yambda-discovery-ranking-two-tower-base / codex-sol-high-cli-0-153-4-r03-2026-09-18 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-19T00:04:11.478Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-19T00:04:13.830Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-19T00:04:13.830Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-19T00:04:13.830Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-19T00:04:13.830Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-19 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-19", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-19", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-19T00:04:13.849Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-19T00:04:22.036Z ### Thinking unavailable — no text recorded I’ll first profile the data sizes, schemas, cutoff distributions, and evaluator mechanics. Then I’ll build a scalable sampled-training two-tower baseline, score it publicly, and iterate only where validation evidence supports the change. I’ll keep a valid submission artifact in place early. ### exec · 2026-09-19T00:04:25.191Z { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:04:25.255Z Script completed Wall time 0.1 seconds Output: /app candidate_catalog.parquet 349352 bytes target_users.parquet 84127 bytes test_eligible_candidates.npz 7736341 bytes test_preference_state.parquet 1092329 bytes train_dislikes.parquet 857213 bytes train_likes.parquet 5374652 bytes train_listens.parquet 319183255 bytes train_undislikes.parquet 155988 bytes train_unlikes.parquet 1855885 bytes val_dislikes.parquet 36571 bytes val_likes.parquet 298484 bytes val_listens.parquet 9947609 bytes val_undislikes.parquet 7845 bytes val_unlikes.parquet 72536 bytes validation_eligible_candidates.npz 7573821 bytes validation_preference_state.parquet 1061761 bytes """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd17e0b3087d1ad24e8bcb778fb42", "usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 21, "total_tokens": 14794 }, "turn_token_usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 21, "total_tokens": 14794 }, "thread_token_usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 21, "total_tokens": 14794 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c6b54e21-e0e4-4c61-bf22-d04888840fa0", "process_id": "87177", "command": [ "/bin/sh", "-lc", "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/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# 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": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/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# 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": 4820 }, "formatted_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n#!/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# 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" } } ## Step 8 · agent · 2026-09-19T00:04:34.451Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:04:40.762Z { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n m=pq.ParquetFile(p).metadata\\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'schema',pq.ParquetFile(p).schema_arrow)\\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\\n z=np.load(p)\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n print('uids first/last',z['uids'][:3],z['uids'][-3:],'items first',z['item_ids'][:10])\\n packed=z['packed_eligible']; n=len(z['item_ids'])\\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(), pd.read_parquet(D/'target_users.parquet').shape)\\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:04:40.893Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pyarrow' ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd189a85487d198e2722252b98d44", "usage": { "input_tokens": 18800, "cached_input_tokens": 14464, "cache_write_input_tokens": 0, "output_tokens": 398, "reasoning_output_tokens": 38, "total_tokens": 19198 }, "turn_token_usage": { "input_tokens": 33390, "cached_input_tokens": 25600, "cache_write_input_tokens": 0, "output_tokens": 602, "reasoning_output_tokens": 59, "total_tokens": 33992 }, "thread_token_usage": { "input_tokens": 33390, "cached_input_tokens": 25600, "cache_write_input_tokens": 0, "output_tokens": 602, "reasoning_output_tokens": 59, "total_tokens": 33992 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6db20b76-1330-49de-bcf0-bfbbfd85f3bc", "process_id": "45515", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'schema',pq.ParquetFile(p).schema_arrow)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uids first/last',z['uids'][:3],z['uids'][-3:],'items first',z['item_ids'][:10])\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(), pd.read_parquet(D/'target_users.parquet').shape)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'schema',pq.ParquetFile(p).schema_arrow)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uids first/last',z['uids'][:3],z['uids'][-3:],'items first',z['item_ids'][:10])\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(), pd.read_parquet(D/'target_users.parquet').shape)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').tail())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 9090 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pyarrow'\n" } } ## Step 9 · agent · 2026-09-19T00:04:48.437Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:04:50.105Z { "code": "const r = await tools.exec_command({\"cmd\":\"which python; python --version; ls -la /task | head -40; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head -20; command -v uv || true; command -v python3\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:04:50.160Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python Python 3.12.13 total 36 dr-xr-xr-x 1 root root 4096 Sep 19 00:01 . drwxr-xr-x 1 root root 4096 Sep 19 00:04 .. -r--r--r-- 1 root root 0 Sep 18 23:47 .keep dr-xr-xr-x 1 root root 4096 Sep 18 23:47 data -r--r--r-- 1 root root 5774 Sep 18 23:47 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 18 23:47 ranking_evaluation.py -r--r--r-- 1 root root 1295 Sep 18 23:47 requirements.txt /usr/local/bin/python3 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd19921f087d1a03b1d4739f1c6a0", "usage": { "input_tokens": 19254, "cached_input_tokens": 18560, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 17, "total_tokens": 19372 }, "turn_token_usage": { "input_tokens": 52644, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 720, "reasoning_output_tokens": 76, "total_tokens": 53364 }, "thread_token_usage": { "input_tokens": 52644, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 720, "reasoning_output_tokens": 76, "total_tokens": 53364 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-938eb07f-71f9-44d1-ae1e-3a04465e5ea6", "process_id": "45952", "command": [ "/bin/sh", "-lc", "which python; python --version; ls -la /task | head -40; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head -20; command -v uv || true; command -v python3" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; python --version; ls -la /task | head -40; find / -maxdepth 3 -type f -name 'activate' 2>/dev/null | head -20; command -v uv || true; command -v python3" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:04 ..\n-r--r--r-- 1 root root 0 Sep 18 23:47 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:47 data\n-r--r--r-- 1 root root 5774 Sep 18 23:47 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:47 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:47 requirements.txt\n/usr/local/bin/python3\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:04 ..\n-r--r--r-- 1 root root 0 Sep 18 23:47 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:47 data\n-r--r--r-- 1 root root 5774 Sep 18 23:47 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:47 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:47 requirements.txt\n/usr/local/bin/python3\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4240 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:04 ..\n-r--r--r-- 1 root root 0 Sep 18 23:47 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:47 data\n-r--r--r-- 1 root root 5774 Sep 18 23:47 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:47 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:47 requirements.txt\n/usr/local/bin/python3\n" } } ## Step 10 · agent · 2026-09-19T00:04:58.605Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:00.167Z { "code": "const r = await tools.exec_command({\"cmd\":\"python -m pip list --format=columns | sed -n '1,160p'; find / -maxdepth 4 -type d \\\\( -name 'site-packages' -o -name '.venv' \\\\) 2>/dev/null\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:00.932Z Script completed Wall time 0.8 seconds Output: Package Version ------- ------- pip 25.0.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1a2761487d1bd7ffe0438e6e2cf", "usage": { "input_tokens": 19577, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 113, "reasoning_output_tokens": 13, "total_tokens": 19690 }, "turn_token_usage": { "input_tokens": 72221, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 833, "reasoning_output_tokens": 89, "total_tokens": 73054 }, "thread_token_usage": { "input_tokens": 72221, "cached_input_tokens": 63232, "cache_write_input_tokens": 0, "output_tokens": 833, "reasoning_output_tokens": 89, "total_tokens": 73054 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-02e1255f-9716-4361-a6a4-d1a3bed2f4aa", "process_id": "78565", "command": [ "/bin/sh", "-lc", "python -m pip list --format=columns | sed -n '1,160p'; find / -maxdepth 4 -type d \\( -name 'site-packages' -o -name '.venv' \\) 2>/dev/null" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list --format=columns | sed -n '1,160p'; find / -maxdepth 4 -type d \\( -name 'site-packages' -o -name '.venv' \\) 2>/dev/null" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Package Version\n------- -------\npip 25.0.1\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 598216078 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\n" } } ## Step 11 · agent · 2026-09-19T00:05:09.334Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:10.788Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type f -path '*/bin/python' 2>/dev/null | head -40; find / -type d -name 'torch' 2>/dev/null | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:11.229Z Script completed Wall time 0.4 seconds Output: /opt/task-venv/lib/python3.12/site-packages/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch /opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch /opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch /opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1ad1f7c87d1a05c91a43e6b3f87", "usage": { "input_tokens": 19727, "cached_input_tokens": 19456, "cache_write_input_tokens": 0, "output_tokens": 110, "reasoning_output_tokens": 22, "total_tokens": 19837 }, "turn_token_usage": { "input_tokens": 91948, "cached_input_tokens": 82688, "cache_write_input_tokens": 0, "output_tokens": 943, "reasoning_output_tokens": 111, "total_tokens": 92891 }, "thread_token_usage": { "input_tokens": 91948, "cached_input_tokens": 82688, "cache_write_input_tokens": 0, "output_tokens": 943, "reasoning_output_tokens": 111, "total_tokens": 92891 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f5ef00fd-6ccc-4d07-b337-e0aa226aaf15", "process_id": "42704", "command": [ "/bin/sh", "-lc", "find / -type f -path '*/bin/python' 2>/dev/null | head -40; find / -type d -name 'torch' 2>/dev/null | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type f -path '*/bin/python' 2>/dev/null | head -40; find / -type d -name 'torch' 2>/dev/null | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n", "stderr": "", "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 273061468 }, "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n" } } ## Step 12 · agent · 2026-09-19T00:05:19.098Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:20.570Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv /opt/task-venv/bin | head -80; ls -l /opt/task-venv/lib/python3.12/site-packages/pyarrow | head\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:20.617Z Script completed Wall time 0.0 seconds Output: /opt/task-venv: total 32 dr-xr-xr-x 1 root root 4096 Sep 19 00:01 . drwxr-xr-x 1 root root 4096 Sep 19 00:00 .. -r--r--r-- 1 root root 0 Sep 19 00:01 .lock dr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin dr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc dr-xr-xr-x 1 root root 4096 Sep 19 00:00 include dr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib lrwxrwxrwx 1 root root 3 Sep 19 00:00 lib64 -> lib -r--r--r-- 1 root root 171 Sep 19 00:00 pyvenv.cfg dr-xr-xr-x 1 root root 4096 Sep 19 00:01 share /opt/task-venv/bin: total 124 dr-xr-xr-x 1 root root 4096 Sep 19 00:01 . dr-xr-xr-x 1 root root 4096 Sep 19 00:01 .. -r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 19 00:01 activate -r--r--r-- 1 root root 920 Sep 19 00:01 activate.csh -r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish -r-xr-xr-x 1 root root 302 Sep 19 00:01 chardetect -r-xr-xr-x 1 root root 304 Sep 19 00:01 dotenv -r-xr-xr-x 1 root root 308 Sep 19 00:01 f2py -r-xr-xr-x 1 root root 299 Sep 19 00:01 idna -r-xr-xr-x 1 root root 297 Sep 19 00:01 isympy -r-xr-xr-x 1 root root 305 Sep 19 00:01 jsonschema -r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter -r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter-migrate -r-xr-xr-x 1 root root 316 Sep 19 00:01 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Sep 19 00:01 jupyter-trust -r-xr-xr-x 1 root root 311 Sep 19 00:01 jupytext -r-xr-xr-x 1 