001 seconds 69.24412679672241\ntest examples 50009 seconds 69.24641823768616\ntest saved 51855 70.40952014923096\n-rw-r--r-- 1 agent agent 136M Sep 10 18:01 /app/solution/raw_positive.parquet\n-rw-r--r-- 1 agent agent 1.7K Sep 10 18:02 /app/solution/temporal_prepare.log\n-rw-r--r-- 1 agent agent 2.9K Sep 10 18:00 /app/solution/temporal_prepare.py\n-rw-r--r-- 1 agent agent 3.4K Sep 10 18:02 /app/solution/temporal_train.py\n-rw-r--r-- 1 agent agent 259M Sep 10 18:02 /app/solution/test_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 /app/solution/validation_temporal.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4150 }, "formatted_output": "{\"epoch\": 14, \"loss\": 7.595434989684668, \"seconds\": 257.1}\n{\"epoch\": 15, \"loss\": 7.5414970043378, \"seconds\": 293.8}\nVALID 15 0.01945074695797453\n{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\ntest examples 40000 seconds 64.73566842079163\ntest examples 40009 seconds 64.74145293235779\ntest examples 40009 seconds 64.74506378173828\ntest examples 50001 seconds 69.24412679672241\ntest examples 50009 seconds 69.24641823768616\ntest saved 51855 70.40952014923096\n-rw-r--r-- 1 agent agent 136M Sep 10 18:01 /app/solution/raw_positive.parquet\n-rw-r--r-- 1 agent agent 1.7K Sep 10 18:02 /app/solution/temporal_prepare.log\n-rw-r--r-- 1 agent agent 2.9K Sep 10 18:00 /app/solution/temporal_prepare.py\n-rw-r--r-- 1 agent agent 3.4K Sep 10 18:02 /app/solution/temporal_train.py\n-rw-r--r-- 1 agent agent 259M Sep 10 18:02 /app/solution/test_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 /app/solution/validation_temporal.npz\n" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-10T18:03:06.690Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:03:06.690Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:03:08.781Z", "callId": "call_WCfM2I9De7zzzBHpxSAauiRy", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.current; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 > /app/solution/temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:03:09.969Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"8746ee\",\"wall_time_seconds\":1.000952508,\"session_id\":50351,\"original_token_count\":3,\"output\":\"2442715136\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:03:08.838Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0d1cbd087d19a875a22c0041087", "usage": { "input_tokens": 43453, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 275, "reasoning_output_tokens": 176, "total_tokens": 43728 }, "turn_token_usage": { "input_tokens": 973515, "cached_input_tokens": 914304, "cache_write_input_tokens": 0, "output_tokens": 15283, "reasoning_output_tokens": 5724, "total_tokens": 988798 }, "thread_token_usage": { "input_tokens": 973515, "cached_input_tokens": 914304, "cache_write_input_tokens": 0, "output_tokens": 15283, "reasoning_output_tokens": 5724, "total_tokens": 988798 } } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-10T18:03:25.034Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:03:25.034Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:03:34.954Z", "callId": "call_22YLODkiPhMJQrLzOTMhjg4M", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# Made for You: CPU PyTorch two-tower retrieval\\n\\nWork in progress; the final configuration and validation result will be recorded after model selection.\\n\\nThe user tower pools weighted interaction-history item embeddings (no user-ID lookup), applies tanh and a learned projection to a 256-dimensional representation. The independent item tower applies a trainable embedding, tanh, and a learned linear projection. Appending a constant to the user vector and a learned item-bias coordinate to the item vector gives an exact 257-dimensional dot-product score. Item vectors can be cached offline for maximum inner-product retrieval.\\n\\n`prepare.py` builds ragged histories without constructing a global user-item interaction matrix. `model.py` trains both towers with a denoising multinomial objective using minibatch scores. It provides a sampled-catalog training option for larger catalogs. `temporal_prepare.py` and `temporal_train.py` add historical weekly discovery tasks with strictly earlier histories and novel organic outcomes. Validation rankings use training history only. Final ranking uses train plus validation history and the test eligibility mask.\\n\\nAn initial checkpoint achieved public NDCG@10 **0.016856**. The saved initial `submission.parquet` has passed all structural and test-cutoff eligibility checks.\\n\\nEnvironment Python: `/opt/task-venv/bin/python`. All generated artifacts stay in `/app/solution`.\\nMD\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:03:35.064Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"3faae2\",\"wall_time_seconds\":0.00000403,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:03:35.037Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0c5a6249-a56b-4fc3-907b-0365c1d11ddb", "process_id": "65252", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# Made for You: CPU PyTorch two-tower retrieval\n\nWork in progress; the final configuration and validation result will be recorded after model selection.\n\nThe user tower pools weighted interaction-history item embeddings (no user-ID lookup), applies tanh and a learned projection to a 256-dimensional representation. The independent item tower applies a trainable embedding, tanh, and a learned linear projection. Appending a constant to the user vector and a learned item-bias coordinate to the item vector gives an exact 257-dimensional dot-product score. Item vectors can be cached offline for maximum inner-product retrieval.\n\n`prepare.py` builds ragged histories without constructing a global user-item interaction matrix. `model.py` trains both towers with a denoising multinomial objective using minibatch scores. It provides a sampled-catalog training option for larger catalogs. `temporal_prepare.py` and `temporal_train.py` add historical weekly discovery tasks with strictly earlier histories and novel organic outcomes. Validation rankings use training history only. Final ranking uses train plus validation history and the test eligibility mask.\n\nAn initial checkpoint achieved public NDCG@10 **0.016856**. The saved initial `submission.parquet` has passed all structural and test-cutoff eligibility checks.\n\nEnvironment Python: `/opt/task-venv/bin/python`. All generated artifacts stay in `/app/solution`.\nMD" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# Made for You: CPU PyTorch two-tower retrieval\n\nWork in progress; the final configuration and validation result will be recorded after model selection.\n\nThe user tower pools weighted interaction-history item embeddings (no user-ID lookup), applies tanh and a learned projection to a 256-dimensional representation. The independent item tower applies a trainable embedding, tanh, and a learned linear projection. Appending a constant to the user vector and a learned item-bias coordinate to the item vector gives an exact 257-dimensional dot-product score. Item vectors can be cached offline for maximum inner-product retrieval.\n\n`prepare.py` builds ragged histories without constructing a global user-item interaction matrix. `model.py` trains both towers with a denoising multinomial objective using minibatch scores. It provides a sampled-catalog training option for larger catalogs. `temporal_prepare.py` and `temporal_train.py` add historical weekly discovery tasks with strictly earlier histories and novel organic outcomes. Validation rankings use training history only. Final ranking uses train plus validation history and the test eligibility mask.\n\nAn initial checkpoint achieved public NDCG@10 **0.016856**. The saved initial `submission.parquet` has passed all structural and test-cutoff eligibility checks.\n\nEnvironment Python: `/opt/task-venv/bin/python`. All generated artifacts stay in `/app/solution`.\nMD" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4030 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:03:35.044Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0de5f2487d1b7716c9ee44c7159", "usage": { "input_tokens": 43789, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 591, "reasoning_output_tokens": 264, "total_tokens": 44380 }, "turn_token_usage": { "input_tokens": 1017304, "cached_input_tokens": 957568, "cache_write_input_tokens": 0, "output_tokens": 15874, "reasoning_output_tokens": 5988, "total_tokens": 1033178 }, "thread_token_usage": { "input_tokens": 1017304, "cached_input_tokens": 957568, "cache_write_input_tokens": 0, "output_tokens": 15874, "reasoning_output_tokens": 5988, "total_tokens": 1033178 } } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-10T18:03:38.850Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:03:38.850Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:03:41.127Z", "text": "The baseline has reached 0.01945 NDCG@10. The rolling backtest produced about 50,000 training examples, and I’ve started fine-tuning both towers on them. During this training, history tracks are excluded from the loss denominator to better match the platform’s new-track candidate boundary.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:03:42.914Z", "callId": "call_j9h4EskhzZzzQHHbAlOw6AqI", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:03:43.012Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5e0142\",\"wall_time_seconds\":0.0000081,\"exit_code\":0,\"original_token_count\":84,\"output\":\"{\\\"epoch\\\": 16, \\\"loss\\\": 7.502994879698142, \\\"seconds\\\": 328.0}\\n{\\\"epoch\\\": 17, \\\"loss\\\": 7.4713649810888825, \\\"seconds\\\": 339.7}\\n{\\\"epoch\\\": 18, \\\"loss\\\": 7.428267032672197, \\\"seconds\\\": 351.5}\\n{\\\"epoch\\\": 19, \\\"loss\\\": 7.39779397768852, \\\"seconds\\\": 363.5}\\n{\\\"epoch\\\": 20, \\\"loss\\\": 7.366746688500429, \\\"seconds\\\": 377.2}\\nVALID 20 0.01821621312291726\\n4638048256\\n\"}\n{\"current_time\":\"2026-09-10 18:03:42 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:03:42.989Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8b00177b-9772-469a-a55c-2d4301bbe9bc", "process_id": "98494", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/dae.log; tail -6 /app/solution/temporal.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n4638048256\n", "stderr": "", "aggregated_output": "{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n4638048256\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8100 }, "formatted_output": "{\"epoch\": 16, \"loss\": 7.502994879698142, \"seconds\": 328.0}\n{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n4638048256\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:03:43.007Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0f76fb487d1ab8b4ace47955f89", "usage": { "input_tokens": 44437, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 150, "reasoning_output_tokens": 11, "total_tokens": 44587 }, "turn_token_usage": { "input_tokens": 1061741, "cached_input_tokens": 1001216, "cache_write_input_tokens": 0, "output_tokens": 16024, "reasoning_output_tokens": 5999, "total_tokens": 1077765 }, "thread_token_usage": { "input_tokens": 1061741, "cached_input_tokens": 1001216, "cache_write_input_tokens": 0, "output_tokens": 16024, "reasoning_output_tokens": 5999, "total_tokens": 1077765 } } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-10T18:04:00.554Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:04:00.554Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:04:02.998Z", "callId": "call_kofL5cEW68Ql9NIBNXppbDFE", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/dae.log; tail -5 /app/solution/temporal.log; python - <<'PY'\\np='/app/solution/model.py';s=open(p).read().replace('torch.set_num_threads(8)','torch.set_num_threads(4)');open(p,'w').write(s)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:04:03.165Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"da1f2a\",\"wall_time_seconds\":0.00000807,\"exit_code\":0,\"original_token_count\":67,\"output\":\"{\\\"epoch\\\": 17, \\\"loss\\\": 7.4713649810888825, \\\"seconds\\\": 339.7}\\n{\\\"epoch\\\": 18, \\\"loss\\\": 7.428267032672197, \\\"seconds\\\": 351.5}\\n{\\\"epoch\\\": 19, \\\"loss\\\": 7.39779397768852, \\\"seconds\\\": 363.5}\\n{\\\"epoch\\\": 20, \\\"loss\\\": 7.366746688500429, \\\"seconds\\\": 377.2}\\nVALID 20 0.01821621312291726\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:04:03.094Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f0ff4fc487d18beddb1bcbc8a74c", "usage": { "input_tokens": 44815, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 535, "reasoning_output_tokens": 437, "total_tokens": 45350 }, "turn_token_usage": { "input_tokens": 1106556, "cached_input_tokens": 1045504, "cache_write_input_tokens": 0, "output_tokens": 16559, "reasoning_output_tokens": 6436, "total_tokens": 1123115 }, "thread_token_usage": { "input_tokens": 1106556, "cached_input_tokens": 1045504, "cache_write_input_tokens": 0, "output_tokens": 16559, "reasoning_output_tokens": 6436, "total_tokens": 1123115 } } }, { "kind": "context", "timestamp": "2026-09-10T18:04:03.139Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-53947a49-7203-4f4c-92cf-3308386cd72a", "process_id": "4896", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/dae.log; tail -5 /app/solution/temporal.log; python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace('torch.set_num_threads(8)','torch.set_num_threads(4)');open(p,'w').write(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/dae.log; tail -5 /app/solution/temporal.log; python - <<'PY'\np='/app/solution/model.py';s=open(p).read().replace('torch.set_num_threads(8)','torch.set_num_threads(4)');open(p,'w').write(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n", "stderr": "", "aggregated_output": "{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8070 }, "formatted_output": "{\"epoch\": 17, \"loss\": 7.4713649810888825, \"seconds\": 339.7}\n{\"epoch\": 18, \"loss\": 7.428267032672197, \"seconds\": 351.5}\n{\"epoch\": 19, \"loss\": 7.39779397768852, \"seconds\": 363.5}\n{\"epoch\": 20, \"loss\": 7.366746688500429, \"seconds\": 377.2}\nVALID 20 0.01821621312291726\n" } } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-10T18:04:07.503Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:04:07.503Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:04:11.852Z", "callId": "call_tbjmqZwiVjeytwjnXis0I50l", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().replace(b'\\\\0',b' ').decode()\\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'):\\n print(p.name,cmd,[(x) for x in (p/'status').read_text().splitlines() if x.startswith(('State:','VmRSS:','Threads:'))])\\n except:pass\\nPY\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-10T18:04:12.056Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"dbc1e1\",\"wall_time_seconds\":0.018182472,\"exit_code\":0,\"original_token_count\":83,\"output\":\"417 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 ['State:\\\\tR (running)', 'VmRSS:\\\\t 1549664 kB', 'Threads:\\\\t25']\\n685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 ['State:\\\\tR (running)', 'VmRSS:\\\\t 2180212 kB', 'Threads:\\\\t21']\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:04:11.954Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1139f9c87d1945aec6d3193f379", "usage": { "input_tokens": 45529, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 211, "reasoning_output_tokens": 64, "total_tokens": 45740 }, "turn_token_usage": { "input_tokens": 1152085, "cached_input_tokens": 1090176, "cache_write_input_tokens": 0, "output_tokens": 16770, "reasoning_output_tokens": 6500, "total_tokens": 1168855 }, "thread_token_usage": { "input_tokens": 1152085, "cached_input_tokens": 1090176, "cache_write_input_tokens": 0, "output_tokens": 16770, "reasoning_output_tokens": 6500, "total_tokens": 1168855 } } }, { "kind": "context", "timestamp": "2026-09-10T18:04:12.057Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0a80df5b-96d4-4ac7-9426-144ec190c487", "process_id": "10919", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'):\n print(p.name,cmd,[(x) for x in (p/'status').read_text().splitlines() if x.startswith(('State:','VmRSS:','Threads:'))])\n except:pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'):\n print(p.name,cmd,[(x) for x in (p/'status').read_text().splitlines() if x.startswith(('State:','VmRSS:','Threads:'))])\n except:pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "417 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 ['State:\\tR (running)', 'VmRSS:\\t 1549664 kB', 'Threads:\\t25']\n685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 ['State:\\tR (running)', 'VmRSS:\\t 2180212 kB', 'Threads:\\t21']\n", "stderr": "", "aggregated_output": "417 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 ['State:\\tR (running)', 'VmRSS:\\t 1549664 kB', 'Threads:\\t25']\n685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 ['State:\\tR (running)', 'VmRSS:\\t 2180212 kB', 'Threads:\\t21']\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 22151422 }, "formatted_output": "417 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 ['State:\\tR (running)', 'VmRSS:\\t 1549664 kB', 'Threads:\\t25']\n685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 ['State:\\tR (running)', 'VmRSS:\\t 2180212 kB', 'Threads:\\t21']\n" } } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-10T18:04:17.625Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:04:17.625Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:04:21.016Z", "callId": "call_Kgsr9eDCuD3gJ5SvHawGWF1s", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill -TERM 417\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/model.py --name vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 70 --eval-every 5 > /app/solution/vae.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:04:22.249Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"33dcf4\",\"wall_time_seconds\":1.0018498,\"session_id\":71460,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:04:21.077Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f11c3df887d198ccc2d68481fcad", "usage": { "input_tokens": 45917, "cached_input_tokens": 45312, "cache_write_input_tokens": 0, "output_tokens": 172, "reasoning_output_tokens": 48, "total_tokens": 46089 }, "turn_token_usage": { "input_tokens": 1198002, "cached_input_tokens": 1135488, "cache_write_input_tokens": 0, "output_tokens": 16942, "reasoning_output_tokens": 6548, "total_tokens": 1214944 }, "thread_token_usage": { "input_tokens": 1198002, "cached_input_tokens": 1135488, "cache_write_input_tokens": 0, "output_tokens": 16942, "reasoning_output_tokens": 6548, "total_tokens": 1214944 } } }, { "kind": "context", "timestamp": "2026-09-10T18:04:21.230Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fdf353aa-96fa-47c6-ba6b-25e953d204a3", "process_id": "81291", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/model.py'\ns=open(p).read().replace(\"groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\", \"groups=f.groupby('uid',sort=True).size();groups=groups.reindex(np.union1d(groups.index.to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()),fill_value=0);uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/model.py'\ns=open(p).read().replace(\"groups=f.groupby('uid',sort=True).size();uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\", \"groups=f.groupby('uid',sort=True).size();groups=groups.reindex(np.union1d(groups.index.to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()),fill_value=0);uids=groups.index.to_numpy();ptr=np.r_[0,np.cumsum(groups.values)]\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/model.py --name dae --epochs 50 --eval-every 5 > /app/solution/dae.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 453, "nanos": 351637281 }, "formatted_output": "" } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-10T18:04:33.240Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:04:33.240Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:04:34.215Z", "callId": "call_eYFDx6sd2J7zdVgi0d6SjrvA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/validation_temporal_history.npz\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:04:34.313Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5056b1\",\"wall_time_seconds\":0.00000461,\"exit_code\":0,\"original_token_count\":22,\"output\":\"-rw-r--r-- 1 agent agent 66M Sep 10 18:03 /app/solution/validation_temporal_history.npz\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:04:34.273Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-249d41d0-e46a-467f-9832-f3226088be7f", "process_id": "92622", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/validation_temporal_history.npz" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/validation_temporal_history.npz" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 66M Sep 10 18:03 /app/solution/validation_temporal_history.npz\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 66M Sep 10 18:03 /app/solution/validation_temporal_history.npz\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4610 }, "formatted_output": "-rw-r--r-- 1 agent agent 66M Sep 10 18:03 /app/solution/validation_temporal_history.npz\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:04:34.301Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f12668a887d19a44bed1132e856c", "usage": { "input_tokens": 46147, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 273, "reasoning_output_tokens": 208, "total_tokens": 46420 }, "turn_token_usage": { "input_tokens": 1244149, "cached_input_tokens": 1181184, "cache_write_input_tokens": 0, "output_tokens": 17215, "reasoning_output_tokens": 6756, "total_tokens": 1261364 }, "thread_token_usage": { "input_tokens": 1244149, "cached_input_tokens": 1181184, "cache_write_input_tokens": 0, "output_tokens": 17215, "reasoning_output_tokens": 6756, "total_tokens": 1261364 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-10T18:05:02.132Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:05:02.132Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:05:03.927Z", "text": "The baseline peaked at epoch 15 and then declined, so I stopped that run and kept its best checkpoint. The temporal model is training from that checkpoint. I’ve also started a variational version of the history encoder to test whether stronger regularization improves generalization.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:05:04.910Z", "callId": "call_JoUWEbpcyYjufGtHMsz2Nnu4", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log; tail -6 /app/solution/vae.log; tail -3 /app/solution/dae.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:05:04.998Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"4e2ed8\",\"wall_time_seconds\":0.00000689,\"exit_code\":0,\"original_token_count\":46,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.898980984321007, \\\"seconds\\\": 20.6}\\nVALID 1 0.01280853042654181\\nVALID 20 0.01821621312291726\\n{\\\"epoch\\\": 21, \\\"loss\\\": 7.34021161764096, \\\"seconds\\\": 441.5}\\nTerminated\\n\"}\n{\"current_time\":\"2026-09-10 18:05:04 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:05:04.962Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0ed47aea-2ebe-49bf-bac8-76e9e55c32e7", "process_id": "17598", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/temporal.log; tail -6 /app/solution/vae.log; tail -3 /app/solution/dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/temporal.log; tail -6 /app/solution/vae.log; tail -3 /app/solution/dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\nVALID 20 0.01821621312291726\n{\"epoch\": 21, \"loss\": 7.34021161764096, \"seconds\": 441.5}\nTerminated\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\nVALID 20 0.01821621312291726\n{\"epoch\": 21, \"loss\": 7.34021161764096, \"seconds\": 441.5}\nTerminated\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6890 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\nVALID 20 0.01821621312291726\n{\"epoch\": 21, \"loss\": 7.34021161764096, \"seconds\": 441.5}\nTerminated\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:05:04.986Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1328cbc87d18cc44a06d295cdd9", "usage": { "input_tokens": 46508, "cached_input_tokens": 45952, "cache_write_input_tokens": 0, "output_tokens": 646, "reasoning_output_tokens": 516, "total_tokens": 47154 }, "turn_token_usage": { "input_tokens": 1290657, "cached_input_tokens": 1227136, "cache_write_input_tokens": 0, "output_tokens": 17861, "reasoning_output_tokens": 7272, "total_tokens": 1308518 }, "thread_token_usage": { "input_tokens": 1290657, "cached_input_tokens": 1227136, "cache_write_input_tokens": 0, "output_tokens": 17861, "reasoning_output_tokens": 7272, "total_tokens": 1308518 } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-10T18:05:29.525Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:05:29.525Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:06:06.438Z", "callId": "call_3p1vAUTGbnIBohyDQVX63szK", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/retrieve.py <<'PY'\\n\\\"\\\"\\\"Export independent item vectors and rank by one exact tower dot product.\\n\\nAn ensemble remains a two-tower model: concatenate the component user vectors\\nand component item vectors. The production serving path can index item vectors\\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\\nis a bounded-batch reference implementation for the supplied small catalog.\\n\\\"\\\"\\\"\\nfrom model import *\\nfrom temporal_train import temporal_data\\n\\nclass UserRetrievalTower(nn.Module):\\n def __init__(self,models,weights):\\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\\n def forward(self,histories):\\n vec=[]\\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\\n\\nclass ItemRetrievalTower(nn.Module):\\n def __init__(self,models,features,pop,trend):\\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(2,1,bias=False)\\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend]]))\\n def forward(self,ids):\\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\\n\\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.):\\n models=[];datas=[];configs=[]\\n for path in paths:\\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\\n datas.append(temporal_data(stage) if 'temporal_args' in c else load_data(stage,cfg))\\n f=pd.read_parquet(O/f'{stage}_events.parquet');g=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\\n features=torch.tensor(np.stack([np.log(g['count'].to_numpy()+10),np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)],1),dtype=torch.float32)\\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend).eval(),datas,configs\\n\\n@torch.no_grad()\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\\n for st in range(0,len(uids),128):\\n histories=[]\\n for r,(_,ptr,ids,w) in zip(rows,datas):\\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\\n uv=ut(histories);uvecs.append(uv.numpy());s=uv@emb.T\\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy())\\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\\n if scoresout is not None:scoresout.flush()\\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));torch.save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\\n # Serving-time user encoding benchmark, excluding the history-store read.\\n times=[]\\n for _ in range(25):\\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\\n print('100-item history encoder median ms',np.median(times),flush=True)\\nif __name__=='__main__':main()\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:06:06.526Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"334c9a\",\"wall_time_seconds\":0.00001006,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:06:06.504Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3814a715-ce39-448c-89cf-ef9dd99dc949", "process_id": "38448", "command": [ "/bin/sh", "-lc", "cat > /app/solution/retrieve.py <<'PY'\n\"\"\"Export independent item vectors and rank by one exact tower dot product.\n\nAn ensemble remains a two-tower model: concatenate the component user vectors\nand component item vectors. The production serving path can index item vectors\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\nis a bounded-batch reference implementation for the supplied small catalog.\n\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\n\nclass UserRetrievalTower(nn.Module):\n def __init__(self,models,weights):\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\n def forward(self,histories):\n vec=[]\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\n\nclass ItemRetrievalTower(nn.Module):\n def __init__(self,models,features,pop,trend):\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(2,1,bias=False)\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend]]))\n def forward(self,ids):\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\n\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.):\n models=[];datas=[];configs=[]\n for path in paths:\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\n datas.append(temporal_data(stage) if 'temporal_args' in c else load_data(stage,cfg))\n f=pd.read_parquet(O/f'{stage}_events.parquet');g=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\n features=torch.tensor(np.stack([np.log(g['count'].to_numpy()+10),np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)],1),dtype=torch.float32)\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend).eval(),datas,configs\n\n@torch.no_grad()\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\n for st in range(0,len(uids),128):\n histories=[]\n for r,(_,ptr,ids,w) in zip(rows,datas):\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\n uv=ut(histories);uvecs.append(uv.numpy());s=uv@emb.T\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy())\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\n if scoresout is not None:scoresout.flush()\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));torch.save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\n # Serving-time user encoding benchmark, excluding the history-store read.\n times=[]\n for _ in range(25):\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\n print('100-item history encoder median ms',np.median(times),flush=True)\nif __name__=='__main__':main()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/retrieve.py <<'PY'\n\"\"\"Export independent item vectors and rank by one exact tower dot product.\n\nAn ensemble remains a two-tower model: concatenate the component user vectors\nand component item vectors. The production serving path can index item vectors\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\nis a bounded-batch reference implementation for the supplied small catalog.\n\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\n\nclass UserRetrievalTower(nn.Module):\n def __init__(self,models,weights):\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\n def forward(self,histories):\n vec=[]\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\n\nclass ItemRetrievalTower(nn.Module):\n def __init__(self,models,features,pop,trend):\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(2,1,bias=False)\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend]]))\n def forward(self,ids):\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\n\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.):\n models=[];datas=[];configs=[]\n for path in paths:\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\n datas.append(temporal_data(stage) if 'temporal_args' in c else load_data(stage,cfg))\n f=pd.read_parquet(O/f'{stage}_events.parquet');g=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\n features=torch.tensor(np.stack([np.log(g['count'].to_numpy()+10),np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)],1),dtype=torch.float32)\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend).eval(),datas,configs\n\n@torch.no_grad()\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\n for st in range(0,len(uids),128):\n histories=[]\n for r,(_,ptr,ids,w) in zip(rows,datas):\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\n uv=ut(histories);uvecs.append(uv.numpy());s=uv@emb.T\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy())\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\n if scoresout is not None:scoresout.flush()\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));torch.save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\n # Serving-time user encoding benchmark, excluding the history-store read.\n times=[]\n for _ in range(25):\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\n print('100-item history encoder median ms',np.median(times),flush=True)\nif __name__=='__main__':main()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 10060 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:06:06.523Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f151447487d1ac75508a383d4424", "usage": { "input_tokens": 47312, "cached_input_tokens": 46336, "cache_write_input_tokens": 0, "output_tokens": 1981, "reasoning_output_tokens": 516, "total_tokens": 49293 }, "turn_token_usage": { "input_tokens": 1337969, "cached_input_tokens": 1273472, "cache_write_input_tokens": 0, "output_tokens": 19842, "reasoning_output_tokens": 7788, "total_tokens": 1357811 }, "thread_token_usage": { "input_tokens": 1337969, "cached_input_tokens": 1273472, "cache_write_input_tokens": 0, "output_tokens": 19842, "reasoning_output_tokens": 7788, "total_tokens": 1357811 } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-10T18:06:09.417Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:06:09.417Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:06:11.164Z", "callId": "call_WlJeylqjZu1nDCVX0fUD76yF", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/temporal.log; tail -8 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:06:11.265Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"d2c4c4\",\"wall_time_seconds\":0.00000513,\"exit_code\":0,\"original_token_count\":88,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\n{\\\"epoch\\\": 1, \\\"loss\\\": 9.898980984321007, \\\"seconds\\\": 20.6}\\nVALID 1 0.01280853042654181\\n{\\\"epoch\\\": 2, \\\"loss\\\": 9.134680980291122, \\\"seconds\\\": 59.9}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.79036762775519, \\\"seconds\\\": 76.5}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.595540083371676, \\\"seconds\\\": 94.5}\\n4677562368\\n\"}\n{\"current_time\":\"2026-09-10 18:06:11 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:06:11.223Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-83a9a15a-a6e8-42f7-9675-053ec0da21dd", "process_id": "42338", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/temporal.log; tail -8 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/temporal.log; tail -8 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n4677562368\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n4677562368\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5130 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n4677562368\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:06:11.261Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f18ecb2487d19aee48d8b60b84da", "usage": { "input_tokens": 49350, "cached_input_tokens": 47104, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 32, "total_tokens": 49451 }, "turn_token_usage": { "input_tokens": 1387319, "cached_input_tokens": 1320576, "cache_write_input_tokens": 0, "output_tokens": 19943, "reasoning_output_tokens": 7820, "total_tokens": 1407262 }, "thread_token_usage": { "input_tokens": 1387319, "cached_input_tokens": 1320576, "cache_write_input_tokens": 0, "output_tokens": 19943, "reasoning_output_tokens": 7820, "total_tokens": 1407262 } } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-10T18:06:18.333Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:06:18.333Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:06:20.741Z", "callId": "call_Ef7vMEOdrTAdKhBBdHCc3BuU", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --name interim_temporal --load /app/solution/temporal.pt --infer --threads 1 > /app/solution/interim_temporal.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:06:21.928Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"5a49df\",\"wall_time_seconds\":1.000315267,\"session_id\":57462,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:06:20.805Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f193842c87d1a23d63534b6e1b12", "usage": { "input_tokens": 49689, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 263, "reasoning_output_tokens": 166, "total_tokens": 49952 }, "turn_token_usage": { "input_tokens": 1437008, "cached_input_tokens": 1369728, "cache_write_input_tokens": 0, "output_tokens": 20206, "reasoning_output_tokens": 7986, "total_tokens": 1457214 }, "thread_token_usage": { "input_tokens": 1437008, "cached_input_tokens": 1369728, "cache_write_input_tokens": 0, "output_tokens": 20206, "reasoning_output_tokens": 7986, "total_tokens": 1457214 } } }, { "kind": "context", "timestamp": "2026-09-10T18:06:47.929Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1bee376f-6f4b-4aa2-ba67-de4406718d82", "process_id": "57462", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --name interim_temporal --load /app/solution/temporal.pt --infer --threads 1 > /app/solution/interim_temporal.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --name interim_temporal --load /app/solution/temporal.pt --infer --threads 1 > /app/solution/interim_temporal.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 27, "nanos": 5035661 }, "formatted_output": "" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-10T18:06:47.999Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:06:47.999Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:06:49.774Z", "text": "The first temporal fine-tuning epoch reached 0.03381 NDCG@10, a substantial improvement over the baseline’s 0.01945. I’m updating the saved submission with this model’s test-cutoff ranking while training continues. The score still comes entirely from independently computed user and item vectors.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:06:50.803Z", "callId": "call_zKk39KsCkCB2DfhV8SY6gHg8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/interim_temporal.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:06:50.913Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ecd1e1\",\"wall_time_seconds\":0.00000449,\"exit_code\":0,\"original_token_count\":88,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\n{\\\"epoch\\\": 2, \\\"loss\\\": 9.134680980291122, \\\"seconds\\\": 59.9}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.79036762775519, \\\"seconds\\\": 76.5}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.595540083371676, \\\"seconds\\\": 94.5}\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.480866273244223, \\\"seconds\\\": 115.3}\\nVALID 5 0.0198911908453955\\n5098889216\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:06:50.878Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f19e244487d1a3fe32c2e5d02125", "usage": { "input_tokens": 50010, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 626, "reasoning_output_tokens": 483, "total_tokens": 50636 }, "turn_token_usage": { "input_tokens": 1487018, "cached_input_tokens": 1419264, "cache_write_input_tokens": 0, "output_tokens": 20832, "reasoning_output_tokens": 8469, "total_tokens": 1507850 }, "thread_token_usage": { "input_tokens": 1487018, "cached_input_tokens": 1419264, "cache_write_input_tokens": 0, "output_tokens": 20832, "reasoning_output_tokens": 8469, "total_tokens": 1507850 } } }, { "kind": "context", "timestamp": "2026-09-10T18:06:50.885Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76bdab81-356a-4796-8536-f6d5badd90cb", "process_id": "97235", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/interim_temporal.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/interim_temporal.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n5098889216\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n5098889216\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4490 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n5098889216\n" } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-10T18:07:06.365Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:07:06.365Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:07:07.768Z", "callId": "call_ncoWCY9eC3ImDcpsshoBHuYA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/temporal.log; tail -8 /app/solution/vae.log; ls -lh /app/solution/submission.parquet; tail /app/solution/interim_temporal.