root root 315 Sep 19 00:01 jupytext-config -r-xr-xr-x 1 root root 301 Sep 19 00:01 kaggle -r-xr-xr-x 1 root root 312 Sep 19 00:01 markdown-it -r-xr-xr-x 1 root root 325 Sep 19 00:01 normalizer -r-xr-xr-x 1 root root 308 Sep 19 00:01 numpy-config -r-xr-xr-x 1 root root 313 Sep 19 00:01 pip -r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3 -r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3.12 lrwxrwxrwx 1 root root 21 Sep 19 00:00 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 19 00:00 python3 -> python lrwxrwxrwx 1 root root 6 Sep 19 00:00 python3.12 -> python -r-xr-xr-x 1 root root 307 Sep 19 00:01 slugify -r-xr-xr-x 1 root root 321 Sep 19 00:01 torchfrtrace -r-xr-xr-x 1 root root 312 Sep 19 00:01 torchrun -r-xr-xr-x 1 root root 299 Sep 19 00:01 tqdm total 120204 -r--r--r-- 1 root root 2195 Sep 19 00:01 __init__.pxd -r--r--r-- 1 root root 18119 Sep 19 00:01 __init__.py -r-xr-xr-x 1 root root 327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so -r--r--r-- 1 root root 1440 Sep 19 00:01 _acero.pxd -r--r--r-- 1 root root 21301 Sep 19 00:01 _acero.pyx -r-xr-xr-x 1 root root 102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so -r--r--r-- 1 root root 5909 Sep 19 00:01 _azurefs.pyx -r-xr-xr-x 1 root root 1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so -r--r--r-- 1 root root 1949 Sep 19 00:01 _compute.pxd ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-58d2dfd0-2df4-47e7-a005-e90433f938c1", "process_id": "14677", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv /opt/task-venv/bin | head -80; ls -l /opt/task-venv/lib/python3.12/site-packages/pyarrow | head" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -la /opt/task-venv /opt/task-venv/bin", "path": "task-venv" }, { "type": "list_files", "cmd": "ls -l /opt/task-venv/lib/python3.12/site-packages/pyarrow", "path": "pyarrow" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root 0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root 3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root 171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root 920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root 302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root 304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root 297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root 305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root 315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root 301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root 325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root 21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root 321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root 2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root 18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root 327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root 21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root 102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root 1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1949 Sep 19 00:01 _compute.pxd\n", "stderr": "", "aggregated_output": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root 0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root 3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root 171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root 920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root 302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root 304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root 297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root 305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root 315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root 301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root 325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root 21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root 321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root 2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root 18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root 327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root 21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root 102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root 1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1949 Sep 19 00:01 _compute.pxd\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3290 }, "formatted_output": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root 0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root 3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root 171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root 920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root 302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root 304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root 297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root 305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root 311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root 315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root 301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root 325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root 308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root 313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root 21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root 6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root 321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root 312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root 299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root 2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root 18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root 327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root 21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root 102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root 1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root 1949 Sep 19 00:01 _compute.pxd\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1b7846487d1a5e59ecbb8583ff5", "usage": { "input_tokens": 19975, "cached_input_tokens": 19584, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 10, "total_tokens": 20075 }, "turn_token_usage": { "input_tokens": 111923, "cached_input_tokens": 102272, "cache_write_input_tokens": 0, "output_tokens": 1043, "reasoning_output_tokens": 121, "total_tokens": 112966 }, "thread_token_usage": { "input_tokens": 111923, "cached_input_tokens": 102272, "cache_write_input_tokens": 0, "output_tokens": 1043, "reasoning_output_tokens": 121, "total_tokens": 112966 } } ## Step 13 · agent · 2026-09-19T00:05:28.105Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:29.482Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys, torch, pyarrow\\nprint(sys.prefix,torch.__version__,pyarrow.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:32.479Z Script completed Wall time 3.0 seconds Output: /opt/task-venv 2.8.0+cpu 18.1.0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1c0dd3c87d1a1a60d10a2d58a7c", "usage": { "input_tokens": 21379, "cached_input_tokens": 19840, "cache_write_input_tokens": 0, "output_tokens": 127, "reasoning_output_tokens": 42, "total_tokens": 21506 }, "turn_token_usage": { "input_tokens": 133302, "cached_input_tokens": 122112, "cache_write_input_tokens": 0, "output_tokens": 1170, "reasoning_output_tokens": 163, "total_tokens": 134472 }, "thread_token_usage": { "input_tokens": 133302, "cached_input_tokens": 122112, "cache_write_input_tokens": 0, "output_tokens": 1170, "reasoning_output_tokens": 163, "total_tokens": 134472 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-74b075ae-151b-4601-a858-743b0abc17d9", "process_id": "81660", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys, torch, pyarrow\nprint(sys.prefix,torch.__version__,pyarrow.__version__)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys, torch, pyarrow\nprint(sys.prefix,torch.__version__,pyarrow.__version__)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/opt/task-venv 2.8.0+cpu 18.1.0\n", "stderr": "", "aggregated_output": "/opt/task-venv 2.8.0+cpu 18.1.0\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 828608950 }, "formatted_output": "/opt/task-venv 2.8.0+cpu 18.1.0\n" } } ## Step 14 · agent · 2026-09-19T00:05:41.100Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:46.816Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p); m=pf.metadata\\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'cols',pf.schema_arrow.names)\\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\\n z=np.load(p)\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n packed=z['packed_eligible']; n=len(z['item_ids'])\\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\\nt=pd.read_parquet(D/'target_users.parquet'); c=pd.read_parquet(D/'candidate_catalog.parquet')\\nprint('targets',t.head(), t.shape, t.dtypes)\\nprint('catalog head/tail\\\\n',c.head(),'\\\\n',c.tail(),c.dtypes)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:49.148Z Script completed Wall time 2.3 seconds Output: candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] target_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens'] test_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state'] train_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic'] validation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state'] validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible count [24657. 28038.5 29242. 29852. 30000. ] 29067.306291390727 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} eligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947 targets uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 (8456, 3) uid uint32 recent_positive_listens float64 history_positive_listens int64 dtype: object catalog head/tail item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 3 3542184 24075 17699 4 4 9378983 24036 18195 5 item_id positive_listens organic_positive_listens popularity_rank 29995 1275692 163 81 29996 29996 1280011 163 76 29997 29997 1309246 163 138 29998 29998 1634958 163 46 29999 29999 1670524 163 73 30000 item_id uint32 positive_listens int64 organic_positive_listens uint64 popularity_rank int32 dtype: object ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1cca59887d1b6b23659a58a1dac", "usage": { "input_tokens": 21549, "cached_input_tokens": 21248, "cache_write_input_tokens": 0, "output_tokens": 336, "reasoning_output_tokens": 6, "total_tokens": 21885 }, "turn_token_usage": { "input_tokens": 154851, "cached_input_tokens": 143360, "cache_write_input_tokens": 0, "output_tokens": 1506, "reasoning_output_tokens": 169, "total_tokens": 156357 }, "thread_token_usage": { "input_tokens": 154851, "cached_input_tokens": 143360, "cache_write_input_tokens": 0, "output_tokens": 1506, "reasoning_output_tokens": 169, "total_tokens": 156357 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be0fd6a5-1042-44d7-9619-c5295e8a0a94", "process_id": "37718", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p); m=pf.metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'cols',pf.schema_arrow.names)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nt=pd.read_parquet(D/'target_users.parquet'); c=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets',t.head(), t.shape, t.dtypes)\nprint('catalog head/tail\\n',c.head(),'\\n',c.tail(),c.dtypes)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p); m=pf.metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'cols',pf.schema_arrow.names)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nt=pd.read_parquet(D/'target_users.parquet'); c=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets',t.head(), t.shape, t.dtypes)\nprint('catalog head/tail\\n',c.head(),'\\n',c.tail(),c.dtypes)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657. 28038.5 29242. 29852. 30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 (8456, 3) uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657. 28038.5 29242. 29852. 30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 (8456, 3) uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 163212256 }, "formatted_output": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657. 28038.5 29242. 29852. 30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\ntargets uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114 (8456, 3) uid uint32\nrecent_positive_listens float64\nhistory_positive_listens int64\ndtype: object\ncatalog head/tail\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5 \n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000 item_id uint32\npositive_listens int64\norganic_positive_listens uint64\npopularity_rank int32\ndtype: object\n" } } ## Step 15 · agent · 2026-09-19T00:06:05.848Z ### Thinking unavailable — no text recorded The dataset has 8,456 target users, 30,000 catalog items, and roughly 29,000 eligible items per user. A full score pass is therefore about 254 million dot products, manageable in blocks; training will use event samples and history bags, never a global interaction matrix. I’m establishing popularity and history-overlap baselines first so later neural gains are measured against something concrete. ### exec · 2026-09-19T00:06:13.216Z { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; /opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, time\\nfrom pathlib import Path\\nD=Path('/task/data')\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\nfor name in ['train_listens','val_listens']:\\n t=time.time(); pf=pq.ParquetFile(D/f'{name}.parquet')\\n n=nt=nc=npos=nstrong=0; users=set(); mint=10**20; maxt=0\\n for b in pf.iter_batches(batch_size=1_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\\n x=b.to_pandas(); n+=len(x); users.update(x.uid.unique().tolist()); mint=min(mint,int(x.timestamp.min())); maxt=max(maxt,int(x.timestamp.max()))\\n m=x.uid.isin(target); nt+=int(m.sum()); nc+=int((m & x.item_id.isin(cat)).sum()); npos+=int((m & (x.played_ratio_pct>=50)).sum()); nstrong+=int((m & (x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\\n print(name,dict(rows=n,unique_users=len(users),target_rows=nt,target_catalog=nc,target_pos50=npos,target_organic80=nstrong,min_ts=mint,max_ts=maxt,secs=round(time.time()-t,2)))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:06:22.282Z Script completed Wall time 9.1 seconds Output: /bin/sh: 1: free: not found train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2} val_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1dd64e087d1bf88dc7036555f5a", "usage": { "input_tokens": 22864, "cached_input_tokens": 21376, "cache_write_input_tokens": 0, "output_tokens": 883, "reasoning_output_tokens": 393, "total_tokens": 23747 }, "turn_token_usage": { "input_tokens": 177715, "cached_input_tokens": 164736, "cache_write_input_tokens": 0, "output_tokens": 2389, "reasoning_output_tokens": 562, "total_tokens": 180104 }, "thread_token_usage": { "input_tokens": 177715, "cached_input_tokens": 164736, "cache_write_input_tokens": 0, "output_tokens": 2389, "reasoning_output_tokens": 562, "total_tokens": 180104 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e18bf95-bbfa-484d-b175-ebbe65216bfd", "process_id": "49286", "command": [ "/bin/sh", "-lc", "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nfor name in ['train_listens','val_listens']:\n t=time.time(); pf=pq.ParquetFile(D/f'{name}.parquet')\n n=nt=nc=npos=nstrong=0; users=set(); mint=10**20; maxt=0\n for b in pf.iter_batches(batch_size=1_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); n+=len(x); users.update(x.uid.unique().tolist()); mint=min(mint,int(x.timestamp.min())); maxt=max(maxt,int(x.timestamp.max()))\n m=x.uid.isin(target); nt+=int(m.sum()); nc+=int((m & x.item_id.isin(cat)).sum()); npos+=int((m & (x.played_ratio_pct>=50)).sum()); nstrong+=int((m & (x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\n print(name,dict(rows=n,unique_users=len(users),target_rows=nt,target_catalog=nc,target_pos50=npos,target_organic80=nstrong,min_ts=mint,max_ts=maxt,secs=round(time.time()-t,2)))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nfor name in ['train_listens','val_listens']:\n t=time.time(); pf=pq.ParquetFile(D/f'{name}.parquet')\n n=nt=nc=npos=nstrong=0; users=set(); mint=10**20; maxt=0\n for b in pf.iter_batches(batch_size=1_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); n+=len(x); users.update(x.uid.unique().tolist()); mint=min(mint,int(x.timestamp.min())); maxt=max(maxt,int(x.timestamp.max()))\n m=x.uid.isin(target); nt+=int(m.sum()); nc+=int((m & x.item_id.isin(cat)).sum()); npos+=int((m & (x.played_ratio_pct>=50)).sum()); nstrong+=int((m & (x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\n print(name,dict(rows=n,unique_users=len(users),target_rows=nt,target_catalog=nc,target_pos50=npos,target_organic80=nstrong,min_ts=mint,max_ts=maxt,secs=round(time.time()-t,2)))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n", "stderr": "", "aggregated_output": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 897843438 }, "formatted_output": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n" } } ## Step 16 · agent · 2026-09-19T00:06:40.965Z ### Thinking unavailable — no text recorded ## Step 17 · agent · 2026-09-19T00:06:45.481Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:06:50.226Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create a structurally valid popularity ranking for either cutoff.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ parser.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ args = parser.parse_args()\\n+ archive = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ uids = archive[\\\"uids\\\"].astype(np.int64)\\n+ items = archive[\\\"item_ids\\\"].astype(np.int64)\\n+ packed = archive[\\\"packed_eligible\\\"]\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ eligible = np.unpackbits(\\n+ packed[r], count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ # The archive item order matches the popularity-ordered catalog.\\n+ chosen = items[np.flatnonzero(eligible)[:100]]\\n+ rows.append(\\n+ pd.DataFrame(\\n+ {\\\"uid\\\": uid, \\\"item_id\\\": chosen, \\\"rank\\\": np.arange(1, 101)}\\n+ )\\n+ )\\n+ args.output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.concat(rows, ignore_index=True).astype(\\n+ {\\\"uid\\\": \\\"int64\\\", \\\"item_id\\\": \\\"int64\\\", \\\"rank\\\": \\\"int64\\\"}\\n+ ).to_parquet(args.output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:06:50.271Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7a76aa95-80d0-44ef-a742-e1faf755cc6a", "changes": { "/app/solution/baseline.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Create a structurally valid popularity ranking for either cutoff.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\n\nDATA = Path(\"/task/data\")\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n parser.add_argument(\"--output\", type=Path, required=True)\n args = parser.parse_args()\n archive = np.load(DATA / f\"{args.stage}_eligible_candidates.npz\")\n uids = archive[\"uids\"].astype(np.int64)\n items = archive[\"item_ids\"].astype(np.int64)\n packed = archive[\"packed_eligible\"]\n rows = []\n for r, uid in enumerate(uids):\n eligible = np.unpackbits(\n packed[r], count=len(items), bitorder=\"little\"\n ).astype(bool)\n # The archive item order matches the popularity-ordered catalog.\n chosen = items[np.flatnonzero(eligible)[:100]]\n rows.append(\n pd.DataFrame(\n {\"uid\": uid, \"item_id\": chosen, \"rank\": np.arange(1, 101)}\n )\n )\n args.output.parent.mkdir(parents=True, exist_ok=True)\n pd.concat(rows, ignore_index=True).astype(\n {\"uid\": \"int64\", \"item_id\": \"int64\", \"rank\": \"int64\"}\n ).to_parquet(args.output, index=False)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd1fe93d887d1a317f13fbf6aa93b", "usage": { "input_tokens": 23944, "cached_input_tokens": 22656, "cache_write_input_tokens": 0, "output_tokens": 1161, "reasoning_output_tokens": 696, "total_tokens": 25105 }, "turn_token_usage": { "input_tokens": 201659, "cached_input_tokens": 187392, "cache_write_input_tokens": 0, "output_tokens": 3550, "reasoning_output_tokens": 1258, "total_tokens": 205209 }, "thread_token_usage": { "input_tokens": 201659, "cached_input_tokens": 187392, "cache_write_input_tokens": 0, "output_tokens": 3550, "reasoning_output_tokens": 1258, "total_tokens": 205209 } } ## Step 18 · agent · 2026-09-19T00:06:57.792Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:06:59.732Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:08.943Z Script completed Wall time 9.2 seconds Output: { "model": "solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd21a9b9887d1acd5b0c813ea3eeb", "usage": { "input_tokens": 25129, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 131, "reasoning_output_tokens": 15, "total_tokens": 25260 }, "turn_token_usage": { "input_tokens": 226788, "cached_input_tokens": 211200, "cache_write_input_tokens": 0, "output_tokens": 3681, "reasoning_output_tokens": 1273, "total_tokens": 230469 }, "thread_token_usage": { "input_tokens": 226788, "cached_input_tokens": 211200, "cache_write_input_tokens": 0, "output_tokens": 3681, "reasoning_output_tokens": 1273, "total_tokens": 230469 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-36cfb31a-e144-4eeb-be04-ab7af5b5ec16", "process_id": "37824", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 43207233 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 19 · agent · 2026-09-19T00:07:29.745Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:07:33.595Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,8p' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\n# Compare exclusion with every historical catalog listen for a handful of users.\\nuids=pd.read_parquet(D/'target_users.parquet').uid.astype(int).iloc[:20].tolist()\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'],filters=[('uid','in',uids)])\\nz=np.load(D/'validation_eligible_candidates.npz'); items=z['item_ids']; col={int(v):i for i,v in enumerate(items)}; row={int(v):i for i,v in enumerate(z['uids'])}\\nfor uid,g in x.groupby('uid'):\\n uniq=set(map(int,g.item_id)) & set(col)\\n bits=np.unpackbits(z['packed_eligible'][row[int(uid)]],count=len(items),bitorder='little')\\n excluded={int(items[i]) for i in np.flatnonzero(bits==0)}\\n print(uid,len(uniq),len(excluded),len(uniq&excluded),len(excluded-uniq))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:37.097Z Script completed Wall time 3.5 seconds Output: MemTotal: 15988976 kB MemFree: 1790884 kB MemAvailable: 14877056 kB Buffers: 228152 kB Cached: 12561140 kB SwapCached: 0 kB Active: 500152 kB Inactive: 12560080 kB 100 682 682 682 0 200 168 168 168 0 300 69 69 69 0 600 680 680 680 0 700 1653 1653 1653 0 800 2524 2524 2524 0 900 334 334 334 0 1000 499 499 499 0 1100 1440 1440 1440 0 1400 1262 1264 1262 2 1500 524 524 524 0 1600 1517 1524 1517 7 1700 2164 2164 2164 0 1900 2040 2041 2040 1 2000 374 374 374 0 2100 644 645 644 1 2200 1096 1096 1096 0 2300 1405 1406 1405 1 2400 471 471 471 0 2500 636 636 636 0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd22d201887d1b1d4535d9509e639", "usage": { "input_tokens": 25327, "cached_input_tokens": 24960, "cache_write_input_tokens": 0, "output_tokens": 801, "reasoning_output_tokens": 492, "total_tokens": 26128 }, "turn_token_usage": { "input_tokens": 252115, "cached_input_tokens": 236160, "cache_write_input_tokens": 0, "output_tokens": 4482, "reasoning_output_tokens": 1765, "total_tokens": 256597 }, "thread_token_usage": { "input_tokens": 252115, "cached_input_tokens": 236160, "cache_write_input_tokens": 0, "output_tokens": 4482, "reasoning_output_tokens": 1765, "total_tokens": 256597 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1713f71f-42d8-4551-8690-b6b0f6102b49", "process_id": "75032", "command": [ "/bin/sh", "-lc", "sed -n '1,8p' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\n# Compare exclusion with every historical catalog listen for a handful of users.\nuids=pd.read_parquet(D/'target_users.parquet').uid.astype(int).iloc[:20].tolist()\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'],filters=[('uid','in',uids)])\nz=np.load(D/'validation_eligible_candidates.npz'); items=z['item_ids']; col={int(v):i for i,v in enumerate(items)}; row={int(v):i for i,v in enumerate(z['uids'])}\nfor uid,g in x.groupby('uid'):\n uniq=set(map(int,g.item_id)) & set(col)\n bits=np.unpackbits(z['packed_eligible'][row[int(uid)]],count=len(items),bitorder='little')\n excluded={int(items[i]) for i in np.flatnonzero(bits==0)}\n print(uid,len(uniq),len(excluded),len(uniq&excluded),len(excluded-uniq))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,8p' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\n# Compare exclusion with every historical catalog listen for a handful of users.\nuids=pd.read_parquet(D/'target_users.parquet').uid.astype(int).iloc[:20].tolist()\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'],filters=[('uid','in',uids)])\nz=np.load(D/'validation_eligible_candidates.npz'); items=z['item_ids']; col={int(v):i for i,v in enumerate(items)}; row={int(v):i for i,v in enumerate(z['uids'])}\nfor uid,g in x.groupby('uid'):\n uniq=set(map(int,g.item_id)) & set(col)\n bits=np.unpackbits(z['packed_eligible'][row[int(uid)]],count=len(items),bitorder='little')\n excluded={int(items[i]) for i in np.flatnonzero(bits==0)}\n print(uid,len(uniq),len(excluded),len(uniq&excluded),len(excluded-uniq))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "100 682 682 682 0\n200 168 168 168 0\n300 69 69 69 0\n600 680 680 680 0\n700 1653 1653 1653 0\n800 2524 2524 2524 0\n900 334 334 334 0\n1000 499 499 499 0\n1100 1440 1440 1440 0\n1400 1262 1264 1262 2\n1500 524 524 524 0\n1600 1517 1524 1517 7\n1700 2164 2164 2164 0\n1900 2040 2041 2040 1\n2000 374 374 374 0\n2100 644 645 644 1\n2200 1096 1096 1096 0\n2300 1405 1406 1405 1\n2400 471 471 471 0\n2500 636 636 636 0\n", "stderr": "", "aggregated_output": "100 682 682 682 0\n200 168 168 168 0\n300 69 69 69 0\n600 680 680 680 0\n700 1653 1653 1653 0\n800 2524 2524 2524 0\n900 334 334 334 0\n1000 499 499 499 0\n1100 1440 1440 1440 0\n1400 1262 1264 1262 2\n1500 524 524 524 0\n1600 1517 1524 1517 7\n1700 2164 2164 2164 0\n1900 2040 2041 2040 1\n2000 374 374 374 0\n2100 644 645 644 1\n2200 1096 1096 1096 0\n2300 1405 1406 1405 1\n2400 471 471 471 0\n2500 636 636 636 0\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 334035451 }, "formatted_output": "100 682 682 682 0\n200 168 168 168 0\n300 69 69 69 0\n600 680 680 680 0\n700 1653 1653 1653 0\n800 2524 2524 2524 0\n900 334 334 334 0\n1000 499 499 499 0\n1100 1440 1440 1440 0\n1400 1262 1264 1262 2\n1500 524 524 524 0\n1600 1517 1524 1517 7\n1700 2164 2164 2164 0\n1900 2040 2041 2040 1\n2000 374 374 374 0\n2100 644 645 644 1\n2200 1096 1096 1096 0\n2300 1405 1406 1405 1\n2400 471 471 471 0\n2500 636 636 636 0\n" } } ## Step 20 · agent · 2026-09-19T00:07:50.561Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:07:52.278Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nfor rg in [0,1,20,41]:\\n x=p.