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:07:07.894Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ded29d\",\"wall_time_seconds\":0.00000405,\"exit_code\":0,\"original_token_count\":140,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\n{\\\"epoch\\\": 1, \\\"loss\\\": 9.898980984321007, \\\"seconds\\\": 20.6}\\nVALID 1 0.01280853042654181\\n{\\\"epoch\\\": 2, \\\"loss\\\": 9.134680980291122, \\\"seconds\\\": 59.9}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.79036762775519, \\\"seconds\\\": 76.5}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.595540083371676, \\\"seconds\\\": 94.5}\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.480866273244223, \\\"seconds\\\": 115.3}\\nVALID 5 0.0198911908453955\\n{\\\"epoch\\\": 6, \\\"loss\\\": 8.393172361911871, \\\"seconds\\\": 157.8}\\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:06 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-10 18:07:07 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:07:07.826Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1bb51e087d1bcfe8141b5dde957", "usage": { "input_tokens": 50854, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 370, "reasoning_output_tokens": 288, "total_tokens": 51224 }, "turn_token_usage": { "input_tokens": 1537872, "cached_input_tokens": 1469056, "cache_write_input_tokens": 0, "output_tokens": 21202, "reasoning_output_tokens": 8757, "total_tokens": 1559074 }, "thread_token_usage": { "input_tokens": 1537872, "cached_input_tokens": 1469056, "cache_write_input_tokens": 0, "output_tokens": 21202, "reasoning_output_tokens": 8757, "total_tokens": 1559074 } } }, { "kind": "context", "timestamp": "2026-09-10T18:07:07.865Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b38ecb30-d70d-48b8-bc40-d42c2b112fd2", "process_id": "23722", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/temporal.log; tail -8 /app/solution/vae.log; ls -lh /app/solution/submission.parquet; tail /app/solution/interim_temporal.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/temporal.log; tail -8 /app/solution/vae.log; ls -lh /app/solution/submission.parquet; tail /app/solution/interim_temporal.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:06 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:06 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4050 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\n{\"epoch\": 1, \"loss\": 9.898980984321007, \"seconds\": 20.6}\nVALID 1 0.01280853042654181\n{\"epoch\": 2, \"loss\": 9.134680980291122, \"seconds\": 59.9}\n{\"epoch\": 3, \"loss\": 8.79036762775519, \"seconds\": 76.5}\n{\"epoch\": 4, \"loss\": 8.595540083371676, \"seconds\": 94.5}\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n-rw-r--r-- 1 agent agent 1.6M Sep 10 18:06 /app/solution/submission.parquet\n" } } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-10T18:07:14.020Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:07:14.020Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:07:14.304Z", "callId": "call_TiOqDyDk2uMiExju6lXwjj2B", "name": "sleep", "input": { "duration_ms": 35000 }, "result": { "timestamp": "2026-09-10T18:07:49.362Z", "content": "Wall time: 35.0219 seconds\nSleep completed.", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:07:14.432Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1cc2c9487d1bcb7215bd8c28768", "usage": { "input_tokens": 51552, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 111, "reasoning_output_tokens": 90, "total_tokens": 51663 }, "turn_token_usage": { "input_tokens": 1589424, "cached_input_tokens": 1519744, "cache_write_input_tokens": 0, "output_tokens": 21313, "reasoning_output_tokens": 8847, "total_tokens": 1610737 }, "thread_token_usage": { "input_tokens": 1589424, "cached_input_tokens": 1519744, "cache_write_input_tokens": 0, "output_tokens": 21313, "reasoning_output_tokens": 8847, "total_tokens": 1610737 } } }, { "kind": "context", "timestamp": "2026-09-10T18:07:49.331Z", "data": { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_TiOqDyDk2uMiExju6lXwjj2B", "durationMs": 35000 } } }, { "kind": "tool", "timestamp": "2026-09-10T18:07:53.183Z", "callId": "call_s0INhdNOPAnjeDdpWIJ1P7En", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/interim_temporal_scores.npy\",\"max_output_tokens\":1200}));\n" }, "result": { "timestamp": "2026-09-10T18:07:53.300Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"df5b18\",\"wall_time_seconds\":0.00000636,\"exit_code\":0,\"original_token_count\":129,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\\nVALID 2 0.03211637453089786\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.480866273244223, \\\"seconds\\\": 115.3}\\nVALID 5 0.0198911908453955\\n{\\\"epoch\\\": 6, \\\"loss\\\": 8.393172361911871, \\\"seconds\\\": 157.8}\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.307161746880947, \\\"seconds\\\": 178.5}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 8.237603322053568, \\\"seconds\\\": 197.4}\\n-rw-r--r-- 1 agent agent 484M Sep 10 18:06 /app/solution/interim_temporal_scores.npy\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:07:53.278Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d48dc40c-2c86-48f6-ae0e-38d622754874", "process_id": "91201", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/interim_temporal_scores.npy" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; ls -lh /app/solution/interim_temporal_scores.npy" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n-rw-r--r-- 1 agent agent 484M Sep 10 18:06 /app/solution/interim_temporal_scores.npy\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n-rw-r--r-- 1 agent agent 484M Sep 10 18:06 /app/solution/interim_temporal_scores.npy\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6360 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 5, \"loss\": 8.480866273244223, \"seconds\": 115.3}\nVALID 5 0.0198911908453955\n{\"epoch\": 6, \"loss\": 8.393172361911871, \"seconds\": 157.8}\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n-rw-r--r-- 1 agent agent 484M Sep 10 18:06 /app/solution/interim_temporal_scores.npy\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:07:53.297Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1f590d087d186e4b27b1b62d872", "usage": { "input_tokens": 51686, "cached_input_tokens": 12160, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 51748 }, "turn_token_usage": { "input_tokens": 1641110, "cached_input_tokens": 1531904, "cache_write_input_tokens": 0, "output_tokens": 21375, "reasoning_output_tokens": 8847, "total_tokens": 1662485 }, "thread_token_usage": { "input_tokens": 1641110, "cached_input_tokens": 1531904, "cache_write_input_tokens": 0, "output_tokens": 21375, "reasoning_output_tokens": 8847, "total_tokens": 1662485 } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-10T18:08:08.455Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:08:08.455Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:08:13.633Z", "callId": "call_44aCIlxdFDs9xZc6cLVWPnDy", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/tune.py';s=open(p).read().replace('from model import *','from model import *\\\\nfrom temporal_train import temporal_data')\\ns=s.replace(\\\"data=load_data('validation',cfg)\\\",\\\"data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg)\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 1 > /app/solution/temporal_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:08:14.830Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"f3759b\",\"wall_time_seconds\":1.000887873,\"session_id\":71674,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:08:13.767Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f1f98fac87d1a422813f6b2d8cc5", "usage": { "input_tokens": 52028, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 627, "reasoning_output_tokens": 443, "total_tokens": 52655 }, "turn_token_usage": { "input_tokens": 1693138, "cached_input_tokens": 1583360, "cache_write_input_tokens": 0, "output_tokens": 22002, "reasoning_output_tokens": 9290, "total_tokens": 1715140 }, "thread_token_usage": { "input_tokens": 1693138, "cached_input_tokens": 1583360, "cache_write_input_tokens": 0, "output_tokens": 22002, "reasoning_output_tokens": 9290, "total_tokens": 1715140 } } } ] }, { "id": 48, "role": "agent", "timestamp": "2026-09-10T18:08:33.341Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:08:33.341Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:08:35.282Z", "text": "Temporal fine-tuning peaked at its first completed epoch and dipped at the second. I’ll check earlier points in a fresh run and test simple popularity and trend calibration. Any calibration will be part of the item vector, preserving the exact dot-product score and offline item computation.\n" }, { "kind": "context", "timestamp": "2026-09-10T18:08:37.601Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d07aeeec-efd8-4667-a49a-edd123c29596", "process_id": "71674", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/tune.py';s=open(p).read().replace('from model import *','from model import *\\nfrom temporal_train import temporal_data')\ns=s.replace(\"data=load_data('validation',cfg)\",\"data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 1 > /app/solution/temporal_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/tune.py';s=open(p).read().replace('from model import *','from model import *\\nfrom temporal_train import temporal_data')\ns=s.replace(\"data=load_data('validation',cfg)\",\"data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 1 > /app/solution/temporal_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 23, "nanos": 770324613 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:08:50.677Z", "callId": "call_e61TUP8qtLd9OSRz3rdt2aHF", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/temporal_train.py';s=open(p).read();s=s.replace(\\\"p.add_argument('--infer',action='store_true');a=p.parse_args()\\\", \\\"p.add_argument('--infer',action='store_true');p.add_argument('--eval-steps',type=int,default=0);p.add_argument('--max-steps',type=int,default=0);p.add_argument('--dropout',type=float,default=-1);a=p.parse_args()\\\")\\ns=s.replace(\\\"cfg=argparse.Namespace(**c['args']);model=TwoTower\\\", \\\"cfg=argparse.Namespace(**c['args']);cfg.dropout=a.dropout if a.dropout>=0 else cfg.dropout;model=TwoTower\\\")\\ns=s.replace(\\\"for epoch in range(1,a.epochs+1):\\\", \\\"step=0\\\\n for epoch in range(1,a.epochs+1):\\\")\\ns=s.replace(\\\"opt.step();losses.append(loss.item())\\\", \\\"\\\"\\\"opt.step();losses.append(loss.item());step+=1\\n if (a.eval_steps and step%a.eval_steps==0) or (a.max_steps and step==a.max_steps):\\n if a.stage=='validation':\\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('STEP VALID',step,score,flush=True)\\n if score>best:\\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\\n else:\\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\\n model.train()\\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\\\"\\\"\\\")\\nopen(p,'w').write(s)\\nPY\\ntail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/temporal_calibration.log\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-10T18:08:50.938Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"2274ca\",\"wall_time_seconds\":0.000006711,\"exit_code\":0,\"original_token_count\":111,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\\nVALID 2 0.03211637453089786\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.307161746880947, \\\"seconds\\\": 178.5}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 8.237603322053568, \\\"seconds\\\": 197.4}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 8.170881289702196, \\\"seconds\\\": 216.8}\\n{\\\"epoch\\\": 10, \\\"loss\\\": 8.105766657071236, \\\"seconds\\\": 239.3}\\nVALID 10 0.020586506817820396\\nKilled\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:08:50.723Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f20f1be487d1bf84257dfb38c82f", "usage": { "input_tokens": 52713, "cached_input_tokens": 51840, "cache_write_input_tokens": 0, "output_tokens": 1032, "reasoning_output_tokens": 449, "total_tokens": 53745 }, "turn_token_usage": { "input_tokens": 1745851, "cached_input_tokens": 1635200, "cache_write_input_tokens": 0, "output_tokens": 23034, "reasoning_output_tokens": 9739, "total_tokens": 1768885 }, "thread_token_usage": { "input_tokens": 1745851, "cached_input_tokens": 1635200, "cache_write_input_tokens": 0, "output_tokens": 23034, "reasoning_output_tokens": 9739, "total_tokens": 1768885 } } }, { "kind": "context", "timestamp": "2026-09-10T18:08:50.875Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-91800bd7-d8ac-489e-9eb3-2fafaadc09bc", "process_id": "49861", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read();s=s.replace(\"p.add_argument('--infer',action='store_true');a=p.parse_args()\", \"p.add_argument('--infer',action='store_true');p.add_argument('--eval-steps',type=int,default=0);p.add_argument('--max-steps',type=int,default=0);p.add_argument('--dropout',type=float,default=-1);a=p.parse_args()\")\ns=s.replace(\"cfg=argparse.Namespace(**c['args']);model=TwoTower\", \"cfg=argparse.Namespace(**c['args']);cfg.dropout=a.dropout if a.dropout>=0 else cfg.dropout;model=TwoTower\")\ns=s.replace(\"for epoch in range(1,a.epochs+1):\", \"step=0\\n for epoch in range(1,a.epochs+1):\")\ns=s.replace(\"opt.step();losses.append(loss.item())\", \"\"\"opt.step();losses.append(loss.item());step+=1\n if (a.eval_steps and step%a.eval_steps==0) or (a.max_steps and step==a.max_steps):\n if a.stage=='validation':\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('STEP VALID',step,score,flush=True)\n if score>best:\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\n model.train()\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\"\"\")\nopen(p,'w').write(s)\nPY\ntail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/temporal_calibration.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/temporal_train.py';s=open(p).read();s=s.replace(\"p.add_argument('--infer',action='store_true');a=p.parse_args()\", \"p.add_argument('--infer',action='store_true');p.add_argument('--eval-steps',type=int,default=0);p.add_argument('--max-steps',type=int,default=0);p.add_argument('--dropout',type=float,default=-1);a=p.parse_args()\")\ns=s.replace(\"cfg=argparse.Namespace(**c['args']);model=TwoTower\", \"cfg=argparse.Namespace(**c['args']);cfg.dropout=a.dropout if a.dropout>=0 else cfg.dropout;model=TwoTower\")\ns=s.replace(\"for epoch in range(1,a.epochs+1):\", \"step=0\\n for epoch in range(1,a.epochs+1):\")\ns=s.replace(\"opt.step();losses.append(loss.item())\", \"\"\"opt.step();losses.append(loss.item());step+=1\n if (a.eval_steps and step%a.eval_steps==0) or (a.max_steps and step==a.max_steps):\n if a.stage=='validation':\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('STEP VALID',step,score,flush=True)\n if score>best:\n best=score;torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n torch.save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/'submission.parquet')\n model.train()\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\"\"\")\nopen(p,'w').write(s)\nPY\ntail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log; tail -5 /app/solution/temporal_calibration.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\nKilled\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\nKilled\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6711 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 7, \"loss\": 8.307161746880947, \"seconds\": 178.5}\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\nKilled\n" } } } ] }, { "id": 49, "role": "agent", "timestamp": "2026-09-10T18:08:59.143Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:08:59.143Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:09:02.787Z", "callId": "call_WJ2LC89dOs9L6EYpNIctpHm8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; python - <<'PY'\\nfrom pathlib import Path\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().replace(b'\\\\0',b' ').decode()\\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'): print(p.name,cmd)\\n except:pass\\nPY\",\"max_output_tokens\":1500}));\n" }, "result": { "timestamp": "2026-09-10T18:09:03.047Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"3e203e\",\"wall_time_seconds\":0.028463846,\"exit_code\":0,\"original_token_count\":96,\"output\":\"low 0\\nhigh 0\\nmax 1240\\noom 1\\noom_kill 1\\noom_group_kill 0\\nsock_throttled 0\\n3393515520\\n685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 \\n748 /opt/task-venv/bin/python /app/solution/model.py --name vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 70 --eval-every 5 \\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:09:02.883Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f233378087d18dcf9e20050318bb", "usage": { "input_tokens": 53995, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 245, "reasoning_output_tokens": 113, "total_tokens": 54240 }, "turn_token_usage": { "input_tokens": 1799846, "cached_input_tokens": 1687680, "cache_write_input_tokens": 0, "output_tokens": 23279, "reasoning_output_tokens": 9852, "total_tokens": 1823125 }, "thread_token_usage": { "input_tokens": 1799846, "cached_input_tokens": 1687680, "cache_write_input_tokens": 0, "output_tokens": 23279, "reasoning_output_tokens": 9852, "total_tokens": 1823125 } } }, { "kind": "context", "timestamp": "2026-09-10T18:09:03.032Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b2021b67-5763-450c-8dd6-26c6c49fc7a3", "process_id": "98133", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'): print(p.name,cmd)\n except:pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.events; cat /sys/fs/cgroup/memory.current; python - <<'PY'\nfrom pathlib import Path\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/'): print(p.name,cmd)\n except:pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 \n748 /opt/task-venv/bin/python /app/solution/model.py --name vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 70 --eval-every 5 \n", "stderr": "", "aggregated_output": "685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 \n748 /opt/task-venv/bin/python /app/solution/model.py --name vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 70 --eval-every 5 \n", "exit_code": 0, "duration": { "secs": 0, "nanos": 30362378 }, "formatted_output": "685 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 \n748 /opt/task-venv/bin/python /app/solution/model.py --name vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 70 --eval-every 5 \n" } } } ] }, { "id": 50, "role": "agent", "timestamp": "2026-09-10T18:09:12.320Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:09:12.320Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:09:13.774Z", "callId": "call_mRwv3GJv5R7NV54u7Cwayv2F", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:09:13.904Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"dec7c2\",\"wall_time_seconds\":0.00000508,\"exit_code\":0,\"original_token_count\":109,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\\nVALID 2 0.03211637453089786\\n{\\\"epoch\\\": 8, \\\"loss\\\": 8.237603322053568, \\\"seconds\\\": 197.4}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 8.170881289702196, \\\"seconds\\\": 216.8}\\n{\\\"epoch\\\": 10, \\\"loss\\\": 8.105766657071236, \\\"seconds\\\": 239.3}\\nVALID 10 0.020586506817820396\\n{\\\"epoch\\\": 11, \\\"loss\\\": 8.040272529308613, \\\"seconds\\\": 278.9}\\n\"}\n{\"current_time\":\"2026-09-10 18:09:13 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:09:13.849Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5660a8b3-e364-4c00-ac0c-c0d0411508f6", "process_id": "65107", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5080 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\n{\"epoch\": 8, \"loss\": 8.237603322053568, \"seconds\": 197.4}\n{\"epoch\": 9, \"loss\": 8.170881289702196, \"seconds\": 216.8}\n{\"epoch\": 10, \"loss\": 8.105766657071236, \"seconds\": 239.3}\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:09:13.898Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f23f3ef487d1a5b382e58f4aa092", "usage": { "input_tokens": 54445, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 268, "reasoning_output_tokens": 209, "total_tokens": 54713 }, "turn_token_usage": { "input_tokens": 1854291, "cached_input_tokens": 1741440, "cache_write_input_tokens": 0, "output_tokens": 23547, "reasoning_output_tokens": 10061, "total_tokens": 1877838 }, "thread_token_usage": { "input_tokens": 1854291, "cached_input_tokens": 1741440, "cache_write_input_tokens": 0, "output_tokens": 23547, "reasoning_output_tokens": 10061, "total_tokens": 1877838 } } } ] }, { "id": 51, "role": "agent", "timestamp": "2026-09-10T18:09:33.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:09:33.865Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:09:33.988Z", "callId": "call_U0TLviACxisL7q9BIkDoZMCH", "name": "sleep", "input": { "duration_ms": 30000 }, "result": { "timestamp": "2026-09-10T18:10:04.033Z", "content": "Wall time: 30.0256 seconds\nSleep completed.", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:09:34.062Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f24a1a7887d19e01e20dc402772b", "usage": { "input_tokens": 54980, "cached_input_tokens": 54272, "cache_write_input_tokens": 0, "output_tokens": 526, "reasoning_output_tokens": 505, "total_tokens": 55506 }, "turn_token_usage": { "input_tokens": 1909271, "cached_input_tokens": 1795712, "cache_write_input_tokens": 0, "output_tokens": 24073, "reasoning_output_tokens": 10566, "total_tokens": 1933344 }, "thread_token_usage": { "input_tokens": 1909271, "cached_input_tokens": 1795712, "cache_write_input_tokens": 0, "output_tokens": 24073, "reasoning_output_tokens": 10566, "total_tokens": 1933344 } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:04.004Z", "data": { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_U0TLviACxisL7q9BIkDoZMCH", "durationMs": 30000 } } }, { "kind": "tool", "timestamp": "2026-09-10T18:10:08.283Z", "callId": "call_B3bwYzMXmwtnBEJplyYngcRG", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:10:08.419Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"b8f260\",\"wall_time_seconds\":0.00000574,\"exit_code\":0,\"original_token_count\":131,\"output\":\"EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\\nVALID 1 0.03381411485331253\\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\\nVALID 2 0.03211637453089786\\nEPOCH 3 loss 7.679805579811636 seconds 379.2435493469238\\nVALID 3 0.03251125590759313\\nVALID 10 0.020586506817820396\\n{\\\"epoch\\\": 11, \\\"loss\\\": 8.040272529308613, \\\"seconds\\\": 278.9}\\n{\\\"epoch\\\": 12, \\\"loss\\\": 7.970042748328967, \\\"seconds\\\": 299.0}\\n{\\\"epoch\\\": 13, \\\"loss\\\": 7.912647504072923, \\\"seconds\\\": 317.1}\\n{\\\"epoch\\\": 14, \\\"loss\\\": 7.8598926434150105, \\\"seconds\\\": 336.2}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:10:08.358Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a5baca9d-4231-4b24-926c-cf7fce498f08", "process_id": "75224", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/temporal.log; tail -5 /app/solution/vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\nEPOCH 3 loss 7.679805579811636 seconds 379.2435493469238\nVALID 3 0.03251125590759313\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\nEPOCH 3 loss 7.679805579811636 seconds 379.2435493469238\nVALID 3 0.03251125590759313\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5740 }, "formatted_output": "EPOCH 1 loss 8.264096314256841 seconds 131.98639750480652\nVALID 1 0.03381411485331253\nEPOCH 2 loss 7.907028165730563 seconds 259.51356744766235\nVALID 2 0.03211637453089786\nEPOCH 3 loss 7.679805579811636 seconds 379.2435493469238\nVALID 3 0.03251125590759313\nVALID 10 0.020586506817820396\n{\"epoch\": 11, \"loss\": 8.040272529308613, \"seconds\": 278.9}\n{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:08.404Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f27c6e6487d1946847c849de6248", "usage": { "input_tokens": 55529, "cached_input_tokens": 54784, "cache_write_input_tokens": 0, "output_tokens": 48, "reasoning_output_tokens": 0, "total_tokens": 55577 }, "turn_token_usage": { "input_tokens": 1964800, "cached_input_tokens": 1850496, "cache_write_input_tokens": 0, "output_tokens": 24121, "reasoning_output_tokens": 10566, "total_tokens": 1988921 }, "thread_token_usage": { "input_tokens": 1964800, "cached_input_tokens": 1850496, "cache_write_input_tokens": 0, "output_tokens": 24121, "reasoning_output_tokens": 10566, "total_tokens": 1988921 } } } ] }, { "id": 52, "role": "agent", "timestamp": "2026-09-10T18:10:12.620Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:10:12.620Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:10:14.606Z", "text": "The saved submission now uses the stronger temporal checkpoint. Further temporal epochs remain below its first-epoch score, while the variational model has reached 0.02059 on its own. I’m keeping the best checkpoints and checking calibration before spending more time on long training runs.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:10:17.188Z", "callId": "call_DgGIk6MlOkA3Vwao8ha1ECyY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"kill -TERM 685\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:10:18.407Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"79a1cb\",\"wall_time_seconds\":1.000516389,\"session_id\":52331,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:10:17.260Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f280a4e087d1aa467fd05ce16e00", "usage": { "input_tokens": 55863, "cached_input_tokens": 55296, "cache_write_input_tokens": 0, "output_tokens": 180, "reasoning_output_tokens": 23, "total_tokens": 56043 }, "turn_token_usage": { "input_tokens": 2020663, "cached_input_tokens": 1905792, "cache_write_input_tokens": 0, "output_tokens": 24301, "reasoning_output_tokens": 10589, "total_tokens": 2044964 }, "thread_token_usage": { "input_tokens": 2020663, "cached_input_tokens": 1905792, "cache_write_input_tokens": 0, "output_tokens": 24301, "reasoning_output_tokens": 10589, "total_tokens": 2044964 } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:17.473Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-043d11ac-55eb-4a01-a06f-0c3faa2b485a", "process_id": "50351", "command": [ "/bin/sh", "-lc", "cat /sys/fs/cgroup/memory.current; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 > /app/solution/temporal.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /sys/fs/cgroup/memory.current; OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --epochs 6 --threads 4 > /app/solution/temporal.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 143, "duration": { "secs": 428, "nanos": 510407839 }, "formatted_output": "" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-10T18:10:21.349Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:10:21.349Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:10:23.307Z", "callId": "call_VsKsgw2njWMwnMj0C1viRtei", "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/temporal_validation.parquet > /app/solution/public_temporal.json\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:10:24.501Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"450edb\",\"wall_time_seconds\":1.002368785,\"session_id\":8892,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:10:23.425Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f28a9f7887d191a74dd649678751", "usage": { "input_tokens": 56101, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 98, "reasoning_output_tokens": 23, "total_tokens": 56199 }, "turn_token_usage": { "input_tokens": 2076764, "cached_input_tokens": 1961472, "cache_write_input_tokens": 0, "output_tokens": 24399, "reasoning_output_tokens": 10612, "total_tokens": 2101163 }, "thread_token_usage": { "input_tokens": 2076764, "cached_input_tokens": 1961472, "cache_write_input_tokens": 0, "output_tokens": 24399, "reasoning_output_tokens": 10612, "total_tokens": 2101163 } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:28.117Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da7a8246-f08e-4589-ab39-e3dbc49b6d7f", "process_id": "8892", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/temporal_validation.parquet > /app/solution/public_temporal.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/temporal_validation.parquet > /app/solution/public_temporal.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 4, "nanos": 632361358 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:34.166Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1a4e5895-7224-4585-89dd-2241c94da449", "process_id": "52331", "command": [ "/bin/sh", "-lc", "kill -TERM 685\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "kill -TERM 685\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 16, "nanos": 753124431 }, "formatted_output": "" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-10T18:10:38.791Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:10:38.791Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:10:40.099Z", "callId": "call_VuxSUTaKvSkoz9daGGukrdlq", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/vae.log; tail -8 /app/solution/temporal_calibration.log; cat /app/solution/public_temporal.json\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-10T18:10:40.235Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ad5f21\",\"wall_time_seconds\":0.000004261,\"exit_code\":0,\"original_token_count\":103,\"output\":\"{\\\"epoch\\\": 12, \\\"loss\\\": 7.970042748328967, \\\"seconds\\\": 299.0}\\n{\\\"epoch\\\": 13, \\\"loss\\\": 7.912647504072923, \\\"seconds\\\": 317.1}\\n{\\\"epoch\\\": 14, \\\"loss\\\": 7.8598926434150105, \\\"seconds\\\": 336.2}\\n{\\\"epoch\\\": 15, \\\"loss\\\": 7.805606689208593, \\\"seconds\\\": 353.5}\\nVALID 15 0.02162968673159766\\nKilled\\n{\\n \\\"model\\\": \\\"/app/solution/temporal_validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.033814,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:10:40.193Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f290b5e887d1b6e913be05fcd756", "usage": { "input_tokens": 56256, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 474, "reasoning_output_tokens": 412, "total_tokens": 56730 }, "turn_token_usage": { "input_tokens": 2133020, "cached_input_tokens": 2017408, "cache_write_input_tokens": 0, "output_tokens": 24873, "reasoning_output_tokens": 11024, "total_tokens": 2157893 }, "thread_token_usage": { "input_tokens": 2133020, "cached_input_tokens": 2017408, "cache_write_input_tokens": 0, "output_tokens": 24873, "reasoning_output_tokens": 11024, "total_tokens": 2157893 } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:40.205Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e4299999-dc10-49af-9a36-cbe1c108bd10", "process_id": "73774", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/vae.log; tail -8 /app/solution/temporal_calibration.log; cat /app/solution/public_temporal.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/vae.log; tail -8 /app/solution/temporal_calibration.log; cat /app/solution/public_temporal.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n{\"epoch\": 15, \"loss\": 7.805606689208593, \"seconds\": 353.5}\nVALID 15 0.02162968673159766\nKilled\n{\n \"model\": \"/app/solution/temporal_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.033814,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n{\"epoch\": 15, \"loss\": 7.805606689208593, \"seconds\": 353.5}\nVALID 15 0.02162968673159766\nKilled\n{\n \"model\": \"/app/solution/temporal_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.033814,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4261 }, "formatted_output": "{\"epoch\": 12, \"loss\": 7.970042748328967, \"seconds\": 299.0}\n{\"epoch\": 13, \"loss\": 7.912647504072923, \"seconds\": 317.1}\n{\"epoch\": 14, \"loss\": 7.8598926434150105, \"seconds\": 336.2}\n{\"epoch\": 15, \"loss\": 7.805606689208593, \"seconds\": 353.5}\nVALID 15 0.02162968673159766\nKilled\n{\n \"model\": \"/app/solution/temporal_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.033814,\n \"quality_users\": 4263\n }\n}\n" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-10T18:10:54.376Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:10:54.376Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:10:55.540Z", "callId": "call_TVjFo7HpVrGBaX3Gpo4GcJVz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/tune.py; cat /sys/fs/cgroup/memory.events\",\"max_output_tokens\":3000}));\n" }, "result": { "timestamp": "2026-09-10T18:10:55.670Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"be8ab3\",\"wall_time_seconds\":0.00000539,\"exit_code\":0,\"original_token_count\":604,\"output\":\"\\\"\\\"\\\"Validation-only search of history aggregation and item prior calibration.\\\"\\\"\\\"\\nfrom model import *\\nfrom temporal_train import temporal_data\\nimport itertools\\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\\nseed(42);torch.set_num_threads(a.threads)\\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\\nwith torch.no_grad():\\n emb=model.item_tower(torch.arange(N))\\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\\n cfg.decay=decay;cfg.power=power;data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\\n for start in range(0,len(rows),128):\\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\\n for (pop,tr),top in zip(params,tops):\\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\\n if score>best:best=score;print('BEST',r,flush=True)\\n print('DONE',decay,power,flush=True)\\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\\nlow 0\\nhigh 0\\nmax 2289\\noom 3\\noom_kill 2\\noom_group_kill 0\\nsock_throttled 0\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:10:55.595Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-032e066d-6e43-495b-b4a8-423d1e98dfe7", "process_id": "54035", "command": [ "/bin/sh", "-lc", "cat /app/solution/tune.py; cat /sys/fs/cgroup/memory.events" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/tune.py", "name": "tune.py", "path": "/app/solution/tune.py" }, { "type": "read", "cmd": "cat /sys/fs/cgroup/memory.events", "name": "memory.events", "path": "/sys/fs/cgroup/memory.events" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\"\"\"Validation-only search of history aggregation and item prior calibration.\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\nimport itertools\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\nseed(42);torch.set_num_threads(a.threads)\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\nwith torch.no_grad():\n emb=model.item_tower(torch.arange(N))\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\n cfg.decay=decay;cfg.power=power;data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\n for (pop,tr),top in zip(params,tops):\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\n if score>best:best=score;print('BEST',r,flush=True)\n print('DONE',decay,power,flush=True)\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\nlow 0\nhigh 0\nmax 2289\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 0\n", "stderr": "", "aggregated_output": "\"\"\"Validation-only search of history aggregation and item prior calibration.\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\nimport itertools\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\nseed(42);torch.set_num_threads(a.threads)\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\nwith torch.no_grad():\n emb=model.item_tower(torch.arange(N))\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\n cfg.decay=decay;cfg.power=power;data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\n for (pop,tr),top in zip(params,tops):\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\n if score>best:best=score;print('BEST',r,flush=True)\n print('DONE',decay,power,flush=True)\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\nlow 0\nhigh 0\nmax 2289\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 0\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5390 }, "formatted_output": "\"\"\"Validation-only search of history aggregation and item prior calibration.\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\nimport itertools\np=argparse.ArgumentParser();p.add_argument('checkpoint');p.add_argument('--decays',default='180');p.add_argument('--powers',default='0.3');p.add_argument('--name',default='tune');p.add_argument('--threads',type=int,default=4);a=p.parse_args()\nseed(42);torch.set_num_threads(a.threads)\nc=torch.load(a.checkpoint,weights_only=False);cfg=argparse.Namespace(**c['args']);model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);model.eval();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];\nf=pd.read_parquet(O/'validation_events.parquet');cnt=f.groupby('iid').agg(count=('count','sum'),organic=('organic','sum'),recent=('recent','sum'),org_recent=('org_recent','sum')).reindex(range(N),fill_value=0)\nlogpop=torch.tensor(np.log(cnt['count'].to_numpy()+10),dtype=torch.float32);trend=torch.tensor(np.log(cnt.org_recent.to_numpy()+5)-np.log(cnt.organic.to_numpy()+10),dtype=torch.float32)\nparams=list(itertools.product([-.5,-.25,0,.25,.5],[-.5,0,.5,1.,1.5]));results=[];best=-1\nwith torch.no_grad():\n emb=model.item_tower(torch.arange(N))\n for decay,power in itertools.product(map(float,a.decays.split(',')),map(float,a.powers.split(','))):\n cfg.decay=decay;cfg.power=power;data=temporal_data('validation') if 'temporal_args' in c else load_data('validation',cfg);uids,ptr,ids,w=data;rows=pd.Index(uids).get_indexer(z['uids']);tops=[[] for _ in params]\n for start in range(0,len(rows),128):\n bi,off,bw,_,_=batch_data(rows[start:start+128],ptr,ids,w);uv,_=model.users(bi,off,bw);s=uv@emb.T;eligible=torch.tensor(np.unpackbits(z['packed_eligible'][start:start+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9)\n for ix,(pop,tr) in enumerate(params):tops[ix].append(torch.topk(s+pop*logpop+tr*trend,10,dim=1).indices.numpy())\n for (pop,tr),top in zip(params,tops):\n score=metrics(np.concatenate(top),gold,ideal);r=dict(decay=decay,power=power,pop=pop,trend=tr,score=score);results.append(r)\n if score>best:best=score;print('BEST',r,flush=True)\n print('DONE',decay,power,flush=True)\njson.dump(sorted(results,key=lambda x:-x['score']),open(O/(a.name+'.json'),'w'),indent=2)\nlow 0\nhigh 0\nmax 2289\noom 3\noom_kill 2\noom_group_kill 0\nsock_throttled 0\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:10:55.665Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f2a07d5487d1a48b9a96a44a3671", "usage": { "input_tokens": 56964, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 442, "reasoning_output_tokens": 396, "total_tokens": 57406 }, "turn_token_usage": { "input_tokens": 2189984, "cached_input_tokens": 2073472, "cache_write_input_tokens": 0, "output_tokens": 25315, "reasoning_output_tokens": 11420, "total_tokens": 2215299 }, "thread_token_usage": { "input_tokens": 2189984, "cached_input_tokens": 2073472, "cache_write_input_tokens": 0, "output_tokens": 25315, "reasoning_output_tokens": 11420, "total_tokens": 2215299 } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-10T18:11:04.006Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:11:04.006Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:11:10.386Z", "callId": "call_42ChqoR8u7EcSGnctHfRmoX8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/tune.py';s=open(p).read().replace('import itertools','import itertools, resource\\\\ndef mem(label): print(label,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\\\\nmem(\\\"start\\\")')\\ns=s.replace(\\\"params=list\\\", \\\"mem('loaded')\\\\ndel f\\\\nparams=list\\\").replace(\\\"for start in range(0,len(rows),128):\\\", \\\"mem('data ready')\\\\n for start in range(0,len(rows),128):\\\\n if start%512==0:mem(start)\\\")\\nopen(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:11:11.570Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"e57a90\",\"wall_time_seconds\":1.001782609,\"session_id\":41685,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:11:10.483Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f2afe10887d1974977e11180182e", "usage": { "input_tokens": 58199, "cached_input_tokens": 56832, "cache_write_input_tokens": 0, "output_tokens": 448, "reasoning_output_tokens": 213, "total_tokens": 58647 }, "turn_token_usage": { "input_tokens": 2248183, "cached_input_tokens": 2130304, "cache_write_input_tokens": 0, "output_tokens": 25763, "reasoning_output_tokens": 11633, "total_tokens": 2273946 }, "thread_token_usage": { "input_tokens": 2248183, "cached_input_tokens": 2130304, "cache_write_input_tokens": 0, "output_tokens": 25763, "reasoning_output_tokens": 11633, "total_tokens": 2273946 } } } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-10T18:11:22.384Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:11:22.384Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:11:23.263Z", "callId": "call_Qp0apxCaVRFxZAJx1uJEiagh", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -20 /app/solution/temporal_calibration.log; tail -4 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:11:23.384Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"adf318\",\"wall_time_seconds\":0.000005261,\"exit_code\":0,\"original_token_count\":81,\"output\":\"start 285340\\nloaded 977544\\ndata ready 977544\\n0 977544\\n512 1674364\\n1024 2408416\\n1536 3563300\\n2048 4403340\\nVALID 15 0.02162968673159766\\n{\\\"epoch\\\": 16, \\\"loss\\\": 7.7631748884152145, \\\"seconds\\\": 383.8}\\n{\\\"epoch\\\": 17, \\\"loss\\\": 7.72749183116815, \\\"seconds\\\": 397.1}\\n{\\\"epoch\\\": 18, \\\"loss\\\": 7.7009535019214335, \\\"seconds\\\": 411.9}\\n6140035072\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:11:23.327Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dab05339-3dd2-46b4-841a-40277b69326d", "process_id": "27811", "command": [ "/bin/sh", "-lc", "tail -20 /app/solution/temporal_calibration.log; tail -4 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -20 /app/solution/temporal_calibration.log; tail -4 /app/solution/vae.