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\\n print(rg,len(x),x.head(2).to_dict('records'),x.tail(2).to_dict('records'),x.timestamp.min(),x.timestamp.max(),x.uid.nunique())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:53.908Z Script completed Wall time 1.6 seconds Output: 0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242 1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225 20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251 41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd24945c087d184093d02e21e9ead", "usage": { "input_tokens": 26471, "cached_input_tokens": 25088, "cache_write_input_tokens": 0, "output_tokens": 456, "reasoning_output_tokens": 283, "total_tokens": 26927 }, "turn_token_usage": { "input_tokens": 278586, "cached_input_tokens": 261248, "cache_write_input_tokens": 0, "output_tokens": 4938, "reasoning_output_tokens": 2048, "total_tokens": 283524 }, "thread_token_usage": { "input_tokens": 278586, "cached_input_tokens": 261248, "cache_write_input_tokens": 0, "output_tokens": 4938, "reasoning_output_tokens": 2048, "total_tokens": 283524 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d0caf9c-35c8-484e-ac2f-189f4d1585c6", "process_id": "51257", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in [0,1,20,41]:\n x=p.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\n print(rg,len(x),x.head(2).to_dict('records'),x.tail(2).to_dict('records'),x.timestamp.min(),x.timestamp.max(),x.uid.nunique())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in [0,1,20,41]:\n x=p.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\n print(rg,len(x),x.head(2).to_dict('records'),x.tail(2).to_dict('records'),x.timestamp.min(),x.timestamp.max(),x.uid.nunique())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242\n1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225\n20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251\n41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98\n", "stderr": "", "aggregated_output": "0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242\n1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225\n20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251\n41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 461318269 }, "formatted_output": "0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242\n1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225\n20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251\n41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98\n" } } ## Step 21 · agent · 2026-09-19T00:08:13.874Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:08:26.688Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/experiment_popularity.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Validation-only popularity recency sweep used during model selection.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path('/task/data')\\n+O = Path('/app/solution')\\n+END = 24786800\\n+cat = pd.read_parquet(D/'candidate_catalog.parquet')\\n+items = cat.item_id.to_numpy(np.int64)\\n+idx = pd.Index(items)\\n+scores = {f'listen_{days}d': np.zeros(len(items), np.float64) for days in [3,7,14,30,60,90,180]}\\n+scores['organic_all'] = cat.organic_positive_listens.to_numpy(np.float64)\\n+scores['positive_all'] = cat.positive_listens.to_numpy(np.float64)\\n+for batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=1_000_000,\\n+ columns=['timestamp','item_id','is_organic','played_ratio_pct']):\\n+ x=batch.to_pandas()\\n+ pos=idx.get_indexer(x.item_id)\\n+ good=(pos>=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\\n+ age=END-x.timestamp.to_numpy()\\n+ for days in [3,7,14,30,60,90,180]:\\n+ m=good&(age < days*86400)\\n+ scores[f'listen_{days}d'] += np.bincount(pos[m], minlength=len(items))\\n+likes=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic'])\\n+lp=idx.get_indexer(likes.item_id); age=END-likes.timestamp.to_numpy()\\n+for days in [7,30,90]:\\n+ m=(lp>=0)&(likes.is_organic.to_numpy()==1)&(age=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\n age=END-x.timestamp.to_numpy()\n for days in [3,7,14,30,60,90,180]:\n m=good&(age < days*86400)\n scores[f'listen_{days}d'] += np.bincount(pos[m], minlength=len(items))\nlikes=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic'])\nlp=idx.get_indexer(likes.item_id); age=END-likes.timestamp.to_numpy()\nfor days in [7,30,90]:\n m=(lp>=0)&(likes.is_organic.to_numpy()==1)&(age None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+def load_events(stage: str, item_index: pd.Index, uid_index: pd.Index):\\n+ \\\"\\\"\\\"Read compact catalog event arrays, retaining no dense interaction grid.\\\"\\\"\\\"\\n+ families = [\\\"train_listens\\\"] + ([\\\"val_listens\\\"] if stage == \\\"test\\\" else [])\\n+ chunks = {k: [] for k in (\\\"uid\\\", \\\"time\\\", \\\"item\\\", \\\"ratio\\\", \\\"organic\\\")}\\n+ for family in families:\\n+ pf = pq.ParquetFile(DATA / f\\\"{family}.parquet\\\")\\n+ for batch in pf.iter_batches(\\n+ batch_size=1_000_000,\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ x = batch.to_pandas()\\n+ ii = item_index.get_indexer(x.item_id)\\n+ ui = uid_index.get_indexer(x.uid)\\n+ keep = (ii >= 0) & (ui >= 0)\\n+ chunks[\\\"uid\\\"].append(ui[keep].astype(np.int16))\\n+ chunks[\\\"time\\\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\\n+ chunks[\\\"item\\\"].append(ii[keep].astype(np.int16))\\n+ chunks[\\\"ratio\\\"].append(x.played_ratio_pct.to_numpy()[keep].astype(np.int16))\\n+ chunks[\\\"organic\\\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\\n+ ev = {k: np.concatenate(v) for k, v in chunks.items()}\\n+ # Files are user-major individually. Sorting also merges train and val for refit.\\n+ order = np.lexsort((ev[\\\"time\\\"], ev[\\\"uid\\\"]))\\n+ ev = {k: v[order] for k, v in ev.items()}\\n+ return ev\\n+\\n+\\n+def load_action(stage: str, family: str, item_index: pd.Index, uid_index: pd.Index):\\n+ names = [f\\\"train_{family}\\\"] + ([f\\\"val_{family}\\\"] if stage == \\\"test\\\" else [])\\n+ out = {k: [] for k in (\\\"uid\\\", \\\"time\\\", \\\"item\\\", \\\"organic\\\")}\\n+ for name in names:\\n+ x = pd.read_parquet(DATA / f\\\"{name}.parquet\\\")\\n+ ii = item_index.get_indexer(x.item_id)\\n+ ui = uid_index.get_indexer(x.uid)\\n+ keep = (ii >= 0) & (ui >= 0)\\n+ out[\\\"uid\\\"].append(ui[keep].astype(np.int16))\\n+ out[\\\"time\\\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\\n+ out[\\\"item\\\"].append(ii[keep].astype(np.int16))\\n+ out[\\\"organic\\\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\\n+ out = {k: np.concatenate(v) if v else np.empty(0, np.int32) for k, v in out.items()}\\n+ order = np.lexsort((out[\\\"time\\\"], out[\\\"uid\\\"]))\\n+ return {k: v[order] for k, v in out.items()}\\n+\\n+\\n+def group_bounds(uid_array: np.ndarray, n_users: int):\\n+ counts = np.bincount(uid_array.astype(np.int64), minlength=n_users)\\n+ ends = np.cumsum(counts)\\n+ return np.r_[0, ends[:-1]], ends\\n+\\n+\\n+def recent_unique(values: np.ndarray, limit: int) -> np.ndarray:\\n+ if len(values) == 0:\\n+ return values.astype(np.int64)\\n+ rev = values[::-1]\\n+ _, first = np.unique(rev, return_index=True)\\n+ return rev[np.sort(first)[:limit]].astype(np.int64)\\n+\\n+\\n+def frequent_unique(values: np.ndarray, limit: int) -> np.ndarray:\\n+ if len(values) == 0:\\n+ return values.astype(np.int64)\\n+ ids, counts = np.unique(values, return_counts=True)\\n+ # Stable item-id tie breaking makes the cache and training reproducible.\\n+ order = np.lexsort((ids, -counts))[:limit]\\n+ return ids[order].astype(np.int64)\\n+\\n+\\n+def population_features(ev, likes, cutoffs: np.ndarray, n_items: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Cutoff-correct item features used by the trainable item MLP.\\\"\\\"\\\"\\n+ feats = []\\n+ t, item = ev[\\\"time\\\"], ev[\\\"item\\\"].astype(np.int64)\\n+ strong = (ev[\\\"organic\\\"] == 1) & (ev[\\\"ratio\\\"] >= 80)\\n+ lt, li = likes[\\\"time\\\"], likes[\\\"item\\\"].astype(np.int64)\\n+ lo = likes[\\\"organic\\\"] == 1\\n+ for cutoff in cutoffs:\\n+ cols = []\\n+ for days in (3, 7, 14, 30):\\n+ m = strong & (t < cutoff) & (t >= cutoff - days * 86400)\\n+ cols.append(np.bincount(item[m], minlength=n_items))\\n+ m = lo & (lt < cutoff) & (lt >= cutoff - 7 * 86400)\\n+ cols.append(np.bincount(li[m], minlength=n_items))\\n+ m = strong & (t < cutoff)\\n+ cols.append(np.bincount(item[m], minlength=n_items))\\n+ x = np.stack(cols, axis=1).astype(np.float32)\\n+ x = np.log1p(x)\\n+ # Cross-sectional standardization keeps different historical cutoffs aligned.\\n+ x = (x - x.mean(0, keepdims=True)) / (x.std(0, keepdims=True) + 1e-5)\\n+ feats.append(x)\\n+ return np.stack(feats)\\n+\\n+\\n+def make_profiles(stage: str, ev, likes, n_users: int, n_items: int):\\n+ end = TRAIN_END if stage == \\\"validation\\\" else FINAL_END\\n+ # Validation uses only earlier pseudo-futures. Refit adds the actual public\\n+ # window as a training future via cutoff=end-(WINDOW+GAP).\\n+ cutoffs = np.asarray([end - (j + 1) * (WINDOW + GAP) for j in range(12)], np.int64)\\n+ pop = population_features(ev, likes, np.r_[cutoffs, end], n_items)\\n+ es, ee = group_bounds(ev[\\\"uid\\\"], n_users)\\n+ ls, le = group_bounds(likes[\\\"uid\\\"], n_users)\\n+ histories, recents, user_feats, label_lists, cutoff_ids = [], [], [], [], []\\n+ for ci, cutoff in enumerate(cutoffs):\\n+ y0, y1 = cutoff + GAP, cutoff + GAP + WINDOW\\n+ for u in range(n_users):\\n+ sl = slice(es[u], ee[u])\\n+ tt, ii = ev[\\\"time\\\"][sl], ev[\\\"item\\\"][sl].astype(np.int64)\\n+ before = tt < cutoff\\n+ if not before.any():\\n+ continue\\n+ seen = np.unique(ii[before])\\n+ pos = before & (ev[\\\"ratio\\\"][sl] >= 50)\\n+ pos_items = ii[pos]\\n+ if len(pos_items) < 5:\\n+ continue\\n+ target_m = (\\n+ (tt >= y0) & (tt < y1) & (ev[\\\"organic\\\"][sl] == 1)\\n+ & (ev[\\\"ratio\\\"][sl] >= 80)\\n+ )\\n+ targets = np.setdiff1d(np.unique(ii[target_m]), seen, assume_unique=False)\\n+ # Organic likes are oversampled to respect their seven-fold DCG gain.\\n+ lsl = slice(ls[u], le[u])\\n+ ltt, li = likes[\\\"time\\\"][lsl], likes[\\\"item\\\"][lsl].astype(np.int64)\\n+ lm = (ltt >= y0) & (ltt < y1) & (likes[\\\"organic\\\"][lsl] == 1)\\n+ liked_targets = np.setdiff1d(np.unique(li[lm]), seen, assume_unique=False)\\n+ if len(targets) == 0 and len(liked_targets) == 0:\\n+ continue\\n+ labels = np.r_[targets, np.repeat(liked_targets, 4)].astype(np.int64)\\n+ histories.append(frequent_unique(pos_items, PAD_HISTORY))\\n+ # Recent channel only considers the last 60 days before this cutoff.\\n+ rpos = pos & (tt >= cutoff - 60 * 86400)\\n+ recents.append(recent_unique(ii[rpos], PAD_RECENT))\\n+ nstrong = int((before & (ev[\\\"organic\\\"][sl] == 1) & (ev[\\\"ratio\\\"][sl] >= 80)).sum())\\n+ user_feats.append([\\n+ math.log1p(len(np.unique(pos_items))),\\n+ math.log1p(int(pos.sum())),\\n+ math.log1p(nstrong),\\n+ len(recents[-1]) / PAD_RECENT,\\n+ ])\\n+ label_lists.append(labels)\\n+ cutoff_ids.append(ci)\\n+ return cutoffs, pop, histories, recents, np.asarray(user_feats, np.float32), label_lists, np.asarray(cutoff_ids)\\n+\\n+\\n+def make_inference_profiles(stage: str, ev, n_users: int):\\n+ cutoff = TRAIN_END if stage == \\\"validation\\\" else FINAL_END\\n+ es, ee = group_bounds(ev[\\\"uid\\\"], n_users)\\n+ histories, recents, feats = [], [], []\\n+ for u in range(n_users):\\n+ sl = slice(es[u], ee[u]); tt = ev[\\\"time\\\"][sl]\\n+ ii = ev[\\\"item\\\"][sl].astype(np.int64)\\n+ before = tt <= cutoff\\n+ pos = before & (ev[\\\"ratio\\\"][sl] >= 50)\\n+ pi = ii[pos]\\n+ histories.append(frequent_unique(pi, PAD_HISTORY))\\n+ recents.append(recent_unique(ii[pos & (tt >= cutoff - 60 * 86400)], PAD_RECENT))\\n+ nstrong = int((before & (ev[\\\"organic\\\"][sl] == 1) & (ev[\\\"ratio\\\"][sl] >= 80)).sum())\\n+ feats.append([math.log1p(len(np.unique(pi))), math.log1p(int(pos.sum())),\\n+ math.log1p(nstrong), len(recents[-1]) / PAD_RECENT])\\n+ return histories, recents, np.asarray(feats, np.float32)\\n+\\n+\\n+def pad_bags(bags: list[np.ndarray], width: int, pad: int) -> np.ndarray:\\n+ out = np.full((len(bags), width), pad, dtype=np.int64)\\n+ for r, x in enumerate(bags):\\n+ out[r, : min(width, len(x))] = x[:width]\\n+ return out\\n+\\n+\\n+class UserTower(nn.Module):\\n+ \\\"\\\"\\\"History-only user encoder; there is intentionally no user-ID table.\\\"\\\"\\\"\\n+ def __init__(self, n_items: int, dim: int = 64):\\n+ super().__init__()\\n+ self.pad = n_items\\n+ self.long_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.recent_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.numeric = nn.Sequential(nn.Linear(4, 16), nn.ReLU())\\n+ self.mlp = nn.Sequential(nn.Linear(dim * 2 + 16, 128), nn.ReLU(),\\n+ nn.Linear(128, dim))\\n+\\n+ @staticmethod\\n+ def masked_mean(table: nn.Embedding, ids: torch.Tensor, pad: int):\\n+ mask = (ids != pad).unsqueeze(-1)\\n+ return (table(ids) * mask).sum(1) / mask.sum(1).clamp_min(1)\\n+\\n+ def forward(self, history, recent, numeric):\\n+ h = self.masked_mean(self.long_embedding, history, self.pad)\\n+ r = self.masked_mean(self.recent_embedding, recent, self.pad)\\n+ return self.mlp(torch.cat([h, r, self.numeric(numeric)], dim=1))\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ \\\"\\\"\\\"Offline-precomputable candidate encoder.\\\"\\\"\\\"\\n+ def __init__(self, n_items: int, n_features: int, dim: int = 64):\\n+ super().__init__()\\n+ self.id_embedding = nn.Embedding(n_items, dim)\\n+ self.feature_mlp = nn.Sequential(nn.Linear(n_features, 32), nn.ReLU(),\\n+ nn.Linear(32, dim))\\n+ self.combine = nn.Sequential(nn.Linear(dim * 2, 96), nn.ReLU(),\\n+ nn.Linear(96, dim))\\n+\\n+ def forward(self, item_ids, features):\\n+ return self.combine(torch.cat([self.id_embedding(item_ids),\\n+ self.feature_mlp(features)], dim=-1))\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, n_features: int, dim: int = 64):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, n_features, dim)\\n+ self.log_scale = nn.Parameter(torch.tensor(math.log(1.0 / math.sqrt(dim))))\\n+\\n+ def scores(self, u, v):\\n+ return (u.unsqueeze(1) * v).sum(-1) * self.log_scale.exp().clamp(max=2.0)\\n+\\n+\\n+def train_model(model, histories, recents, user_feats, labels, cutoff_ids, pop,\\n+ epochs: int, seed: int):\\n+ h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, pop.shape[1]))\\n+ r = torch.from_numpy(pad_bags(recents, PAD_RECENT, pop.shape[1]))\\n+ uf = torch.from_numpy(user_feats)\\n+ pfeat = torch.from_numpy(pop.astype(np.float32))\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\\n+ rng = np.random.default_rng(seed)\\n+ n, batch_size, candidates = len(labels), 256, 40\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(n); losses = []\\n+ for start in range(0, n, batch_size):\\n+ rows = order[start:start + batch_size]\\n+ b = len(rows)\\n+ positives = np.asarray([x[rng.integers(len(x))] for x in (labels[j] for j in rows)])\\n+ neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\\n+ cand = np.concatenate([positives[:, None], neg], axis=1)\\n+ # Put the positive at a random column to prevent positional shortcuts.\\n+ pos_col = rng.integers(candidates, size=b)\\n+ cand[np.arange(b), 0], cand[np.arange(b), pos_col] = (\\n+ cand[np.arange(b), pos_col].copy(), cand[np.arange(b), 0].copy())\\n+ bt = torch.from_numpy(rows)\\n+ ct = torch.from_numpy(cand)\\n+ cids = torch.from_numpy(cutoff_ids[rows]).long()\\n+ u = model.user_tower(h[bt], r[bt], uf[bt])\\n+ cf = pfeat[cids[:, None], ct]\\n+ v = model.item_tower(ct, cf)\\n+ logits = model.scores(u, v)\\n+ loss = F.cross_entropy(logits, torch.from_numpy(pos_col).long())\\n+ opt.zero_grad(set_to_none=True); loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ opt.step(); losses.append(float(loss))\\n+ print(f\\\"epoch={epoch + 1} profiles={n} loss={np.mean(losses):.5f}\\\", flush=True)\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def rank(model, stage: str, histories, recents, user_feats, final_pop,\\n+ uids: np.ndarray, items: np.ndarray, packed: np.ndarray, output: Path):\\n+ model.eval(); n_items = len(items)\\n+ h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, n_items))\\n+ r = torch.from_numpy(pad_bags(recents, PAD_RECENT, n_items))\\n+ uf = torch.from_numpy(user_feats)\\n+ item_ids = torch.arange(n_items)\\n+ item_vec = model.item_tower(item_ids, torch.from_numpy(final_pop.astype(np.float32)))\\n+ frames = []\\n+ for start in range(0, len(uids), 128):\\n+ stop = min(len(uids), start + 128)\\n+ user_vec = model.user_tower(h[start:stop], r[start:stop], uf[start:stop])\\n+ scores = user_vec @ item_vec.T\\n+ eligibility = np.unpackbits(packed[start:stop], axis=1, count=n_items,\\n+ bitorder=\\\"little\\\").astype(bool)\\n+ scores[~torch.from_numpy(eligibility)] = -torch.inf\\n+ top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\\n+ for j, row in enumerate(top):\\n+ frames.append(pd.DataFrame({\\\"uid\\\": int(uids[start + j]),\\n+ \\\"item_id\\\": items[row],\\n+ \\\"rank\\\": np.arange(1, 101)}))\\n+ pd.concat(frames, ignore_index=True).astype(\\n+ {\\\"uid\\\": \\\"int64\\\", \\\"item_id\\\": \\\"int64\\\", \\\"rank\\\": \\\"int64\\\"}\\n+ ).to_parquet(output, index=False)\\n+\\n+\\n+def main():\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ ap.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ ap.add_argument(\\\"--epochs\\\", type=int, default=8)\\n+ ap.add_argument(\\\"--seed\\\", type=int, default=20260919)\\n+ args = ap.parse_args(); seed_everything(args.seed)\\n+ torch.set_num_threads(min(16, torch.get_num_threads()))\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ items = catalog.item_id.to_numpy(np.int64); uids = targets.uid.to_numpy(np.int64)\\n+ print(\\\"loading compact event streams\\\", flush=True); started = time.time()\\n+ ev = load_events(args.stage, pd.Index(items), pd.Index(uids))\\n+ likes = load_action(args.stage, \\\"likes\\\", pd.Index(items), pd.Index(uids))\\n+ print(f\\\"catalog listens={len(ev['uid']):,} likes={len(likes['uid']):,} seconds={time.time()-started:.1f}\\\", flush=True)\\n+ cutoffs, pop, histories, recents, uf, labels, cids = make_profiles(\\n+ args.stage, ev, likes, len(uids), len(items))\\n+ print(f\\\"training profiles={len(labels):,} mean_labels={np.mean([len(x) for x in labels]):.2f}\\\", flush=True)\\n+ model = TwoTower(len(items), pop.shape[-1])\\n+ train_model(model, histories, recents, uf, labels, cids, pop, args.epochs, args.seed)\\n+ ih, ir, iuf = make_inference_profiles(args.stage, ev, len(uids))\\n+ z = np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\")\\n+ rank(model, args.stage, ih, ir, iuf, pop[-1], z[\\\"uids\\\"].astype(np.int64),\\n+ z[\\\"item_ids\\\"].astype(np.int64), z[\\\"packed_eligible\\\"], args.output)\\n+ torch.save({\\\"user_tower\\\": model.user_tower.state_dict(),\\n+ \\\"item_tower\\\": model.item_tower.state_dict(),\\n+ \\\"config\\\": {\\\"dimension\\\": 64, \\\"item_features\\\": int(pop.shape[-1]),\\n+ \\\"stage\\\": args.stage, \\\"seed\\\": args.seed}},\\n+ OUT / f\\\"two_tower_{args.stage}.pt\\\")\\n+ print(json.dumps({\\\"output\\\": str(args.output), \\\"seconds\\\": round(time.time()-started, 1)}))\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:13:39.296Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-771b161b-b636-4bf6-afec-a51e00b2be6a", "changes": { "/app/solution/train_two_tower.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-based PyTorch two-tower ranker.\n\nThe script deliberately works with event streams and bounded history bags. It\nnever creates a user-by-item matrix. Run with ``--stage validation`` for model\nselection and ``--stage test`` for the final refit on train + validation data.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nWINDOW = 604_800\nGAP = 1_800\nFINAL_END = TRAIN_END + GAP + WINDOW\nPAD_HISTORY = 256\nPAD_RECENT = 96\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef load_events(stage: str, item_index: pd.Index, uid_index: pd.Index):\n \"\"\"Read compact catalog event arrays, retaining no dense interaction grid.\"\"\"\n families = [\"train_listens\"] + ([\"val_listens\"] if stage == \"test\" else [])\n chunks = {k: [] for k in (\"uid\", \"time\", \"item\", \"ratio\", \"organic\")}\n for family in families:\n pf = pq.ParquetFile(DATA / f\"{family}.parquet\")\n for batch in pf.iter_batches(\n batch_size=1_000_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n x = batch.to_pandas()\n ii = item_index.get_indexer(x.item_id)\n ui = uid_index.get_indexer(x.uid)\n keep = (ii >= 0) & (ui >= 0)\n chunks[\"uid\"].append(ui[keep].astype(np.int16))\n chunks[\"time\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\n chunks[\"item\"].append(ii[keep].astype(np.int16))\n chunks[\"ratio\"].append(x.played_ratio_pct.to_numpy()[keep].astype(np.int16))\n chunks[\"organic\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\n ev = {k: np.concatenate(v) for k, v in chunks.items()}\n # Files are user-major individually. Sorting also merges train and val for refit.\n order = np.lexsort((ev[\"time\"], ev[\"uid\"]))\n ev = {k: v[order] for k, v in ev.items()}\n return ev\n\n\ndef load_action(stage: str, family: str, item_index: pd.Index, uid_index: pd.Index):\n names = [f\"train_{family}\"] + ([f\"val_{family}\"] if stage == \"test\" else [])\n out = {k: [] for k in (\"uid\", \"time\", \"item\", \"organic\")}\n for name in names:\n x = pd.read_parquet(DATA / f\"{name}.parquet\")\n ii = item_index.get_indexer(x.item_id)\n ui = uid_index.get_indexer(x.uid)\n keep = (ii >= 0) & (ui >= 0)\n out[\"uid\"].append(ui[keep].astype(np.int16))\n out[\"time\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\n out[\"item\"].append(ii[keep].astype(np.int16))\n out[\"organic\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\n out = {k: np.concatenate(v) if v else np.empty(0, np.int32) for k, v in out.items()}\n order = np.lexsort((out[\"time\"], out[\"uid\"]))\n return {k: v[order] for k, v in out.items()}\n\n\ndef group_bounds(uid_array: np.ndarray, n_users: int):\n counts = np.bincount(uid_array.astype(np.int64), minlength=n_users)\n ends = np.cumsum(counts)\n return np.r_[0, ends[:-1]], ends\n\n\ndef recent_unique(values: np.ndarray, limit: int) -> np.ndarray:\n if len(values) == 0:\n return values.astype(np.int64)\n rev = values[::-1]\n _, first = np.unique(rev, return_index=True)\n return rev[np.sort(first)[:limit]].astype(np.int64)\n\n\ndef frequent_unique(values: np.ndarray, limit: int) -> np.ndarray:\n if len(values) == 0:\n return values.astype(np.int64)\n ids, counts = np.unique(values, return_counts=True)\n # Stable item-id tie breaking makes the cache and training reproducible.\n order = np.lexsort((ids, -counts))[:limit]\n return ids[order].astype(np.int64)\n\n\ndef population_features(ev, likes, cutoffs: np.ndarray, n_items: int) -> np.ndarray:\n \"\"\"Cutoff-correct item features used by the trainable item MLP.\"\"\"\n feats = []\n t, item = ev[\"time\"], ev[\"item\"].astype(np.int64)\n strong = (ev[\"organic\"] == 1) & (ev[\"ratio\"] >= 80)\n lt, li = likes[\"time\"], likes[\"item\"].astype(np.int64)\n lo = likes[\"organic\"] == 1\n for cutoff in cutoffs:\n cols = []\n for days in (3, 7, 14, 30):\n m = strong & (t < cutoff) & (t >= cutoff - days * 86400)\n cols.append(np.bincount(item[m], minlength=n_items))\n m = lo & (lt < cutoff) & (lt >= cutoff - 7 * 86400)\n cols.append(np.bincount(li[m], minlength=n_items))\n m = strong & (t < cutoff)\n cols.append(np.bincount(item[m], minlength=n_items))\n x = np.stack(cols, axis=1).astype(np.float32)\n x = np.log1p(x)\n # Cross-sectional standardization keeps different historical cutoffs aligned.\n x = (x - x.mean(0, keepdims=True)) / (x.std(0, keepdims=True) + 1e-5)\n feats.append(x)\n return np.stack(feats)\n\n\ndef make_profiles(stage: str, ev, likes, n_users: int, n_items: int):\n end = TRAIN_END if stage == \"validation\" else FINAL_END\n # Validation uses only earlier pseudo-futures. Refit adds the actual public\n # window as a training future via cutoff=end-(WINDOW+GAP).\n cutoffs = np.asarray([end - (j + 1) * (WINDOW + GAP) for j in range(12)], np.int64)\n pop = population_features(ev, likes, np.r_[cutoffs, end], n_items)\n es, ee = group_bounds(ev[\"uid\"], n_users)\n ls, le = group_bounds(likes[\"uid\"], n_users)\n histories, recents, user_feats, label_lists, cutoff_ids = [], [], [], [], []\n for ci, cutoff in enumerate(cutoffs):\n y0, y1 = cutoff + GAP, cutoff + GAP + WINDOW\n for u in range(n_users):\n sl = slice(es[u], ee[u])\n tt, ii = ev[\"time\"][sl], ev[\"item\"][sl].astype(np.int64)\n before = tt < cutoff\n if not before.any():\n continue\n seen = np.unique(ii[before])\n pos = before & (ev[\"ratio\"][sl] >= 50)\n pos_items = ii[pos]\n if len(pos_items) < 5:\n continue\n target_m = (\n (tt >= y0) & (tt < y1) & (ev[\"organic\"][sl] == 1)\n & (ev[\"ratio\"][sl] >= 80)\n )\n targets = np.setdiff1d(np.unique(ii[target_m]), seen, assume_unique=False)\n # Organic likes are oversampled to respect their seven-fold DCG gain.\n lsl = slice(ls[u], le[u])\n ltt, li = likes[\"time\"][lsl], likes[\"item\"][lsl].astype(np.int64)\n lm = (ltt >= y0) & (ltt < y1) & (likes[\"organic\"][lsl] == 1)\n liked_targets = np.setdiff1d(np.unique(li[lm]), seen, assume_unique=False)\n if len(targets) == 0 and len(liked_targets) == 0:\n continue\n labels = np.r_[targets, np.repeat(liked_targets, 4)].astype(np.int64)\n histories.append(frequent_unique(pos_items, PAD_HISTORY))\n # Recent channel only considers the last 60 days before this cutoff.\n rpos = pos & (tt >= cutoff - 60 * 86400)\n recents.append(recent_unique(ii[rpos], PAD_RECENT))\n nstrong = int((before & (ev[\"organic\"][sl] == 1) & (ev[\"ratio\"][sl] >= 80)).sum())\n user_feats.append([\n math.log1p(len(np.unique(pos_items))),\n math.log1p(int(pos.sum())),\n math.log1p(nstrong),\n len(recents[-1]) / PAD_RECENT,\n ])\n label_lists.append(labels)\n cutoff_ids.append(ci)\n return cutoffs, pop, histories, recents, np.asarray(user_feats, np.float32), label_lists, np.asarray(cutoff_ids)\n\n\ndef make_inference_profiles(stage: str, ev, n_users: int):\n cutoff = TRAIN_END if stage == \"validation\" else FINAL_END\n es, ee = group_bounds(ev[\"uid\"], n_users)\n histories, recents, feats = [], [], []\n for u in range(n_users):\n sl = slice(es[u], ee[u]); tt = ev[\"time\"][sl]\n ii = ev[\"item\"][sl].astype(np.int64)\n before = tt <= cutoff\n pos = before & (ev[\"ratio\"][sl] >= 50)\n pi = ii[pos]\n histories.append(frequent_unique(pi, PAD_HISTORY))\n recents.append(recent_unique(ii[pos & (tt >= cutoff - 60 * 86400)], PAD_RECENT))\n nstrong = int((before & (ev[\"organic\"][sl] == 1) & (ev[\"ratio\"][sl] >= 80)).sum())\n feats.append([math.log1p(len(np.unique(pi))), math.log1p(int(pos.sum())),\n math.log1p(nstrong), len(recents[-1]) / PAD_RECENT])\n return histories, recents, np.asarray(feats, np.float32)\n\n\ndef pad_bags(bags: list[np.ndarray], width: int, pad: int) -> np.ndarray:\n out = np.full((len(bags), width), pad, dtype=np.int64)\n for r, x in enumerate(bags):\n out[r, : min(width, len(x))] = x[:width]\n return out\n\n\nclass UserTower(nn.Module):\n \"\"\"History-only user encoder; there is intentionally no user-ID table.\"\"\"\n def __init__(self, n_items: int, dim: int = 64):\n super().