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\nVALID 15 0.02162968673159766\n{\"epoch\": 16, \"loss\": 7.7631748884152145, \"seconds\": 383.8}\n{\"epoch\": 17, \"loss\": 7.72749183116815, \"seconds\": 397.1}\n{\"epoch\": 18, \"loss\": 7.7009535019214335, \"seconds\": 411.9}\n6140035072\n", "stderr": "", "aggregated_output": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\nVALID 15 0.02162968673159766\n{\"epoch\": 16, \"loss\": 7.7631748884152145, \"seconds\": 383.8}\n{\"epoch\": 17, \"loss\": 7.72749183116815, \"seconds\": 397.1}\n{\"epoch\": 18, \"loss\": 7.7009535019214335, \"seconds\": 411.9}\n6140035072\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5261 }, "formatted_output": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\nVALID 15 0.02162968673159766\n{\"epoch\": 16, \"loss\": 7.7631748884152145, \"seconds\": 383.8}\n{\"epoch\": 17, \"loss\": 7.72749183116815, \"seconds\": 397.1}\n{\"epoch\": 18, \"loss\": 7.7009535019214335, \"seconds\": 411.9}\n6140035072\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:11:23.373Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f2bfc59c87d185ac9e1c6083a933", "usage": { "input_tokens": 58705, "cached_input_tokens": 57984, "cache_write_input_tokens": 0, "output_tokens": 192, "reasoning_output_tokens": 130, "total_tokens": 58897 }, "turn_token_usage": { "input_tokens": 2306888, "cached_input_tokens": 2188288, "cache_write_input_tokens": 0, "output_tokens": 25955, "reasoning_output_tokens": 11763, "total_tokens": 2332843 }, "thread_token_usage": { "input_tokens": 2306888, "cached_input_tokens": 2188288, "cache_write_input_tokens": 0, "output_tokens": 25955, "reasoning_output_tokens": 11763, "total_tokens": 2332843 } } }, { "kind": "context", "timestamp": "2026-09-10T18:11:29.144Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d7f27c86-5cff-4a54-93c1-44b29e611698", "process_id": "41685", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/tune.py';s=open(p).read().replace('import itertools','import itertools, resource\\ndef mem(label): print(label,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\\nmem(\"start\")')\ns=s.replace(\"params=list\", \"mem('loaded')\\ndel f\\nparams=list\").replace(\"for start in range(0,len(rows),128):\", \"mem('data ready')\\n for start in range(0,len(rows),128):\\n if start%512==0:mem(start)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/tune.py';s=open(p).read().replace('import itertools','import itertools, resource\\ndef mem(label): print(label,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,flush=True)\\nmem(\"start\")')\ns=s.replace(\"params=list\", \"mem('loaded')\\ndel f\\nparams=list\").replace(\"for start in range(0,len(rows),128):\", \"mem('data ready')\\n for start in range(0,len(rows),128):\\n if start%512==0:mem(start)\")\nopen(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/tune.py /app/solution/temporal.pt --name temporal_calibration --threads 4 > /app/solution/temporal_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 137, "duration": { "secs": 18, "nanos": 569931692 }, "formatted_output": "" } } } ] }, { "id": 58, "role": "agent", "timestamp": "2026-09-10T18:11:36.605Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:11:36.605Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:11:40.489Z", "callId": "call_5iCpPtwlAKfOtr3mplFkDbsQ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -15 /app/solution/temporal_calibration.log; python - <<'PY'\\nfrom pathlib import Path\\nimport os,signal\\nfor p in Path('/proc').glob('[0-9]*'):\\n try:\\n cmd=(p/'cmdline').read_bytes().replace(b'\\\\0',b' ').decode()\\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/tune.py'):os.kill(int(p.name),signal.SIGTERM)\\n except:pass\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:11:40.758Z", "content": "Script completed\nWall time 0.3 seconds\nOutput:\n\n{\"chunk_id\":\"4649c4\",\"wall_time_seconds\":0.00000928,\"exit_code\":0,\"original_token_count\":45,\"output\":\"start 285340\\nloaded 977544\\ndata ready 977544\\n0 977544\\n512 1674364\\n1024 2408416\\n1536 3563300\\n2048 4403340\\n2560 5138372\\n3072 5753352\\n3584 6158276\\n4096 6488280\\n4608 6855780\\nKilled\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:11:40.592Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f2cba66487d19c4a7582b635d471", "usage": { "input_tokens": 59101, "cached_input_tokens": 58496, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 230, "total_tokens": 59469 }, "turn_token_usage": { "input_tokens": 2365989, "cached_input_tokens": 2246784, "cache_write_input_tokens": 0, "output_tokens": 26323, "reasoning_output_tokens": 11993, "total_tokens": 2392312 }, "thread_token_usage": { "input_tokens": 2365989, "cached_input_tokens": 2246784, "cache_write_input_tokens": 0, "output_tokens": 26323, "reasoning_output_tokens": 11993, "total_tokens": 2392312 } } }, { "kind": "context", "timestamp": "2026-09-10T18:11:40.723Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cc697c81-3c05-4bd1-99b7-c7870e9bc68e", "process_id": "9698", "command": [ "/bin/sh", "-lc", "tail -15 /app/solution/temporal_calibration.log; python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/tune.py'):os.kill(int(p.name),signal.SIGTERM)\n except:pass\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -15 /app/solution/temporal_calibration.log; python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*'):\n try:\n cmd=(p/'cmdline').read_bytes().replace(b'\\0',b' ').decode()\n if cmd.startswith('/opt/task-venv/bin/python /app/solution/tune.py'):os.kill(int(p.name),signal.SIGTERM)\n except:pass\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\n2560 5138372\n3072 5753352\n3584 6158276\n4096 6488280\n4608 6855780\nKilled\n", "stderr": "", "aggregated_output": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\n2560 5138372\n3072 5753352\n3584 6158276\n4096 6488280\n4608 6855780\nKilled\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 9280 }, "formatted_output": "start 285340\nloaded 977544\ndata ready 977544\n0 977544\n512 1674364\n1024 2408416\n1536 3563300\n2048 4403340\n2560 5138372\n3072 5753352\n3584 6158276\n4096 6488280\n4608 6855780\nKilled\n" } } } ] }, { "id": 59, "role": "agent", "timestamp": "2026-09-10T18:11:52.303Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:11:52.303Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:11:57.396Z", "callId": "call_bQJWgNCo2I4Emx4exc4QVIWc", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\\nimport torch,resource,numpy as np,gc\\ntorch.set_num_threads(1)\\ns=torch.randn(128,30000);pop=torch.randn(30000);l=[]\\nwith torch.no_grad():\\n for st in range(100):\\n a=torch.topk(s+pop,10,dim=1).indices.numpy();l.append(a)\\n if st%20==0:print(st,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,a.nbytes,a.base.untyped_storage().nbytes(),a.base.grad_fn,flush=True)\\nPY\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:11:58.572Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"2bcc49\",\"wall_time_seconds\":1.000647109,\"session_id\":22920,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:11:57.516Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f2de6f2c87d1b8c88befadf56750", "usage": { "input_tokens": 59610, "cached_input_tokens": 58880, "cache_write_input_tokens": 0, "output_tokens": 416, "reasoning_output_tokens": 236, "total_tokens": 60026 }, "turn_token_usage": { "input_tokens": 2425599, "cached_input_tokens": 2305664, "cache_write_input_tokens": 0, "output_tokens": 26739, "reasoning_output_tokens": 12229, "total_tokens": 2452338 }, "thread_token_usage": { "input_tokens": 2425599, "cached_input_tokens": 2305664, "cache_write_input_tokens": 0, "output_tokens": 26739, "reasoning_output_tokens": 12229, "total_tokens": 2452338 } } }, { "kind": "context", "timestamp": "2026-09-10T18:12:01.810Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-950f477d-bfa6-4582-88d3-4280dd2c5117", "process_id": "22920", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport torch,resource,numpy as np,gc\ntorch.set_num_threads(1)\ns=torch.randn(128,30000);pop=torch.randn(30000);l=[]\nwith torch.no_grad():\n for st in range(100):\n a=torch.topk(s+pop,10,dim=1).indices.numpy();l.append(a)\n if st%20==0:print(st,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,a.nbytes,a.base.untyped_storage().nbytes(),a.base.grad_fn,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nimport torch,resource,numpy as np,gc\ntorch.set_num_threads(1)\ns=torch.randn(128,30000);pop=torch.randn(30000);l=[]\nwith torch.no_grad():\n for st in range(100):\n a=torch.topk(s+pop,10,dim=1).indices.numpy();l.append(a)\n if st%20==0:print(st,resource.getrusage(resource.RUSAGE_SELF).ru_maxrss,a.nbytes,a.base.untyped_storage().nbytes(),a.base.grad_fn,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 246148 10240 10240 None\n20 501724 10240 10240 None\n40 801756 10240 10240 None\n60 1101788 10240 10240 None\n80 1221724 10240 10240 None\n", "stderr": "", "aggregated_output": "0 246148 10240 10240 None\n20 501724 10240 10240 None\n40 801756 10240 10240 None\n60 1101788 10240 10240 None\n80 1221724 10240 10240 None\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 239522124 }, "formatted_output": "0 246148 10240 10240 None\n20 501724 10240 10240 None\n40 801756 10240 10240 None\n60 1101788 10240 10240 None\n80 1221724 10240 10240 None\n" } } } ] }, { "id": 60, "role": "agent", "timestamp": "2026-09-10T18:12:03.572Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:12:03.572Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:12:05.596Z", "text": "The public evaluator confirms 0.033814 NDCG@10 for the temporal checkpoint. A calibration run exhausted memory and stopped; the saved model and submission are intact. I’m changing the calibration search to score batches incrementally so it stays within the memory limit.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:12:06.826Z", "callId": "call_iMnn1v7ZFE5qoYHWLSseAN8m", "name": "exec", "input": { "code": "text(await tools.write_stdin({session_id:22920,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:12:06.945Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"c74fad\",\"wall_time_seconds\":0.0000034,\"exit_code\":0,\"original_token_count\":34,\"output\":\"0 246148 10240 10240 None\\n20 501724 10240 10240 None\\n40 801756 10240 10240 None\\n60 1101788 10240 10240 None\\n80 1221724 10240 10240 None\\n\"}\n{\"current_time\":\"2026-09-10 18:12:06 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:12:06.942Z", "data": { "type": "token_usage_record", "thread_id": 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7.586650567177014, \\\"seconds\\\": 483.6}\\n{\\\"epoch\\\": 23, \\\"loss\\\": 7.571064955148941, \\\"seconds\\\": 501.4}\\nKilled\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:13:15.465Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2bf1e325-48f0-4104-ae02-f929d00f4caf", "process_id": "10528", "command": [ "/bin/sh", "-lc", "tail -12 /app/solution/temporal_calibration.log; tail -5 /app/solution/vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -12 /app/solution/temporal_calibration.log; tail -5 /app/solution/vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "3072 1427652\n3584 1488500\n4096 2042552\n4608 2957616\n5120 3437712\n5632 3783788\n6144 4547804\n6656 4907604\n7168 5762776\n7680 6377780\n8192 7068864\nKilled\nVALID 20 0.02211889188780754\n{\"epoch\": 21, \"loss\": 7.63392929541759, \"seconds\": 469.6}\n{\"epoch\": 22, \"loss\": 7.586650567177014, \"seconds\": 483.6}\n{\"epoch\": 23, \"loss\": 7.571064955148941, \"seconds\": 501.4}\nKilled\n", "stderr": "", "aggregated_output": "3072 1427652\n3584 1488500\n4096 2042552\n4608 2957616\n5120 3437712\n5632 3783788\n6144 4547804\n6656 4907604\n7168 5762776\n7680 6377780\n8192 7068864\nKilled\nVALID 20 0.02211889188780754\n{\"epoch\": 21, \"loss\": 7.63392929541759, \"seconds\": 469.6}\n{\"epoch\": 22, \"loss\": 7.586650567177014, \"seconds\": 483.6}\n{\"epoch\": 23, \"loss\": 7.571064955148941, \"seconds\": 501.4}\nKilled\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4320 }, "formatted_output": "3072 1427652\n3584 1488500\n4096 2042552\n4608 2957616\n5120 3437712\n5632 3783788\n6144 4547804\n6656 4907604\n7168 5762776\n7680 6377780\n8192 7068864\nKilled\nVALID 20 0.02211889188780754\n{\"epoch\": 21, \"loss\": 7.63392929541759, \"seconds\": 469.6}\n{\"epoch\": 22, \"loss\": 7.586650567177014, \"seconds\": 483.6}\n{\"epoch\": 23, \"loss\": 7.571064955148941, \"seconds\": 501.4}\nKilled\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:13:15.532Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f3363cb487d1af368f2a8af073ca", "usage": { "input_tokens": 61479, "cached_input_tokens": 61184, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 61529 }, "turn_token_usage": { "input_tokens": 2790533, "cached_input_tokens": 2667520, "cache_write_input_tokens": 0, "output_tokens": 27658, "reasoning_output_tokens": 12505, "total_tokens": 2818191 }, "thread_token_usage": { "input_tokens": 2790533, "cached_input_tokens": 2667520, "cache_write_input_tokens": 0, "output_tokens": 27658, "reasoning_output_tokens": 12505, "total_tokens": 2818191 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-10T18:13:42.682Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:13:42.682Z", "text": "" } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-10T18:13:57.010Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:13:57.010Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:13:59.725Z", "callId": "call_IfY9hgUI00tyrZre55kOV2V2", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --name temporal_early --epochs 1 --eval-steps 50 --max-steps 300 --threads 4 > /app/solution/temporal_early.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/retrieve.py --models /app/solution/temporal.pt --stage validation --name temporal_v --save-scores --output /app/solution/temporal_validation.parquet > /app/solution/temporal_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:14:02.072Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"f7be86\",\"wall_time_seconds\":1.001359148,\"session_id\":54691,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"39fdbe\",\"wall_time_seconds\":1.001195415,\"session_id\":31896,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:13:59.784Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f3439d2487d1b6a3e4b466b2d8d4", "usage": { "input_tokens": 61782, "cached_input_tokens": 16640, "cache_write_input_tokens": 0, "output_tokens": 1134, "reasoning_output_tokens": 934, "total_tokens": 62916 }, "turn_token_usage": { "input_tokens": 2852315, "cached_input_tokens": 2684160, "cache_write_input_tokens": 0, "output_tokens": 28792, "reasoning_output_tokens": 13439, "total_tokens": 2881107 }, "thread_token_usage": { "input_tokens": 2852315, "cached_input_tokens": 2684160, "cache_write_input_tokens": 0, "output_tokens": 28792, "reasoning_output_tokens": 13439, "total_tokens": 2881107 } } } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-10T18:14:12.434Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:14:12.434Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T18:14:20.630Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e8b5153a-790b-44dc-9cc8-1abf1c725fbb", "process_id": "31896", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt --stage validation --name temporal_v --save-scores --output /app/solution/temporal_validation.parquet > /app/solution/temporal_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt --stage validation --name temporal_v --save-scores --output /app/solution/temporal_validation.parquet > /app/solution/temporal_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 19, "nanos": 560522773 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:14:33.432Z", "callId": "call_yMOEwgpQ3zqswS58Nx6sR5WK", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/calibrate.py <<'PY'\\n\\\"\\\"\\\"Stream cached validation predictions for calibration/ensemble selection.\\n\\nThe cache contains model scores, never a training interaction matrix.\\n\\\"\\\"\\\"\\nfrom model import *\\nimport itertools,gc\\np=argparse.ArgumentParser();p.add_argument('--scores',nargs='+',required=True);p.add_argument('--name',default='calibration');p.add_argument('--weights',default='1');p.add_argument('--pops',default='-0.5,-0.25,0,0.25,0.5');p.add_argument('--trends',default='-0.5,0,0.5,1,1.5,2');a=p.parse_args();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];arrays=[np.load(s,mmap_mode='r') for s in a.scores]\\nf=pd.read_parquet(O/'validation_events.parquet',columns=['iid','count','organic','org_recent']);g=f.groupby('iid').sum().reindex(range(N),fill_value=0);pop=np.log(g['count'].to_numpy()+10).astype('float32');trend=(np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)).astype('float32');del f,g;gc.collect()\\nws=[float(s) for s in a.weights.split(',')];mixes=[[1.]] if len(arrays)==1 else [[v,1-v] for v in ws];params=list(itertools.product(mixes,map(float,a.pops.split(',')),map(float,a.trends.split(','))));sums=np.zeros(len(params));discount=1/np.log2(np.arange(2,12));users=np.array(sorted(gold))\\nfor st in range(0,len(users),64):\\n us=users[st:st+64];eligible=np.unpackbits(packed[us],axis=1,count=N,bitorder='little').astype(bool);source=[np.array(s[us],dtype='float32') for s in arrays]\\n for j,(weights,p,t) in enumerate(params):\\n scores=sum(w*x for w,x in zip(weights,source))+p*pop+t*trend;scores[~eligible]=-1e9;idx=np.argpartition(scores,-10,axis=1)[:,-10:].copy();vals=np.take_along_axis(scores,idx,axis=1);idx=np.take_along_axis(idx,np.argsort(-vals,axis=1),axis=1)\\n sums[j]+=sum(sum((2**gold[u].get(int(i),0)-1)*d for i,d in zip(row,discount))/ideal[u] for u,row in zip(us,idx))\\n if st%640==0:print('users',st,flush=True)\\nresults=[dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s in zip(params,sums)];results.sort(key=lambda r:-r['score']);json.dump(results,open(O/(a.name+'.json'),'w'),indent=2);print(results[:10],flush=True)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:14:33.517Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"706497\",\"wall_time_seconds\":0.00000304,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:14:33.479Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-adb56fbc-c13e-43b4-82de-f50632d3591d", "process_id": "42158", "command": [ "/bin/sh", "-lc", "cat > /app/solution/calibrate.py <<'PY'\n\"\"\"Stream cached validation predictions for calibration/ensemble selection.\n\nThe cache contains model scores, never a training interaction matrix.\n\"\"\"\nfrom model import *\nimport itertools,gc\np=argparse.ArgumentParser();p.add_argument('--scores',nargs='+',required=True);p.add_argument('--name',default='calibration');p.add_argument('--weights',default='1');p.add_argument('--pops',default='-0.5,-0.25,0,0.25,0.5');p.add_argument('--trends',default='-0.5,0,0.5,1,1.5,2');a=p.parse_args();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];arrays=[np.load(s,mmap_mode='r') for s in a.scores]\nf=pd.read_parquet(O/'validation_events.parquet',columns=['iid','count','organic','org_recent']);g=f.groupby('iid').sum().reindex(range(N),fill_value=0);pop=np.log(g['count'].to_numpy()+10).astype('float32');trend=(np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)).astype('float32');del f,g;gc.collect()\nws=[float(s) for s in a.weights.split(',')];mixes=[[1.]] if len(arrays)==1 else [[v,1-v] for v in ws];params=list(itertools.product(mixes,map(float,a.pops.split(',')),map(float,a.trends.split(','))));sums=np.zeros(len(params));discount=1/np.log2(np.arange(2,12));users=np.array(sorted(gold))\nfor st in range(0,len(users),64):\n us=users[st:st+64];eligible=np.unpackbits(packed[us],axis=1,count=N,bitorder='little').astype(bool);source=[np.array(s[us],dtype='float32') for s in arrays]\n for j,(weights,p,t) in enumerate(params):\n scores=sum(w*x for w,x in zip(weights,source))+p*pop+t*trend;scores[~eligible]=-1e9;idx=np.argpartition(scores,-10,axis=1)[:,-10:].copy();vals=np.take_along_axis(scores,idx,axis=1);idx=np.take_along_axis(idx,np.argsort(-vals,axis=1),axis=1)\n sums[j]+=sum(sum((2**gold[u].get(int(i),0)-1)*d for i,d in zip(row,discount))/ideal[u] for u,row in zip(us,idx))\n if st%640==0:print('users',st,flush=True)\nresults=[dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s in zip(params,sums)];results.sort(key=lambda r:-r['score']);json.dump(results,open(O/(a.name+'.json'),'w'),indent=2);print(results[:10],flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/calibrate.py <<'PY'\n\"\"\"Stream cached validation predictions for calibration/ensemble selection.\n\nThe cache contains model scores, never a training interaction matrix.\n\"\"\"\nfrom model import *\nimport itertools,gc\np=argparse.ArgumentParser();p.add_argument('--scores',nargs='+',required=True);p.add_argument('--name',default='calibration');p.add_argument('--weights',default='1');p.add_argument('--pops',default='-0.5,-0.25,0,0.25,0.5');p.add_argument('--trends',default='-0.5,0,0.5,1,1.5,2');a=p.parse_args();gold,ideal=get_truth();z=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];arrays=[np.load(s,mmap_mode='r') for s in a.scores]\nf=pd.read_parquet(O/'validation_events.parquet',columns=['iid','count','organic','org_recent']);g=f.groupby('iid').sum().reindex(range(N),fill_value=0);pop=np.log(g['count'].to_numpy()+10).astype('float32');trend=(np.log(g.org_recent.to_numpy()+5)-np.log(g.organic.to_numpy()+10)).astype('float32');del f,g;gc.collect()\nws=[float(s) for s in a.weights.split(',')];mixes=[[1.]] if len(arrays)==1 else [[v,1-v] for v in ws];params=list(itertools.product(mixes,map(float,a.pops.split(',')),map(float,a.trends.split(','))));sums=np.zeros(len(params));discount=1/np.log2(np.arange(2,12));users=np.array(sorted(gold))\nfor st in range(0,len(users),64):\n us=users[st:st+64];eligible=np.unpackbits(packed[us],axis=1,count=N,bitorder='little').astype(bool);source=[np.array(s[us],dtype='float32') for s in arrays]\n for j,(weights,p,t) in enumerate(params):\n scores=sum(w*x for w,x in zip(weights,source))+p*pop+t*trend;scores[~eligible]=-1e9;idx=np.argpartition(scores,-10,axis=1)[:,-10:].copy();vals=np.take_along_axis(scores,idx,axis=1);idx=np.take_along_axis(idx,np.argsort(-vals,axis=1),axis=1)\n sums[j]+=sum(sum((2**gold[u].get(int(i),0)-1)*d for i,d in zip(row,discount))/ideal[u] for u,row in zip(us,idx))\n if st%640==0:print('users',st,flush=True)\nresults=[dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s in zip(params,sums)];results.sort(key=lambda r:-r['score']);json.dump(results,open(O/(a.name+'.json'),'w'),indent=2);print(results[:10],flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3040 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:14:33.515Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f36a4ff487d1a5c803ef55a5509d", "usage": { "input_tokens": 63008, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 976, "reasoning_output_tokens": 267, "total_tokens": 63984 }, "turn_token_usage": { "input_tokens": 2915323, "cached_input_tokens": 2745728, "cache_write_input_tokens": 0, "output_tokens": 29768, "reasoning_output_tokens": 13706, "total_tokens": 2945091 }, "thread_token_usage": { "input_tokens": 2915323, "cached_input_tokens": 2745728, "cache_write_input_tokens": 0, "output_tokens": 29768, "reasoning_output_tokens": 13706, "total_tokens": 2945091 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-10T18:14:57.013Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:14:57.013Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:14:58.586Z", "text": "I’ve isolated the calibration memory issue and moved the grid search to bounded NumPy batches. I’m also checking temporal checkpoints every 50 updates to see whether the best model occurs before the first full epoch. The confirmed 0.033814 model and its submission remain saved.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:14:59.488Z", "callId": "call_7VEZN15amzMSeIqSR57XE6gt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/temporal_early.log; tail -8 /app/solution/temporal_export.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:14:59.556Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"ebb20d\",\"wall_time_seconds\":0.0000037,\"exit_code\":0,\"original_token_count\":59,\"output\":\"STEP VALID 50 0.031248352877365968\\nSTEP VALID 100 0.033157784868847254\\nSaved /app/solution/temporal_validation.parquet vector dimension 258\\nNDCG@10 0.03381411485331253\\n100-item history encoder median ms 0.14513400037685642\\n3670867968\\n\"}\n{\"current_time\":\"2026-09-10 18:14:59 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:14:59.532Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-94262d76-a515-478e-bcfc-217704ab1fd3", "process_id": "90117", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/temporal_early.log; tail -8 /app/solution/temporal_export.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/temporal_early.log; tail -8 /app/solution/temporal_export.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "STEP VALID 50 0.031248352877365968\nSTEP VALID 100 0.033157784868847254\nSaved /app/solution/temporal_validation.parquet vector dimension 258\nNDCG@10 0.03381411485331253\n100-item history encoder median ms 0.14513400037685642\n3670867968\n", "stderr": "", "aggregated_output": "STEP VALID 50 0.031248352877365968\nSTEP VALID 100 0.033157784868847254\nSaved /app/solution/temporal_validation.parquet vector dimension 258\nNDCG@10 0.03381411485331253\n100-item history encoder median ms 0.14513400037685642\n3670867968\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3700 }, "formatted_output": "STEP VALID 50 0.031248352877365968\nSTEP VALID 100 0.033157784868847254\nSaved /app/solution/temporal_validation.parquet vector dimension 258\nNDCG@10 0.03381411485331253\n100-item history encoder median ms 0.14513400037685642\n3670867968\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:14:59.546Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f38a2ca887d1a02b5ef9dcdb4c91", "usage": { "input_tokens": 64039, "cached_input_tokens": 62848, "cache_write_input_tokens": 0, "output_tokens": 651, 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"stdout": "STEP VALID 50 0.031248352877365968\nSTEP VALID 100 0.033157784868847254\nSTEP VALID 150 0.03228593583689389\nSTEP VALID 200 0.03223523640564804\nSTEP VALID 250 0.03233558881411224\nSTEP VALID 300 0.03229635665630695\nBEST 0.033157784868847254\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.0, 'trend': 2.0, 'score': 0.04344294306660555}, {'weights': [1.0], 'pop': 0.25, 'trend': 2.0, 'score': 0.043183523813076184}, {'weights': [1.0], 'pop': 0.0, 'trend': 1.5, 'score': 0.04224923756099519}, {'weights': [1.0], 'pop': 0.5, 'trend': 2.0, 'score': 0.042173331483097806}, {'weights': [1.0], 'pop': -0.25, 'trend': 2.0, 'score': 0.04203172422514686}, {'weights': [1.0], 'pop': 0.25, 'trend': 1.5, 'score': 0.04162966349730606}, {'weights': [1.0], 'pop': 0.5, 'trend': 1.5, 'score': 0.04138013052531437}, {'weights': [1.0], 'pop': -0.25, 'trend': 1.5, 'score': 0.04042939053538561}, {'weights': [1.0], 'pop': 0.25, 'trend': 1.0, 'score': 0.040232876512441555}, {'weights': [1.0], 'pop': 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"2026-09-10T18:16:44.944Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-12c65ddb-1050-41b4-b2fe-ad051cbab7e4", "process_id": "9476", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name temporal_calibration_extended --pops=-0.5,0,0.5,1 --trends=2,2.5,3,4,5 > /app/solution/calibration_extended.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name temporal_calibration_extended --pops=-0.5,0,0.5,1 --trends=2,2.5,3,4,5 > /app/solution/calibration_extended.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 21, "nanos": 936659582 }, "formatted_output": "" } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-10T18:16:45.120Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:16:45.120Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:16:47.060Z", "text": "Calibration raised public NDCG@10 to 0.04344. The improvement comes from a recent-organic-interest feature computed from training history and included in the item tower’s bias coordinate. I’ve started refitting the selected baseline on train plus validation, while testing whether the variational encoder adds value after temporal fine-tuning.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:16:48.071Z", "callId": "call_NCMo2WkdktgWrZw6NZNAVyaW", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/final_dae.log; tail -5 /app/solution/temporal_vae.log; tail -4 /app/solution/calibration_extended.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:16:48.195Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"fa9d6e\",\"wall_time_seconds\":0.00000472,\"exit_code\":0,\"original_token_count\":200,\"output\":\"users 2560\\nusers 3200\\nusers 3840\\n[{'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 4.0, 'score': 0.044687489774290136}, {'weights': [1.0], 'pop': 0.0, 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"nanos": 4720 }, "formatted_output": "users 2560\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 4.0, 'score': 0.044687489774290136}, {'weights': [1.0], 'pop': 0.0, 'trend': 5.0, 'score': 0.04418400528854336}, {'weights': [1.0], 'pop': 0.0, 'trend': 4.0, 'score': 0.04413516357360457}, {'weights': [1.0], 'pop': 0.0, 'trend': 3.0, 'score': 0.0439390493264054}, {'weights': [1.0], 'pop': 0.0, 'trend': 2.5, 'score': 0.043668523679801105}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'score': 0.04354242065810706}, {'weights': [1.0], 'pop': 0.0, 'trend': 2.0, 'score': 0.04344294306660555}, {'weights': [1.0], 'pop': 1.0, 'trend': 5.0, 'score': 0.04339170935717074}, {'weights': [1.0], 'pop': 0.5, 'trend': 2.5, 'score': 0.043307332433615606}]\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:16:48.192Z", "data": { "type": "token_usage_record", "thread_id": 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"2026-09-10T18:17:18.020Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4bb81f49-43cf-47e4-85a0-1c4730e34d2c", "process_id": "32890", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_dae.log; tail -6 /app/solution/temporal_vae.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_dae.log; tail -6 /app/solution/temporal_vae.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\nSTEP VALID 100 0.03179238340391338\n5299200000\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\nSTEP VALID 100 0.03179238340391338\n5299200000\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5670 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\nSTEP VALID 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109.3}\\nSTEP VALID 100 0.03179238340391338\\nSTEP VALID 200 0.033632722639901996\\n\"}\n{\"current_time\":\"2026-09-10 18:18:18 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:18:18.487Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ca8a6981-536f-4240-b45a-d67615acdfc0", "process_id": "9298", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/final_dae.log; tail -8 /app/solution/temporal_vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/final_dae.log; tail -8 /app/solution/temporal_vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\n{\"epoch\": 2, \"loss\": 8.895250357114351, \"seconds\": 51.1}\n{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\n{\"epoch\": 2, \"loss\": 8.895250357114351, \"seconds\": 51.1}\n{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4780 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.604410733932104, \"seconds\": 25.2}\n{\"epoch\": 2, \"loss\": 8.895250357114351, \"seconds\": 51.1}\n{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:18:18.538Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f46248c887d1ad161f645626689f", "usage": { "input_tokens": 71305, "cached_input_tokens": 69504, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 141, "total_tokens": 71509 }, "turn_token_usage": { "input_tokens": 3586232, "cached_input_tokens": 3406720, "cache_write_input_tokens": 0, "output_tokens": 35573, "reasoning_output_tokens": 17646, "total_tokens": 3621805 }, "thread_token_usage": { "input_tokens": 3586232, "cached_input_tokens": 3406720, "cache_write_input_tokens": 0, "output_tokens": 35573, "reasoning_output_tokens": 17646, "total_tokens": 3621805 } } } ] }, { "id": 81, "role": "agent", "timestamp": "2026-09-10T18:18:36.392Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:18:36.392Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:18:38.260Z", "text": "The best calibrated score is now 0.04485 NDCG@10. Final refitting is underway. I’m also testing more weight on recent history and a blend with the variational model; the blend would concatenate user and item vectors and still use a single dot product.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:18:38.421Z", "callId": "call_ipLziWu6qejtPrWHGwmuVG7w", "name": "sleep", "input": { "duration_ms": 30000 }, "result": { "timestamp": "2026-09-10T18:19:08.477Z", "content": "Wall time: 30.0371 seconds\nSleep completed.", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:18:38.551Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f46adb4887d19687a2ea031861c1", "usage": { "input_tokens": 71746, "cached_input_tokens": 71168, "cache_write_input_tokens": 0, "output_tokens": 601, "reasoning_output_tokens": 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"2026-09-10T18:19:13.115Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"3f03f5\",\"wall_time_seconds\":0.000011181,\"exit_code\":0,\"original_token_count\":123,\"output\":\"{\\\"epoch\\\": 3, \\\"loss\\\": 8.540017617054474, \\\"seconds\\\": 72.6}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.31898185534355, \\\"seconds\\\": 88.5}\\n{\\\"epoch\\\": 5, \\\"loss\\\": 8.197496243012257, \\\"seconds\\\": 109.3}\\n{\\\"epoch\\\": 6, \\\"loss\\\": 8.107794138101431, \\\"seconds\\\": 128.1}\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.018903573354086, \\\"seconds\\\": 150.0}\\nSTEP VALID 100 0.03179238340391338\\nSTEP VALID 200 0.033632722639901996\\nSTEP VALID 300 0.033537824006443354\\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\\nVALID 1 0.03448803517643607\\n5175099392\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:19:13.011Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-af550750-036b-4b90-abfc-13e865869838", "process_id": "80397", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/final_dae.log; tail -6 /app/solution/temporal_vae.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/final_dae.log; tail -6 /app/solution/temporal_vae.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\nSTEP VALID 300 0.033537824006443354\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\nVALID 1 0.03448803517643607\n5175099392\n", "stderr": "", "aggregated_output": "{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\nSTEP VALID 300 0.033537824006443354\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\nVALID 1 0.03448803517643607\n5175099392\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11181 }, "formatted_output": "{\"epoch\": 3, \"loss\": 8.540017617054474, \"seconds\": 72.6}\n{\"epoch\": 4, \"loss\": 8.31898185534355, \"seconds\": 88.5}\n{\"epoch\": 5, \"loss\": 8.197496243012257, \"seconds\": 109.3}\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\nSTEP VALID 300 0.033537824006443354\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\nVALID 1 0.03448803517643607\n5175099392\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:19:13.105Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f49ca92c87d1a1b59d504d1135a4", "usage": { "input_tokens": 72370, "cached_input_tokens": 71552, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 0, "total_tokens": 72432 }, "turn_token_usage": { "input_tokens": 3730348, "cached_input_tokens": 3549440, "cache_write_input_tokens": 0, "output_tokens": 36236, "reasoning_output_tokens": 18162, "total_tokens": 3766584 }, "thread_token_usage": { "input_tokens": 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8.107794138101431, \\\"seconds\\\": 128.1}\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.018903573354086, \\\"seconds\\\": 150.0}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 7.945269211744651, \\\"seconds\\\": 167.0}\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:19:19.267Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aa33530b-2275-42c8-a76f-dc7b4e2459a5", "process_id": "56907", "command": [ "/bin/sh", "-lc", "rg 'saved' /app/solution/temporal_prepare.log; tail -3 /app/solution/final_dae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "rg 'saved' /app/solution/temporal_prepare.log; tail -3 /app/solution/final_dae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation saved 50581 43.78077960014343\ntest saved 51855 70.40952014923096\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\n{\"epoch\": 8, \"loss\": 7.945269211744651, \"seconds\": 167.0}\n", "stderr": "", "aggregated_output": "validation saved 50581 43.78077960014343\ntest saved 51855 70.40952014923096\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\n{\"epoch\": 8, \"loss\": 7.945269211744651, \"seconds\": 167.0}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5970 }, "formatted_output": "validation saved 50581 43.78077960014343\ntest saved 51855 70.40952014923096\n{\"epoch\": 6, \"loss\": 8.107794138101431, \"seconds\": 128.1}\n{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\n{\"epoch\": 8, \"loss\": 7.945269211744651, \"seconds\": 167.0}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:19:19.315Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": 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"call_9UsjpGTqAA7ivMItzCFrtvWt", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name temporal_calibration_high --pops=0,0.5,1 --trends=5,6,8,10 > /app/solution/calibration_high.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:19:39.028Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"42c358\",\"wall_time_seconds\":1.000804602,\"session_id\":50057,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:19:37.976Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": 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--name temporal_calibration_high --pops=0,0.5,1 --trends=5,6,8,10 > /app/solution/calibration_high.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name temporal_calibration_high --pops=0,0.5,1 --trends=5,6,8,10 > /app/solution/calibration_high.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 18, "nanos": 898149107 }, "formatted_output": "" } } } ] }, { "id": 84, "role": "agent", "timestamp": "2026-09-10T18:20:00.152Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:20:00.152Z", "text": "" } ] }, { "id": 85, "role": "agent", "timestamp": "2026-09-10T18:20:21.637Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:20:21.637Z", "text": "" } ] }, { "id": 86, "role": "agent", "timestamp": "2026-09-10T18:20:24.358Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:20:24.358Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:20:31.968Z", "callId": "call_hCyerkkbNOxvafoxaDtEMOLj", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/item_features.py <<'PY'\\n\\\"\\\"\\\"Streaming item-only trend statistics from observed events.\\\"\\\"\\\"\\nimport numpy as np,pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data');O=Path('/app/solution');N=30000\\nfor stage,cut in [('validation',24786800),('test',25393400)]:\\n out=O/f'{stage}_item_features.npz'\\n if out.exists():continue\\n stats={k:np.zeros(N) for k in ['count','organic','likes']+[f'{k}_{d}' for k in ['organic','likes'] for d in [7,14,30,60]]}\\n for b in pq.ParquetFile(O/'raw_positive.parquet').iter_batches(batch_size=500000,columns=['iid','timestamp','like','quality']):\\n f=b.to_pandas();f=f[f.timestamp /app/solution/item_features.