__init__()\n self.pad = n_items\n self.long_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n self.recent_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n self.numeric = nn.Sequential(nn.Linear(4, 16), nn.ReLU())\n self.mlp = nn.Sequential(nn.Linear(dim * 2 + 16, 128), nn.ReLU(),\n nn.Linear(128, dim))\n\n @staticmethod\n def masked_mean(table: nn.Embedding, ids: torch.Tensor, pad: int):\n mask = (ids != pad).unsqueeze(-1)\n return (table(ids) * mask).sum(1) / mask.sum(1).clamp_min(1)\n\n def forward(self, history, recent, numeric):\n h = self.masked_mean(self.long_embedding, history, self.pad)\n r = self.masked_mean(self.recent_embedding, recent, self.pad)\n return self.mlp(torch.cat([h, r, self.numeric(numeric)], dim=1))\n\n\nclass ItemTower(nn.Module):\n \"\"\"Offline-precomputable candidate encoder.\"\"\"\n def __init__(self, n_items: int, n_features: int, dim: int = 64):\n super().__init__()\n self.id_embedding = nn.Embedding(n_items, dim)\n self.feature_mlp = nn.Sequential(nn.Linear(n_features, 32), nn.ReLU(),\n nn.Linear(32, dim))\n self.combine = nn.Sequential(nn.Linear(dim * 2, 96), nn.ReLU(),\n nn.Linear(96, dim))\n\n def forward(self, item_ids, features):\n return self.combine(torch.cat([self.id_embedding(item_ids),\n self.feature_mlp(features)], dim=-1))\n\n\nclass TwoTower(nn.Module):\n def __init__(self, n_items: int, n_features: int, dim: int = 64):\n super().__init__()\n self.user_tower = UserTower(n_items, dim)\n self.item_tower = ItemTower(n_items, n_features, dim)\n self.log_scale = nn.Parameter(torch.tensor(math.log(1.0 / math.sqrt(dim))))\n\n def scores(self, u, v):\n return (u.unsqueeze(1) * v).sum(-1) * self.log_scale.exp().clamp(max=2.0)\n\n\ndef train_model(model, histories, recents, user_feats, labels, cutoff_ids, pop,\n epochs: int, seed: int):\n h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, pop.shape[1]))\n r = torch.from_numpy(pad_bags(recents, PAD_RECENT, pop.shape[1]))\n uf = torch.from_numpy(user_feats)\n pfeat = torch.from_numpy(pop.astype(np.float32))\n opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\n rng = np.random.default_rng(seed)\n n, batch_size, candidates = len(labels), 256, 40\n model.train()\n for epoch in range(epochs):\n order = rng.permutation(n); losses = []\n for start in range(0, n, batch_size):\n rows = order[start:start + batch_size]\n b = len(rows)\n positives = np.asarray([x[rng.integers(len(x))] for x in (labels[j] for j in rows)])\n neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\n cand = np.concatenate([positives[:, None], neg], axis=1)\n # Put the positive at a random column to prevent positional shortcuts.\n pos_col = rng.integers(candidates, size=b)\n cand[np.arange(b), 0], cand[np.arange(b), pos_col] = (\n cand[np.arange(b), pos_col].copy(), cand[np.arange(b), 0].copy())\n bt = torch.from_numpy(rows)\n ct = torch.from_numpy(cand)\n cids = torch.from_numpy(cutoff_ids[rows]).long()\n u = model.user_tower(h[bt], r[bt], uf[bt])\n cf = pfeat[cids[:, None], ct]\n v = model.item_tower(ct, cf)\n logits = model.scores(u, v)\n loss = F.cross_entropy(logits, torch.from_numpy(pos_col).long())\n opt.zero_grad(set_to_none=True); loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n opt.step(); losses.append(float(loss))\n print(f\"epoch={epoch + 1} profiles={n} loss={np.mean(losses):.5f}\", flush=True)\n return model\n\n\n@torch.inference_mode()\ndef rank(model, stage: str, histories, recents, user_feats, final_pop,\n uids: np.ndarray, items: np.ndarray, packed: np.ndarray, output: Path):\n model.eval(); n_items = len(items)\n h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, n_items))\n r = torch.from_numpy(pad_bags(recents, PAD_RECENT, n_items))\n uf = torch.from_numpy(user_feats)\n item_ids = torch.arange(n_items)\n item_vec = model.item_tower(item_ids, torch.from_numpy(final_pop.astype(np.float32)))\n frames = []\n for start in range(0, len(uids), 128):\n stop = min(len(uids), start + 128)\n user_vec = model.user_tower(h[start:stop], r[start:stop], uf[start:stop])\n scores = user_vec @ item_vec.T\n eligibility = np.unpackbits(packed[start:stop], axis=1, count=n_items,\n bitorder=\"little\").astype(bool)\n scores[~torch.from_numpy(eligibility)] = -torch.inf\n top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\n for j, row in enumerate(top):\n frames.append(pd.DataFrame({\"uid\": int(uids[start + j]),\n \"item_id\": items[row],\n \"rank\": np.arange(1, 101)}))\n pd.concat(frames, ignore_index=True).astype(\n {\"uid\": \"int64\", \"item_id\": \"int64\", \"rank\": \"int64\"}\n ).to_parquet(output, index=False)\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n ap.add_argument(\"--output\", type=Path, required=True)\n ap.add_argument(\"--epochs\", type=int, default=8)\n ap.add_argument(\"--seed\", type=int, default=20260919)\n args = ap.parse_args(); seed_everything(args.seed)\n torch.set_num_threads(min(16, torch.get_num_threads()))\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n targets = pd.read_parquet(DATA / \"target_users.parquet\")\n items = catalog.item_id.to_numpy(np.int64); uids = targets.uid.to_numpy(np.int64)\n print(\"loading compact event streams\", flush=True); started = time.time()\n ev = load_events(args.stage, pd.Index(items), pd.Index(uids))\n likes = load_action(args.stage, \"likes\", pd.Index(items), pd.Index(uids))\n print(f\"catalog listens={len(ev['uid']):,} likes={len(likes['uid']):,} seconds={time.time()-started:.1f}\", flush=True)\n cutoffs, pop, histories, recents, uf, labels, cids = make_profiles(\n args.stage, ev, likes, len(uids), len(items))\n print(f\"training profiles={len(labels):,} mean_labels={np.mean([len(x) for x in labels]):.2f}\", flush=True)\n model = TwoTower(len(items), pop.shape[-1])\n train_model(model, histories, recents, uf, labels, cids, pop, args.epochs, args.seed)\n ih, ir, iuf = make_inference_profiles(args.stage, ev, len(uids))\n z = np.load(DATA / f\"{args.stage}_eligible_candidates.npz\")\n rank(model, args.stage, ih, ir, iuf, pop[-1], z[\"uids\"].astype(np.int64),\n z[\"item_ids\"].astype(np.int64), z[\"packed_eligible\"], args.output)\n torch.save({\"user_tower\": model.user_tower.state_dict(),\n \"item_tower\": model.item_tower.state_dict(),\n \"config\": {\"dimension\": 64, \"item_features\": int(pop.shape[-1]),\n \"stage\": args.stage, \"seed\": args.seed}},\n OUT / f\"two_tower_{args.stage}.pt\")\n print(json.dumps({\"output\": str(args.output), \"seconds\": round(time.time()-started, 1)}))\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_two_tower.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd30ccb5087d1bb50229cd03c3d87", "usage": { "input_tokens": 30980, "cached_input_tokens": 30464, "cache_write_input_tokens": 0, "output_tokens": 8781, "reasoning_output_tokens": 3475, "total_tokens": 39761 }, "turn_token_usage": { "input_tokens": 485458, "cached_input_tokens": 462336, "cache_write_input_tokens": 0, "output_tokens": 15580, "reasoning_output_tokens": 6129, "total_tokens": 501038 }, "thread_token_usage": { "input_tokens": 485458, "cached_input_tokens": 462336, "cache_write_input_tokens": 0, "output_tokens": 15580, "reasoning_output_tokens": 6129, "total_tokens": 501038 } } ## Step 32 · agent · 2026-09-19T00:13:47.498Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:13:48.698Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_two_tower.parquet --epochs 6\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:13:49.866Z Script completed Wall time 1.2 seconds Output: SESSION_ID=53033 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd3b3c7dc87d1ba21a0a027c4de8d", "usage": { "input_tokens": 39785, "cached_input_tokens": 30848, "cache_write_input_tokens": 0, "output_tokens": 184, "reasoning_output_tokens": 92, "total_tokens": 39969 }, "turn_token_usage": { "input_tokens": 525243, "cached_input_tokens": 493184, "cache_write_input_tokens": 0, "output_tokens": 15764, "reasoning_output_tokens": 6221, "total_tokens": 541007 }, "thread_token_usage": { "input_tokens": 525243, "cached_input_tokens": 493184, "cache_write_input_tokens": 0, "output_tokens": 15764, "reasoning_output_tokens": 6221, "total_tokens": 541007 } } ## Step 33 · agent · 2026-09-19T00:13:58.236Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:13:59.034Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":53033,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:14:29.042Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"43dded","wall_time_seconds":30.001131018,"session_id":53033,"original_token_count":32} loading compact event streams catalog listens=31,062,540 likes=472,889 seconds=3.9 training profiles=44,451 mean_labels=12.70 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd3be164887d1885875ad630f6edd", "usage": { "input_tokens": 39997, "cached_input_tokens": 39552, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 8, "total_tokens": 40057 }, "turn_token_usage": { "input_tokens": 565240, "cached_input_tokens": 532736, "cache_write_input_tokens": 0, "output_tokens": 15824, "reasoning_output_tokens": 6229, "total_tokens": 581064 }, "thread_token_usage": { "input_tokens": 565240, "cached_input_tokens": 532736, "cache_write_input_tokens": 0, "output_tokens": 15824, "reasoning_output_tokens": 6229, "total_tokens": 581064 } } ## Step 34 · agent · 2026-09-19T00:14:36.543Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:14:37.320Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":53033,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:15:07.333Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"8a56e2","wall_time_seconds":30.001955314,"session_id":53033,"original_token_count":134} /app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) epoch=1 profiles=44451 loss=3.09883 epoch=2 profiles=44451 loss=2.94168 epoch=3 profiles=44451 loss=2.83037 epoch=4 profiles=44451 loss=2.76422 epoch=5 profiles=44451 loss=2.71072 epoch=6 profiles=44451 loss=2.65645 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd3e53e7887d18fa7426416e0c8d9", "usage": { "input_tokens": 40147, "cached_input_tokens": 39808, "cache_write_input_tokens": 0, "output_tokens": 62, "reasoning_output_tokens": 10, "total_tokens": 40209 }, "turn_token_usage": { "input_tokens": 605387, "cached_input_tokens": 572544, "cache_write_input_tokens": 0, "output_tokens": 15886, "reasoning_output_tokens": 6239, "total_tokens": 621273 }, "thread_token_usage": { "input_tokens": 605387, "cached_input_tokens": 572544, "cache_write_input_tokens": 0, "output_tokens": 15886, "reasoning_output_tokens": 6239, "total_tokens": 621273 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31088a0a-bc52-42d1-8437-68db0234614e", "process_id": "53033", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_two_tower.parquet --epochs 6" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_two_tower.parquet --epochs 6" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.9\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.09883\nepoch=2 profiles=44451 loss=2.94168\nepoch=3 profiles=44451 loss=2.83037\nepoch=4 profiles=44451 loss=2.76422\nepoch=5 profiles=44451 loss=2.71072\nepoch=6 profiles=44451 loss=2.65645\n{\"output\": \"solution/validation_two_tower.parquet\", \"seconds\": 78.1}\n", "stderr": "", "aggregated_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.9\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.09883\nepoch=2 profiles=44451 loss=2.94168\nepoch=3 profiles=44451 loss=2.83037\nepoch=4 profiles=44451 loss=2.76422\nepoch=5 profiles=44451 loss=2.71072\nepoch=6 profiles=44451 loss=2.65645\n{\"output\": \"solution/validation_two_tower.parquet\", \"seconds\": 78.1}\n", "exit_code": 0, "duration": { "secs": 82, "nanos": 170860787 }, "formatted_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.9\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.09883\nepoch=2 profiles=44451 loss=2.94168\nepoch=3 profiles=44451 loss=2.83037\nepoch=4 profiles=44451 loss=2.76422\nepoch=5 profiles=44451 loss=2.71072\nepoch=6 profiles=44451 loss=2.65645\n{\"output\": \"solution/validation_two_tower.parquet\", \"seconds\": 78.1}\n" } } ## Step 35 · agent · 2026-09-19T00:15:14.633Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:15:15.411Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":53033,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:15:15.517Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"43f515","wall_time_seconds":0.00000316,"exit_code":0,"original_token_count":18} {"output": "solution/validation_two_tower.parquet", "seconds": 78.1} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd40ba48487d18b5e3f9c3f34b674", "usage": { "input_tokens": 40416, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 8, "total_tokens": 40476 }, "turn_token_usage": { "input_tokens": 645803, "cached_input_tokens": 612480, "cache_write_input_tokens": 0, "output_tokens": 15946, "reasoning_output_tokens": 6247, "total_tokens": 661749 }, "thread_token_usage": { "input_tokens": 645803, "cached_input_tokens": 612480, "cache_write_input_tokens": 0, "output_tokens": 15946, "reasoning_output_tokens": 6247, "total_tokens": 661749 } } ## Step 36 · agent · 2026-09-19T00:15:22.881Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:15:23.941Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_two_tower.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:15:27.365Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_two_tower.parquet", "scores": { "quality_ndcg@10": 0.02361, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd413ba8887d1bd6728c033318a5a", "usage": { "input_tokens": 40549, "cached_input_tokens": 40192, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 6, "total_tokens": 40624 }, "turn_token_usage": { "input_tokens": 686352, "cached_input_tokens": 652672, "cache_write_input_tokens": 0, "output_tokens": 16021, "reasoning_output_tokens": 6253, "total_tokens": 702373 }, "thread_token_usage": { "input_tokens": 686352, "cached_input_tokens": 652672, "cache_write_input_tokens": 0, "output_tokens": 16021, "reasoning_output_tokens": 6253, "total_tokens": 702373 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e0b8b037-10be-4a3c-8365-98c6761d2b12", "process_id": "2394", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_two_tower.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_two_tower.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_two_tower.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02361,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_two_tower.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02361,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 257003600 }, "formatted_output": "{\n \"model\": \"solution/validation_two_tower.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02361,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 37 · agent · 2026-09-19T00:15:45.807Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-19T00:15:54.789Z ### Thinking unavailable — no text recorded The initial neural ranker reaches 0.0236—well above lifetime popularity but below the 0.0312 short-term trend baseline. Its collaborative component is useful, but its sampled objective underweights the abrupt current-week trend. I’m now calibrating a separable popularity coordinate alongside the learned embedding (implemented as one extra item/user-vector dimension), then I’ll refit the selected combination on train plus validation. ### exec · 2026-09-19T00:16:16.973Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/calibrate_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Calibrate a separable current-popularity coordinate for a fitted ranker.\\\"\\\"\\\"\\n+import argparse\\n+from pathlib import Path\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+from train_two_tower import (DATA, TRAIN_END, FINAL_END, TwoTower, load_events,\\n+ load_action, make_inference_profiles, pad_bags,\\n+ PAD_HISTORY, PAD_RECENT)\\n+\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\\n+ ap.add_argument('--checkpoint',type=Path,required=True); ap.add_argument('--prefix',type=Path,required=True)\\n+ ap.add_argument('--alphas',default='0,0.1,0.25,0.5,0.75,1,1.5,2,3')\\n+ a=ap.parse_args(); torch.set_num_threads(min(16,torch.get_num_threads()))\\n+ cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); targets=pd.read_parquet(DATA/'target_users.parquet')\\n+ items=cat.item_id.to_numpy(np.int64); uids=targets.uid.to_numpy(np.int64)\\n+ ev=load_events(a.stage,pd.Index(items),pd.Index(uids)); likes=load_action(a.stage,'likes',pd.Index(items),pd.Index(uids))\\n+ cutoff=TRAIN_END if a.stage=='validation' else FINAL_END\\n+ t=ev['time']; it=ev['item'].astype(np.int64)\\n+ m=(ev['organic']==1)&(ev['ratio']>=80)&(t=cutoff-7*86400)\\n+ listen=np.bincount(it[m],minlength=len(items)).astype(float)\\n+ lt=likes['time']; li=likes['item'].astype(np.int64)\\n+ m=(likes['organic']==1)&(lt=cutoff-7*86400)\\n+ like=np.bincount(li[m],minlength=len(items)).astype(float)\\n+ trend=np.log1p(listen)+np.log1p(like)\\n+ trend=(trend-trend.mean())/(trend.std()+1e-8)\\n+ # Recreate the current cutoff-standardized six item features.\\n+ cols=[]\\n+ for days in (3,7,14,30):\\n+ m=(ev['organic']==1)&(ev['ratio']>=80)&(t=cutoff-days*86400)\\n+ cols.append(np.bincount(it[m],minlength=len(items)))\\n+ m=(likes['organic']==1)&(lt=cutoff-7*86400)\\n+ cols.append(np.bincount(li[m],minlength=len(items)))\\n+ m=(ev['organic']==1)&(ev['ratio']>=80)&(t=80)&(t=cutoff-7*86400)\n listen=np.bincount(it[m],minlength=len(items)).astype(float)\n lt=likes['time']; li=likes['item'].astype(np.int64)\n m=(likes['organic']==1)&(lt=cutoff-7*86400)\n like=np.bincount(li[m],minlength=len(items)).astype(float)\n trend=np.log1p(listen)+np.log1p(like)\n trend=(trend-trend.mean())/(trend.std()+1e-8)\n # Recreate the current cutoff-standardized six item features.\n cols=[]\n for days in (3,7,14,30):\n m=(ev['organic']==1)&(ev['ratio']>=80)&(t=cutoff-days*86400)\n cols.append(np.bincount(it[m],minlength=len(items)))\n m=(likes['organic']==1)&(lt=cutoff-7*86400)\n cols.append(np.bincount(li[m],minlength=len(items)))\n m=(ev['organic']==1)&(ev['ratio']>=80)&(t 0:\\n+ neg_prob = np.power(label_counts + 0.1, negative_power)\\n+ neg_prob /= neg_prob.sum()\\n+ else:\\n+ neg_prob = None\\n@@\\n- neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\\n+ if neg_prob is None:\\n+ neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\\n+ else:\\n+ neg = rng.choice(pop.shape[1], size=(b, candidates - 1), p=neg_prob)\\n@@\\n ap.add_argument(\\\"--seed\\\", type=int, default=20260919)\\n+ ap.add_argument(\\\"--negative-power\\\", type=float, default=0.75,\\n+ help=\\\"target-frequency exponent for sampled negatives; 0 is uniform\\\")\\n@@\\n- train_model(model, histories, recents, uf, labels, cids, pop, args.epochs, args.seed)\\n+ train_model(model, histories, recents, uf, labels, cids, pop, args.epochs,\\n+ args.seed, args.negative_power)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:20:18.321Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-3dd870ae-4f68-45d7-bb4c-3995ace25e1b", "changes": { "/app/solution/train_two_tower.py": { "type": "update", "unified_diff": "@@ -254,3 +254,3 @@\n def train_model(model, histories, recents, user_feats, labels, cutoff_ids, pop,\n- epochs: int, seed: int):\n+ epochs: int, seed: int, negative_power: float):\n h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, pop.shape[1]))\n@@ -262,2 +262,8 @@\n n, batch_size, candidates = len(labels), 256, 40\n+ label_counts = np.bincount(np.concatenate(labels), minlength=pop.shape[1]).astype(np.float64)\n+ if negative_power > 0:\n+ neg_prob = np.power(label_counts + 0.1, negative_power)\n+ neg_prob /= neg_prob.sum()\n+ else:\n+ neg_prob = None\n model.train()\n@@ -269,3 +275,6 @@\n positives = np.asarray([x[rng.integers(len(x))] for x in (labels[j] for j in rows)])\n- neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\n+ if neg_prob is None:\n+ neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\n+ else:\n+ neg = rng.choice(pop.shape[1], size=(b, candidates - 1), p=neg_prob)\n cand = np.concatenate([positives[:, None], neg], axis=1)\n@@ -323,2 +332,4 @@\n ap.add_argument(\"--seed\", type=int, default=20260919)\n+ ap.add_argument(\"--negative-power\", type=float, default=0.75,\n+ help=\"target-frequency exponent for sampled negatives; 0 is uniform\")\n args = ap.parse_args(); seed_everything(args.seed)\n@@ -336,3 +347,4 @@\n model = TwoTower(len(items), pop.shape[-1])\n- train_model(model, histories, recents, uf, labels, cids, pop, args.epochs, args.seed)\n+ train_model(model, histories, recents, uf, labels, cids, pop, args.epochs,\n+ args.seed, args.negative_power)\n ih, ir, iuf = make_inference_profiles(args.stage, ev, len(uids))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd52b85e887d1839f5711eb6b3771", "usage": { "input_tokens": 46307, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 818, "reasoning_output_tokens": 382, "total_tokens": 47125 }, "turn_token_usage": { "input_tokens": 1352704, "cached_input_tokens": 1310592, "cache_write_input_tokens": 0, "output_tokens": 20754, "reasoning_output_tokens": 7789, "total_tokens": 1373458 }, "thread_token_usage": { "input_tokens": 1352704, "cached_input_tokens": 1310592, "cache_write_input_tokens": 0, "output_tokens": 20754, "reasoning_output_tokens": 7789, "total_tokens": 1373458 } } ## Step 46 · agent · 2026-09-19T00:20:24.150Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:20:26.142Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_debiased.parquet --epochs 8 --negative-power 0.75 && mv solution/two_tower_validation.pt solution/two_tower_validation_debiased.pt\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:20:27.307Z Script completed Wall time 1.2 seconds Output: SESSION_ID=36096 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd542bf6487d1a6f3772d4a82d38a", "usage": { "input_tokens": 47149, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 10, "total_tokens": 47278 }, "turn_token_usage": { "input_tokens": 1399853, "cached_input_tokens": 1356672, "cache_write_input_tokens": 0, "output_tokens": 20883, "reasoning_output_tokens": 7799, "total_tokens": 1420736 }, "thread_token_usage": { "input_tokens": 1399853, "cached_input_tokens": 1356672, "cache_write_input_tokens": 0, "output_tokens": 20883, "reasoning_output_tokens": 7799, "total_tokens": 1420736 } } ### exec · 2026-09-19T00:20:33.766Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":36096,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:21:03.774Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"cf38f2","wall_time_seconds":30.000360744,"session_id":36096,"original_token_count":21} loading compact event streams catalog listens=31,062,540 likes=472,889 seconds=3.8 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd54b7b7c87d1add91d8e6f94b8a1", "usage": { "input_tokens": 47306, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 47356 }, "turn_token_usage": { "input_tokens": 1447159, "cached_input_tokens": 1403648, "cache_write_input_tokens": 0, "output_tokens": 20933, "reasoning_output_tokens": 7799, "total_tokens": 1468092 }, "thread_token_usage": { "input_tokens": 1447159, "cached_input_tokens": 1403648, "cache_write_input_tokens": 0, "output_tokens": 20933, "reasoning_output_tokens": 7799, "total_tokens": 1468092 } } ### exec · 2026-09-19T00:21:10.235Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":36096,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:21:40.247Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"183633","wall_time_seconds":30.001401741,"session_id":36096,"original_token_count":135} training profiles=44,451 mean_labels=12.70 /app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) epoch=1 profiles=44451 loss=3.57682 epoch=2 profiles=44451 loss=3.50454 epoch=3 profiles=44451 loss=3.43755 epoch=4 profiles=44451 loss=3.37458 epoch=5 profiles=44451 loss=3.31198 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd56ffa8487d18ba27045b2082678", "usage": { "input_tokens": 47434, "cached_input_tokens": 47104, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 47484 }, "turn_token_usage": { "input_tokens": 1494593, "cached_input_tokens": 1450752, "cache_write_input_tokens": 0, "output_tokens": 20983, "reasoning_output_tokens": 7799, "total_tokens": 1515576 }, "thread_token_usage": { "input_tokens": 1494593, "cached_input_tokens": 1450752, "cache_write_input_tokens": 0, "output_tokens": 20983, "reasoning_output_tokens": 7799, "total_tokens": 1515576 } } ### exec · 2026-09-19T00:21:45.984Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":36096,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:22:03.296Z Script completed Wall time 17.3 seconds Output: {"chunk_id":"62d732","wall_time_seconds":17.292444214,"exit_code":0,"original_token_count":47} epoch=6 profiles=44451 loss=3.25675 epoch=7 profiles=44451 loss=3.20034 epoch=8 profiles=44451 loss=3.13725 {"output": "solution/validation_twotower_debiased.parquet", "seconds": 92.9} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd594803887d196ef8a954d0b81f1", "usage": { "input_tokens": 47687, "cached_input_tokens": 47232, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 47737 }, "turn_token_usage": { "input_tokens": 1542280, "cached_input_tokens": 1497984, "cache_write_input_tokens": 0, "output_tokens": 21033, "reasoning_output_tokens": 7799, "total_tokens": 1563313 }, "thread_token_usage": { "input_tokens": 1542280, "cached_input_tokens": 1497984, "cache_write_input_tokens": 0, "output_tokens": 21033, "reasoning_output_tokens": 7799, "total_tokens": 1563313 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5f9bb41b-d6fb-4f40-856d-a2d926fe7318", "process_id": "36096", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_debiased.parquet --epochs 8 --negative-power 0.75 && mv solution/two_tower_validation.pt solution/two_tower_validation_debiased.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_debiased.parquet --epochs 8 --negative-power 0.75 && mv solution/two_tower_validation.pt solution/two_tower_validation_debiased.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.57682\nepoch=2 profiles=44451 loss=3.50454\nepoch=3 profiles=44451 loss=3.43755\nepoch=4 profiles=44451 loss=3.37458\nepoch=5 profiles=44451 loss=3.31198\nepoch=6 profiles=44451 loss=3.25675\nepoch=7 profiles=44451 loss=3.20034\nepoch=8 profiles=44451 loss=3.13725\n{\"output\": \"solution/validation_twotower_debiased.parquet\", \"seconds\": 92.9}\n", "stderr": "", "aggregated_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.57682\nepoch=2 profiles=44451 loss=3.50454\nepoch=3 profiles=44451 loss=3.43755\nepoch=4 profiles=44451 loss=3.37458\nepoch=5 profiles=44451 loss=3.31198\nepoch=6 profiles=44451 loss=3.25675\nepoch=7 profiles=44451 loss=3.20034\nepoch=8 profiles=44451 loss=3.13725\n{\"output\": \"solution/validation_twotower_debiased.parquet\", \"seconds\": 92.9}\n", "exit_code": 0, "duration": { "secs": 96, "nanos": 980777308 }, "formatted_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.57682\nepoch=2 profiles=44451 loss=3.50454\nepoch=3 profiles=44451 loss=3.43755\nepoch=4 profiles=44451 loss=3.37458\nepoch=5 profiles=44451 loss=3.31198\nepoch=6 profiles=44451 loss=3.25675\nepoch=7 profiles=44451 loss=3.20034\nepoch=8 profiles=44451 loss=3.13725\n{\"output\": \"solution/validation_twotower_debiased.parquet\", \"seconds\": 92.9}\n" } } ## Step 47 · agent · 2026-09-19T00:22:07.294Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:22:09.921Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_debiased.