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:20:33.151Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"d6b1ec\",\"wall_time_seconds\":1.000562526,\"session_id\":16723,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:20:32.057Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f4bb421887d180f7cce16b6ac655", "usage": { "input_tokens": 73524, "cached_input_tokens": 72832, "cache_write_input_tokens": 0, "output_tokens": 1582, "reasoning_output_tokens": 1104, "total_tokens": 75106 }, "turn_token_usage": { "input_tokens": 3949589, "cached_input_tokens": 3767040, "cache_write_input_tokens": 0, "output_tokens": 38412, "reasoning_output_tokens": 19696, "total_tokens": 3988001 }, "thread_token_usage": { "input_tokens": 3949589, "cached_input_tokens": 3767040, "cache_write_input_tokens": 0, "output_tokens": 38412, "reasoning_output_tokens": 19696, "total_tokens": 3988001 } } } ] }, { "id": 87, "role": "agent", "timestamp": "2026-09-10T18:20:37.961Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:20:37.961Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:20:40.212Z", "callId": "call_pwczCLa4cWaLuXbawWDUHxTY", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/final_dae.log; tail -8 /app/solution/temporal_vae.log; tail -3 /app/solution/calibration_high.log; cat /app/solution/item_features.log\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:20:40.389Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"2bbd56\",\"wall_time_seconds\":0.0000054,\"exit_code\":0,\"original_token_count\":355,\"output\":\"{\\\"epoch\\\": 7, \\\"loss\\\": 8.018903573354086, \\\"seconds\\\": 150.0}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 7.945269211744651, \\\"seconds\\\": 167.0}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 7.877113886368581, \\\"seconds\\\": 183.8}\\n{\\\"epoch\\\": 10, \\\"loss\\\": 7.81897558310093, \\\"seconds\\\": 208.2}\\n{\\\"epoch\\\": 11, \\\"loss\\\": 7.762079141078851, \\\"seconds\\\": 225.8}\\n{\\\"epoch\\\": 12, \\\"loss\\\": 7.716447243323693, \\\"seconds\\\": 247.8}\\nSTEP VALID 100 0.03179238340391338\\nSTEP VALID 200 0.033632722639901996\\nSTEP VALID 300 0.033537824006443354\\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\\nVALID 1 0.03448803517643607\\nSTEP VALID 400 0.033657834151121384\\nSTEP VALID 500 0.03482751795000102\\nusers 3200\\nusers 3840\\n[{'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 8.0, 'score': 0.04468778579881119}, {'weights': [1.0], 'pop': 1.0, 'trend': 8.0, 'score': 0.04433298262046629}, {'weights': [1.0], 'pop': 0.0, 'trend': 5.0, 'score': 0.04418400528854336}, {'weights': [1.0], 'pop': 1.0, 'trend': 6.0, 'score': 0.04407503137935032}, {'weights': [1.0], 'pop': 0.0, 'trend': 6.0, 'score': 0.04393927487710176}, {'weights': [1.0], 'pop': 0.5, 'trend': 10.0, 'score': 0.04388788912738301}, {'weights': [1.0], 'pop': 1.0, 'trend': 10.0, 'score': 0.04385379339200116}, {'weights': [1.0], 'pop': 0.0, 'trend': 8.0, 'score': 0.043414130123955626}]\\nsaved validation\\n\"}\n{\"current_time\":\"2026-09-10 18:20:40 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:20:40.322Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-29683982-c3bc-4606-9745-ad24e0a87b4d", "process_id": "28102", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/final_dae.log; tail -8 /app/solution/temporal_vae.log; tail -3 /app/solution/calibration_high.log; cat /app/solution/item_features.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/final_dae.log; tail -8 /app/solution/temporal_vae.log; tail -3 /app/solution/calibration_high.log; cat /app/solution/item_features.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\n{\"epoch\": 8, \"loss\": 7.945269211744651, \"seconds\": 167.0}\n{\"epoch\": 9, \"loss\": 7.877113886368581, \"seconds\": 183.8}\n{\"epoch\": 10, \"loss\": 7.81897558310093, \"seconds\": 208.2}\n{\"epoch\": 11, \"loss\": 7.762079141078851, \"seconds\": 225.8}\n{\"epoch\": 12, \"loss\": 7.716447243323693, \"seconds\": 247.8}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\nSTEP VALID 300 0.033537824006443354\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\nVALID 1 0.03448803517643607\nSTEP VALID 400 0.033657834151121384\nSTEP VALID 500 0.03482751795000102\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 8.0, 'score': 0.04468778579881119}, {'weights': [1.0], 'pop': 1.0, 'trend': 8.0, 'score': 0.04433298262046629}, {'weights': [1.0], 'pop': 0.0, 'trend': 5.0, 'score': 0.04418400528854336}, {'weights': [1.0], 'pop': 1.0, 'trend': 6.0, 'score': 0.04407503137935032}, {'weights': [1.0], 'pop': 0.0, 'trend': 6.0, 'score': 0.04393927487710176}, {'weights': [1.0], 'pop': 0.5, 'trend': 10.0, 'score': 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{'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 8.0, 'score': 0.04468778579881119}, {'weights': [1.0], 'pop': 1.0, 'trend': 8.0, 'score': 0.04433298262046629}, {'weights': [1.0], 'pop': 0.0, 'trend': 5.0, 'score': 0.04418400528854336}, {'weights': [1.0], 'pop': 1.0, 'trend': 6.0, 'score': 0.04407503137935032}, {'weights': [1.0], 'pop': 0.0, 'trend': 6.0, 'score': 0.04393927487710176}, {'weights': [1.0], 'pop': 0.5, 'trend': 10.0, 'score': 0.04388788912738301}, {'weights': [1.0], 'pop': 1.0, 'trend': 10.0, 'score': 0.04385379339200116}, {'weights': [1.0], 'pop': 0.0, 'trend': 8.0, 'score': 0.043414130123955626}]\nsaved validation\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5400 }, "formatted_output": "{\"epoch\": 7, \"loss\": 8.018903573354086, \"seconds\": 150.0}\n{\"epoch\": 8, \"loss\": 7.945269211744651, \"seconds\": 167.0}\n{\"epoch\": 9, \"loss\": 7.877113886368581, \"seconds\": 183.8}\n{\"epoch\": 10, \"loss\": 7.81897558310093, \"seconds\": 208.2}\n{\"epoch\": 11, \"loss\": 7.762079141078851, \"seconds\": 225.8}\n{\"epoch\": 12, \"loss\": 7.716447243323693, \"seconds\": 247.8}\nSTEP VALID 100 0.03179238340391338\nSTEP VALID 200 0.033632722639901996\nSTEP VALID 300 0.033537824006443354\nEPOCH 1 loss 8.357705217419248 seconds 151.3408489227295\nVALID 1 0.03448803517643607\nSTEP VALID 400 0.033657834151121384\nSTEP VALID 500 0.03482751795000102\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 5.0, 'score': 0.044850304556712114}, {'weights': [1.0], 'pop': 0.5, 'trend': 8.0, 'score': 0.04468778579881119}, {'weights': [1.0], 'pop': 1.0, 'trend': 8.0, 'score': 0.04433298262046629}, {'weights': [1.0], 'pop': 0.0, 'trend': 5.0, 'score': 0.04418400528854336}, {'weights': [1.0], 'pop': 1.0, 'trend': 6.0, 'score': 0.04407503137935032}, {'weights': [1.0], 'pop': 0.0, 'trend': 6.0, 'score': 0.04393927487710176}, {'weights': [1.0], 'pop': 0.5, 'trend': 10.0, 'score': 0.04388788912738301}, {'weights': [1.0], 'pop': 1.0, 'trend': 10.0, 'score': 0.04385379339200116}, {'weights': [1.0], 'pop': 0.0, 'trend': 8.0, 'score': 0.043414130123955626}]\nsaved validation\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:20:40.360Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f4f1625487d1adb1039bab0286fa", "usage": { "input_tokens": 75165, "cached_input_tokens": 73344, "cache_write_input_tokens": 0, "output_tokens": 115, "reasoning_output_tokens": 31, "total_tokens": 75280 }, "turn_token_usage": { "input_tokens": 4024754, "cached_input_tokens": 3840384, "cache_write_input_tokens": 0, "output_tokens": 38527, "reasoning_output_tokens": 19727, "total_tokens": 4063281 }, "thread_token_usage": { "input_tokens": 4024754, "cached_input_tokens": 3840384, "cache_write_input_tokens": 0, "output_tokens": 38527, "reasoning_output_tokens": 19727, "total_tokens": 4063281 } } }, { "kind": "context", "timestamp": "2026-09-10T18:20:42.045Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8c889a2d-c440-44ec-9e56-ede1adc8d69b", "process_id": "16723", "command": [ "/bin/sh", "-lc", "cat > /app/solution/item_features.py <<'PY'\n\"\"\"Streaming item-only trend statistics from observed events.\"\"\"\nimport numpy as np,pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');N=30000\nfor stage,cut in [('validation',24786800),('test',25393400)]:\n out=O/f'{stage}_item_features.npz'\n if out.exists():continue\n stats={k:np.zeros(N) for k in ['count','organic','likes']+[f'{k}_{d}' for k in ['organic','likes'] for d in [7,14,30,60]]}\n for b in pq.ParquetFile(O/'raw_positive.parquet').iter_batches(batch_size=500000,columns=['iid','timestamp','like','quality']):\n f=b.to_pandas();f=f[f.timestamp /app/solution/item_features.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/item_features.py <<'PY'\n\"\"\"Streaming item-only trend statistics from observed events.\"\"\"\nimport numpy as np,pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');N=30000\nfor stage,cut in [('validation',24786800),('test',25393400)]:\n out=O/f'{stage}_item_features.npz'\n if out.exists():continue\n stats={k:np.zeros(N) for k in ['count','organic','likes']+[f'{k}_{d}' for k in ['organic','likes'] for d in [7,14,30,60]]}\n for b in pq.ParquetFile(O/'raw_positive.parquet').iter_batches(batch_size=500000,columns=['iid','timestamp','like','quality']):\n f=b.to_pandas();f=f[f.timestamp /app/solution/item_features.log 2>&1" } ], "source": 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"status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 273, "nanos": 718378706 }, "formatted_output": "" } } } ] }, { "id": 88, "role": "agent", "timestamp": "2026-09-10T18:20:56.348Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:20:56.348Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:21:18.948Z", "callId": "call_HsOBO0LWCCPA1hTP6K7UcA7c", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/calibrate.py';s=open(p).read().replace(\\\"a=p.parse_args();gold\\\", \\\"p.add_argument('--feature-decays',default='30');p.add_argument('--feature-likes',default='0');a=p.parse_args();gold\\\")\\nstart=s.index(\\\"f=pd.read_parquet(O/'validation_events.parquet'\\\");end=s.index(\\\"ws=[float\\\",start)\\ns=s[:start]+\\\"\\\"\\\"features=np.load(O/'validation_item_features.npz');pop=np.log(features['count']+10).astype('float32');feature_decays=list(map(int,a.feature_decays.split(',')));feature_likes=list(map(float,a.feature_likes.split(',')));trends={(d,l):(np.log(features[f'organic_{d}']+l*features[f'likes_{d}']+5)-np.log(features['organic']+l*features['likes']+10)).astype('float32') for d in feature_decays for l in feature_likes}\\n\\\"\\\"\\\"+s[end:]\\ns=s.replace(\\\"map(float,a.trends.split(','))));\\\", \\\"map(float,a.trends.split(',')),feature_decays,feature_likes));\\\").replace('for j,(weights,p,t) in enumerate(params):','for j,(weights,p,t,fd,fl) in enumerate(params):').replace('+p*pop+t*trend;', '+p*pop+t*trends[(fd,fl)];').replace(\\\"dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s\\\", \\\"dict(weights=w,pop=p,trend=t,feature_decay=fd,feature_like=fl,score=float(s/len(users))) for (w,p,t,fd,fl),s\\\")\\nopen(p,'w').write(s)\\np='/app/solution/retrieve.py';s=open(p).read().replace('history_decays=None):','history_decays=None,feature_decay=30,feature_like=0.):')\\nstart=s.index(\\\" f=pd.read_parquet(O/f'{stage}_events.parquet')\\\");end=s.index(' return UserRetrievalTower',start)\\ns=s[:start]+\\\"\\\"\\\" stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10)],1),dtype=torch.float32)\\n\\\"\\\"\\\"+s[end:]\\ns=s.replace(\\\"a=p.parse_args();seed(42);weights\\\", \\\"p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);a=p.parse_args();seed(42);weights\\\").replace('a.pop,a.trend,a.history_decays);z=', 'a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like);z=');open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name feature_search --pops=0.5 --trends=3,6,9 --feature-decays=7,14,30,60 --feature-likes=0,7,20 > /app/solution/feature_search.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:21:20.119Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"99b1da\",\"wall_time_seconds\":1.001019127,\"session_id\":20165,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:21:19.080Z", "data": { "type": "token_usage_record", "thread_id": 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"2026-09-10T18:21:30.897Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:21:31.966Z", "callId": "call_C5eUkmmJUvVOr77aic3uOUzA", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/temporal_vae.log; tail -3 /app/solution/final_dae.log; cat /app/solution/item_features.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:21:32.027Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"cefbac\",\"wall_time_seconds\":0.00000467,\"exit_code\":0,\"original_token_count\":91,\"output\":\"VALID 1 0.03448803517643607\\nSTEP VALID 400 0.033657834151121384\\nSTEP VALID 500 0.03482751795000102\\nSTEP VALID 600 0.03270451592393891\\nBEST 0.03482751795000102\\n{\\\"epoch\\\": 13, \\\"loss\\\": 7.663483344591581, \\\"seconds\\\": 267.3}\\n{\\\"epoch\\\": 14, \\\"loss\\\": 7.621985600544856, \\\"seconds\\\": 281.5}\\n{\\\"epoch\\\": 15, \\\"loss\\\": 7.580834462092473, \\\"seconds\\\": 295.9}\\nsaved validation\\nsaved test\\n\"}\n{\"current_time\":\"2026-09-10 18:21:32 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:21:32.006Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bcbe8d45-5e0c-4937-9076-b4af0e9ab73a", "process_id": "23951", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/temporal_vae.log; tail -3 /app/solution/final_dae.log; cat /app/solution/item_features.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/temporal_vae.log; tail -3 /app/solution/final_dae.log; cat /app/solution/item_features.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "VALID 1 0.03448803517643607\nSTEP VALID 400 0.033657834151121384\nSTEP VALID 500 0.03482751795000102\nSTEP VALID 600 0.03270451592393891\nBEST 0.03482751795000102\n{\"epoch\": 13, \"loss\": 7.663483344591581, \"seconds\": 267.3}\n{\"epoch\": 14, \"loss\": 7.621985600544856, \"seconds\": 281.5}\n{\"epoch\": 15, \"loss\": 7.580834462092473, \"seconds\": 295.9}\nsaved validation\nsaved test\n", "stderr": "", "aggregated_output": "VALID 1 0.03448803517643607\nSTEP VALID 400 0.033657834151121384\nSTEP VALID 500 0.03482751795000102\nSTEP VALID 600 0.03270451592393891\nBEST 0.03482751795000102\n{\"epoch\": 13, \"loss\": 7.663483344591581, \"seconds\": 267.3}\n{\"epoch\": 14, \"loss\": 7.621985600544856, \"seconds\": 281.5}\n{\"epoch\": 15, \"loss\": 7.580834462092473, \"seconds\": 295.9}\nsaved validation\nsaved test\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4670 }, "formatted_output": "VALID 1 0.03448803517643607\nSTEP VALID 400 0.033657834151121384\nSTEP VALID 500 0.03482751795000102\nSTEP VALID 600 0.03270451592393891\nBEST 0.03482751795000102\n{\"epoch\": 13, \"loss\": 7.663483344591581, \"seconds\": 267.3}\n{\"epoch\": 14, \"loss\": 7.621985600544856, \"seconds\": 281.5}\n{\"epoch\": 15, \"loss\": 7.580834462092473, \"seconds\": 295.9}\nsaved validation\nsaved test\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:21:32.024Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f5204ea487d1b52f80c4df587ebf", "usage": { "input_tokens": 77249, "cached_input_tokens": 75776, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 143, "total_tokens": 77464 }, "turn_token_usage": { "input_tokens": 4178011, "cached_input_tokens": 3991168, "cache_write_input_tokens": 0, "output_tokens": 39925, "reasoning_output_tokens": 20269, "total_tokens": 4217936 }, "thread_token_usage": { "input_tokens": 4178011, "cached_input_tokens": 3991168, "cache_write_input_tokens": 0, "output_tokens": 39925, "reasoning_output_tokens": 20269, "total_tokens": 4217936 } } }, { "kind": "context", "timestamp": "2026-09-10T18:21:32.686Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f26da22c-afed-488b-935e-ead849deb52b", "process_id": "31470", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/model.py --stage test --name final_dae --epochs 15 --eval-every 15 > /app/solution/final_dae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/model.py --stage test --name final_dae --epochs 15 --eval-every 15 > /app/solution/final_dae.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 312, "nanos": 5310725 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:21:42.663Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3796005-a619-4171-9d7a-9172ea244503", "process_id": "20165", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"a=p.parse_args();gold\", \"p.add_argument('--feature-decays',default='30');p.add_argument('--feature-likes',default='0');a=p.parse_args();gold\")\nstart=s.index(\"f=pd.read_parquet(O/'validation_events.parquet'\");end=s.index(\"ws=[float\",start)\ns=s[:start]+\"\"\"features=np.load(O/'validation_item_features.npz');pop=np.log(features['count']+10).astype('float32');feature_decays=list(map(int,a.feature_decays.split(',')));feature_likes=list(map(float,a.feature_likes.split(',')));trends={(d,l):(np.log(features[f'organic_{d}']+l*features[f'likes_{d}']+5)-np.log(features['organic']+l*features['likes']+10)).astype('float32') for d in feature_decays for l in feature_likes}\n\"\"\"+s[end:]\ns=s.replace(\"map(float,a.trends.split(','))));\", \"map(float,a.trends.split(',')),feature_decays,feature_likes));\").replace('for j,(weights,p,t) in enumerate(params):','for j,(weights,p,t,fd,fl) in enumerate(params):').replace('+p*pop+t*trend;', '+p*pop+t*trends[(fd,fl)];').replace(\"dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s\", \"dict(weights=w,pop=p,trend=t,feature_decay=fd,feature_like=fl,score=float(s/len(users))) for (w,p,t,fd,fl),s\")\nopen(p,'w').write(s)\np='/app/solution/retrieve.py';s=open(p).read().replace('history_decays=None):','history_decays=None,feature_decay=30,feature_like=0.):')\nstart=s.index(\" f=pd.read_parquet(O/f'{stage}_events.parquet')\");end=s.index(' return UserRetrievalTower',start)\ns=s[:start]+\"\"\" stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10)],1),dtype=torch.float32)\n\"\"\"+s[end:]\ns=s.replace(\"a=p.parse_args();seed(42);weights\", \"p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);a=p.parse_args();seed(42);weights\").replace('a.pop,a.trend,a.history_decays);z=', 'a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like);z=');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name feature_search --pops=0.5 --trends=3,6,9 --feature-decays=7,14,30,60 --feature-likes=0,7,20 > /app/solution/feature_search.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"a=p.parse_args();gold\", \"p.add_argument('--feature-decays',default='30');p.add_argument('--feature-likes',default='0');a=p.parse_args();gold\")\nstart=s.index(\"f=pd.read_parquet(O/'validation_events.parquet'\");end=s.index(\"ws=[float\",start)\ns=s[:start]+\"\"\"features=np.load(O/'validation_item_features.npz');pop=np.log(features['count']+10).astype('float32');feature_decays=list(map(int,a.feature_decays.split(',')));feature_likes=list(map(float,a.feature_likes.split(',')));trends={(d,l):(np.log(features[f'organic_{d}']+l*features[f'likes_{d}']+5)-np.log(features['organic']+l*features['likes']+10)).astype('float32') for d in feature_decays for l in feature_likes}\n\"\"\"+s[end:]\ns=s.replace(\"map(float,a.trends.split(','))));\", \"map(float,a.trends.split(',')),feature_decays,feature_likes));\").replace('for j,(weights,p,t) in enumerate(params):','for j,(weights,p,t,fd,fl) in enumerate(params):').replace('+p*pop+t*trend;', '+p*pop+t*trends[(fd,fl)];').replace(\"dict(weights=w,pop=p,trend=t,score=float(s/len(users))) for (w,p,t),s\", \"dict(weights=w,pop=p,trend=t,feature_decay=fd,feature_like=fl,score=float(s/len(users))) for (w,p,t,fd,fl),s\")\nopen(p,'w').write(s)\np='/app/solution/retrieve.py';s=open(p).read().replace('history_decays=None):','history_decays=None,feature_decay=30,feature_like=0.):')\nstart=s.index(\" f=pd.read_parquet(O/f'{stage}_events.parquet')\");end=s.index(' return UserRetrievalTower',start)\ns=s[:start]+\"\"\" stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10)],1),dtype=torch.float32)\n\"\"\"+s[end:]\ns=s.replace(\"a=p.parse_args();seed(42);weights\", \"p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);a=p.parse_args();seed(42);weights\").replace('a.pop,a.trend,a.history_decays);z=', 'a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like);z=');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_v_scores.npy --name feature_search --pops=0.5 --trends=3,6,9 --feature-decays=7,14,30,60 --feature-likes=0,7,20 > /app/solution/feature_search.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 23, "nanos": 545318718 }, "formatted_output": "" } } } ] }, { "id": 90, "role": "agent", "timestamp": "2026-09-10T18:21:43.184Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:21:43.184Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:21:45.455Z", "text": "The variational model reached 0.03483 before calibration, and the calibrated baseline reached 0.04524. I’m refitting both candidates on all observed history. I’m also comparing shorter item-trend windows and organic-like activity to check whether they improve on the current 30-day play-based feature.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:21:54.509Z", "callId": "call_Tz7t7FHixd0GOEZIypV5RxFh", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --load /app/solution/final_dae.pt --name final_temporal --epochs 1 --threads 4 > /app/solution/final_temporal.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/model.py --stage test --name final_vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 20 --eval-every 20 > /app/solution/final_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/retrieve.py --models /app/solution/temporal_vae.pt --stage validation --name temporal_vae_v --save-scores --output /app/solution/temporal_vae_validation.parquet > /app/solution/temporal_vae_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:21:57.998Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\"chunk_id\":\"181b0a\",\"wall_time_seconds\":1.000948577,\"session_id\":85546,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"362131\",\"wall_time_seconds\":1.001214094,\"session_id\":49834,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"4ca35c\",\"wall_time_seconds\":1.000839244,\"session_id\":29911,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:21:54.623Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f52ce00c87d1ab6c7ddd06c119dd", "usage": { "input_tokens": 77690, "cached_input_tokens": 77056, "cache_write_input_tokens": 0, "output_tokens": 639, "reasoning_output_tokens": 257, "total_tokens": 78329 }, "turn_token_usage": { "input_tokens": 4255701, "cached_input_tokens": 4068224, "cache_write_input_tokens": 0, "output_tokens": 40564, "reasoning_output_tokens": 20526, "total_tokens": 4296265 }, "thread_token_usage": { "input_tokens": 4255701, "cached_input_tokens": 4068224, "cache_write_input_tokens": 0, "output_tokens": 40564, "reasoning_output_tokens": 20526, "total_tokens": 4296265 } } }, { "kind": "context", "timestamp": "2026-09-10T18:22:18.872Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aab9b064-c97f-4858-b457-e707ff73a43d", "process_id": "29911", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --stage validation --name temporal_vae_v --save-scores --output /app/solution/temporal_vae_validation.parquet > /app/solution/temporal_vae_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --stage validation --name temporal_vae_v --save-scores --output /app/solution/temporal_vae_validation.parquet > /app/solution/temporal_vae_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 21, "nanos": 876091999 }, "formatted_output": "" } } } ] }, { "id": 91, "role": "agent", "timestamp": "2026-09-10T18:22:19.657Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:22:19.657Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:22:20.884Z", "callId": "call_hdfiYKGs6T43ldEqyxhcsxlo", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/feature_search.log; tail -4 /app/solution/temporal_vae_export.log; tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; cat /sys/fs/cgroup/memory.current\",\"max_output_tokens\":2500}));\n" }, "result": { "timestamp": "2026-09-10T18:22:21.039Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"0b2ace\",\"wall_time_seconds\":0.00001199,\"exit_code\":0,\"original_token_count\":347,\"output\":\"users 2560\\nusers 3200\\nusers 3840\\n[{'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04705273375419932}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 7.0, 'score': 0.04651643296117765}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 20.0, 'score': 0.04588397882359283}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 20.0, 'score': 0.045690916126388696}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 20.0, 'score': 0.04563504338754502}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 7.0, 'score': 0.04557125640165742}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 7.0, 'score': 0.04549286782837285}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 0.0, 'score': 0.04538946572525092}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 0.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 7.0, 'score': 0.045026574863654106}]\\nSaved /app/solution/temporal_vae_validation.parquet vector dimension 202\\nNDCG@10 0.03482751795000102\\n100-item history encoder median ms 0.30414899993047584\\n7081136128\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:22:20.979Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf6f1c29-a844-4c79-ab16-9d60f55bbb4f", "process_id": "31383", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/feature_search.log; tail -4 /app/solution/temporal_vae_export.log; tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; cat /sys/fs/cgroup/memory.current" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/feature_search.log; tail -4 /app/solution/temporal_vae_export.log; tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; cat /sys/fs/cgroup/memory.current" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 2560\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04705273375419932}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 7.0, 'score': 0.04651643296117765}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 20.0, 'score': 0.04588397882359283}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 20.0, 'score': 0.045690916126388696}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 20.0, 'score': 0.04563504338754502}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 7.0, 'score': 0.04557125640165742}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 7.0, 'score': 0.04549286782837285}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 0.0, 'score': 0.04538946572525092}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 0.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 7.0, 'score': 0.045026574863654106}]\nSaved /app/solution/temporal_vae_validation.parquet vector dimension 202\nNDCG@10 0.03482751795000102\n100-item history encoder median ms 0.30414899993047584\n7081136128\n", "stderr": "", "aggregated_output": "users 2560\nusers 3200\nusers 3840\n[{'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04705273375419932}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 7.0, 'score': 0.04651643296117765}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 20.0, 'score': 0.04588397882359283}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 20.0, 'score': 0.045690916126388696}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 20.0, 'score': 0.04563504338754502}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 7.0, 'score': 0.04557125640165742}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 7, 'feature_like': 7.0, 'score': 0.04549286782837285}, {'weights': [1.0], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 0.0, 'score': 0.04538946572525092}, {'weights': [1.0], 'pop': 0.5, 'trend': 6.0, 'feature_decay': 30, 'feature_like': 0.0, 'score': 0.04523697617512317}, {'weights': [1.0], 'pop': 0.5, 'trend': 9.0, 'feature_decay': 60, 'feature_like': 7.0, 'score': 0.045026574863654106}]\nSaved /app/solution/temporal_vae_validation.parquet vector dimension 202\nNDCG@10 0.03482751795000102\n100-item history encoder median ms 0.30414899993047584\n7081136128\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 11990 }, "formatted_output": "users 2560\nusers 3200\nusers 3840\n[{'weights': [1.0], 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3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04784395356904407}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04781587388558954}, {'weights': [0.25, 0.75], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04759905634586774}, {'weights': [0.75, 0.25], 'pop': 0.0, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04717926134290475}, {'weights': [0.5, 0.5], 'pop': 0.0, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04713977343931826}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 4.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.047015695194627095}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 4.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04672450803271763}]\n{\"epoch\": 1, \"loss\": 9.899569645906107, \"seconds\": 22.8}\n{\"epoch\": 2, \"loss\": 9.126480004726313, \"seconds\": 61.9}\n{\"epoch\": 3, \"loss\": 8.789817272088467, \"seconds\": 87.2}\nlow 0\nhigh 0\nmax 6207\noom 22\noom_kill 5\noom_group_kill 0\nsock_throttled 0\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:23:31.730Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f59d980c87d1a70b457d2c44937c", "usage": { "input_tokens": 80951, "cached_input_tokens": 80256, "cache_write_input_tokens": 0, "output_tokens": 97, "reasoning_output_tokens": 0, "total_tokens": 81048 }, "turn_token_usage": { "input_tokens": 4655230, "cached_input_tokens": 4463744, "cache_write_input_tokens": 0, "output_tokens": 42237, "reasoning_output_tokens": 21677, "total_tokens": 4697467 }, "thread_token_usage": { "input_tokens": 4655230, "cached_input_tokens": 4463744, "cache_write_input_tokens": 0, "output_tokens": 42237, "reasoning_output_tokens": 21677, "total_tokens": 4697467 } } } ] }, { "id": 95, "role": "agent", "timestamp": "2026-09-10T18:23:46.323Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:23:46.323Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:23:47.833Z", "text": "The two-model blend reached 0.04842 NDCG@10. Giving more weight to recent user history also improved the baseline before calibration, so I’m checking that combination. The current best blend uses a 14-day item-trend feature that includes organic likes.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:23:55.314Z", "callId": "call_TgVOJYSjkmawJ6HbItmMF67e", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_recent_v_scores.npy /app/solution/temporal_vae_v_scores.npy --name recent_ensemble_calibration --weights=0.5,0.75,1 --pops=0.25,0.5 --trends=2,3,4 --feature-decays=14 --feature-likes=20 > /app/solution/recent_ensemble_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/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 30 --stage validation --name temporal_vae_recent_v --save-scores --output /app/solution/temporal_vae_recent_validation.parquet > /app/solution/temporal_vae_recent_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:23:57.704Z", "content": "Script completed\nWall time 2.4 seconds\nOutput:\n\n{\"chunk_id\":\"575f60\",\"wall_time_seconds\":1.000693987,\"session_id\":40674,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"4d4d38\",\"wall_time_seconds\":1.000910312,\"session_id\":67810,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:23:55.448Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f5a40af087d1b634ed1cb3cb4198", "usage": { "input_tokens": 81882, "cached_input_tokens": 80768, "cache_write_input_tokens": 0, 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/app/solution/temporal_vae_recent_export.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:24:08.578Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"62d222\",\"wall_time_seconds\":0.00000599,\"exit_code\":0,\"original_token_count\":77,\"output\":\"{\\\"epoch\\\": 1, \\\"loss\\\": 9.899569645906107, \\\"seconds\\\": 22.8}\\n{\\\"epoch\\\": 2, \\\"loss\\\": 9.126480004726313, \\\"seconds\\\": 61.9}\\n{\\\"epoch\\\": 3, \\\"loss\\\": 8.789817272088467, \\\"seconds\\\": 87.2}\\n{\\\"epoch\\\": 4, \\\"loss\\\": 8.618656598604643, \\\"seconds\\\": 108.0}\\nls: cannot access '/app/solution/final_temporal.pt': No such file or directory\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:24:08.499Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f5be001887d1b22c5f3aacc465ce", "usage": { "input_tokens": 82653, "cached_input_tokens": 81664, "cache_write_input_tokens": 0, "output_tokens": 226, "reasoning_output_tokens": 144, "total_tokens": 82879 }, "turn_token_usage": { "input_tokens": 4819765, "cached_input_tokens": 4626176, "cache_write_input_tokens": 0, "output_tokens": 43141, "reasoning_output_tokens": 22179, "total_tokens": 4862906 }, "thread_token_usage": { "input_tokens": 4819765, "cached_input_tokens": 4626176, "cache_write_input_tokens": 0, "output_tokens": 43141, "reasoning_output_tokens": 22179, "total_tokens": 4862906 } } }, { "kind": "context", "timestamp": "2026-09-10T18:24:08.543Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-44272b86-6384-4ae5-8e8d-9025a08bd5de", "process_id": "4173", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_temporal.log; tail -4 /app/solution/final_vae.log; ls -lh /app/solution/final_temporal.pt; tail -3 /app/solution/temporal_vae_recent_export.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_temporal.log; tail -4 /app/solution/final_vae.log; ls -lh /app/solution/final_temporal.pt; tail -3 /app/solution/temporal_vae_recent_export.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\"epoch\": 1, \"loss\": 9.899569645906107, \"seconds\": 22.8}\n{\"epoch\": 2, \"loss\": 9.126480004726313, \"seconds\": 61.9}\n{\"epoch\": 3, \"loss\": 8.789817272088467, \"seconds\": 87.2}\n{\"epoch\": 4, \"loss\": 8.618656598604643, \"seconds\": 108.0}\nls: cannot access '/app/solution/final_temporal.pt': No such file or directory\n", "stderr": "", "aggregated_output": "{\"epoch\": 1, \"loss\": 9.899569645906107, \"seconds\": 22.8}\n{\"epoch\": 2, \"loss\": 9.126480004726313, \"seconds\": 61.9}\n{\"epoch\": 3, \"loss\": 8.789817272088467, \"seconds\": 87.2}\n{\"epoch\": 4, \"loss\": 8.618656598604643, \"seconds\": 108.0}\nls: cannot access '/app/solution/final_temporal.pt': No such file or directory\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 5990 }, "formatted_output": "{\"epoch\": 1, \"loss\": 9.899569645906107, \"seconds\": 22.8}\n{\"epoch\": 2, \"loss\": 9.126480004726313, \"seconds\": 61.9}\n{\"epoch\": 3, \"loss\": 8.789817272088467, \"seconds\": 87.2}\n{\"epoch\": 4, \"loss\": 8.618656598604643, \"seconds\": 108.0}\nls: cannot access '/app/solution/final_temporal.pt': No such file or directory\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:24:21.309Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9e7e3d97-260c-4780-98dc-c37f6df341b8", "process_id": "40674", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_recent_v_scores.npy /app/solution/temporal_vae_v_scores.npy --name recent_ensemble_calibration --weights=0.5,0.75,1 --pops=0.25,0.5 --trends=2,3,4 --feature-decays=14 --feature-likes=20 > /app/solution/recent_ensemble_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_recent_v_scores.npy /app/solution/temporal_vae_v_scores.npy --name recent_ensemble_calibration --weights=0.5,0.75,1 --pops=0.25,0.5 --trends=2,3,4 --feature-decays=14 --feature-likes=20 > /app/solution/recent_ensemble_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 25, "nanos": 804705807 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:24:25.321Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-009f991f-48b3-4cde-a2d4-27a5613853e7", 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"session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f5c8cc1487d19a8adfaeb0b0f0a3", "usage": { "input_tokens": 83062, "cached_input_tokens": 82432, "cache_write_input_tokens": 0, "output_tokens": 1280, "reasoning_output_tokens": 1259, "total_tokens": 84342 }, "turn_token_usage": { "input_tokens": 4902827, "cached_input_tokens": 4708608, "cache_write_input_tokens": 0, "output_tokens": 44421, "reasoning_output_tokens": 23438, "total_tokens": 4947248 }, "thread_token_usage": { "input_tokens": 4902827, "cached_input_tokens": 4708608, "cache_write_input_tokens": 0, "output_tokens": 44421, "reasoning_output_tokens": 23438, "total_tokens": 4947248 } } }, { "kind": "context", "timestamp": "2026-09-10T18:25:18.984Z", "data": { "type": "codex_item_completed", "item": { "type": "Extension", "kind": "clock.sleep", "id": "call_kJ012gfYMvno0U0K22xadaTl", "durationMs": 20000 } } }, { "kind": "tool", "timestamp": "2026-09-10T18:25:24.908Z", "callId": "call_GKAOQnnK25iTgWOpJlu9XwdJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; tail -3 /app/solution/recent_ensemble_calibration.log; tail -3 /app/solution/temporal_vae_recent_export.log\",\"max_output_tokens\":2500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:25:25.336Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"032d34\",\"wall_time_seconds\":0.00000452,\"exit_code\":0,\"original_token_count\":420,\"output\":\"EPOCH 1 loss 8.25409788216276 seconds 135.61107754707336\\nBEST -1\\n{\\\"epoch\\\": 7, \\\"loss\\\": 8.322509961250502, \\\"seconds\\\": 165.5}\\n{\\\"epoch\\\": 8, \\\"loss\\\": 8.2558698287377, \\\"seconds\\\": 178.7}\\n{\\\"epoch\\\": 9, \\\"loss\\\": 8.193523229696812, \\\"seconds\\\": 191.7}\\nusers 3200\\nusers 3840\\n[{'weights': 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{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 4.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04709143438957054}, {'weights': [1.0, 0.0], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04701786480299606}]\\nSaved /app/solution/temporal_vae_recent_validation.parquet vector dimension 202\\nNDCG@10 0.03616415614136258\\n100-item history encoder median ms 0.2409260000604263\\n\"}\n{\"current_time\":\"2026-09-10 18:25:24 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:25:24.975Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-58565923-4672-4ecc-99cd-46e3531ca808", "process_id": "35175", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; tail -3 /app/solution/recent_ensemble_calibration.log; tail -3 /app/solution/temporal_vae_recent_export.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/final_temporal.log; tail -3 /app/solution/final_vae.log; tail -3 /app/solution/recent_ensemble_calibration.log; tail -3 /app/solution/temporal_vae_recent_export.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.25409788216276 seconds 135.61107754707336\nBEST -1\n{\"epoch\": 7, \"loss\": 8.322509961250502, \"seconds\": 165.5}\n{\"epoch\": 8, \"loss\": 8.2558698287377, \"seconds\": 178.7}\n{\"epoch\": 9, \"loss\": 8.193523229696812, \"seconds\": 191.7}\nusers 3200\nusers 3840\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04890285313004543}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04863192688056305}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04862209516682046}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 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"aggregated_output": "EPOCH 1 loss 8.25409788216276 seconds 135.61107754707336\nBEST -1\n{\"epoch\": 7, \"loss\": 8.322509961250502, \"seconds\": 165.5}\n{\"epoch\": 8, \"loss\": 8.2558698287377, \"seconds\": 178.7}\n{\"epoch\": 9, \"loss\": 8.193523229696812, \"seconds\": 191.7}\nusers 3200\nusers 3840\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04890285313004543}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04863192688056305}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04862209516682046}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04848772552136662}, {'weights': [0.5, 0.5], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04820705401058758}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 3.0, 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"reasoning_output_tokens": 23564, "total_tokens": 5117376 }, "thread_token_usage": { "input_tokens": 5072472, "cached_input_tokens": 4875776, "cache_write_input_tokens": 0, "output_tokens": 44904, "reasoning_output_tokens": 23564, "total_tokens": 5117376 } } }, { "kind": "context", "timestamp": "2026-09-10T18:26:03.571Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e18b6760-17b0-40ec-a285-84a964b470ca", "process_id": "91869", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/final_temporal.pt --stage test --history-decays 30 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 20 --name refitted_baseline > /app/solution/refitted_baseline.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/final_temporal.pt --stage test --history-decays 30 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 20 --name refitted_baseline > /app/solution/refitted_baseline.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 23, "nanos": 87159852 }, "formatted_output": "" } } } ] }, { "id": 101, "role": "agent", "timestamp": "2026-09-10T18:26:05.323Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:26:05.323Z", "text": "" } ] }, { "id": 102, "role": "agent", "timestamp": "2026-09-10T18:26:10.441Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:26:10.441Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:26:12.033Z", "text": "The first fully refitted model is ready, and I’m saving its calibrated test ranking. The best validation blend has reached 0.04890. The variational model also improved with more recent history, so I’m checking both models together with that weighting.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:26:12.900Z", "callId": "call_l7tIIaWTIaiCIgqNFJxmWMC6", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/refitted_baseline.log; tail -3 /app/solution/both_recent_calibration.log; tail -3 /app/solution/final_vae.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:26:13.043Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"57e709\",\"wall_time_seconds\":0.00000479,\"exit_code\":0,\"original_token_count\":81,\"output\":\"Saved /app/solution/submission.parquet vector dimension 258\\n100-item history encoder median ms 0.14701400004923926\\nusers 0\\nusers 640\\nusers 1280\\n{\\\"epoch\\\": 10, \\\"loss\\\": 8.129330384425629, \\\"seconds\\\": 205.0}\\n{\\\"epoch\\\": 11, 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0\nusers 640\nusers 1280\n{\"epoch\": 10, \"loss\": 8.129330384425629, \"seconds\": 205.0}\n{\"epoch\": 11, \"loss\": 8.065889639732164, \"seconds\": 218.0}\n{\"epoch\": 12, \"loss\": 8.008641817630865, \"seconds\": 236.3}\n", "stderr": "", "aggregated_output": "Saved /app/solution/submission.parquet vector dimension 258\n100-item history encoder median ms 0.14701400004923926\nusers 0\nusers 640\nusers 1280\n{\"epoch\": 10, \"loss\": 8.129330384425629, \"seconds\": 205.0}\n{\"epoch\": 11, \"loss\": 8.065889639732164, \"seconds\": 218.0}\n{\"epoch\": 12, \"loss\": 8.008641817630865, \"seconds\": 236.3}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4790 }, "formatted_output": "Saved /app/solution/submission.parquet vector dimension 258\n100-item history encoder median ms 0.14701400004923926\nusers 0\nusers 640\nusers 1280\n{\"epoch\": 10, \"loss\": 8.129330384425629, \"seconds\": 205.0}\n{\"epoch\": 11, \"loss\": 8.065889639732164, \"seconds\": 218.0}\n{\"epoch\": 12, \"loss\": 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5203921 } } } ] }, { "id": 103, "role": "agent", "timestamp": "2026-09-10T18:26:37.538Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:26:37.538Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T18:26:44.467Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cf205bda-4031-4d27-b651-0a7eea958d86", "process_id": "91004", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_recent_v_scores.npy /app/solution/temporal_vae_recent_v_scores.npy --name both_recent_calibration --weights=0,0.25,0.5,0.75 --pops=0.25,0.5 --trends=1.5,2,2.5,3 --feature-decays=14 --feature-likes=20,50 > /app/solution/both_recent_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores 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'feature_decay': 14, 'feature_like': 50.0, 'score': 0.049388184199123816}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04921511965270798}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04916112066648021}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.049159012904870535}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04914632224674964}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.049011392203177295}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04885109475838859}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04884335326125479}, {'weights': [0.5, 0.5], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04881864558107175}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04880483722698378}]\\nSaved /app/solution/temporal_14_validation.parquet vector dimension 258\\nNDCG@10 0.03611252884063253\\n100-item history encoder median ms 0.16871499974513426\\n{\\\"epoch\\\": 17, \\\"loss\\\": 7.782534470924964, \\\"seconds\\\": 312.3}\\n{\\\"epoch\\\": 18, \\\"loss\\\": 7.747487110969348, \\\"seconds\\\": 326.1}\\n{\\\"epoch\\\": 19, \\\"loss\\\": 7.7211297658773566, \\\"seconds\\\": 339.2}\\n\"}\n{\"current_time\":\"2026-09-10 18:27:49 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:27:49.225Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-71c5501d-986a-4541-9256-26afa2204c7a", "process_id": "71564", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/both_recent_calibration.log; tail -3 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0.04914632224674964}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.049011392203177295}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04885109475838859}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04884335326125479}, {'weights': [0.5, 0.5], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04881864558107175}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04880483722698378}]\nSaved /app/solution/temporal_14_validation.parquet vector dimension 258\nNDCG@10 0.03611252884063253\n100-item history encoder median ms 0.16871499974513426\n{\"epoch\": 17, \"loss\": 7.782534470924964, \"seconds\": 312.3}\n{\"epoch\": 18, \"loss\": 7.747487110969348, \"seconds\": 326.1}\n{\"epoch\": 19, \"loss\": 7.7211297658773566, \"seconds\": 339.2}\n", "stderr": "", "aggregated_output": "users 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.049388184199123816}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04921511965270798}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04916112066648021}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.049159012904870535}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04914632224674964}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.049011392203177295}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04885109475838859}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 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'feature_like': 20.0, 'score': 0.04921511965270798}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04916112066648021}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.049159012904870535}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04914632224674964}, {'weights': [0.75, 0.25], 'pop': 0.25, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.049011392203177295}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04885109475838859}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 3.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04884335326125479}, {'weights': [0.5, 0.5], 'pop': 0.25, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'score': 0.04881864558107175}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'score': 0.04880483722698378}]\nSaved /app/solution/temporal_14_validation.parquet vector dimension 258\nNDCG@10 0.03611252884063253\n100-item history encoder median ms 0.16871499974513426\n{\"epoch\": 17, \"loss\": 7.782534470924964, \"seconds\": 312.3}\n{\"epoch\": 18, \"loss\": 7.747487110969348, \"seconds\": 326.1}\n{\"epoch\": 19, \"loss\": 7.7211297658773566, \"seconds\": 339.2}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:27:49.305Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f67394d487d1aa80a28ba509ef67", "usage": { "input_tokens": 87876, "cached_input_tokens": 86528, "cache_write_input_tokens": 0, "output_tokens": 1250, "reasoning_output_tokens": 1172, "total_tokens": 89126 }, 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--org 1 --decay 120 --kl .001 --epochs 20 --eval-every 20 > /app/solution/final_vae.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/model.py --stage test --name final_vae --vae --hidden 600 --dim 200 --dropout .5 --power 0 --like 2 --org 1 --decay 120 --kl .001 --epochs 20 --eval-every 20 > /app/solution/final_vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 367, "nanos": 655526740 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:28:19.633Z", "callId": "call_JRpCPI4i8dikeCaXRZYyaOfv", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/calibrate.py';s=open(p).read().replace(\\\"a=p.parse_args();gold\\\", \\\"p.add_argument('--organic-shares',default='0');a=p.parse_args();gold\\\")\\ns=s.replace(\\\"ws=[float\\\", \\\"shares={l:np.log((features['organic']+l*features['likes']+10)/(features['count']+l*features['likes']+20)).astype('float32') for l in feature_likes}\\\\nws=[float\\\")\\ns=s.replace('feature_decays,feature_likes));','feature_decays,feature_likes,map(float,a.organic_shares.split(\\\\',\\\\'))));').replace('(weights,p,t,fd,fl) in enumerate','(weights,p,t,fd,fl,osh) in enumerate').replace('+t*trends[(fd,fl)];','+t*trends[(fd,fl)]+osh*shares[fl];').replace('feature_like=fl,score=', 'feature_like=fl,organic_share=osh,score=').replace('for (w,p,t,fd,fl),s','for (w,p,t,fd,fl,osh),s');open(p,'w').write(s)\\np='/app/solution/retrieve.py';s=open(p).read().replace('features,pop,trend):','features,pop,trend,organic_share=0.):').replace('nn.Linear(2,1,bias=False)','nn.Linear(3,1,bias=False)').replace('[[pop,trend]]','[[pop,trend,organic_share]]').replace('feature_like=0.):','feature_like=0.,organic_share=0.):')\\ns=s.replace(\\\"np.log(stats['organic']+feature_like*stats['likes']+10)],1)\\\", \\\"np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1)\\\")\\ns=s.replace('ItemRetrievalTower(models,features,pop,trend).eval()', 'ItemRetrievalTower(models,features,pop,trend,organic_share).eval()').replace(\\\"a=p.parse_args();seed(42);weights\\\", \\\"p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights\\\").replace('a.feature_decay,a.feature_like);z=', 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"text": "The best blend has reached 0.04939 NDCG@10. A fully refitted single-model submission is saved and passes the eligibility checks. The variational model’s refit is nearly through pretraining; its temporal fine-tuning is the last training stage needed for the blend.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:28:30.077Z", "callId": "call_5MSrvWBxDLhNDG8YAxGotymn", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_vae.log; /opt/task-venv/bin/python - <<'PY'\\nimport torch,numpy as np\\nc=torch.load('/app/solution/final_vae.pt',weights_only=False);print('final VAE checkpoint epoch',c['epoch'])\\na=np.load('/task/data/validation_eligible_candidates.npz');b=np.load('/task/data/test_eligible_candidates.npz');print('catalog alignment',np.array_equal(a['item_ids'],b['item_ids']))\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:28:33.594Z", "content": "Script completed\nWall time 3.5 seconds\nOutput:\n\n{\"chunk_id\":\"c84c78\",\"wall_time_seconds\":3.330164867,\"exit_code\":0,\"original_token_count\":60,\"output\":\"{\\\"epoch\\\": 18, \\\"loss\\\": 7.747487110969348, \\\"seconds\\\": 326.1}\\n{\\\"epoch\\\": 19, \\\"loss\\\": 7.7211297658773566, \\\"seconds\\\": 339.2}\\n{\\\"epoch\\\": 20, \\\"loss\\\": 7.694623824877617, \\\"seconds\\\": 352.4}\\nBEST -1\\nfinal VAE checkpoint epoch 20\\ncatalog alignment True\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:28:30.199Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f6c5001487d19af2de3d5f8bcf9c", "usage": { "input_tokens": 90857, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 220, "reasoning_output_tokens": 14, "total_tokens": 91077 }, "turn_token_usage": { "input_tokens": 5513639, "cached_input_tokens": 5310464, "cache_write_input_tokens": 0, "output_tokens": 49067, "reasoning_output_tokens": 26438, "total_tokens": 5562706 }, "thread_token_usage": { "input_tokens": 5513639, "cached_input_tokens": 5310464, "cache_write_input_tokens": 0, "output_tokens": 49067, "reasoning_output_tokens": 26438, "total_tokens": 5562706 } } }, { "kind": "context", "timestamp": "2026-09-10T18:28:33.580Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fc5576bf-7b9e-43b3-ab3a-7fe4951adff9", "process_id": "15936", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_vae.log; /opt/task-venv/bin/python - <<'PY'\nimport torch,numpy as np\nc=torch.load('/app/solution/final_vae.pt',weights_only=False);print('final VAE checkpoint epoch',c['epoch'])\na=np.load('/task/data/validation_eligible_candidates.npz');b=np.load('/task/data/test_eligible_candidates.npz');print('catalog alignment',np.array_equal(a['item_ids'],b['item_ids']))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_vae.log; /opt/task-venv/bin/python - <<'PY'\nimport torch,numpy as np\nc=torch.load('/app/solution/final_vae.pt',weights_only=False);print('final VAE checkpoint epoch',c['epoch'])\na=np.load('/task/data/validation_eligible_candidates.npz');b=np.load('/task/data/test_eligible_candidates.npz');print('catalog alignment',np.array_equal(a['item_ids'],b['item_ids']))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "final VAE checkpoint epoch 20\ncatalog alignment True\n", "stderr": "", "aggregated_output": "final VAE checkpoint epoch 20\ncatalog alignment True\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 330156187 }, "formatted_output": "final VAE checkpoint epoch 20\ncatalog alignment True\n" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:28:42.261Z", "callId": "call_ScVKTzY3fwZVvB7lUrWedHqx", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --load /app/solution/final_vae.pt --name final_temporal_vae --epochs 2 --max-steps 513 --threads 4 > /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/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 14 --stage validation --name temporal_vae_14_v --save-scores --output /app/solution/temporal_vae_14_validation.parquet > /app/solution/temporal_vae_14_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:28:44.606Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"075452\",\"wall_time_seconds\":1.00086014,\"session_id\":23078,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"46e657\",\"wall_time_seconds\":1.000738157,\"session_id\":82028,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:28:42.395Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f6d1f76087d19026b0a7f6d48c48", "usage": { "input_tokens": 91231, "cached_input_tokens": 90624, "cache_write_input_tokens": 0, "output_tokens": 220, "reasoning_output_tokens": 0, "total_tokens": 91451 }, "turn_token_usage": { "input_tokens": 5604870, "cached_input_tokens": 5401088, "cache_write_input_tokens": 0, "output_tokens": 49287, "reasoning_output_tokens": 26438, "total_tokens": 5654157 }, "thread_token_usage": { "input_tokens": 5604870, "cached_input_tokens": 5401088, "cache_write_input_tokens": 0, "output_tokens": 49287, "reasoning_output_tokens": 26438, "total_tokens": 5654157 } } }, { "kind": "context", "timestamp": "2026-09-10T18:28:52.803Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ac38c5b-d271-4c95-89e2-a3e9b09efc1f", "process_id": "43695", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"a=p.parse_args();gold\", \"p.add_argument('--organic-shares',default='0');a=p.parse_args();gold\")\ns=s.replace(\"ws=[float\", \"shares={l:np.log((features['organic']+l*features['likes']+10)/(features['count']+l*features['likes']+20)).astype('float32') for l in feature_likes}\\nws=[float\")\ns=s.replace('feature_decays,feature_likes));','feature_decays,feature_likes,map(float,a.organic_shares.split(\\',\\'))));').replace('(weights,p,t,fd,fl) in enumerate','(weights,p,t,fd,fl,osh) in enumerate').replace('+t*trends[(fd,fl)];','+t*trends[(fd,fl)]+osh*shares[fl];').replace('feature_like=fl,score=', 'feature_like=fl,organic_share=osh,score=').replace('for (w,p,t,fd,fl),s','for (w,p,t,fd,fl,osh),s');open(p,'w').write(s)\np='/app/solution/retrieve.py';s=open(p).read().replace('features,pop,trend):','features,pop,trend,organic_share=0.):').replace('nn.Linear(2,1,bias=False)','nn.Linear(3,1,bias=False)').replace('[[pop,trend]]','[[pop,trend,organic_share]]').replace('feature_like=0.):','feature_like=0.,organic_share=0.):')\ns=s.replace(\"np.log(stats['organic']+feature_like*stats['likes']+10)],1)\", \"np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1)\")\ns=s.replace('ItemRetrievalTower(models,features,pop,trend).eval()', 'ItemRetrievalTower(models,features,pop,trend,organic_share).eval()').replace(\"a=p.parse_args();seed(42);weights\", \"p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights\").replace('a.feature_decay,a.feature_like);z=', 'a.feature_decay,a.feature_like,a.organic_share);z=');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_recent_v_scores.npy --name organic_calibration --weights=0.5,0.75 --pops=0.5 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=20,50 --organic-shares=0,0.5,1,2 > /app/solution/organic_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/calibrate.py';s=open(p).read().replace(\"a=p.parse_args();gold\", \"p.add_argument('--organic-shares',default='0');a=p.parse_args();gold\")\ns=s.replace(\"ws=[float\", \"shares={l:np.log((features['organic']+l*features['likes']+10)/(features['count']+l*features['likes']+20)).astype('float32') for l in feature_likes}\\nws=[float\")\ns=s.replace('feature_decays,feature_likes));','feature_decays,feature_likes,map(float,a.organic_shares.split(\\',\\'))));').replace('(weights,p,t,fd,fl) in enumerate','(weights,p,t,fd,fl,osh) in enumerate').replace('+t*trends[(fd,fl)];','+t*trends[(fd,fl)]+osh*shares[fl];').replace('feature_like=fl,score=', 'feature_like=fl,organic_share=osh,score=').replace('for (w,p,t,fd,fl),s','for (w,p,t,fd,fl,osh),s');open(p,'w').write(s)\np='/app/solution/retrieve.py';s=open(p).read().replace('features,pop,trend):','features,pop,trend,organic_share=0.):').replace('nn.Linear(2,1,bias=False)','nn.Linear(3,1,bias=False)').replace('[[pop,trend]]','[[pop,trend,organic_share]]').replace('feature_like=0.):','feature_like=0.,organic_share=0.):')\ns=s.replace(\"np.log(stats['organic']+feature_like*stats['likes']+10)],1)\", \"np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1)\")\ns=s.replace('ItemRetrievalTower(models,features,pop,trend).eval()', 'ItemRetrievalTower(models,features,pop,trend,organic_share).eval()').replace(\"a=p.parse_args();seed(42);weights\", \"p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights\").replace('a.feature_decay,a.feature_like);z=', 'a.feature_decay,a.feature_like,a.organic_share);z=');open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_recent_v_scores.npy --name organic_calibration --weights=0.5,0.75 --pops=0.5 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=20,50 --organic-shares=0,0.5,1,2 > /app/solution/organic_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 32, "nanos": 995484135 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:29:05.050Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76be41be-3b7e-416d-b735-887d4e654e55", "process_id": "82028", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 14 --stage validation --name temporal_vae_14_v --save-scores --output /app/solution/temporal_vae_14_validation.parquet > /app/solution/temporal_vae_14_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 14 --stage validation --name temporal_vae_14_v --save-scores --output /app/solution/temporal_vae_14_validation.parquet > /app/solution/temporal_vae_14_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 21, "nanos": 446147978 }, "formatted_output": "" } } } ] }, { "id": 110, "role": "agent", "timestamp": "2026-09-10T18:29:10.864Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:29:10.864Z", "text": "" } ] }, { "id": 111, "role": "agent", "timestamp": "2026-09-10T18:29:31.088Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:29:31.088Z", "text": "" } ] }, { "id": 112, "role": "agent", "timestamp": "2026-09-10T18:29:38.338Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:29:38.338Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:29:39.443Z", "callId": "call_s2hqiiwcM0OFZNAjLi2UbRW1", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/organic_calibration.log; tail -3 /app/solution/temporal_vae_14_export.log; tail -3 /app/solution/final_temporal_vae.log\",\"max_output_tokens\":2500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:29:39.586Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"d5fc09\",\"wall_time_seconds\":0.000014061,\"exit_code\":0,\"original_token_count\":411,\"output\":\"users 3200\\nusers 3840\\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050103819237872736}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05008473881645227}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04999666517063951}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04990632903331294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04990102938823372}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.04985566740835615}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.04984435906151916}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.049785323626168304}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 0.5, 'score': 0.04977837332336719}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04973408593984676}]\\nSaved /app/solution/temporal_vae_14_validation.parquet vector dimension 202\\nNDCG@10 0.03705266641234422\\n100-item history encoder median ms 0.21495600003618165\\n\"}\n{\"current_time\":\"2026-09-10 18:29:39 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:29:39.487Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0f6ce11c-8d2a-4ed1-9fb4-2225a195ee44", "process_id": "6747", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/organic_calibration.log; tail -3 /app/solution/temporal_vae_14_export.log; tail -3 /app/solution/final_temporal_vae.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/organic_calibration.log; tail -3 /app/solution/temporal_vae_14_export.log; tail -3 /app/solution/final_temporal_vae.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050103819237872736}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05008473881645227}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04999666517063951}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04990632903331294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04990102938823372}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.04985566740835615}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.04984435906151916}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.049785323626168304}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 0.5, 'score': 0.04977837332336719}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04973408593984676}]\nSaved /app/solution/temporal_vae_14_validation.parquet vector dimension 202\nNDCG@10 0.03705266641234422\n100-item history encoder median ms 0.21495600003618165\n", "stderr": "", "aggregated_output": "users 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050103819237872736}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05008473881645227}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04999666517063951}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04990632903331294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04990102938823372}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.04985566740835615}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.04984435906151916}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.049785323626168304}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 0.5, 'score': 0.04977837332336719}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04973408593984676}]\nSaved /app/solution/temporal_vae_14_validation.parquet vector dimension 202\nNDCG@10 0.03705266641234422\n100-item history encoder median ms 0.21495600003618165\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 14061 }, "formatted_output": "users 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050103819237872736}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05008473881645227}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04999666517063951}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04990632903331294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04990102938823372}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.04985566740835615}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.04984435906151916}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 1.0, 'score': 0.049785323626168304}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 20.0, 'organic_share': 0.5, 'score': 0.04977837332336719}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 2.0, 'score': 0.04973408593984676}]\nSaved /app/solution/temporal_vae_14_validation.parquet vector dimension 202\nNDCG@10 0.03705266641234422\n100-item history encoder median ms 0.21495600003618165\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:29:39.583Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f6dcd0fc87d1b5182a1c4dadab2d", "usage": { "input_tokens": 91541, "cached_input_tokens": 91008, "cache_write_input_tokens": 0, "output_tokens": 1297, "reasoning_output_tokens": 1216, "total_tokens": 92838 }, "turn_token_usage": { "input_tokens": 5696411, "cached_input_tokens": 5492096, "cache_write_input_tokens": 0, "output_tokens": 50584, "reasoning_output_tokens": 27654, "total_tokens": 5746995 }, "thread_token_usage": { "input_tokens": 5696411, "cached_input_tokens": 5492096, "cache_write_input_tokens": 0, "output_tokens": 50584, "reasoning_output_tokens": 27654, "total_tokens": 5746995 } } } ] }, { "id": 113, "role": "agent", "timestamp": "2026-09-10T18:30:00.487Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:30:00.487Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:30:04.478Z", "callId": "call_ZX61VvLp3XY8F5MUoQ7fe9iJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name both_14_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50 --organic-shares=0,0.5,1 > /app/solution/both_14_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/retrieve.py --models /app/solution/temporal.pt --history-decays 7 --stage validation --name temporal_7_v --save-scores --output /app/solution/temporal_7_validation.parquet > /app/solution/temporal_7_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:30:06.814Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"41a102\",\"wall_time_seconds\":1.000440492,\"session_id\":60714,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"8dd71f\",\"wall_time_seconds\":1.000765971,\"session_id\":31180,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:30:04.575Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f713dd6c87d1ad118eb2da333130", "usage": { "input_tokens": 93646, "cached_input_tokens": 91392, "cache_write_input_tokens": 0, "output_tokens": 729, "reasoning_output_tokens": 459, "total_tokens": 94375 }, "turn_token_usage": { "input_tokens": 5790057, "cached_input_tokens": 5583488, "cache_write_input_tokens": 0, "output_tokens": 51313, "reasoning_output_tokens": 28113, "total_tokens": 5841370 }, "thread_token_usage": { "input_tokens": 5790057, "cached_input_tokens": 5583488, "cache_write_input_tokens": 0, "output_tokens": 51313, "reasoning_output_tokens": 28113, "total_tokens": 5841370 } } } ] }, { "id": 114, "role": "agent", "timestamp": "2026-09-10T18:30:29.945Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:30:29.945Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T18:30:33.849Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-54732684-2e49-4443-a168-924e01a11a98", "process_id": "31180", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt --history-decays 7 --stage validation --name temporal_7_v --save-scores --output /app/solution/temporal_7_validation.parquet > /app/solution/temporal_7_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt --history-decays 7 --stage validation --name temporal_7_v --save-scores --output /app/solution/temporal_7_validation.parquet > /app/solution/temporal_7_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 28, "nanos": 35074070 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:30:33.987Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aa805ee0-d81b-4f4f-9b0e-7f45d7755d58", "process_id": "60714", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name both_14_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50 --organic-shares=0,0.5,1 > /app/solution/both_14_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name both_14_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50 --organic-shares=0,0.5,1 > /app/solution/both_14_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 29, "nanos": 339752244 }, "formatted_output": "" } } } ] }, { "id": 115, "role": "agent", "timestamp": "2026-09-10T18:30:38.660Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:30:38.660Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:30:40.066Z", "text": "Public NDCG@10 has passed 0.05010. Shorter history weighting is helping both encoders, and a small item-level adjustment for organic activity adds a further gain. I’m finishing those comparisons and checking that the exported vectors reproduce the ranking scores.\n" }, { "kind": "context", "timestamp": "2026-09-10T18:30:43.831Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-651ea65b-7458-4028-b869-6c7567ac874e", "process_id": "23078", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --stage test --load /app/solution/final_vae.pt --name final_temporal_vae --epochs 2 --max-steps 513 --threads 4 > /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_train.py --stage test --load /app/solution/final_vae.pt --name final_temporal_vae --epochs 2 --max-steps 513 --threads 4 > /app/solution/final_temporal_vae.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 121, "nanos": 396631033 }, "formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:30:48.006Z", "callId": "call_hK5BZpA6k2LzsEZoHVNeJjzS", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/model.py';s=open(p).read();old=\\\"f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\\\";new=\\\"\\\"\\\"cache=O/f'val_{fam}_gold.parquet'\\n if not cache.exists():\\n f=pd.read_parquet(D/f'val_{fam}.parquet');f=f[f.is_organic==1]\\n if fam=='listens':f=f[f.played_ratio_pct>=80]\\n r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id);ok=(r>=0)&(c>=0);f=f.loc[ok,['uid','item_id']];r=r[ok];c=c[ok]\\n ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool);f.loc[ok].drop_duplicates().to_parquet(cache,index=False)\\n f=pd.read_parquet(cache);r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\\\"\\\"\\\";assert old in s;s=s.replace(old,new).replace('gold,ideal=get_truth();best=-1;',\\\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1;\\\");open(p,'w').write(s)\\np='/app/solution/temporal_train.py';s=open(p).read().replace('gold,ideal=get_truth();best=-1',\\\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\\\");open(p,'w').write(s)\\np='/app/solution/item_features.py';s=open(p).read().replace('N=30000', \\\"N=pq.ParquetFile(D/'candidate_catalog.parquet').metadata.num_rows\\\");open(p,'w').write(s)\\nPY\\ntail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/temporal_7_export.log; tail -3 /app/solution/both_14_calibration.log\",\"max_output_tokens\":2500}));\n" }, "result": { "timestamp": "2026-09-10T18:30:48.124Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"738989\",\"wall_time_seconds\":0.00000308,\"exit_code\":0,\"original_token_count\":427,\"output\":\"EPOCH 1 loss 8.35132726542468 seconds 70.47580695152283\\nBEST -1\\nSaved /app/solution/temporal_7_validation.parquet vector dimension 258\\nNDCG@10 0.03716207136881013\\n100-item history encoder median ms 0.18319599985261448\\nusers 3200\\nusers 3840\\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050378486166519194}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05032138847166572}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.0499929856593927}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04984780208726081}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04981857262871436}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.049764001587552294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.0497391340441023}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04969385088246713}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04968835585601932}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04956914181746576}]\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:30:48.097Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f72f05d887d1a737dd89cddc8c68", "usage": { "input_tokens": 94467, "cached_input_tokens": 93440, "cache_write_input_tokens": 0, "output_tokens": 1286, "reasoning_output_tokens": 725, "total_tokens": 95753 }, "turn_token_usage": { "input_tokens": 5884524, "cached_input_tokens": 5676928, "cache_write_input_tokens": 0, "output_tokens": 52599, "reasoning_output_tokens": 28838, "total_tokens": 5937123 }, "thread_token_usage": { "input_tokens": 5884524, "cached_input_tokens": 5676928, "cache_write_input_tokens": 0, "output_tokens": 52599, "reasoning_output_tokens": 28838, "total_tokens": 5937123 } } }, { "kind": "context", "timestamp": "2026-09-10T18:30:48.101Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-acfbfe28-dd56-4a7d-a328-b7c20c4b7bcc", "process_id": "54453", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();old=\"f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\";new=\"\"\"cache=O/f'val_{fam}_gold.parquet'\n if not cache.exists():\n f=pd.read_parquet(D/f'val_{fam}.parquet');f=f[f.is_organic==1]\n if fam=='listens':f=f[f.played_ratio_pct>=80]\n r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id);ok=(r>=0)&(c>=0);f=f.loc[ok,['uid','item_id']];r=r[ok];c=c[ok]\n ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool);f.loc[ok].drop_duplicates().to_parquet(cache,index=False)\n f=pd.read_parquet(cache);r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\"\"\";assert old in s;s=s.replace(old,new).replace('gold,ideal=get_truth();best=-1;',\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1;\");open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace('gold,ideal=get_truth();best=-1',\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\");open(p,'w').write(s)\np='/app/solution/item_features.py';s=open(p).read().replace('N=30000', \"N=pq.ParquetFile(D/'candidate_catalog.parquet').metadata.num_rows\");open(p,'w').write(s)\nPY\ntail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/temporal_7_export.log; tail -3 /app/solution/both_14_calibration.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/model.py';s=open(p).read();old=\"f=pd.read_parquet(O/f'val_{fam}_gold.parquet');r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\";new=\"\"\"cache=O/f'val_{fam}_gold.parquet'\n if not cache.exists():\n f=pd.read_parquet(D/f'val_{fam}.parquet');f=f[f.is_organic==1]\n if fam=='listens':f=f[f.played_ratio_pct>=80]\n r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id);ok=(r>=0)&(c>=0);f=f.loc[ok,['uid','item_id']];r=r[ok];c=c[ok]\n ok=((z['packed_eligible'][r,c//8]>>(c%8))&1).astype(bool);f.loc[ok].drop_duplicates().to_parquet(cache,index=False)\n f=pd.read_parquet(cache);r=ui.get_indexer(f.uid);c=ii.get_indexer(f.item_id)\"\"\";assert old in s;s=s.replace(old,new).replace('gold,ideal=get_truth();best=-1;',\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1;\");open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace('gold,ideal=get_truth();best=-1',\"gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\");open(p,'w').write(s)\np='/app/solution/item_features.py';s=open(p).read().replace('N=30000', \"N=pq.ParquetFile(D/'candidate_catalog.parquet').metadata.num_rows\");open(p,'w').write(s)\nPY\ntail -4 /app/solution/final_temporal_vae.log; tail -3 /app/solution/temporal_7_export.log; tail -3 /app/solution/both_14_calibration.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "EPOCH 1 loss 8.35132726542468 seconds 70.47580695152283\nBEST -1\nSaved /app/solution/temporal_7_validation.parquet vector dimension 258\nNDCG@10 0.03716207136881013\n100-item history encoder median ms 0.18319599985261448\nusers 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050378486166519194}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05032138847166572}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.0499929856593927}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04984780208726081}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04981857262871436}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.049764001587552294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.0497391340441023}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04969385088246713}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04968835585601932}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04956914181746576}]\n", "stderr": "", "aggregated_output": "EPOCH 1 loss 8.35132726542468 seconds 70.47580695152283\nBEST -1\nSaved /app/solution/temporal_7_validation.parquet vector dimension 258\nNDCG@10 0.03716207136881013\n100-item history encoder median ms 0.18319599985261448\nusers 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050378486166519194}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05032138847166572}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.0499929856593927}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04984780208726081}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04981857262871436}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.049764001587552294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.0497391340441023}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04969385088246713}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04968835585601932}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04956914181746576}]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3080 }, "formatted_output": "EPOCH 1 loss 8.35132726542468 seconds 70.47580695152283\nBEST -1\nSaved /app/solution/temporal_7_validation.parquet vector dimension 258\nNDCG@10 0.03716207136881013\n100-item history encoder median ms 0.18319599985261448\nusers 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050378486166519194}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.05032138847166572}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.0499929856593927}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04984780208726081}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04981857262871436}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.049764001587552294}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.0, 'score': 0.0497391340441023}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04969385088246713}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04968835585601932}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04956914181746576}]\n" } } } ] }, { "id": 116, "role": "agent", "timestamp": "2026-09-10T18:30:56.889Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:30:56.889Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:31:05.340Z", "callId": "call_oVkophAySV0IcdpzKCcYZVwA", "name": "exec", "input": { "code": "const results=await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt --weights 0.5 0.5 --stage test --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name final > /app/solution/final_retrieval.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/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]);results.forEach(text);\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:31:06.535Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"a4995d\",\"wall_time_seconds\":1.00145971,\"session_id\":42981,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"9f8d1e\",\"wall_time_seconds\":1.000448962,\"session_id\":76205,\"original_token_count\":0,\"output\":\"\"}}\n{\"current_time\":\"2026-09-10 18:31:06 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:31:05.488Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f7588cd487d194ca900b7f38c2cc", "usage": { "input_tokens": 96566, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 501, "reasoning_output_tokens": 208, "total_tokens": 97067 }, "turn_token_usage": { "input_tokens": 5981090, "cached_input_tokens": 5771264, "cache_write_input_tokens": 0, "output_tokens": 53100, "reasoning_output_tokens": 29046, "total_tokens": 6034190 }, "thread_token_usage": { "input_tokens": 5981090, "cached_input_tokens": 5771264, "cache_write_input_tokens": 0, "output_tokens": 53100, "reasoning_output_tokens": 29046, "total_tokens": 6034190 } } }, { "kind": "context", "timestamp": "2026-09-10T18:31:21.646Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-43cad785-7035-4b01-9505-55c11d4b90f9", "process_id": "76205", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 16, "nanos": 127504342 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:31:24.640Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-97f5305e-7b6e-498e-92ec-0080062d46d6", "process_id": "42981", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt --weights 0.5 0.5 --stage test --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name final > /app/solution/final_retrieval.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt --weights 0.5 0.5 --stage test --history-decays 14 14 --pop 0.5 --trend 2 --feature-decay 14 --feature-like 50 --organic-share 1 --name final > /app/solution/final_retrieval.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 19, "nanos": 122622052 }, "formatted_output": "" } } } ] }, { "id": 117, "role": "agent", "timestamp": "2026-09-10T18:31:39.647Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:31:39.647Z", "text": "" } ] }, { "id": 118, "role": "agent", "timestamp": "2026-09-10T18:32:01.283Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:32:01.283Z", "text": "" } ] }, { "id": 119, "role": "agent", "timestamp": "2026-09-10T18:32:11.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:32:11.805Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:32:19.137Z", "callId": "call_Pitzk7MtZotexoIVz5EQlsjW", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/temporal_prepare.py';s=open(p).read().replace('import numpy as np,pandas as pd,pyarrow.parquet as pq,time','import numpy as np,pandas as pd,pyarrow.parquet as pq,time,argparse\\\\nap=argparse.ArgumentParser();ap.add_argument(\\\"--decay\\\",type=float,default=90);ap.add_argument(\\\"--stage\\\",default=\\\"both\\\");args=ap.parse_args()').replace(\\\"out=O/f'{stage}_temporal.npz'\\\", \\\"out=O/(f'{stage}_temporal.npz' if args.decay==90 else f'{stage}_temporal_{args.decay:g}.npz')\\\\n if args.stage not in ['both',stage]:continue\\\").replace('dec=np.exp(-age/90)', 'dec=np.exp(-age/args.decay)');open(p,'w').write(s)\\np='/app/solution/temporal_train.py';s=open(p).read().replace(\\\"a=p.parse_args();seed(a.seed)\\\", \\\"p.add_argument('--history-decay',type=float,default=90);a=p.parse_args();seed(a.seed)\\\").replace('data=temporal_data(a.stage);gold', 'data=temporal_data(a.stage,a.history_decay);gold').replace(\\\"z=np.load(O/f'{a.stage}_temporal.npz');\\\", \\\"z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));\\\");open(p,'w').write(s)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py --decay 7 > /app/solution/temporal_short_prepare.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:32:20.308Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"6574ed\",\"wall_time_seconds\":1.001856806,\"session_id\":6876,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:32:19.254Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f76ac55487d1acbf6c853887a4f6", "usage": { "input_tokens": 97192, "cached_input_tokens": 96384, "cache_write_input_tokens": 0, "output_tokens": 1731, "reasoning_output_tokens": 1304, "total_tokens": 98923 }, "turn_token_usage": { "input_tokens": 6078282, "cached_input_tokens": 5867648, "cache_write_input_tokens": 0, "output_tokens": 54831, "reasoning_output_tokens": 30350, "total_tokens": 6133113 }, "thread_token_usage": { "input_tokens": 6078282, "cached_input_tokens": 5867648, "cache_write_input_tokens": 0, "output_tokens": 54831, "reasoning_output_tokens": 30350, "total_tokens": 6133113 } } } ] }, { "id": 120, "role": "agent", "timestamp": "2026-09-10T18:32:42.785Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:32:42.785Z", "text": "" } ] }, { "id": 121, "role": "agent", "timestamp": "2026-09-10T18:32:47.610Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:32:47.610Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:32:48.762Z", "callId": "call_lJcZ2aJ1kZH2utmZTMGaDsDJ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -4 /app/solution/final_retrieval.log; tail -4 /app/solution/selected_validation.log; tail -3 /app/solution/temporal_short_prepare.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:32:48.928Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"d48544\",\"wall_time_seconds\":0.00000439,\"exit_code\":0,\"original_token_count\":97,\"output\":\"Saved /app/solution/submission.parquet vector dimension 459\\n100-item history encoder median ms 0.294748000214895\\nSaved /app/solution/validation.parquet vector dimension 459\\nNDCG@10 0.05037848616651931\\n100-item history encoder median ms 0.3344089996062394\\ntest examples 7 seconds 21.991068363189697\\ntest examples 11 seconds 21.99194359779358\\ntest examples 10004 seconds 26.038624048233032\\n\"}\n{\"current_time\":\"2026-09-10 18:32:48 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:32:48.806Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4750bccc-4937-42aa-bb2a-769fa17fe4f6", "process_id": "22833", "command": [ "/bin/sh", "-lc", "tail -4 /app/solution/final_retrieval.log; tail -4 /app/solution/selected_validation.log; tail -3 /app/solution/temporal_short_prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -4 /app/solution/final_retrieval.log; tail -4 /app/solution/selected_validation.log; tail -3 /app/solution/temporal_short_prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Saved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.294748000214895\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.05037848616651931\n100-item history encoder median ms 0.3344089996062394\ntest examples 7 seconds 21.991068363189697\ntest examples 11 seconds 21.99194359779358\ntest examples 10004 seconds 26.038624048233032\n", "stderr": "", "aggregated_output": "Saved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.294748000214895\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.05037848616651931\n100-item history encoder median ms 0.3344089996062394\ntest examples 7 seconds 21.991068363189697\ntest examples 11 seconds 21.99194359779358\ntest examples 10004 seconds 26.038624048233032\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4390 }, "formatted_output": "Saved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.294748000214895\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.05037848616651931\n100-item history encoder median ms 0.3344089996062394\ntest examples 7 seconds 21.991068363189697\ntest examples 11 seconds 21.99194359779358\ntest examples 10004 seconds 26.038624048233032\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:32:48.925Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f7b48b5c87d1b7088dd772f641ab", "usage": { "input_tokens": 98980, "cached_input_tokens": 97024, "cache_write_input_tokens": 0, "output_tokens": 694, "reasoning_output_tokens": 618, "total_tokens": 99674 }, "turn_token_usage": { "input_tokens": 6177262, "cached_input_tokens": 5964672, "cache_write_input_tokens": 0, "output_tokens": 55525, "reasoning_output_tokens": 30968, "total_tokens": 6232787 }, "thread_token_usage": { "input_tokens": 6177262, "cached_input_tokens": 5964672, "cache_write_input_tokens": 0, "output_tokens": 55525, "reasoning_output_tokens": 30968, "total_tokens": 6232787 } } }, { "kind": "context", "timestamp": "2026-09-10T18:33:04.090Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1f2523ea-b43d-4829-b297-3e5e8dbe69ff", "process_id": "6876", "command": [ "/bin/sh", "-lc", "python - <<'PY'\np='/app/solution/temporal_prepare.py';s=open(p).read().replace('import numpy as np,pandas as pd,pyarrow.parquet as pq,time','import numpy as np,pandas as pd,pyarrow.parquet as pq,time,argparse\\nap=argparse.ArgumentParser();ap.add_argument(\"--decay\",type=float,default=90);ap.add_argument(\"--stage\",default=\"both\");args=ap.parse_args()').replace(\"out=O/f'{stage}_temporal.npz'\", \"out=O/(f'{stage}_temporal.npz' if args.decay==90 else f'{stage}_temporal_{args.decay:g}.npz')\\n if args.stage not in ['both',stage]:continue\").replace('dec=np.exp(-age/90)', 'dec=np.exp(-age/args.decay)');open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"a=p.parse_args();seed(a.seed)\", \"p.add_argument('--history-decay',type=float,default=90);a=p.parse_args();seed(a.seed)\").replace('data=temporal_data(a.stage);gold', 'data=temporal_data(a.stage,a.history_decay);gold').replace(\"z=np.load(O/f'{a.stage}_temporal.npz');\", \"z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py --decay 7 > /app/solution/temporal_short_prepare.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/temporal_prepare.py';s=open(p).read().replace('import numpy as np,pandas as pd,pyarrow.parquet as pq,time','import numpy as np,pandas as pd,pyarrow.parquet as pq,time,argparse\\nap=argparse.ArgumentParser();ap.add_argument(\"--decay\",type=float,default=90);ap.add_argument(\"--stage\",default=\"both\");args=ap.parse_args()').replace(\"out=O/f'{stage}_temporal.npz'\", \"out=O/(f'{stage}_temporal.npz' if args.decay==90 else f'{stage}_temporal_{args.decay:g}.npz')\\n if args.stage not in ['both',stage]:continue\").replace('dec=np.exp(-age/90)', 'dec=np.exp(-age/args.decay)');open(p,'w').write(s)\np='/app/solution/temporal_train.py';s=open(p).read().replace(\"a=p.parse_args();seed(a.seed)\", \"p.add_argument('--history-decay',type=float,default=90);a=p.parse_args();seed(a.seed)\").replace('data=temporal_data(a.stage);gold', 'data=temporal_data(a.stage,a.history_decay);gold').replace(\"z=np.load(O/f'{a.stage}_temporal.npz');\", \"z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_prepare.py --decay 7 > /app/solution/temporal_short_prepare.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 44, "nanos": 784448834 }, "formatted_output": "" } } } ] }, { "id": 122, "role": "agent", "timestamp": "2026-09-10T18:33:14.284Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:33:14.284Z", "text": "" } ] }, { "id": 123, "role": "agent", "timestamp": "2026-09-10T18:33:32.798Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:33:32.798Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:33:34.161Z", "text": "The full refitted blend is now saved as the submission. Its 459-dimensional vectors reproduce the selected validation ranking, which scores 0.05038 NDCG@10. I’m making one last training comparison with shorter histories, then I’ll finish the reproducibility notes and final checks.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:33:36.883Z", "callId": "call_ZvTXMAX3YIpOMjeygDuBP0WQ", "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:\"/opt/task-venv/bin/python /app/solution/check_submission.py > /app/solution/submission_check.txt\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]);results.forEach(text);\ntext(await tools.exec_command({cmd:\"tail -3 /app/solution/temporal_short_prepare.log\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:33:38.126Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"1d8e89\",\"wall_time_seconds\":1.001834339,\"session_id\":3270,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"7e58b5\",\"wall_time_seconds\":1.001757656,\"session_id\":38405,\"original_token_count\":0,\"output\":\"\"}}\n{\"chunk_id\":\"e6d64b\",\"wall_time_seconds\":0.00000417,\"exit_code\":0,\"original_token_count\":33,\"output\":\"test examples 50001 seconds 42.24179673194885\\ntest examples 50009 seconds 42.244131088256836\\ntest saved 51855 43.265087366104126\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:33:37.050Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f7d1512487d1a521036c7b434622", "usage": { "input_tokens": 99867, "cached_input_tokens": 98816, "cache_write_input_tokens": 0, "output_tokens": 1228, "reasoning_output_tokens": 1003, "total_tokens": 101095 }, "turn_token_usage": { "input_tokens": 6277129, "cached_input_tokens": 6063488, "cache_write_input_tokens": 0, "output_tokens": 56753, "reasoning_output_tokens": 31971, "total_tokens": 6333882 }, "thread_token_usage": { "input_tokens": 6277129, "cached_input_tokens": 6063488, "cache_write_input_tokens": 0, "output_tokens": 56753, "reasoning_output_tokens": 31971, "total_tokens": 6333882 } } }, { "kind": "context", "timestamp": "2026-09-10T18:33:38.111Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-311e164d-e74d-4d79-bae3-7b1a7e9456b0", "process_id": "74544", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/temporal_short_prepare.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/temporal_short_prepare.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "test examples 50001 seconds 42.24179673194885\ntest examples 50009 seconds 42.244131088256836\ntest saved 51855 43.265087366104126\n", "stderr": "", "aggregated_output": "test examples 50001 seconds 42.24179673194885\ntest examples 50009 seconds 42.244131088256836\ntest saved 51855 43.265087366104126\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4170 }, "formatted_output": "test examples 50001 seconds 42.24179673194885\ntest examples 50009 seconds 42.244131088256836\ntest saved 51855 43.265087366104126\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:33:39.004Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-22c7b529-ac2c-4b3f-82dc-431315e103a5", "process_id": "38405", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/check_submission.py > /app/solution/submission_check.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/check_submission.py > /app/solution/submission_check.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 1, "nanos": 931056804 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:33:40.286Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-19fca099-9110-4796-a075-084e06dad7ca", "process_id": "3270", "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": 213687230 }, "formatted_output": "" } } } ] }, { "id": 124, "role": "agent", "timestamp": "2026-09-10T18:33:50.886Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:33:50.886Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:33:56.397Z", "callId": "call_SOJJwg2y1pbFio9bpOOPiEoH", "name": "exec", "input": { "code": "const results=await Promise.allSettled([\ntools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/temporal_train.py --load /app/solution/dae.pt --name temporal_short --history-decay 7 --epochs 2 --eval-steps 100 --max-steps 400 --threads 4 > /app/solution/temporal_short.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/temporal_train.py --load /app/solution/vae.pt --name temporal_vae_short --history-decay 7 --epochs 2 --eval-steps 100 --max-steps 500 --threads 4 > /app/solution/temporal_vae_short.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]);results.forEach(text);\n" }, "result": { "timestamp": "2026-09-10T18:33:57.574Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"9b84bf\",\"wall_time_seconds\":1.000271714,\"session_id\":52963,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"38b3af\",\"wall_time_seconds\":1.001262392,\"session_id\":29217,\"original_token_count\":0,\"output\":\"\"}}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:33:56.555Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f802539487d1a197208b234e3b60", "usage": { "input_tokens": 101282, "cached_input_tokens": 99712, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 286, "total_tokens": 101795 }, "turn_token_usage": { "input_tokens": 6378411, "cached_input_tokens": 6163200, "cache_write_input_tokens": 0, "output_tokens": 57266, "reasoning_output_tokens": 32257, "total_tokens": 6435677 }, "thread_token_usage": { "input_tokens": 6378411, "cached_input_tokens": 6163200, "cache_write_input_tokens": 0, "output_tokens": 57266, "reasoning_output_tokens": 32257, "total_tokens": 6435677 } } } ] }, { "id": 125, "role": "agent", "timestamp": "2026-09-10T18:34:18.707Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:34:18.707Z", "text": "" } ] }, { "id": 126, "role": "agent", "timestamp": "2026-09-10T18:34:41.180Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:34:41.180Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:35:15.663Z", "callId": "call_V1W8VvsoBhe0K8Q4AiPq8XFz", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/selected_config.json <<'JSON'\\n{\\n \\\"public_ndcg_at_10\\\": 0.050378,\\n \\\"models\\\": [\\\"final_temporal.pt\\\", \\\"final_temporal_vae.pt\\\"],\\n \\\"weights\\\": [0.5, 0.5],\\n \\\"history_decays\\\": [14, 14],\\n \\\"pop\\\": 0.5,\\n \\\"trend\\\": 2.0,\\n \\\"feature_decay\\\": 14,\\n \\\"feature_like\\\": 50.0,\\n \\\"organic_share\\\": 1.0,\\n \\\"training\\\": [\\n {\\\"base_name\\\": \\\"final_dae\\\", \\\"base_epochs\\\": 15, \\\"base_args\\\": [], \\\"temporal_name\\\": \\\"final_temporal\\\", \\\"temporal_epochs\\\": 1, \\\"temporal_steps\\\": 0, \\\"history_decay\\\": 90},\\n {\\\"base_name\\\": \\\"final_vae\\\", \\\"base_epochs\\\": 20, \\\"base_args\\\": [\\\"--vae\\\", \\\"--hidden\\\", \\\"600\\\", \\\"--dim\\\", \\\"200\\\", \\\"--power\\\", \\\"0\\\", \\\"--decay\\\", \\\"120\\\", \\\"--kl\\\", \\\"0.001\\\"], \\\"temporal_name\\\": \\\"final_temporal_vae\\\", \\\"temporal_epochs\\\": 2, \\\"temporal_steps\\\": 513, \\\"history_decay\\\": 90}\\n ]\\n}\\nJSON\\ncat > /app/solution/run.py <<'PY'\\n\\\"\\\"\\\"Recreate the selected submission, optionally refitting both neural towers.\\\"\\\"\\\"\\nimport argparse,json,os,subprocess,sys\\nfrom pathlib import Path\\nO=Path(__file__).resolve().parent\\np=argparse.ArgumentParser();p.add_argument('--refit',action='store_true');a=p.parse_args();c=json.loads((O/'selected_config.json').read_text());env=dict(os.environ,OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='4')\\ndef run(script,args=(),log=None):\\n cmd=[sys.executable,str(O/script),*map(str,args)];print('Running',script,flush=True)\\n if log:\\n with (O/log).open('w') as f:subprocess.run(cmd,cwd=O,env=env,check=True,stdout=f,stderr=subprocess.STDOUT)\\n else:subprocess.run(cmd,cwd=O,env=env,check=True)\\nif a.refit:\\n run('prepare.py',log='prepare.log')\\n for decay in sorted({m['history_decay'] for m in c['training']}):run('temporal_prepare.py',['--decay',decay],log=f'prepare_temporal_{decay}.log')\\n run('item_features.py',log='item_features.log')\\n for m in c['training']:\\n run('model.py',['--stage','test','--name',m['base_name'],'--epochs',m['base_epochs'],'--eval-every',m['base_epochs'],*m['base_args']],log=m['base_name']+'.log')\\n run('temporal_train.py',['--stage','test','--load',O/(m['base_name']+'.pt'),'--name',m['temporal_name'],'--epochs',m['temporal_epochs'],'--max-steps',m['temporal_steps'],'--history-decay',m['history_decay']],log=m['temporal_name']+'.log')\\nargs=['--models',*[O/m for m in c['models']],'--weights',*c['weights'],'--history-decays',*c['history_decays'],'--stage','test','--pop',c['pop'],'--trend',c['trend'],'--feature-decay',c['feature_decay'],'--feature-like',c['feature_like'],'--organic-share',c['organic_share'],'--name','final']\\nrun('retrieve.py',args,log='final_retrieval.log');run('check_submission.py')\\nPY\\ncat > /app/solution/verify_vectors.py <<'PY'\\n\\\"\\\"\\\"Verify exported vectors reproduce the final retrieval score and ranking.\\\"\\\"\\\"\\nfrom retrieve import *\\nimport json,time\\nseed(42);torch.set_num_threads(1);c=json.load(open(O/'selected_config.json'));paths=[O/m for m in c['models']]\\nu,i,datas,_=load_retriever(paths,c['weights'],'test',c['pop'],c['trend'],c['history_decays'],c['feature_decay'],c['feature_like'],c['organic_share']);z=np.load(D/'test_eligible_candidates.npz');packed=z['packed_eligible'];savedu=np.load(O/'final_user_vectors.npz');savedi=np.load(O/'final_item_vectors.npz');ranked=pd.read_parquet(O/'submission.parquet');uididx=pd.Index(z['uids']);itemidx=pd.Index(z['item_ids']);timings=[];max_error=0;lengths=[]\\nwith torch.no_grad():\\n for row in np.random.default_rng(42).choice(len(z['uids']),100,replace=False):\\n histories=[];uid=z['uids'][row]\\n for d in datas:\\n r=pd.Index(d[0]).get_indexer([uid]);bi,off,bw,_,_=batch_data(r,*d[1:]);histories.append((bi,off,bw))\\n lengths.append(len(histories[0][0]));start=time.perf_counter();uv=u(histories);timings.append((time.perf_counter()-start)*1000)\\n max_error=max(max_error,float(np.max(np.abs(uv.numpy()[0]-savedu['vectors'][row]))))\\n if len(timings)<=5:\\n emb=i(torch.arange(N));assert np.allclose(emb.numpy(),savedi['vectors'],atol=2e-6,rtol=1e-5)\\n scores=(uv@emb.T).numpy()[0];mask=np.unpackbits(packed[row],count=N,bitorder='little').astype(bool);f=ranked[ranked.uid==uid].sort_values('rank');ix=itemidx.get_indexer(f.item_id)\\n assert (np.diff(scores[ix])<=1e-4).all();mask[ix]=False;assert scores[ix[-1]]+1e-4>=scores[mask].max()\\nassert max_error<1e-4\\nresult=dict(vector_dimension=int(savedu['vectors'].shape[1]),max_user_vector_error=max_error,median_history_items=float(np.median(lengths)),encoder_median_ms=float(np.median(timings)),encoder_p95_ms=float(np.percentile(timings,95)),threads=1,requests=len(timings),ranking_checks=5)\\njson.dump(result,open(O/'vector_verification.json','w'),indent=2);print(result)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:35:15.765Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"9030d4\",\"wall_time_seconds\":0.00000463,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:35:15.716Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-69fe61eb-e5d7-42b0-ad0a-f97c8e0c2e91", "process_id": "14685", "command": [ "/bin/sh", "-lc", "cat > /app/solution/selected_config.json <<'JSON'\n{\n \"public_ndcg_at_10\": 0.050378,\n \"models\": [\"final_temporal.pt\", \"final_temporal_vae.pt\"],\n \"weights\": [0.5, 0.5],\n \"history_decays\": [14, 14],\n \"pop\": 0.5,\n \"trend\": 2.0,\n \"feature_decay\": 14,\n \"feature_like\": 50.0,\n \"organic_share\": 1.0,\n \"training\": [\n {\"base_name\": \"final_dae\", \"base_epochs\": 15, \"base_args\": [], \"temporal_name\": \"final_temporal\", \"temporal_epochs\": 1, \"temporal_steps\": 0, \"history_decay\": 90},\n {\"base_name\": \"final_vae\", \"base_epochs\": 20, \"base_args\": [\"--vae\", \"--hidden\", \"600\", \"--dim\", \"200\", \"--power\", \"0\", \"--decay\", \"120\", \"--kl\", \"0.001\"], \"temporal_name\": \"final_temporal_vae\", \"temporal_epochs\": 2, \"temporal_steps\": 513, \"history_decay\": 90}\n ]\n}\nJSON\ncat > /app/solution/run.py <<'PY'\n\"\"\"Recreate the selected submission, optionally refitting both neural towers.\"\"\"\nimport argparse,json,os,subprocess,sys\nfrom pathlib import Path\nO=Path(__file__).resolve().parent\np=argparse.ArgumentParser();p.add_argument('--refit',action='store_true');a=p.parse_args();c=json.loads((O/'selected_config.json').read_text());env=dict(os.environ,OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='4')\ndef run(script,args=(),log=None):\n cmd=[sys.executable,str(O/script),*map(str,args)];print('Running',script,flush=True)\n if log:\n with (O/log).open('w') as f:subprocess.run(cmd,cwd=O,env=env,check=True,stdout=f,stderr=subprocess.STDOUT)\n else:subprocess.run(cmd,cwd=O,env=env,check=True)\nif a.refit:\n run('prepare.py',log='prepare.log')\n for decay in sorted({m['history_decay'] for m in c['training']}):run('temporal_prepare.py',['--decay',decay],log=f'prepare_temporal_{decay}.log')\n run('item_features.py',log='item_features.log')\n for m in c['training']:\n run('model.py',['--stage','test','--name',m['base_name'],'--epochs',m['base_epochs'],'--eval-every',m['base_epochs'],*m['base_args']],log=m['base_name']+'.log')\n run('temporal_train.py',['--stage','test','--load',O/(m['base_name']+'.pt'),'--name',m['temporal_name'],'--epochs',m['temporal_epochs'],'--max-steps',m['temporal_steps'],'--history-decay',m['history_decay']],log=m['temporal_name']+'.log')\nargs=['--models',*[O/m for m in c['models']],'--weights',*c['weights'],'--history-decays',*c['history_decays'],'--stage','test','--pop',c['pop'],'--trend',c['trend'],'--feature-decay',c['feature_decay'],'--feature-like',c['feature_like'],'--organic-share',c['organic_share'],'--name','final']\nrun('retrieve.py',args,log='final_retrieval.log');run('check_submission.py')\nPY\ncat > /app/solution/verify_vectors.py <<'PY'\n\"\"\"Verify exported vectors reproduce the final retrieval score and ranking.\"\"\"\nfrom retrieve import *\nimport json,time\nseed(42);torch.set_num_threads(1);c=json.load(open(O/'selected_config.json'));paths=[O/m for m in c['models']]\nu,i,datas,_=load_retriever(paths,c['weights'],'test',c['pop'],c['trend'],c['history_decays'],c['feature_decay'],c['feature_like'],c['organic_share']);z=np.load(D/'test_eligible_candidates.npz');packed=z['packed_eligible'];savedu=np.load(O/'final_user_vectors.npz');savedi=np.load(O/'final_item_vectors.npz');ranked=pd.read_parquet(O/'submission.parquet');uididx=pd.Index(z['uids']);itemidx=pd.Index(z['item_ids']);timings=[];max_error=0;lengths=[]\nwith torch.no_grad():\n for row in np.random.default_rng(42).choice(len(z['uids']),100,replace=False):\n histories=[];uid=z['uids'][row]\n for d in datas:\n r=pd.Index(d[0]).get_indexer([uid]);bi,off,bw,_,_=batch_data(r,*d[1:]);histories.append((bi,off,bw))\n lengths.append(len(histories[0][0]));start=time.perf_counter();uv=u(histories);timings.append((time.perf_counter()-start)*1000)\n max_error=max(max_error,float(np.max(np.abs(uv.numpy()[0]-savedu['vectors'][row]))))\n if len(timings)<=5:\n emb=i(torch.arange(N));assert np.allclose(emb.numpy(),savedi['vectors'],atol=2e-6,rtol=1e-5)\n scores=(uv@emb.T).numpy()[0];mask=np.unpackbits(packed[row],count=N,bitorder='little').astype(bool);f=ranked[ranked.uid==uid].sort_values('rank');ix=itemidx.get_indexer(f.item_id)\n assert (np.diff(scores[ix])<=1e-4).all();mask[ix]=False;assert scores[ix[-1]]+1e-4>=scores[mask].max()\nassert max_error<1e-4\nresult=dict(vector_dimension=int(savedu['vectors'].shape[1]),max_user_vector_error=max_error,median_history_items=float(np.median(lengths)),encoder_median_ms=float(np.median(timings)),encoder_p95_ms=float(np.percentile(timings,95)),threads=1,requests=len(timings),ranking_checks=5)\njson.dump(result,open(O/'vector_verification.json','w'),indent=2);print(result)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/selected_config.json <<'JSON'\n{\n \"public_ndcg_at_10\": 0.050378,\n \"models\": [\"final_temporal.pt\", \"final_temporal_vae.pt\"],\n \"weights\": [0.5, 0.5],\n \"history_decays\": [14, 14],\n \"pop\": 0.5,\n \"trend\": 2.0,\n \"feature_decay\": 14,\n \"feature_like\": 50.0,\n \"organic_share\": 1.0,\n \"training\": [\n {\"base_name\": \"final_dae\", \"base_epochs\": 15, \"base_args\": [], \"temporal_name\": \"final_temporal\", \"temporal_epochs\": 1, \"temporal_steps\": 0, \"history_decay\": 90},\n {\"base_name\": \"final_vae\", \"base_epochs\": 20, \"base_args\": [\"--vae\", \"--hidden\", \"600\", \"--dim\", \"200\", \"--power\", \"0\", \"--decay\", \"120\", \"--kl\", \"0.001\"], \"temporal_name\": \"final_temporal_vae\", \"temporal_epochs\": 2, \"temporal_steps\": 513, \"history_decay\": 90}\n ]\n}\nJSON\ncat > /app/solution/run.py <<'PY'\n\"\"\"Recreate the selected submission, optionally refitting both neural towers.\"\"\"\nimport argparse,json,os,subprocess,sys\nfrom pathlib import Path\nO=Path(__file__).resolve().parent\np=argparse.ArgumentParser();p.add_argument('--refit',action='store_true');a=p.parse_args();c=json.loads((O/'selected_config.json').read_text());env=dict(os.environ,OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='4')\ndef run(script,args=(),log=None):\n cmd=[sys.executable,str(O/script),*map(str,args)];print('Running',script,flush=True)\n if log:\n with (O/log).open('w') as f:subprocess.run(cmd,cwd=O,env=env,check=True,stdout=f,stderr=subprocess.STDOUT)\n else:subprocess.run(cmd,cwd=O,env=env,check=True)\nif a.refit:\n run('prepare.py',log='prepare.log')\n for decay in sorted({m['history_decay'] for m in c['training']}):run('temporal_prepare.py',['--decay',decay],log=f'prepare_temporal_{decay}.log')\n run('item_features.py',log='item_features.log')\n for m in c['training']:\n run('model.py',['--stage','test','--name',m['base_name'],'--epochs',m['base_epochs'],'--eval-every',m['base_epochs'],*m['base_args']],log=m['base_name']+'.log')\n run('temporal_train.py',['--stage','test','--load',O/(m['base_name']+'.pt'),'--name',m['temporal_name'],'--epochs',m['temporal_epochs'],'--max-steps',m['temporal_steps'],'--history-decay',m['history_decay']],log=m['temporal_name']+'.log')\nargs=['--models',*[O/m for m in c['models']],'--weights',*c['weights'],'--history-decays',*c['history_decays'],'--stage','test','--pop',c['pop'],'--trend',c['trend'],'--feature-decay',c['feature_decay'],'--feature-like',c['feature_like'],'--organic-share',c['organic_share'],'--name','final']\nrun('retrieve.py',args,log='final_retrieval.log');run('check_submission.py')\nPY\ncat > /app/solution/verify_vectors.py <<'PY'\n\"\"\"Verify exported vectors reproduce the final retrieval score and ranking.\"\"\"\nfrom retrieve import *\nimport json,time\nseed(42);torch.set_num_threads(1);c=json.load(open(O/'selected_config.json'));paths=[O/m for m in c['models']]\nu,i,datas,_=load_retriever(paths,c['weights'],'test',c['pop'],c['trend'],c['history_decays'],c['feature_decay'],c['feature_like'],c['organic_share']);z=np.load(D/'test_eligible_candidates.npz');packed=z['packed_eligible'];savedu=np.load(O/'final_user_vectors.npz');savedi=np.load(O/'final_item_vectors.npz');ranked=pd.read_parquet(O/'submission.parquet');uididx=pd.Index(z['uids']);itemidx=pd.Index(z['item_ids']);timings=[];max_error=0;lengths=[]\nwith torch.no_grad():\n for row in np.random.default_rng(42).choice(len(z['uids']),100,replace=False):\n histories=[];uid=z['uids'][row]\n for d in datas:\n r=pd.Index(d[0]).get_indexer([uid]);bi,off,bw,_,_=batch_data(r,*d[1:]);histories.append((bi,off,bw))\n lengths.append(len(histories[0][0]));start=time.perf_counter();uv=u(histories);timings.append((time.perf_counter()-start)*1000)\n max_error=max(max_error,float(np.max(np.abs(uv.numpy()[0]-savedu['vectors'][row]))))\n if len(timings)<=5:\n emb=i(torch.arange(N));assert np.allclose(emb.numpy(),savedi['vectors'],atol=2e-6,rtol=1e-5)\n scores=(uv@emb.T).numpy()[0];mask=np.unpackbits(packed[row],count=N,bitorder='little').astype(bool);f=ranked[ranked.uid==uid].sort_values('rank');ix=itemidx.get_indexer(f.item_id)\n assert (np.diff(scores[ix])<=1e-4).all();mask[ix]=False;assert scores[ix[-1]]+1e-4>=scores[mask].max()\nassert max_error<1e-4\nresult=dict(vector_dimension=int(savedu['vectors'].shape[1]),max_user_vector_error=max_error,median_history_items=float(np.median(lengths)),encoder_median_ms=float(np.median(timings)),encoder_p95_ms=float(np.percentile(timings,95)),threads=1,requests=len(timings),ranking_checks=5)\njson.dump(result,open(O/'vector_verification.json','w'),indent=2);print(result)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 4630 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:35:15.763Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f815d7c087d1ae9dd35b750b6a17", "usage": { "input_tokens": 101900, "cached_input_tokens": 101120, "cache_write_input_tokens": 0, "output_tokens": 2533, "reasoning_output_tokens": 1034, "total_tokens": 104433 }, "turn_token_usage": { "input_tokens": 6480311, "cached_input_tokens": 6264320, "cache_write_input_tokens": 0, "output_tokens": 59799, "reasoning_output_tokens": 33291, "total_tokens": 6540110 }, "thread_token_usage": { "input_tokens": 6480311, "cached_input_tokens": 6264320, "cache_write_input_tokens": 0, "output_tokens": 59799, "reasoning_output_tokens": 33291, "total_tokens": 6540110 } } } ] }, { "id": 127, "role": "agent", "timestamp": "2026-09-10T18:35:29.237Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:35:29.237Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:35:30.457Z", "callId": "call_QUFrumkNUu86aItYEJ1Eh3Ft", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -6 /app/solution/temporal_short.log; tail -6 /app/solution/temporal_vae_short.log; cat /app/solution/public_score.json; cat /app/solution/submission_check.txt\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:35:30.578Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"c4a60b\",\"wall_time_seconds\":0.000012161,\"exit_code\":0,\"original_token_count\":85,\"output\":\"STEP VALID 100 0.03630188671797824\\nSTEP VALID 200 0.03472028370857776\\nSTEP VALID 100 0.035630173229038255\\nSTEP VALID 200 0.03622143927879816\\n{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.050378,\\n \\\"quality_users\\\": 4263\\n }\\n}\\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\\n\"}\n{\"current_time\":\"2026-09-10 18:35:30 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:35:30.515Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d7301a2-3468-4065-ac6e-ad3d648dfdbc", "process_id": "18455", "command": [ "/bin/sh", "-lc", "tail -6 /app/solution/temporal_short.log; tail -6 /app/solution/temporal_vae_short.log; cat /app/solution/public_score.json; cat /app/solution/submission_check.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -6 /app/solution/temporal_short.log; tail -6 /app/solution/temporal_vae_short.log; cat /app/solution/public_score.json; cat /app/solution/submission_check.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050378,\n \"quality_users\": 4263\n }\n}\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "stderr": "", "aggregated_output": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050378,\n \"quality_users\": 4263\n }\n}\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 12161 }, "formatted_output": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050378,\n \"quality_users\": 4263\n }\n}\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:35:30.568Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f86465ec87d1b7c7f418b4992745", "usage": { "input_tokens": 104490, "cached_input_tokens": 101760, "cache_write_input_tokens": 0, "output_tokens": 333, "reasoning_output_tokens": 250, "total_tokens": 104823 }, "turn_token_usage": { "input_tokens": 6584801, "cached_input_tokens": 6366080, "cache_write_input_tokens": 0, "output_tokens": 60132, "reasoning_output_tokens": 33541, "total_tokens": 6644933 }, "thread_token_usage": { "input_tokens": 6584801, "cached_input_tokens": 6366080, "cache_write_input_tokens": 0, "output_tokens": 60132, "reasoning_output_tokens": 33541, "total_tokens": 6644933 } } } ] }, { "id": 128, "role": "agent", "timestamp": "2026-09-10T18:35:43.982Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:35:43.982Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:35:45.956Z", "callId": "call_vgboGenF3lvdHIMtTGtrZDq4", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 7 --stage validation --name temporal_vae_7_v --save-scores --output /app/solution/temporal_vae_7_validation.parquet > /app/solution/temporal_vae_7_export.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:35:47.148Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"4e412a\",\"wall_time_seconds\":1.001214099,\"session_id\":4513,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:35:46.085Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f872d0b087d1a5b3208517a5ea21", "usage": { "input_tokens": 105027, "cached_input_tokens": 104320, "cache_write_input_tokens": 0, "output_tokens": 416, "reasoning_output_tokens": 292, "total_tokens": 105443 }, "turn_token_usage": { "input_tokens": 6689828, "cached_input_tokens": 6470400, "cache_write_input_tokens": 0, "output_tokens": 60548, "reasoning_output_tokens": 33833, "total_tokens": 6750376 }, "thread_token_usage": { "input_tokens": 6689828, "cached_input_tokens": 6470400, "cache_write_input_tokens": 0, "output_tokens": 60548, "reasoning_output_tokens": 33833, "total_tokens": 6750376 } } }, { "kind": "context", "timestamp": "2026-09-10T18:36:09.592Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5671d66-1ee5-405e-ad56-08477441f673", "process_id": "4513", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 7 --stage validation --name temporal_vae_7_v --save-scores --output /app/solution/temporal_vae_7_validation.parquet > /app/solution/temporal_vae_7_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae.pt --history-decays 7 --stage validation --name temporal_vae_7_v --save-scores --output /app/solution/temporal_vae_7_validation.parquet > /app/solution/temporal_vae_7_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 23, "nanos": 448760461 }, "formatted_output": "" } } } ] }, { "id": 129, "role": "agent", "timestamp": "2026-09-10T18:36:09.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:36:09.865Z", "text": "" } ] }, { "id": 130, "role": "agent", "timestamp": "2026-09-10T18:36:20.660Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:36:20.660Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:36:38.245Z", "callId": "call_CIoM0siG3Ui15bfK1jLmv9yU", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat > /app/solution/README.md <<'MD'\\n# CPU neural two-tower music retrieval\\n\\nPublic validation: **0.050378 graded NDCG@10**, over 4,263 users with eligible relevance labels (`public_score.json`). The final submission contains 845,600 rows: 100 distinct, test-eligible tracks for each of 8,456 target users. Both components were refitted on **train + validation** history.\\n\\n## Architecture and serving\\n\\nThe retrieval vector has **459 dimensions**. It concatenates two neural components, weighted equally:\\n\\n- User towers: weighted history `EmbeddingBag` (512 or 600 units), tanh, and a learned projection to 256 or 200 dimensions. The second component is variational during training and uses its deterministic mean at inference. There is **no user-ID embedding**.\\n- Item towers: trainable item embeddings, tanh, learned linear projection, and a learned item-bias coordinate.\\n- An item-only linear calibration layer uses historical popularity, recent organic activity (including likes), and organic activity share. A constant user coordinate accommodates this bias.\\n\\nThe final score is exactly the dot product of the two vectors. There is no candidate-dependent neural reranker. `final_item_vectors.npz` contains independently precomputed item vectors; use **maximum inner product**, without normalizing these vectors, in a production ANN index. `retrieve.py` uses bounded exhaustive batches for this small benchmark and applies the platform eligibility mask.\\n\\n## Training and temporal separation\\n\\nPretraining uses dropout and weighted multinomial denoising loss with AdamW: 15 epochs for the deterministic component and 20 for the variational component. Fine-tuning uses 12 historical weekly tasks, strictly preceding histories, and novel organic outcomes weighted 1 for strong plays or 7 for likes. History tracks are excluded from the fine-tuning denominator. Final fine-tuning uses one epoch and 513 updates, respectively. The serving history aggregation uses a 14-day event decay, compressed counts, organic activity, and likes.\\n\\nValidation features use only training events. Final features and training include validation events, with cutoff 25,393,400. Eligibility uses the supplied archive for each cutoff. Training uses ragged histories and minibatches; it never constructs or decomposes a global user–item interaction matrix. Both training programs expose `--negatives` for importance-corrected sampled-catalog training at larger scale.\\n\\n## Reproduce\\n\\nGenerate and check the submission from the included fitted models:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/run.py\\n```\\n\\nRebuild the event caches and refit the selected approach first:\\n\\n```sh\\n/opt/task-venv/bin/python /app/solution/run.py --refit\\n```\\n\\n`selected_config.json` records all selected settings. `model.py`, `temporal_train.py`, and `retrieve.py` define, train, and apply both towers. `check_submission.py` checks the complete ranking contract; `verify_vectors.py` checks the exported vectors against the neural computation and ranking.\\nMD\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:36:38.543Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"5c7dca\",\"wall_time_seconds\":0.000003469,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:36:38.289Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-977b98f5-7fca-49bf-bb6d-c052b34f51da", "process_id": "1456", "command": [ "/bin/sh", "-lc", "cat > /app/solution/README.md <<'MD'\n# CPU neural two-tower music retrieval\n\nPublic validation: **0.050378 graded NDCG@10**, over 4,263 users with eligible relevance labels (`public_score.json`). The final submission contains 845,600 rows: 100 distinct, test-eligible tracks for each of 8,456 target users. Both components were refitted on **train + validation** history.\n\n## Architecture and serving\n\nThe retrieval vector has **459 dimensions**. It concatenates two neural components, weighted equally:\n\n- User towers: weighted history `EmbeddingBag` (512 or 600 units), tanh, and a learned projection to 256 or 200 dimensions. The second component is variational during training and uses its deterministic mean at inference. There is **no user-ID embedding**.\n- Item towers: trainable item embeddings, tanh, learned linear projection, and a learned item-bias coordinate.\n- An item-only linear calibration layer uses historical popularity, recent organic activity (including likes), and organic activity share. A constant user coordinate accommodates this bias.\n\nThe final score is exactly the dot product of the two vectors. There is no candidate-dependent neural reranker. `final_item_vectors.npz` contains independently precomputed item vectors; use **maximum inner product**, without normalizing these vectors, in a production ANN index. `retrieve.py` uses bounded exhaustive batches for this small benchmark and applies the platform eligibility mask.\n\n## Training and temporal separation\n\nPretraining uses dropout and weighted multinomial denoising loss with AdamW: 15 epochs for the deterministic component and 20 for the variational component. Fine-tuning uses 12 historical weekly tasks, strictly preceding histories, and novel organic outcomes weighted 1 for strong plays or 7 for likes. History tracks are excluded from the fine-tuning denominator. Final fine-tuning uses one epoch and 513 updates, respectively. The serving history aggregation uses a 14-day event decay, compressed counts, organic activity, and likes.\n\nValidation features use only training events. Final features and training include validation events, with cutoff 25,393,400. Eligibility uses the supplied archive for each cutoff. Training uses ragged histories and minibatches; it never constructs or decomposes a global user–item interaction matrix. Both training programs expose `--negatives` for importance-corrected sampled-catalog training at larger scale.\n\n## Reproduce\n\nGenerate and check the submission from the included fitted models:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run.py\n```\n\nRebuild the event caches and refit the selected approach first:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run.py --refit\n```\n\n`selected_config.json` records all selected settings. `model.py`, `temporal_train.py`, and `retrieve.py` define, train, and apply both towers. `check_submission.py` checks the complete ranking contract; `verify_vectors.py` checks the exported vectors against the neural computation and ranking.\nMD" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/README.md <<'MD'\n# CPU neural two-tower music retrieval\n\nPublic validation: **0.050378 graded NDCG@10**, over 4,263 users with eligible relevance labels (`public_score.json`). The final submission contains 845,600 rows: 100 distinct, test-eligible tracks for each of 8,456 target users. Both components were refitted on **train + validation** history.\n\n## Architecture and serving\n\nThe retrieval vector has **459 dimensions**. It concatenates two neural components, weighted equally:\n\n- User towers: weighted history `EmbeddingBag` (512 or 600 units), tanh, and a learned projection to 256 or 200 dimensions. The second component is variational during training and uses its deterministic mean at inference. There is **no user-ID embedding**.\n- Item towers: trainable item embeddings, tanh, learned linear projection, and a learned item-bias coordinate.\n- An item-only linear calibration layer uses historical popularity, recent organic activity (including likes), and organic activity share. A constant user coordinate accommodates this bias.\n\nThe final score is exactly the dot product of the two vectors. There is no candidate-dependent neural reranker. `final_item_vectors.npz` contains independently precomputed item vectors; use **maximum inner product**, without normalizing these vectors, in a production ANN index. `retrieve.py` uses bounded exhaustive batches for this small benchmark and applies the platform eligibility mask.\n\n## Training and temporal separation\n\nPretraining uses dropout and weighted multinomial denoising loss with AdamW: 15 epochs for the deterministic component and 20 for the variational component. Fine-tuning uses 12 historical weekly tasks, strictly preceding histories, and novel organic outcomes weighted 1 for strong plays or 7 for likes. History tracks are excluded from the fine-tuning denominator. Final fine-tuning uses one epoch and 513 updates, respectively. The serving history aggregation uses a 14-day event decay, compressed counts, organic activity, and likes.\n\nValidation features use only training events. Final features and training include validation events, with cutoff 25,393,400. Eligibility uses the supplied archive for each cutoff. Training uses ragged histories and minibatches; it never constructs or decomposes a global user–item interaction matrix. Both training programs expose `--negatives` for importance-corrected sampled-catalog training at larger scale.\n\n## Reproduce\n\nGenerate and check the submission from the included fitted models:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run.py\n```\n\nRebuild the event caches and refit the selected approach first:\n\n```sh\n/opt/task-venv/bin/python /app/solution/run.py --refit\n```\n\n`selected_config.json` records all selected settings. `model.py`, `temporal_train.py`, and `retrieve.py` define, train, and apply both towers. `check_submission.py` checks the complete ranking contract; `verify_vectors.py` checks the exported vectors against the neural computation and ranking.\nMD" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 3469 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:36:38.541Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f88377cc87d1aaccb508d84f25ba", "usage": { "input_tokens": 105501, "cached_input_tokens": 104832, "cache_write_input_tokens": 0, "output_tokens": 1509, "reasoning_output_tokens": 826, "total_tokens": 107010 }, "turn_token_usage": { "input_tokens": 6795329, "cached_input_tokens": 6575232, "cache_write_input_tokens": 0, "output_tokens": 62057, "reasoning_output_tokens": 34659, "total_tokens": 6857386 }, "thread_token_usage": { "input_tokens": 6795329, "cached_input_tokens": 6575232, "cache_write_input_tokens": 0, "output_tokens": 62057, "reasoning_output_tokens": 34659, "total_tokens": 6857386 } } } ] }, { "id": 131, "role": "agent", "timestamp": "2026-09-10T18:36:44.686Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:36:44.686Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:36:48.291Z", "text": "The public evaluator confirms 0.050378, and the saved refitted submission passes all row, rank, uniqueness, and test-eligibility checks. I’ve added a single-command submission rebuild and a full-refit command. The last experiment is still running; I’ll retain the current blend unless it shows a clear validation gain.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:36:48.867Z", "callId": "call_BJZr8kK5mKbfvmmIAioyNFNC", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -8 /app/solution/temporal_short.log; tail -8 /app/solution/temporal_vae_short.log; tail -3 /app/solution/temporal_vae_7_export.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:36:49.016Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"db35a2\",\"wall_time_seconds\":0.00000414,\"exit_code\":0,\"original_token_count\":136,\"output\":\"STEP VALID 100 0.03630188671797824\\nSTEP VALID 200 0.03472028370857776\\nSTEP VALID 300 0.034923072821116244\\nEPOCH 1 loss 8.318913058801131 seconds 139.59871912002563\\nVALID 1 0.036684308892560656\\nSTEP VALID 100 0.035630173229038255\\nSTEP VALID 200 0.03622143927879816\\nSTEP VALID 300 0.03620758944427156\\nEPOCH 1 loss 8.468400581918582 seconds 148.43994331359863\\nVALID 1 0.03644409038592492\\nSaved /app/solution/temporal_vae_7_validation.parquet vector dimension 202\\nNDCG@10 0.0375504085036477\\n100-item history encoder median ms 0.2833980001923919\\n\"}\n{\"current_time\":\"2026-09-10 18:36:48 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:36:48.912Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-439ce195-2bf8-413c-adf8-2dfb22f548ef", "process_id": "20029", "command": [ "/bin/sh", "-lc", "tail -8 /app/solution/temporal_short.log; tail -8 /app/solution/temporal_vae_short.log; tail -3 /app/solution/temporal_vae_7_export.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -8 /app/solution/temporal_short.log; tail -8 /app/solution/temporal_vae_short.log; tail -3 /app/solution/temporal_vae_7_export.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 300 0.034923072821116244\nEPOCH 1 loss 8.318913058801131 seconds 139.59871912002563\nVALID 1 0.036684308892560656\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\nSTEP VALID 300 0.03620758944427156\nEPOCH 1 loss 8.468400581918582 seconds 148.43994331359863\nVALID 1 0.03644409038592492\nSaved /app/solution/temporal_vae_7_validation.parquet vector dimension 202\nNDCG@10 0.0375504085036477\n100-item history encoder median ms 0.2833980001923919\n", "stderr": "", "aggregated_output": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 300 0.034923072821116244\nEPOCH 1 loss 8.318913058801131 seconds 139.59871912002563\nVALID 1 0.036684308892560656\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\nSTEP VALID 300 0.03620758944427156\nEPOCH 1 loss 8.468400581918582 seconds 148.43994331359863\nVALID 1 0.03644409038592492\nSaved /app/solution/temporal_vae_7_validation.parquet vector dimension 202\nNDCG@10 0.0375504085036477\n100-item history encoder median ms 0.2833980001923919\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4140 }, "formatted_output": "STEP VALID 100 0.03630188671797824\nSTEP VALID 200 0.03472028370857776\nSTEP VALID 300 0.034923072821116244\nEPOCH 1 loss 8.318913058801131 seconds 139.59871912002563\nVALID 1 0.036684308892560656\nSTEP VALID 100 0.035630173229038255\nSTEP VALID 200 0.03622143927879816\nSTEP VALID 300 0.03620758944427156\nEPOCH 1 loss 8.468400581918582 seconds 148.43994331359863\nVALID 1 0.03644409038592492\nSaved /app/solution/temporal_vae_7_validation.parquet vector dimension 202\nNDCG@10 0.0375504085036477\n100-item history encoder median ms 0.2833980001923919\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:36:49.013Z", "data": { "type": "token_usage_record", "thread_id": 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"formatted_output": "" } } }, { "kind": "tool", "timestamp": "2026-09-10T18:37:33.260Z", "callId": "call_QjdKNybCdzArszUUwlhkqaqB", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"python - <<'PY'\\nfor p in ['/app/solution/model.py','/app/solution/temporal_train.py','/app/solution/retrieve.py']:\\n s=open(p).read().replace('torch.save(', 'atomic_save(')\\n if p.endswith('/model.py'):\\n pos=s.index('\\\\nclass UserTower')\\n s=s[:pos]+\\\"\\\\ndef atomic_save(value,path):\\\\n path=Path(path);temporary=path.with_suffix('.tmp.pt');torch.save(value,temporary);os.replace(temporary,path)\\\\n\\\"+s[pos:]\\n open(p,'w').write(s)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:37:33.461Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"79ddd0\",\"wall_time_seconds\":0.000005929,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": 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/app/solution/verify_vectors.py > /app/solution/vector_verification.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:39:32.911Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"86de64\",\"wall_time_seconds\":1.000199185,\"session_id\":41576,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"fdad62\",\"wall_time_seconds\":1.00108867,\"session_id\":16213,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:39:30.750Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f93666ec87d19dea2599e0463998", "usage": { "input_tokens": 111267, "cached_input_tokens": 108672, 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/app/solution/verify_vectors.py > /app/solution/vector_verification.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\np='/app/solution/run.py';s=open(p).read().replace(\"'--history-decay',m['history_decay']]\", \"'--history-decay',m['history_decay'],'--threads',4]\");open(p,'w').write(s)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify_vectors.py > /app/solution/vector_verification.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 864661348 }, "formatted_output": "" } } } ] }, { "id": 139, "role": "agent", "timestamp": "2026-09-10T18:39:42.118Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:39:42.118Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:39:44.551Z", "text": "Seven-day history weighting improved the uncalibrated scores but did not beat the selected 14-day blend after calibration. I’m retaining the 0.050378 submission for now and completing the comparison with models trained on shorter histories. Vector-consistency and CPU encoding checks are also running.\n" }, { "kind": "context", "timestamp": "2026-09-10T18:39:49.613Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b541ae32-53b5-4217-9564-099db8f7f1ce", "process_id": "41576", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae_short.pt --history-decays 7 --stage validation --name trained_vae_short_v --save-scores --output /app/solution/trained_vae_short_validation.parquet > /app/solution/trained_vae_short_export.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal_vae_short.pt --history-decays 7 --stage validation --name trained_vae_short_v --save-scores --output /app/solution/trained_vae_short_validation.parquet > /app/solution/trained_vae_short_export.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 18, "nanos": 870082024 }, "formatted_output": "" } } } ] }, { "id": 140, "role": "agent", "timestamp": "2026-09-10T18:40:06.910Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:40:06.910Z", "text": "" } ] }, { "id": 141, "role": "agent", "timestamp": "2026-09-10T18:40:26.204Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:40:26.204Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:40:27.175Z", "callId": "call_0inFby8nLFHThSKGlKqVp5OQ", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -5 /app/solution/vector_verification.log; tail -3 /app/solution/trained_vae_short_export.log\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:40:27.280Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"36fe1c\",\"wall_time_seconds\":0.00000382,\"exit_code\":0,\"original_token_count\":99,\"output\":\"{'vector_dimension': 459, 'max_user_vector_error': 8.642673492431641e-07, 'median_history_items': 460.0, 'encoder_median_ms': 0.6351424999593291, 'encoder_p95_ms': 1.1482522502546995, 'threads': 1, 'requests': 100, 'ranking_checks': 5}\\nSaved /app/solution/trained_vae_short_validation.parquet vector dimension 202\\nNDCG@10 0.0368477570446434\\n100-item history encoder median ms 0.21203500000410713\\n\"}\n{\"current_time\":\"2026-09-10 18:40:27 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:40:27.223Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-56fa64ff-c77a-4aed-9c56-84c6055f276f", "process_id": "82600", "command": [ "/bin/sh", "-lc", "tail -5 /app/solution/vector_verification.log; tail -3 /app/solution/trained_vae_short_export.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -5 /app/solution/vector_verification.log; tail -3 /app/solution/trained_vae_short_export.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'vector_dimension': 459, 'max_user_vector_error': 8.642673492431641e-07, 'median_history_items': 460.0, 'encoder_median_ms': 0.6351424999593291, 'encoder_p95_ms': 1.1482522502546995, 'threads': 1, 'requests': 100, 'ranking_checks': 5}\nSaved /app/solution/trained_vae_short_validation.parquet vector dimension 202\nNDCG@10 0.0368477570446434\n100-item history encoder median ms 0.21203500000410713\n", "stderr": "", "aggregated_output": "{'vector_dimension': 459, 'max_user_vector_error': 8.642673492431641e-07, 'median_history_items': 460.0, 'encoder_median_ms': 0.6351424999593291, 'encoder_p95_ms': 1.1482522502546995, 'threads': 1, 'requests': 100, 'ranking_checks': 5}\nSaved /app/solution/trained_vae_short_validation.parquet vector dimension 202\nNDCG@10 0.0368477570446434\n100-item history encoder median ms 0.21203500000410713\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3820 }, "formatted_output": "{'vector_dimension': 459, 'max_user_vector_error': 8.642673492431641e-07, 'median_history_items': 460.0, 'encoder_median_ms': 0.6351424999593291, 'encoder_p95_ms': 1.1482522502546995, 'threads': 1, 'requests': 100, 'ranking_checks': 5}\nSaved /app/solution/trained_vae_short_validation.parquet vector dimension 202\nNDCG@10 0.0368477570446434\n100-item history encoder median ms 0.21203500000410713\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:40:27.278Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": 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tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/trained_short_v_scores.npy /app/solution/trained_vae_short_v_scores.npy --name trained_short_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5,3 --feature-decays=14 --feature-likes=50 --organic-shares=0.5,1 > /app/solution/trained_short_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat > /app/solution/check_sampled_training.py <<'PY'\\n\\\"\\\"\\\"Exercise the scalable sampled loss and check gradients reach both towers.\\\"\\\"\\\"\\nfrom model import *\\nseed(2026);torch.set_num_threads(1);c=torch.load(O/'temporal.pt',weights_only=False);a=argparse.Namespace(**c['args']);m=TwoTower(a.hidden,a.dim,a.dropout,a.vae);m.load_state_dict(c['model']);m.train();z=np.load(O/'validation_temporal.npz');rows=np.arange(8)\\nbi,off,bw,br,_=batch_data(rows,z['hptr'],z['hids'].astype('int64'),z['hweights']);bt,_,_,tr,wt=batch_data(rows,z['tptr'],z['tids'].astype('int64'),z['tweights']);uv,kl=m.users(bi,off,bw);negatives=512\\noutids=torch.unique(torch.cat([torch.randperm(N)[:negatives],bt]));logits=uv@m.item_tower(outids).T;forced=torch.isin(outids,torch.unique(bt));logits[:,~forced]-=np.log(negatives/N);hx=torch.searchsorted(outids,bi);ok=(hx0 and ig>0\\nresult=dict(sampled_loss=loss.item(),user_embedding_gradient_norm=ug,item_embedding_gradient_norm=ig,sampled_items=len(outids));json.dump(result,open(O/'sampled_training_check.json','w'),indent=2);print(result)\\nPY\\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/check_sampled_training.py > /app/solution/sampled_training_check.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:40:53.441Z", "content": "Script completed\nWall time 2.3 seconds\nOutput:\n\n{\"chunk_id\":\"c844c7\",\"wall_time_seconds\":1.001841366,\"session_id\":34866,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"fed805\",\"wall_time_seconds\":1.001099166,\"session_id\":8512,\"original_token_count\":0,\"output\":\"\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:40:51.542Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f99baae087d19f21f7e1af67c4ee", "usage": { "input_tokens": 113742, "cached_input_tokens": 112256, "cache_write_input_tokens": 0, "output_tokens": 681, "reasoning_output_tokens": 30, "total_tokens": 114423 }, "turn_token_usage": { "input_tokens": 7564502, "cached_input_tokens": 7272832, "cache_write_input_tokens": 0, "output_tokens": 67726, "reasoning_output_tokens": 38630, "total_tokens": 7632228 }, "thread_token_usage": { "input_tokens": 7564502, "cached_input_tokens": 7272832, "cache_write_input_tokens": 0, "output_tokens": 67726, "reasoning_output_tokens": 38630, "total_tokens": 7632228 } } }, { "kind": "context", "timestamp": "2026-09-10T18:40:58.480Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-612eeea5-fc56-4308-b972-6738f134a83d", "process_id": "8512", "command": [ "/bin/sh", "-lc", "cat > /app/solution/check_sampled_training.py <<'PY'\n\"\"\"Exercise the scalable sampled loss and check gradients reach both towers.\"\"\"\nfrom model import *\nseed(2026);torch.set_num_threads(1);c=torch.load(O/'temporal.pt',weights_only=False);a=argparse.Namespace(**c['args']);m=TwoTower(a.hidden,a.dim,a.dropout,a.vae);m.load_state_dict(c['model']);m.train();z=np.load(O/'validation_temporal.npz');rows=np.arange(8)\nbi,off,bw,br,_=batch_data(rows,z['hptr'],z['hids'].astype('int64'),z['hweights']);bt,_,_,tr,wt=batch_data(rows,z['tptr'],z['tids'].astype('int64'),z['tweights']);uv,kl=m.users(bi,off,bw);negatives=512\noutids=torch.unique(torch.cat([torch.randperm(N)[:negatives],bt]));logits=uv@m.item_tower(outids).T;forced=torch.isin(outids,torch.unique(bt));logits[:,~forced]-=np.log(negatives/N);hx=torch.searchsorted(outids,bi);ok=(hx0 and ig>0\nresult=dict(sampled_loss=loss.item(),user_embedding_gradient_norm=ug,item_embedding_gradient_norm=ig,sampled_items=len(outids));json.dump(result,open(O/'sampled_training_check.json','w'),indent=2);print(result)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/check_sampled_training.py > /app/solution/sampled_training_check.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat > /app/solution/check_sampled_training.py <<'PY'\n\"\"\"Exercise the scalable sampled loss and check gradients reach both towers.\"\"\"\nfrom model import *\nseed(2026);torch.set_num_threads(1);c=torch.load(O/'temporal.pt',weights_only=False);a=argparse.Namespace(**c['args']);m=TwoTower(a.hidden,a.dim,a.dropout,a.vae);m.load_state_dict(c['model']);m.train();z=np.load(O/'validation_temporal.npz');rows=np.arange(8)\nbi,off,bw,br,_=batch_data(rows,z['hptr'],z['hids'].astype('int64'),z['hweights']);bt,_,_,tr,wt=batch_data(rows,z['tptr'],z['tids'].astype('int64'),z['tweights']);uv,kl=m.users(bi,off,bw);negatives=512\noutids=torch.unique(torch.cat([torch.randperm(N)[:negatives],bt]));logits=uv@m.item_tower(outids).T;forced=torch.isin(outids,torch.unique(bt));logits[:,~forced]-=np.log(negatives/N);hx=torch.searchsorted(outids,bi);ok=(hx0 and ig>0\nresult=dict(sampled_loss=loss.item(),user_embedding_gradient_norm=ug,item_embedding_gradient_norm=ig,sampled_items=len(outids));json.dump(result,open(O/'sampled_training_check.json','w'),indent=2);print(result)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/check_sampled_training.py > /app/solution/sampled_training_check.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 6, "nanos": 41872956 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:41:09.700Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-88901efa-9242-47ab-a8e4-9b081ba66dab", "process_id": "34866", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/trained_short_v_scores.npy /app/solution/trained_vae_short_v_scores.npy --name trained_short_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5,3 --feature-decays=14 --feature-likes=50 --organic-shares=0.5,1 > /app/solution/trained_short_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/trained_short_v_scores.npy /app/solution/trained_vae_short_v_scores.npy --name trained_short_calibration --weights=0.25,0.5,0.75 --pops=0.5 --trends=1.5,2,2.5,3 --feature-decays=14 --feature-likes=50 --organic-shares=0.5,1 > /app/solution/trained_short_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 18, "nanos": 431477343 }, "formatted_output": "" } } } ] }, { "id": 143, "role": "agent", "timestamp": "2026-09-10T18:41:19.572Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:41:19.572Z", "text": "" } ] }, { "id": 144, "role": "agent", "timestamp": "2026-09-10T18:41:41.175Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:41:41.175Z", "text": "" } ] }, { "id": 145, "role": "agent", "timestamp": "2026-09-10T18:42:09.716Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:42:09.716Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:42:10.898Z", "callId": "call_3kkzy2FNOHWooQYGANv8v2s0", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"tail -3 /app/solution/trained_short_calibration.log; cat /app/solution/sampled_training_check.log\",\"max_output_tokens\":2500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:42:11.014Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"4369c8\",\"wall_time_seconds\":0.00000331,\"exit_code\":0,\"original_token_count\":415,\"output\":\"users 3200\\nusers 3840\\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.049572027539902225}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049528185382284706}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04948945868841503}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049413162150615225}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04938643933810071}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04937157108447184}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04933608337808821}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04927483705555805}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049238539019211915}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04923171566223708}]\\n{'sampled_loss': 7.8012471199035645, 'user_embedding_gradient_norm': 0.9444960355758667, 'item_embedding_gradient_norm': 5.447116851806641, 'sampled_items': 550}\\n\"}\n{\"current_time\":\"2026-09-10 18:42:10 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:42:10.947Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d26bc15-1c2f-4568-9967-fdca37108e73", "process_id": "70784", "command": [ "/bin/sh", "-lc", "tail -3 /app/solution/trained_short_calibration.log; cat /app/solution/sampled_training_check.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "tail -3 /app/solution/trained_short_calibration.log; cat /app/solution/sampled_training_check.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users 3200\nusers 3840\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.049572027539902225}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049528185382284706}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04948945868841503}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049413162150615225}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04938643933810071}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04937157108447184}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04933608337808821}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04927483705555805}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049238539019211915}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04923171566223708}]\n{'sampled_loss': 7.8012471199035645, 'user_embedding_gradient_norm': 0.9444960355758667, 'item_embedding_gradient_norm': 5.447116851806641, 'sampled_items': 550}\n", "stderr": "", "aggregated_output": "users 3200\nusers 3840\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.049572027539902225}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049528185382284706}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04948945868841503}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049413162150615225}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04938643933810071}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04937157108447184}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04933608337808821}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04927483705555805}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049238539019211915}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04923171566223708}]\n{'sampled_loss': 7.8012471199035645, 'user_embedding_gradient_norm': 0.9444960355758667, 'item_embedding_gradient_norm': 5.447116851806641, 'sampled_items': 550}\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3310 }, "formatted_output": "users 3200\nusers 3840\n[{'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.049572027539902225}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049528185382284706}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04948945868841503}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049413162150615225}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04938643933810071}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04937157108447184}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04933608337808821}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.04927483705555805}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 0.5, 'score': 0.049238539019211915}, {'weights': [0.75, 0.25], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04923171566223708}]\n{'sampled_loss': 7.8012471199035645, 'user_embedding_gradient_norm': 0.9444960355758667, 'item_embedding_gradient_norm': 5.447116851806641, 'sampled_items': 550}\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:42:11.012Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2f9b622ac87d19b45586526c6a707", "usage": { "input_tokens": 114514, "cached_input_tokens": 113536, "cache_write_input_tokens": 0, "output_tokens": 1613, "reasoning_output_tokens": 1552, "total_tokens": 116127 }, "turn_token_usage": { "input_tokens": 7679016, "cached_input_tokens": 7386368, "cache_write_input_tokens": 0, "output_tokens": 69339, "reasoning_output_tokens": 40182, "total_tokens": 7748355 }, "thread_token_usage": { "input_tokens": 7679016, "cached_input_tokens": 7386368, "cache_write_input_tokens": 0, "output_tokens": 69339, "reasoning_output_tokens": 40182, "total_tokens": 7748355 } } } ] }, { "id": 146, "role": "agent", "timestamp": "2026-09-10T18:42:34.734Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:42:34.734Z", "text": "" } ] }, { "id": 147, "role": "agent", "timestamp": "2026-09-10T18:42:56.757Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:42:56.757Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:42:58.862Z", "text": "The short-history training variant topped out at 0.04957, so it will not replace the selected model. The vector check passed, and the sampled training loss sends nonzero gradients to both towers. On 100 sampled histories, CPU user encoding took a median 0.64 ms and p95 1.15 ms on one thread, excluding history loading and retrieval.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:43:05.899Z", "callId": "call_WFUcM8sILiOBNy71f6CMgFsu", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name final_calibration --weights=0.5 --pops=0.25,0.5,0.75,1 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50,100 --organic-shares=1 > /app/solution/final_calibration.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\np='/app/solution/README.md';s=open(p).read().replace('## Training and temporal separation', 'On 100 sampled histories (median 460 distinct tracks), one-thread CPU user encoding took **0.64 ms median / 1.15 ms p95**, excluding history loading and index retrieval. `vector_verification.json` records the measurement and numerical consistency checks.\\\\n\\\\n## Training and temporal separation');open(p,'w').write(s)\\nPY\\nls -lh /app/solution | tail -15\",\"max_output_tokens\":2000}));\n" }, "result": { "timestamp": "2026-09-10T18:43:07.156Z", "content": "Script completed\nWall time 1.2 seconds\nOutput:\n\n{\"chunk_id\":\"2bf1ca\",\"wall_time_seconds\":1.001379647,\"session_id\":91310,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"a6dcaf\",\"wall_time_seconds\":0.00000403,\"exit_code\":0,\"original_token_count\":262,\"output\":\"-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:31 validation.parquet\\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\\n-rw-r--r-- 1 agent agent 2.6M Sep 10 18:20 validation_item_features.npz\\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 validation_temporal.npz\\n-rw-r--r-- 1 agent agent 251M Sep 10 18:32 validation_temporal_7.npz\\n-rw-r--r-- 1 agent agent 66M Sep 10 18:03 validation_temporal_history.npz\\n-rw-r--r-- 1 agent agent 66M Sep 10 18:27 validation_temporal_history_14.npz\\n-rw-r--r-- 1 agent agent 66M Sep 10 18:22 validation_temporal_history_30.npz\\n-rw-r--r-- 1 agent agent 66M Sep 10 18:30 validation_temporal_history_7.npz\\n-rw-r--r-- 1 agent agent 253 Sep 10 18:39 vector_verification.json\\n-rw-r--r-- 1 agent agent 236 Sep 10 18:39 vector_verification.log\\n-rw-r--r-- 1 agent agent 1.9K Sep 10 18:35 verify_vectors.py\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:43:06.109Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2fa03acb087d1a5fa4c148c9e19a6", "usage": { "input_tokens": 116939, "cached_input_tokens": 114304, "cache_write_input_tokens": 0, "output_tokens": 1409, "reasoning_output_tokens": 1034, "total_tokens": 118348 }, "turn_token_usage": { "input_tokens": 7795955, "cached_input_tokens": 7500672, "cache_write_input_tokens": 0, "output_tokens": 70748, "reasoning_output_tokens": 41216, "total_tokens": 7866703 }, "thread_token_usage": { "input_tokens": 7795955, "cached_input_tokens": 7500672, "cache_write_input_tokens": 0, "output_tokens": 70748, "reasoning_output_tokens": 41216, "total_tokens": 7866703 } } }, { "kind": "context", "timestamp": "2026-09-10T18:43:07.136Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3f485fc0-2caf-49bd-a2f1-1f6d4a6828c1", "process_id": "28506", "command": [ "/bin/sh", "-lc", "python - 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"source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:31 validation.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n-rw-r--r-- 1 agent agent 2.6M Sep 10 18:20 validation_item_features.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 validation_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:32 validation_temporal_7.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:03 validation_temporal_history.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:27 validation_temporal_history_14.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:22 validation_temporal_history_30.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:30 validation_temporal_history_7.npz\n-rw-r--r-- 1 agent agent 253 Sep 10 18:39 vector_verification.json\n-rw-r--r-- 1 agent agent 236 Sep 10 18:39 vector_verification.log\n-rw-r--r-- 1 agent agent 1.9K Sep 10 18:35 verify_vectors.py\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:31 validation.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n-rw-r--r-- 1 agent agent 2.6M Sep 10 18:20 validation_item_features.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 validation_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:32 validation_temporal_7.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:03 validation_temporal_history.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:27 validation_temporal_history_14.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:22 validation_temporal_history_30.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:30 validation_temporal_history_7.npz\n-rw-r--r-- 1 agent agent 253 Sep 10 18:39 vector_verification.json\n-rw-r--r-- 1 agent agent 236 Sep 10 18:39 vector_verification.log\n-rw-r--r-- 1 agent agent 1.9K Sep 10 18:35 verify_vectors.py\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4030 }, "formatted_output": "-rw-r--r-- 1 agent agent 5.7K Sep 10 17:53 val_dislikes_gold.parquet\n-rw-r--r-- 1 agent agent 57K Sep 10 17:53 val_likes_gold.parquet\n-rw-r--r-- 1 agent agent 363K Sep 10 17:53 val_listens_gold.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:31 validation.parquet\n-rw-r--r-- 1 agent agent 81M Sep 10 17:54 validation_events.parquet\n-rw-r--r-- 1 agent agent 2.6M Sep 10 18:20 validation_item_features.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:01 validation_temporal.npz\n-rw-r--r-- 1 agent agent 251M Sep 10 18:32 validation_temporal_7.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:03 validation_temporal_history.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:27 validation_temporal_history_14.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:22 validation_temporal_history_30.npz\n-rw-r--r-- 1 agent agent 66M Sep 10 18:30 validation_temporal_history_7.npz\n-rw-r--r-- 1 agent agent 253 Sep 10 18:39 vector_verification.json\n-rw-r--r-- 1 agent agent 236 Sep 10 18:39 vector_verification.log\n-rw-r--r-- 1 agent agent 1.9K Sep 10 18:35 verify_vectors.py\n" } } } ] }, { "id": 148, "role": "agent", "timestamp": "2026-09-10T18:43:32.033Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:43:32.033Z", "text": "" }, { "kind": "context", "timestamp": "2026-09-10T18:43:33.051Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4672fc7-344d-4e93-a560-3e30bacc6505", "process_id": "91310", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name final_calibration --weights=0.5 --pops=0.25,0.5,0.75,1 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50,100 --organic-shares=1 > /app/solution/final_calibration.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/calibrate.py --scores /app/solution/temporal_14_v_scores.npy /app/solution/temporal_vae_14_v_scores.npy --name final_calibration --weights=0.5 --pops=0.25,0.5,0.75,1 --trends=1.5,2,2.5 --feature-decays=14 --feature-likes=50,100 --organic-shares=1 > /app/solution/final_calibration.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 26, "nanos": 975911397 }, "formatted_output": "" } } } ] }, { "id": 149, "role": "agent", "timestamp": "2026-09-10T18:43:57.811Z", "blocks": [ { "kind": "reasoning", "representation": 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0.049831895842828926}]\n15G\t/app/solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6640 }, "formatted_output": "users 3200\nusers 3840\n[{'weights': [0.5, 0.5], 'pop': 0.75, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.05065766584999368}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050378486166519194}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.050317212592504344}, {'weights': [0.5, 0.5], 'pop': 0.75, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.0502415980914881}, {'weights': [0.5, 0.5], 'pop': 0.75, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.050122272387638535}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.05009287362933885}, {'weights': [0.5, 0.5], 'pop': 0.75, 'trend': 2.0, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04993780873570068}, {'weights': [0.5, 0.5], 'pop': 1.0, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.049861840085283535}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 2.5, 'feature_decay': 14, 'feature_like': 50.0, 'organic_share': 1.0, 'score': 0.04984780208726081}, {'weights': [0.5, 0.5], 'pop': 0.5, 'trend': 1.5, 'feature_decay': 14, 'feature_like': 100.0, 'organic_share': 1.0, 'score': 0.049831895842828926}]\n15G\t/app/solution\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:44:06.089Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": 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tools.exec_command({cmd:\"python - <<'PY'\\nimport json\\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(public_ndcg_at_10=0.050658,pop=0.75,trend=2.5,feature_like=100.0);json.dump(c,open(p,'w'),indent=2)\\np='/app/solution/README.md';s=open(p).read().replace('0.050378','0.050658');open(p,'w').write(s)\\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/run.py > /app/solution/reproduction_check.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/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.75 --trend 2.5 --feature-decay 14 --feature-like 100 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]);results.forEach(text);\n" }, "result": { "timestamp": "2026-09-10T18:44:40.551Z", "content": "Script completed\nWall time 1.3 seconds\nOutput:\n\n{\"chunk_id\":\"57edb6\",\"wall_time_seconds\":0.00000155,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"babf6a\",\"wall_time_seconds\":1.001744496,\"session_id\":56383,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"fb28fc\",\"wall_time_seconds\":1.000739988,\"session_id\":6495,\"original_token_count\":0,\"output\":\"\"}}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:44:39.317Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2fa76649087d18e7f7395b77d0341", "usage": { "input_tokens": 120856, "cached_input_tokens": 118656, "cache_write_input_tokens": 0, "output_tokens": 852, "reasoning_output_tokens": 516, "total_tokens": 121708 }, "turn_token_usage": { "input_tokens": 8035662, "cached_input_tokens": 7736064, "cache_write_input_tokens": 0, "output_tokens": 72841, "reasoning_output_tokens": 42916, "total_tokens": 8108503 }, "thread_token_usage": { "input_tokens": 8035662, "cached_input_tokens": 7736064, "cache_write_input_tokens": 0, "output_tokens": 72841, "reasoning_output_tokens": 42916, "total_tokens": 8108503 } } }, { "kind": "context", "timestamp": "2026-09-10T18:44:39.370Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bb0282a0-9f73-4e00-b17d-9c588aa7af03", "process_id": "72295", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport json\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(public_ndcg_at_10=0.050658,pop=0.75,trend=2.5,feature_like=100.0);json.dump(c,open(p,'w'),indent=2)\np='/app/solution/README.md';s=open(p).read().replace('0.050378','0.050658');open(p,'w').write(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport json\np='/app/solution/selected_config.json';c=json.load(open(p));c.update(public_ndcg_at_10=0.050658,pop=0.75,trend=2.5,feature_like=100.0);json.dump(c,open(p,'w'),indent=2)\np='/app/solution/README.md';s=open(p).read().replace('0.050378','0.050658');open(p,'w').write(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 1550 }, "formatted_output": "" } } } ] }, { "id": 152, "role": "agent", "timestamp": "2026-09-10T18:44:49.251Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:44:49.251Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:44:51.244Z", "text": "A final calibration check raised public NDCG@10 to 0.050658. I’m applying those settings to the refitted model and running the documented submission command end to end. After the final checks, I’ll remove the large experiment caches while retaining the fitted models, vectors, code, and validation evidence.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:44:52.254Z", "callId": "call_UbZETNojHzIahU5AkXxOsFwn", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"sed -n '1,180p' /app/solution/retrieve.py; sed -n '1,170p' /app/solution/temporal_train.py\",\"max_output_tokens\":9000}));\n" }, "result": { "timestamp": "2026-09-10T18:44:52.436Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{\"chunk_id\":\"d01754\",\"wall_time_seconds\":0.00001029,\"exit_code\":0,\"original_token_count\":2647,\"output\":\"\\\"\\\"\\\"Export independent item vectors and rank by one exact tower dot product.\\n\\nAn ensemble remains a two-tower model: concatenate the component user vectors\\nand component item vectors. The production serving path can index item vectors\\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\\nis a bounded-batch reference implementation for the supplied small catalog.\\n\\\"\\\"\\\"\\nfrom model import *\\nfrom temporal_train import temporal_data\\n\\nclass UserRetrievalTower(nn.Module):\\n def __init__(self,models,weights):\\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\\n def forward(self,histories):\\n vec=[]\\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\\n\\nclass ItemRetrievalTower(nn.Module):\\n def __init__(self,models,features,pop,trend,organic_share=0.):\\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(3,1,bias=False)\\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend,organic_share]]))\\n def forward(self,ids):\\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\\n\\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.,history_decays=None,feature_decay=30,feature_like=0.,organic_share=0.):\\n models=[];datas=[];configs=[]\\n for component_index,path in enumerate(paths):\\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\\n datas.append(temporal_data(stage,(history_decays[component_index] if history_decays else 90)) if 'temporal_args' in c else load_data(stage,cfg))\\n stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1),dtype=torch.float32)\\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend,organic_share).eval(),datas,configs\\n\\n@torch.no_grad()\\ndef main():\\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');p.add_argument('--history-decays',nargs='+',type=float);p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like,a.organic_share);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\\n for st in range(0,len(uids),128):\\n histories=[]\\n for r,(_,ptr,ids,w) in zip(rows,datas):\\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\\n uv=ut(histories);uvecs.append(uv.numpy().copy());s=uv@emb.T\\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy().copy())\\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\\n if scoresout is not None:scoresout.flush()\\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));atomic_save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\\n # Serving-time user encoding benchmark, excluding the history-store read.\\n times=[]\\n for _ in range(25):\\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\\n print('100-item history encoder median ms',np.median(times),flush=True)\\nif __name__=='__main__':main()\\n\\\"\\\"\\\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\\\"\\\"\\\"\\nfrom model import *\\n\\ndef temporal_data(stage,decay=90):\\n p=O/(f'{stage}_temporal_history.npz' if decay==90 else f'{stage}_temporal_history_{decay:g}.npz')\\n if not p.exists():\\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestamp=0 else cfg.dropout;model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);data=temporal_data(a.stage,a.history_decay);gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\\n if a.infer:\\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else a.name+'_validation.parquet'),save_scores=O/(a.name+'_scores.npy'))\\n if a.stage=='validation':print(metrics(top,gold,ideal),flush=True)\\n return\\n z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));hp=z['hptr'];hi=z['hids'].astype('int64');hw=z['hweights'];tp=z['tptr'];ti=z['tids'].astype('int64');tw=z['tweights'];n=len(hp)-1\\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);start=time.time();allitems=torch.arange(N)\\n step=0\\n for epoch in range(1,a.epochs+1):\\n order=np.random.permutation(n);model.train();losses=[]\\n for st in range(0,n,a.batch):\\n rows=order[st:st+a.batch];bi,off,bw,br,_=batch_data(rows,hp,hi,hw);bt,_,_,tr,wt=batch_data(rows,tp,ti,tw);uv,kl=model.users(bi,off,bw)\\n if a.negatives and a.negativesbest:\\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\\n else:\\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\\n model.train()\\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\\n print('EPOCH',epoch,'loss',np.mean(losses),'seconds',time.time()-start,flush=True)\\n if a.stage=='validation':\\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\\n if score>best:\\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\\n else:\\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\\n print('BEST',best,flush=True)\\nif __name__=='__main__':main()\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:44:52.296Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cd46f7bc-f2cc-4115-9bbe-64cad6ba751c", "process_id": "51477", "command": [ "/bin/sh", "-lc", "sed -n '1,180p' /app/solution/retrieve.py; sed -n '1,170p' /app/solution/temporal_train.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "sed -n '1,180p' /app/solution/retrieve.py", "name": "retrieve.py", "path": "/app/solution/retrieve.py" }, { "type": "read", "cmd": "sed -n '1,170p' /app/solution/temporal_train.py", "name": "temporal_train.py", "path": "/app/solution/temporal_train.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\"\"\"Export independent item vectors and rank by one exact tower dot product.\n\nAn ensemble remains a two-tower model: concatenate the component user vectors\nand component item vectors. The production serving path can index item vectors\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\nis a bounded-batch reference implementation for the supplied small catalog.\n\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\n\nclass UserRetrievalTower(nn.Module):\n def __init__(self,models,weights):\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\n def forward(self,histories):\n vec=[]\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\n\nclass ItemRetrievalTower(nn.Module):\n def __init__(self,models,features,pop,trend,organic_share=0.):\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(3,1,bias=False)\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend,organic_share]]))\n def forward(self,ids):\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\n\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.,history_decays=None,feature_decay=30,feature_like=0.,organic_share=0.):\n models=[];datas=[];configs=[]\n for component_index,path in enumerate(paths):\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\n datas.append(temporal_data(stage,(history_decays[component_index] if history_decays else 90)) if 'temporal_args' in c else load_data(stage,cfg))\n stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1),dtype=torch.float32)\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend,organic_share).eval(),datas,configs\n\n@torch.no_grad()\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');p.add_argument('--history-decays',nargs='+',type=float);p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like,a.organic_share);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\n for st in range(0,len(uids),128):\n histories=[]\n for r,(_,ptr,ids,w) in zip(rows,datas):\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\n uv=ut(histories);uvecs.append(uv.numpy().copy());s=uv@emb.T\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy().copy())\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\n if scoresout is not None:scoresout.flush()\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));atomic_save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\n # Serving-time user encoding benchmark, excluding the history-store read.\n times=[]\n for _ in range(25):\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\n print('100-item history encoder median ms',np.median(times),flush=True)\nif __name__=='__main__':main()\n\"\"\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\"\"\"\nfrom model import *\n\ndef temporal_data(stage,decay=90):\n p=O/(f'{stage}_temporal_history.npz' if decay==90 else f'{stage}_temporal_history_{decay:g}.npz')\n if not p.exists():\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestamp=0 else cfg.dropout;model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);data=temporal_data(a.stage,a.history_decay);gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\n if a.infer:\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else a.name+'_validation.parquet'),save_scores=O/(a.name+'_scores.npy'))\n if a.stage=='validation':print(metrics(top,gold,ideal),flush=True)\n return\n z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));hp=z['hptr'];hi=z['hids'].astype('int64');hw=z['hweights'];tp=z['tptr'];ti=z['tids'].astype('int64');tw=z['tweights'];n=len(hp)-1\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);start=time.time();allitems=torch.arange(N)\n step=0\n for epoch in range(1,a.epochs+1):\n order=np.random.permutation(n);model.train();losses=[]\n for st in range(0,n,a.batch):\n rows=order[st:st+a.batch];bi,off,bw,br,_=batch_data(rows,hp,hi,hw);bt,_,_,tr,wt=batch_data(rows,tp,ti,tw);uv,kl=model.users(bi,off,bw)\n if a.negatives and a.negativesbest:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n model.train()\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\n print('EPOCH',epoch,'loss',np.mean(losses),'seconds',time.time()-start,flush=True)\n if a.stage=='validation':\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n if score>best:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\n", "stderr": "", "aggregated_output": "\"\"\"Export independent item vectors and rank by one exact tower dot product.\n\nAn ensemble remains a two-tower model: concatenate the component user vectors\nand component item vectors. The production serving path can index item vectors\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\nis a bounded-batch reference implementation for the supplied small catalog.\n\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\n\nclass UserRetrievalTower(nn.Module):\n def __init__(self,models,weights):\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\n def forward(self,histories):\n vec=[]\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\n\nclass ItemRetrievalTower(nn.Module):\n def __init__(self,models,features,pop,trend,organic_share=0.):\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(3,1,bias=False)\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend,organic_share]]))\n def forward(self,ids):\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\n\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.,history_decays=None,feature_decay=30,feature_like=0.,organic_share=0.):\n models=[];datas=[];configs=[]\n for component_index,path in enumerate(paths):\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\n datas.append(temporal_data(stage,(history_decays[component_index] if history_decays else 90)) if 'temporal_args' in c else load_data(stage,cfg))\n stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1),dtype=torch.float32)\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend,organic_share).eval(),datas,configs\n\n@torch.no_grad()\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');p.add_argument('--history-decays',nargs='+',type=float);p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like,a.organic_share);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\n for st in range(0,len(uids),128):\n histories=[]\n for r,(_,ptr,ids,w) in zip(rows,datas):\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\n uv=ut(histories);uvecs.append(uv.numpy().copy());s=uv@emb.T\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy().copy())\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\n if scoresout is not None:scoresout.flush()\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));atomic_save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\n # Serving-time user encoding benchmark, excluding the history-store read.\n times=[]\n for _ in range(25):\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\n print('100-item history encoder median ms',np.median(times),flush=True)\nif __name__=='__main__':main()\n\"\"\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\"\"\"\nfrom model import *\n\ndef temporal_data(stage,decay=90):\n p=O/(f'{stage}_temporal_history.npz' if decay==90 else f'{stage}_temporal_history_{decay:g}.npz')\n if not p.exists():\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestamp=0 else cfg.dropout;model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);data=temporal_data(a.stage,a.history_decay);gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\n if a.infer:\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else a.name+'_validation.parquet'),save_scores=O/(a.name+'_scores.npy'))\n if a.stage=='validation':print(metrics(top,gold,ideal),flush=True)\n return\n z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));hp=z['hptr'];hi=z['hids'].astype('int64');hw=z['hweights'];tp=z['tptr'];ti=z['tids'].astype('int64');tw=z['tweights'];n=len(hp)-1\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);start=time.time();allitems=torch.arange(N)\n step=0\n for epoch in range(1,a.epochs+1):\n order=np.random.permutation(n);model.train();losses=[]\n for st in range(0,n,a.batch):\n rows=order[st:st+a.batch];bi,off,bw,br,_=batch_data(rows,hp,hi,hw);bt,_,_,tr,wt=batch_data(rows,tp,ti,tw);uv,kl=model.users(bi,off,bw)\n if a.negatives and a.negativesbest:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n model.train()\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\n print('EPOCH',epoch,'loss',np.mean(losses),'seconds',time.time()-start,flush=True)\n if a.stage=='validation':\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n if score>best:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 10290 }, "formatted_output": "\"\"\"Export independent item vectors and rank by one exact tower dot product.\n\nAn ensemble remains a two-tower model: concatenate the component user vectors\nand component item vectors. The production serving path can index item vectors\nonce and run only UserRetrievalTower for each request. Exhaustive scoring below\nis a bounded-batch reference implementation for the supplied small catalog.\n\"\"\"\nfrom model import *\nfrom temporal_train import temporal_data\n\nclass UserRetrievalTower(nn.Module):\n def __init__(self,models,weights):\n super().__init__();self.components=nn.ModuleList([m.user_tower for m in models]);self.register_buffer('weights',torch.tensor(weights))\n def forward(self,histories):\n vec=[]\n for net,weight,(ids,offsets,values) in zip(self.components,self.weights,histories):\n h,_=net(ids,offsets,values);vec.append(torch.cat([h,torch.ones_like(h[:,:1])],-1)*weight)\n return torch.cat(vec+[torch.ones_like(vec[0][:,:1])],-1)\n\nclass ItemRetrievalTower(nn.Module):\n def __init__(self,models,features,pop,trend,organic_share=0.):\n super().__init__();self.components=nn.ModuleList([m.item_tower for m in models]);self.register_buffer('features',features);self.calibration=nn.Linear(3,1,bias=False)\n with torch.no_grad():self.calibration.weight.copy_(torch.tensor([[pop,trend,organic_share]]))\n def forward(self,ids):\n return torch.cat([net(ids) for net in self.components]+[self.calibration(self.features[ids])],-1)\n\ndef load_retriever(paths,weights,stage,pop=0.,trend=0.,history_decays=None,feature_decay=30,feature_like=0.,organic_share=0.):\n models=[];datas=[];configs=[]\n for component_index,path in enumerate(paths):\n c=torch.load(path,weights_only=False);cfg=argparse.Namespace(**c['args']);m=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);m.load_state_dict(c['model']);models.append(m);configs.append(c)\n datas.append(temporal_data(stage,(history_decays[component_index] if history_decays else 90)) if 'temporal_args' in c else load_data(stage,cfg))\n stats=np.load(O/f'{stage}_item_features.npz');features=torch.tensor(np.stack([np.log(stats['count']+10),np.log(stats[f'organic_{feature_decay}']+feature_like*stats[f'likes_{feature_decay}']+5)-np.log(stats['organic']+feature_like*stats['likes']+10),np.log((stats['organic']+feature_like*stats['likes']+10)/(stats['count']+feature_like*stats['likes']+20))],1),dtype=torch.float32)\n return UserRetrievalTower(models,weights).eval(),ItemRetrievalTower(models,features,pop,trend,organic_share).eval(),datas,configs\n\n@torch.no_grad()\ndef main():\n p=argparse.ArgumentParser();p.add_argument('--models',nargs='+',required=True);p.add_argument('--weights',nargs='+',type=float);p.add_argument('--stage',default='test');p.add_argument('--pop',type=float,default=0);p.add_argument('--trend',type=float,default=0);p.add_argument('--name',default='final');p.add_argument('--save-scores',action='store_true');p.add_argument('--output');p.add_argument('--history-decays',nargs='+',type=float);p.add_argument('--feature-decay',type=int,default=30);p.add_argument('--feature-like',type=float,default=0.);p.add_argument('--organic-share',type=float,default=0.);a=p.parse_args();seed(42);weights=a.weights or [1/len(a.models)]*len(a.models);assert len(weights)==len(a.models)\n ut,it,datas,configs=load_retriever(a.models,weights,a.stage,a.pop,a.trend,a.history_decays,a.feature_decay,a.feature_like,a.organic_share);z=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=z['uids'];items=z['item_ids'];emb=it(torch.arange(N));rows=[pd.Index(d[0]).get_indexer(uids) for d in datas];tops=[];uvecs=[]\n scoresout=np.lib.format.open_memmap(O/(a.name+'_scores.npy'),mode='w+',dtype='float32',shape=(len(uids),N)) if a.save_scores else None\n for st in range(0,len(uids),128):\n histories=[]\n for r,(_,ptr,ids,w) in zip(rows,datas):\n bi,off,bw,_,_=batch_data(r[st:st+128],ptr,ids,w);histories.append((bi,off,bw))\n uv=ut(histories);uvecs.append(uv.numpy().copy());s=uv@emb.T\n if scoresout is not None:scoresout[st:st+len(uv)]=s.numpy()\n eligible=torch.tensor(np.unpackbits(z['packed_eligible'][st:st+128],axis=1,count=N,bitorder='little').astype(bool));s.masked_fill_(~eligible,-1e9);tops.append(torch.topk(s,100,dim=1).indices.numpy().copy())\n top=np.concatenate(tops);out=Path(a.output) if a.output else O/('submission.parquet' if a.stage=='test' else 'validation.parquet');f=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))});tmp=str(out)+'.tmp.parquet';f.to_parquet(tmp,index=False);os.replace(tmp,out)\n if scoresout is not None:scoresout.flush()\n np.savez(O/(a.name+'_item_vectors.npz'),item_ids=items,vectors=emb.numpy());np.savez(O/(a.name+'_user_vectors.npz'),uids=uids,vectors=np.concatenate(uvecs));atomic_save({'user_tower':ut.state_dict(),'item_tower':it.state_dict(),'configuration':vars(a)},O/(a.name+'_retriever.pt'));json.dump(vars(a),open(O/(a.name+'_config.json'),'w'),indent=2)\n print('Saved',out,'vector dimension',emb.shape[1],flush=True)\n if a.stage=='validation':print('NDCG@10',metrics(top,*get_truth()),flush=True)\n # Serving-time user encoding benchmark, excluding the history-store read.\n times=[]\n for _ in range(25):\n st=time.perf_counter();ut([(h[0][:100],torch.tensor([0]),h[2][:100]) for h in histories]);times.append((time.perf_counter()-st)*1000)\n print('100-item history encoder median ms',np.median(times),flush=True)\nif __name__=='__main__':main()\n\"\"\"Fine-tune both towers on past-only weekly novel organic discovery tasks.\"\"\"\nfrom model import *\n\ndef temporal_data(stage,decay=90):\n p=O/(f'{stage}_temporal_history.npz' if decay==90 else f'{stage}_temporal_history_{decay:g}.npz')\n if not p.exists():\n f=pd.read_parquet(O/'raw_positive.parquet');cut=24786800 if stage=='validation' else 25393400;f=f[f.timestamp=0 else cfg.dropout;model=TwoTower(cfg.hidden,cfg.dim,cfg.dropout,cfg.vae);model.load_state_dict(c['model']);data=temporal_data(a.stage,a.history_decay);gold,ideal=get_truth() if a.stage=='validation' else ({},np.empty(0));best=-1\n if a.infer:\n top=rank(model,data,a.stage,O/('submission.parquet' if a.stage=='test' else a.name+'_validation.parquet'),save_scores=O/(a.name+'_scores.npy'))\n if a.stage=='validation':print(metrics(top,gold,ideal),flush=True)\n return\n z=np.load(O/(f'{a.stage}_temporal.npz' if a.history_decay==90 else f'{a.stage}_temporal_{a.history_decay:g}.npz'));hp=z['hptr'];hi=z['hids'].astype('int64');hw=z['hweights'];tp=z['tptr'];ti=z['tids'].astype('int64');tw=z['tweights'];n=len(hp)-1\n opt=torch.optim.AdamW(model.parameters(),lr=a.lr,weight_decay=.01);start=time.time();allitems=torch.arange(N)\n step=0\n for epoch in range(1,a.epochs+1):\n order=np.random.permutation(n);model.train();losses=[]\n for st in range(0,n,a.batch):\n rows=order[st:st+a.batch];bi,off,bw,br,_=batch_data(rows,hp,hi,hw);bt,_,_,tr,wt=batch_data(rows,tp,ti,tw);uv,kl=model.users(bi,off,bw)\n if a.negatives and a.negativesbest:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'step':step},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n model.train()\n if a.max_steps and step>=a.max_steps:print('BEST',best,flush=True);return\n print('EPOCH',epoch,'loss',np.mean(losses),'seconds',time.time()-start,flush=True)\n if a.stage=='validation':\n top=rank(model,data,a.stage);score=metrics(top,gold,ideal);print('VALID',epoch,score,flush=True)\n if score>best:\n best=score;atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch,'score':score},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_validation.parquet'))\n else:\n atomic_save({'model':model.state_dict(),'args':vars(cfg),'temporal_args':vars(a),'epoch':epoch},O/(a.name+'.pt'));rank(model,data,a.stage,O/(a.name+'_submission.parquet'))\n print('BEST',best,flush=True)\nif __name__=='__main__':main()\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:44:52.433Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2fa98fb8887d189daf59f00155688", "usage": { "input_tokens": 121846, "cached_input_tokens": 120704, "cache_write_input_tokens": 0, "output_tokens": 255, "reasoning_output_tokens": 121, "total_tokens": 122101 }, "turn_token_usage": { "input_tokens": 8157508, "cached_input_tokens": 7856768, "cache_write_input_tokens": 0, "output_tokens": 73096, "reasoning_output_tokens": 43037, "total_tokens": 8230604 }, "thread_token_usage": { "input_tokens": 8157508, "cached_input_tokens": 7856768, "cache_write_input_tokens": 0, "output_tokens": 73096, "reasoning_output_tokens": 43037, "total_tokens": 8230604 } } }, { "kind": "context", "timestamp": "2026-09-10T18:44:56.831Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-aaa46a0e-7032-4c2d-9ca5-2b42dcc71a8f", "process_id": "6495", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.75 --trend 2.5 --feature-decay 14 --feature-like 100 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/retrieve.py --models /app/solution/temporal.pt /app/solution/temporal_vae.pt --weights 0.5 0.5 --stage validation --history-decays 14 14 --pop 0.75 --trend 2.5 --feature-decay 14 --feature-like 100 --organic-share 1 --name selected_validation > /app/solution/selected_validation.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 17, "nanos": 215689535 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:45:00.734Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-862146f7-089e-4a74-99bd-2e076766d559", "process_id": "56383", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/run.py > /app/solution/reproduction_check.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/run.py > /app/solution/reproduction_check.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 21, "nanos": 188838268 }, "formatted_output": "" } } } ] }, { "id": 153, "role": "agent", "timestamp": "2026-09-10T18:45:23.384Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:45:23.384Z", "text": "" } ] }, { "id": 154, "role": "agent", "timestamp": "2026-09-10T18:45:44.291Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:45:44.291Z", "text": "" } ] }, { "id": 155, "role": "agent", "timestamp": "2026-09-10T18:46:02.767Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:46:02.767Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:46:04.160Z", "callId": "call_9648OFR3GWBIfEyWuIYI8CNc", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/reproduction_check.log; tail -3 /app/solution/selected_validation.log; tail -3 /app/solution/final_retrieval.log\",\"max_output_tokens\":1500}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:46:04.288Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"f1fb44\",\"wall_time_seconds\":0.00000615,\"exit_code\":0,\"original_token_count\":94,\"output\":\"Running retrieve.py\\nRunning check_submission.py\\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\\nSaved /app/solution/validation.parquet vector dimension 459\\nNDCG@10 0.050657665849993774\\n100-item history encoder median ms 0.31373900037579006\\nSaved /app/solution/submission.parquet vector dimension 459\\n100-item history encoder median ms 0.2993379994222778\\n\"}\n{\"current_time\":\"2026-09-10 18:46:04 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:46:04.207Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e467a245-bc16-4b96-9ee2-a4bef087b75f", "process_id": "35224", "command": [ "/bin/sh", "-lc", "cat /app/solution/reproduction_check.log; tail -3 /app/solution/selected_validation.log; tail -3 /app/solution/final_retrieval.log" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/reproduction_check.log; tail -3 /app/solution/selected_validation.log; tail -3 /app/solution/final_retrieval.log" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.050657665849993774\n100-item history encoder median ms 0.31373900037579006\nSaved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.2993379994222778\n", "stderr": "", "aggregated_output": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.050657665849993774\n100-item history encoder median ms 0.31373900037579006\nSaved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.2993379994222778\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6150 }, "formatted_output": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\nSaved /app/solution/validation.parquet vector dimension 459\nNDCG@10 0.050657665849993774\n100-item history encoder median ms 0.31373900037579006\nSaved /app/solution/submission.parquet vector dimension 459\n100-item history encoder median ms 0.2993379994222778\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:46:04.286Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2faa4b37c87d1a8fd8366f9b844ff", "usage": { "input_tokens": 125354, "cached_input_tokens": 121728, "cache_write_input_tokens": 0, "output_tokens": 1565, "reasoning_output_tokens": 1493, "total_tokens": 126919 }, "turn_token_usage": { "input_tokens": 8282862, "cached_input_tokens": 7978496, "cache_write_input_tokens": 0, "output_tokens": 74661, "reasoning_output_tokens": 44530, "total_tokens": 8357523 }, "thread_token_usage": { "input_tokens": 8282862, "cached_input_tokens": 7978496, "cache_write_input_tokens": 0, "output_tokens": 74661, "reasoning_output_tokens": 44530, "total_tokens": 8357523 } } } ] }, { "id": 156, "role": "agent", "timestamp": "2026-09-10T18:46:11.971Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:46:11.971Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-10T18:46:24.507Z", "callId": "call_hZN9gIA61AsC7MzETuA7lWbQ", "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_vectors.py > /app/solution/vector_verification.log 2>&1\",\"yield_time_ms\":1000,\"max_output_tokens\":1000})\n]);results.forEach(text);\ntext(await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nO=Path('/app/solution')\\nkeeppt={'final_temporal.pt','final_temporal_vae.pt','temporal.pt','temporal_vae.pt'}\\nkeepnpz={'final_item_vectors.npz','final_user_vectors.npz','test_temporal_history_14.npz','validation_temporal_history_14.npz','test_item_features.npz','validation_item_features.npz'}\\nkeeppq={'submission.parquet','validation.parquet','val_listens_gold.parquet','val_likes_gold.parquet'}\\nremoved=0\\nfor p in O.iterdir():\\n if (p.suffix=='.pt' and p.name not in keeppt) or (p.suffix=='.npz' and p.name not in keepnpz) or p.suffix=='.npy' or (p.suffix=='.parquet' and p.name not in keeppq) or p.name in ['tune.py','inspect_data.py']:\\n removed+=p.stat().st_size;p.unlink()\\nprint('Removed experimental artifacts, bytes:',removed)\\np=O/'check_sampled_training.py';s=p.read_text().replace('Exercise the scalable sampled loss and check gradients reach both towers.', 'Exercise sampled loss gradients after running temporal_prepare.py.');p.write_text(s)\\nPY\",\"max_output_tokens\":1000}));\n" }, "result": { "timestamp": "2026-09-10T18:46:26.249Z", "content": "Script completed\nWall time 1.7 seconds\nOutput:\n\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"efcd30\",\"wall_time_seconds\":1.000937484,\"session_id\":91914,\"original_token_count\":0,\"output\":\"\"}}\n{\"status\":\"fulfilled\",\"value\":{\"chunk_id\":\"e19144\",\"wall_time_seconds\":1.000910514,\"session_id\":53592,\"original_token_count\":0,\"output\":\"\"}}\n{\"chunk_id\":\"d11a4b\",\"wall_time_seconds\":0.384078939,\"exit_code\":0,\"original_token_count\":13,\"output\":\"Removed experimental artifacts, bytes: 14972423728\\n\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:46:24.628Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2faecae6487d19dd73db7ed0322f8", "usage": { "input_tokens": 127098, "cached_input_tokens": 125184, "cache_write_input_tokens": 0, "output_tokens": 550, "reasoning_output_tokens": 105, "total_tokens": 127648 }, "turn_token_usage": { "input_tokens": 8409960, "cached_input_tokens": 8103680, "cache_write_input_tokens": 0, "output_tokens": 75211, "reasoning_output_tokens": 44635, "total_tokens": 8485171 }, "thread_token_usage": { "input_tokens": 8409960, "cached_input_tokens": 8103680, "cache_write_input_tokens": 0, "output_tokens": 75211, "reasoning_output_tokens": 44635, "total_tokens": 8485171 } } }, { "kind": "context", "timestamp": "2026-09-10T18:46:26.232Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-40f96302-6a11-48f5-b81d-f6456ec86a0a", "process_id": "8206", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nO=Path('/app/solution')\nkeeppt={'final_temporal.pt','final_temporal_vae.pt','temporal.pt','temporal_vae.pt'}\nkeepnpz={'final_item_vectors.npz','final_user_vectors.npz','test_temporal_history_14.npz','validation_temporal_history_14.npz','test_item_features.npz','validation_item_features.npz'}\nkeeppq={'submission.parquet','validation.parquet','val_listens_gold.parquet','val_likes_gold.parquet'}\nremoved=0\nfor p in O.iterdir():\n if (p.suffix=='.pt' and p.name not in keeppt) or (p.suffix=='.npz' and p.name not in keepnpz) or p.suffix=='.npy' or (p.suffix=='.parquet' and p.name not in keeppq) or p.name in ['tune.py','inspect_data.py']:\n removed+=p.stat().st_size;p.unlink()\nprint('Removed experimental artifacts, bytes:',removed)\np=O/'check_sampled_training.py';s=p.read_text().replace('Exercise the scalable sampled loss and check gradients reach both towers.', 'Exercise sampled loss gradients after running temporal_prepare.py.');p.write_text(s)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nO=Path('/app/solution')\nkeeppt={'final_temporal.pt','final_temporal_vae.pt','temporal.pt','temporal_vae.pt'}\nkeepnpz={'final_item_vectors.npz','final_user_vectors.npz','test_temporal_history_14.npz','validation_temporal_history_14.npz','test_item_features.npz','validation_item_features.npz'}\nkeeppq={'submission.parquet','validation.parquet','val_listens_gold.parquet','val_likes_gold.parquet'}\nremoved=0\nfor p in O.iterdir():\n if (p.suffix=='.pt' and p.name not in keeppt) or (p.suffix=='.npz' and p.name not in keepnpz) or p.suffix=='.npy' or (p.suffix=='.parquet' and p.name not in keeppq) or p.name in ['tune.py','inspect_data.py']:\n removed+=p.stat().st_size;p.unlink()\nprint('Removed experimental artifacts, bytes:',removed)\np=O/'check_sampled_training.py';s=p.read_text().replace('Exercise the scalable sampled loss and check gradients reach both towers.', 'Exercise sampled loss gradients after running temporal_prepare.py.');p.write_text(s)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Removed experimental artifacts, bytes: 14972423728\n", "stderr": "", "aggregated_output": "Removed experimental artifacts, bytes: 14972423728\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 384044528 }, "formatted_output": "Removed experimental artifacts, bytes: 14972423728\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:46:27.919Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-05085188-9cbe-40e4-848b-78a37dff3672", "process_id": "91914", "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": 241290464 }, "formatted_output": "" } } }, { "kind": "context", "timestamp": "2026-09-10T18:46:31.893Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0bba1b3d-baf2-4d49-94eb-4db91012bfbb", "process_id": "53592", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify_vectors.py > /app/solution/vector_verification.log 2>&1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/verify_vectors.py > /app/solution/vector_verification.log 2>&1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 7, "nanos": 210878990 }, "formatted_output": "" } } } ] }, { "id": 157, "role": "agent", "timestamp": "2026-09-10T18:46:54.925Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:46:54.925Z", "text": "" } ] }, { "id": 158, "role": "agent", "timestamp": "2026-09-10T18:47:18.647Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:47:18.647Z", "text": "" } ] }, { "id": 159, "role": "agent", "timestamp": "2026-09-10T18:47:28.754Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:47:28.754Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:47:30.563Z", "text": "The documented rebuild command completed successfully and produced a valid final submission. I removed the large experiment caches and retained the fitted towers, offline item vectors, code, and validation evidence. I’m checking the final score and vector-verification reports after the calibration update.\n" }, { "kind": "tool", "timestamp": "2026-09-10T18:47:32.142Z", "callId": "call_huCXl3CrIj1hBTEhgbEIdrR8", "name": "exec", "input": { "code": "text(await tools.exec_command({cmd:\"cat /app/solution/public_score.json; cat /app/solution/vector_verification.json; du -sh /app/solution; ls -lh /app/solution/submission.parquet /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt\",\"max_output_tokens\":2000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:47:32.268Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n{\"chunk_id\":\"b5813d\",\"wall_time_seconds\":0.00000704,\"exit_code\":0,\"original_token_count\":158,\"output\":\"{\\n \\\"model\\\": \\\"/app/solution/validation.parquet\\\",\\n \\\"scores\\\": {\\n \\\"quality_ndcg@10\\\": 0.050658,\\n \\\"quality_users\\\": 4263\\n }\\n}\\n{\\n \\\"vector_dimension\\\": 459,\\n \\\"max_user_vector_error\\\": 8.642673492431641e-07,\\n \\\"median_history_items\\\": 460.0,\\n \\\"encoder_median_ms\\\": 0.5563899999287969,\\n \\\"encoder_p95_ms\\\": 1.0415060000468657,\\n \\\"threads\\\": 1,\\n \\\"requests\\\": 100,\\n \\\"ranking_checks\\\": 5\\n}572M\\t/app/solution\\n-rw-r--r-- 1 agent agent 89M Sep 10 18:24 /app/solution/final_temporal.pt\\n-rw-r--r-- 1 agent agent 93M Sep 10 18:30 /app/solution/final_temporal_vae.pt\\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:44 /app/solution/submission.parquet\\n\"}\n{\"current_time\":\"2026-09-10 18:47:32 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:47:32.191Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-08524d51-5f81-4ccd-b2bd-de8ff108a2ed", "process_id": "62185", "command": [ "/bin/sh", "-lc", "cat /app/solution/public_score.json; cat /app/solution/vector_verification.json; du -sh /app/solution; ls -lh /app/solution/submission.parquet /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "read", "cmd": "cat /app/solution/public_score.json", "name": "public_score.json", "path": "/app/solution/public_score.json" }, { "type": "read", "cmd": "cat /app/solution/vector_verification.json", "name": "vector_verification.json", "path": "/app/solution/vector_verification.json" }, { "type": "list_files", "cmd": "du -sh /app/solution", "path": "solution" }, { "type": "list_files", "cmd": "ls -lh /app/solution/submission.parquet /app/solution/final_temporal.pt /app/solution/final_temporal_vae.pt", "path": "submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050658,\n \"quality_users\": 4263\n }\n}\n{\n \"vector_dimension\": 459,\n \"max_user_vector_error\": 8.642673492431641e-07,\n \"median_history_items\": 460.0,\n \"encoder_median_ms\": 0.5563899999287969,\n \"encoder_p95_ms\": 1.0415060000468657,\n \"threads\": 1,\n \"requests\": 100,\n \"ranking_checks\": 5\n}572M\t/app/solution\n-rw-r--r-- 1 agent agent 89M Sep 10 18:24 /app/solution/final_temporal.pt\n-rw-r--r-- 1 agent agent 93M Sep 10 18:30 /app/solution/final_temporal_vae.pt\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:44 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050658,\n \"quality_users\": 4263\n }\n}\n{\n \"vector_dimension\": 459,\n \"max_user_vector_error\": 8.642673492431641e-07,\n \"median_history_items\": 460.0,\n \"encoder_median_ms\": 0.5563899999287969,\n \"encoder_p95_ms\": 1.0415060000468657,\n \"threads\": 1,\n \"requests\": 100,\n \"ranking_checks\": 5\n}572M\t/app/solution\n-rw-r--r-- 1 agent agent 89M Sep 10 18:24 /app/solution/final_temporal.pt\n-rw-r--r-- 1 agent agent 93M Sep 10 18:30 /app/solution/final_temporal_vae.pt\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:44 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7040 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.050658,\n \"quality_users\": 4263\n }\n}\n{\n \"vector_dimension\": 459,\n \"max_user_vector_error\": 8.642673492431641e-07,\n \"median_history_items\": 460.0,\n \"encoder_median_ms\": 0.5563899999287969,\n \"encoder_p95_ms\": 1.0415060000468657,\n \"threads\": 1,\n \"requests\": 100,\n \"ranking_checks\": 5\n}572M\t/app/solution\n-rw-r--r-- 1 agent agent 89M Sep 10 18:24 /app/solution/final_temporal.pt\n-rw-r--r-- 1 agent agent 93M Sep 10 18:30 /app/solution/final_temporal_vae.pt\n-rw-r--r-- 1 agent agent 1.5M Sep 10 18:44 /app/solution/submission.parquet\n" } } }, { "kind": "context", "timestamp": "2026-09-10T18:47:32.265Z", "data": { "type": "token_usage_record", "thread_id": 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"input": { "code": "text(await tools.write_stdin({session_id:5796,chars:\"\",\"yield_time_ms\":1000,\"max_output_tokens\":1000}));\ntext(await tools.exec_command({cmd:\"cat /app/solution/reproduction_check.log; python - <<'PY'\\nfrom pathlib import Path\\np=Path('/app/solution/final_retriever.pt')\\nif p.exists():p.unlink()\\nPY\",\"max_output_tokens\":1000}));\ntext(await tools.clock__curr_time({}));\n" }, "result": { "timestamp": "2026-09-10T18:50:02.311Z", "content": "Script completed\nWall time 0.2 seconds\nOutput:\n\n{\"chunk_id\":\"dfb33b\",\"wall_time_seconds\":0.00000128,\"exit_code\":0,\"original_token_count\":0,\"output\":\"\"}\n{\"chunk_id\":\"667388\",\"wall_time_seconds\":0.00000359,\"exit_code\":0,\"original_token_count\":30,\"output\":\"Running retrieve.py\\nRunning check_submission.py\\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\\n\"}\n{\"current_time\":\"2026-09-10 18:50:02 UTC\"}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-10T18:50:02.255Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2fb8ca7e887d197000c8ae616c0f0", "usage": { "input_tokens": 131197, "cached_input_tokens": 129408, "cache_write_input_tokens": 0, "output_tokens": 1567, "reasoning_output_tokens": 1453, "total_tokens": 132764 }, "turn_token_usage": { "input_tokens": 8798534, "cached_input_tokens": 8487680, "cache_write_input_tokens": 0, "output_tokens": 79789, "reasoning_output_tokens": 48815, "total_tokens": 8878323 }, "thread_token_usage": { "input_tokens": 8798534, "cached_input_tokens": 8487680, "cache_write_input_tokens": 0, "output_tokens": 79789, "reasoning_output_tokens": 48815, "total_tokens": 8878323 } } }, { "kind": "context", "timestamp": "2026-09-10T18:50:02.298Z", "data": { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-77e0efb9-07cf-4c37-9863-2859eea38a00", "process_id": "84803", "command": [ "/bin/sh", "-lc", "cat /app/solution/reproduction_check.log; python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/final_retriever.pt')\nif p.exists():p.unlink()\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cat /app/solution/reproduction_check.log; python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/final_retriever.pt')\nif p.exists():p.unlink()\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "stderr": "", "aggregated_output": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3590 }, "formatted_output": "Running retrieve.py\nRunning check_submission.py\nValid: 845600 rows, 8456 users, 100 distinct eligible items per user.\n" } } } ] }, { "id": 166, "role": "agent", "timestamp": "2026-09-10T18:50:16.414Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-10T18:50:16.414Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-10T18:50:21.026Z", "text": "Created [submission.parquet](/app/solution/submission.parquet): 100 eligible tracks for each of 8,456 users. Schema, eligibility, and vector-consistency checks passed.\n\nPublic validation NDCG@10: **0.050658**. Both neural towers were refitted on train + validation.\n\nArchitecture, training details, and reproduction commands are in [README.md](/app/solution/README.md)." }, { "kind": "context", "timestamp": "2026-09-10T18:50:21.145Z", "data": { "type": "token_usage_record", "thread_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "session_id": "01a08c73-1115-7c13-a695-541ce0eca25b", "root_turn_id": "01a08c73-1127-7f82-986f-7be797b759d5", "response_id": "resp_0e42c0c579fa235c016aa2fbdaabac87d19bede980c831de78", "usage": { "input_tokens": 132900, "cached_input_tokens": 131072, "cache_write_input_tokens": 0, "output_tokens": 443, "reasoning_output_tokens": 350, "total_tokens": 133343 }, "turn_token_usage": { "input_tokens": 8931434, "cached_input_tokens": 8618752, "cache_write_input_tokens": 0, "output_tokens": 80232, "reasoning_output_tokens": 49165, "total_tokens": 9011666 }, "thread_token_usage": { "input_tokens": 8931434, "cached_input_tokens": 8618752, "cache_write_input_tokens": 0, "output_tokens": 80232, "reasoning_output_tokens": 49165, "total_tokens": 9011666 } } } ] } ] }