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_debiased.pt --prefix solution/validation_debcal --mode raw --alphas 0,0.5,1,2,3,5,7,10,15,20,30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:22:11.091Z Script completed Wall time 1.2 seconds Output: SESSION_ID=16683 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd5ab792c87d1b8b1c286aad6dd22", "usage": { "input_tokens": 47854, "cached_input_tokens": 47488, "cache_write_input_tokens": 0, "output_tokens": 163, "reasoning_output_tokens": 11, "total_tokens": 48017 }, "turn_token_usage": { "input_tokens": 1590134, "cached_input_tokens": 1545472, "cache_write_input_tokens": 0, "output_tokens": 21196, "reasoning_output_tokens": 7810, "total_tokens": 1611330 }, "thread_token_usage": { "input_tokens": 1590134, "cached_input_tokens": 1545472, "cache_write_input_tokens": 0, "output_tokens": 21196, "reasoning_output_tokens": 7810, "total_tokens": 1611330 } } ### exec · 2026-09-19T00:22:15.732Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":16683,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:22:45.739Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"1a4a03","wall_time_seconds":30.00026507,"session_id":16683,"original_token_count":121} { "model": "solution/validation_twotower_debiased.parquet", "scores": { "quality_ndcg@10": 0.000506, "quality_users": 4263 } } neural score std 8.961896896362305 solution/validation_debcal_a0.parquet solution/validation_debcal_a0.5.parquet solution/validation_debcal_a1.parquet solution/validation_debcal_a2.parquet solution/validation_debcal_a3.parquet solution/validation_debcal_a5.parquet solution/validation_debcal_a7.parquet solution/validation_debcal_a10.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd5b34f2887d19dcf07abf2cc2cf4", "usage": { "input_tokens": 48045, "cached_input_tokens": 47616, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 48095 }, "turn_token_usage": { "input_tokens": 1638179, "cached_input_tokens": 1593088, "cache_write_input_tokens": 0, "output_tokens": 21246, "reasoning_output_tokens": 7810, "total_tokens": 1659425 }, "thread_token_usage": { "input_tokens": 1638179, "cached_input_tokens": 1593088, "cache_write_input_tokens": 0, "output_tokens": 21246, "reasoning_output_tokens": 7810, "total_tokens": 1659425 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a6ca6467-4e53-4196-bace-9b0c73c4cd46", "process_id": "16683", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_debiased.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_debiased.pt --prefix solution/validation_debcal --mode raw --alphas 0,0.5,1,2,3,5,7,10,15,20,30" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_debiased.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_debiased.pt --prefix solution/validation_debcal --mode raw --alphas 0,0.5,1,2,3,5,7,10,15,20,30" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_twotower_debiased.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.000506,\n \"quality_users\": 4263\n }\n}\nneural score std 8.961896896362305\nsolution/validation_debcal_a0.parquet\nsolution/validation_debcal_a0.5.parquet\nsolution/validation_debcal_a1.parquet\nsolution/validation_debcal_a2.parquet\nsolution/validation_debcal_a3.parquet\nsolution/validation_debcal_a5.parquet\nsolution/validation_debcal_a7.parquet\nsolution/validation_debcal_a10.parquet\nsolution/validation_debcal_a15.parquet\nsolution/validation_debcal_a20.parquet\nsolution/validation_debcal_a30.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_twotower_debiased.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.000506,\n \"quality_users\": 4263\n }\n}\nneural score std 8.961896896362305\nsolution/validation_debcal_a0.parquet\nsolution/validation_debcal_a0.5.parquet\nsolution/validation_debcal_a1.parquet\nsolution/validation_debcal_a2.parquet\nsolution/validation_debcal_a3.parquet\nsolution/validation_debcal_a5.parquet\nsolution/validation_debcal_a7.parquet\nsolution/validation_debcal_a10.parquet\nsolution/validation_debcal_a15.parquet\nsolution/validation_debcal_a20.parquet\nsolution/validation_debcal_a30.parquet\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 632712189 }, "formatted_output": "{\n \"model\": \"solution/validation_twotower_debiased.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.000506,\n \"quality_users\": 4263\n }\n}\nneural score std 8.961896896362305\nsolution/validation_debcal_a0.parquet\nsolution/validation_debcal_a0.5.parquet\nsolution/validation_debcal_a1.parquet\nsolution/validation_debcal_a2.parquet\nsolution/validation_debcal_a3.parquet\nsolution/validation_debcal_a5.parquet\nsolution/validation_debcal_a7.parquet\nsolution/validation_debcal_a10.parquet\nsolution/validation_debcal_a15.parquet\nsolution/validation_debcal_a20.parquet\nsolution/validation_debcal_a30.parquet\n" } } ### exec · 2026-09-19T00:22:50.517Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":16683,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:22:50.651Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"39b813","wall_time_seconds":0.00000205,"exit_code":0,"original_token_count":30} solution/validation_debcal_a15.parquet solution/validation_debcal_a20.parquet solution/validation_debcal_a30.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd5d5f0ec87d1ab9b3b3526840df2", "usage": { "input_tokens": 48300, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 48350 }, "turn_token_usage": { "input_tokens": 1686479, "cached_input_tokens": 1640960, "cache_write_input_tokens": 0, "output_tokens": 21296, "reasoning_output_tokens": 7810, "total_tokens": 1707775 }, "thread_token_usage": { "input_tokens": 1686479, "cached_input_tokens": 1640960, "cache_write_input_tokens": 0, "output_tokens": 21296, "reasoning_output_tokens": 7810, "total_tokens": 1707775 } } ## Step 48 · agent · 2026-09-19T00:22:54.034Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:22:56.064Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in solution/validation_debcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:22:57.235Z Script completed Wall time 1.2 seconds Output: SESSION_ID=81643 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd5daf93c87d1aa2cc961aff02c10", "usage": { "input_tokens": 48435, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 119, "reasoning_output_tokens": 6, "total_tokens": 48554 }, "turn_token_usage": { "input_tokens": 1734914, "cached_input_tokens": 1689088, "cache_write_input_tokens": 0, "output_tokens": 21415, "reasoning_output_tokens": 7816, "total_tokens": 1756329 }, "thread_token_usage": { "input_tokens": 1734914, "cached_input_tokens": 1689088, "cache_write_input_tokens": 0, "output_tokens": 21415, "reasoning_output_tokens": 7816, "total_tokens": 1756329 } } ### exec · 2026-09-19T00:23:04.669Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":81643,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:23:33.174Z Script completed Wall time 28.5 seconds Output: {"chunk_id":"5846ba","wall_time_seconds":28.483183097,"exit_code":0,"original_token_count":332} { "model": "solution/validation_debcal_a0.5.parquet", "scores": { "quality_ndcg@10": 0.000737, "quality_users": 4263 } } { "model": "solution/validation_debcal_a0.parquet", "scores": { "quality_ndcg@10": 0.000506, "quality_users": 4263 } } { "model": "solution/validation_debcal_a1.parquet", "scores": { "quality_ndcg@10": 0.001183, "quality_users": 4263 } } { "model": "solution/validation_debcal_a10.parquet", "scores": { "quality_ndcg@10": 0.030706, "quality_users": 4263 } } { "model": "solution/validation_debcal_a15.parquet", "scores": { "quality_ndcg@10": 0.032018, "quality_users": 4263 } } { "model": "solution/validation_debcal_a2.parquet", "scores": { "quality_ndcg@10": 0.002967, "quality_users": 4263 } } { "model": "solution/validation_debcal_a20.parquet", "scores": { "quality_ndcg@10": 0.031899, "quality_users": 4263 } } { "model": "solution/validation_debcal_a3.parquet", "scores": { "quality_ndcg@10": 0.006055, "quality_users": 4263 } } { "model": "solution/validation_debcal_a30.parquet", "scores": { "quality_ndcg@10": 0.031534, "quality_users": 4263 } } { "model": "solution/validation_debcal_a5.parquet", "scores": { "quality_ndcg@10": 0.015209, "quality_users": 4263 } } { "model": "solution/validation_debcal_a7.parquet", "scores": { "quality_ndcg@10": 0.023861, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd5e18b5c87d1bf62660aa04f7250", "usage": { "input_tokens": 48582, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 48632 }, "turn_token_usage": { "input_tokens": 1783496, "cached_input_tokens": 1737344, "cache_write_input_tokens": 0, "output_tokens": 21465, "reasoning_output_tokens": 7816, "total_tokens": 1804961 }, "thread_token_usage": { "input_tokens": 1783496, "cached_input_tokens": 1737344, "cache_write_input_tokens": 0, "output_tokens": 21465, "reasoning_output_tokens": 7816, "total_tokens": 1804961 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-42b69152-1cc8-4fc1-a1a1-bee9e62af2a8", "process_id": "81643", "command": [ "/bin/sh", "-lc", "for f in solution/validation_debcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_debcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{ \"model\": \"solution/validation_debcal_a0.5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000737, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000506, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.001183, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030706, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a15.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032018, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a2.parquet\", \"scores\": { \"quality_ndcg@10\": 0.002967, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031899, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.006055, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031534, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015209, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.023861, \"quality_users\": 4263 } } \n", "stderr": "", "aggregated_output": "{ \"model\": \"solution/validation_debcal_a0.5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000737, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000506, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.001183, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030706, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a15.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032018, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a2.parquet\", \"scores\": { \"quality_ndcg@10\": 0.002967, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031899, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.006055, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031534, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015209, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.023861, \"quality_users\": 4263 } } \n", "exit_code": 0, "duration": { "secs": 36, "nanos": 931935052 }, "formatted_output": "{ \"model\": \"solution/validation_debcal_a0.5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000737, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.000506, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.001183, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030706, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a15.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032018, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a2.parquet\", \"scores\": { \"quality_ndcg@10\": 0.002967, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031899, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.006055, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031534, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015209, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_debcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.023861, \"quality_users\": 4263 } } \n" } } ## Step 49 · agent · 2026-09-19T00:23:50.967Z ### Thinking unavailable — no text recorded ## Step 50 · agent · 2026-09-19T00:24:02.442Z ### Thinking unavailable — no text recorded ## Step 51 · agent · 2026-09-19T00:24:04.193Z ### Thinking unavailable — no text recorded Popularity-matched negative sampling removed too much useful head-item signal and did not improve the blend; I’m discarding that branch. I’m testing one intermediate negative distribution next, since it can preserve broad trend learning while forcing the history encoder to contribute more personalization. The best valid artifact remains untouched while this runs. ### exec · 2026-09-19T00:24:06.551Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_midneg.parquet --epochs 10 --negative-power 0.25 && mv solution/two_tower_validation.pt solution/two_tower_validation_midneg.pt\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:24:07.725Z Script completed Wall time 1.2 seconds Output: SESSION_ID=1107 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd605656c87d1836daa1fa8448eb3", "usage": { "input_tokens": 49158, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 1308, "reasoning_output_tokens": 1120, "total_tokens": 50466 }, "turn_token_usage": { "input_tokens": 1832654, "cached_input_tokens": 1785728, "cache_write_input_tokens": 0, "output_tokens": 22773, "reasoning_output_tokens": 8936, "total_tokens": 1855427 }, "thread_token_usage": { "input_tokens": 1832654, "cached_input_tokens": 1785728, "cache_write_input_tokens": 0, "output_tokens": 22773, "reasoning_output_tokens": 8936, "total_tokens": 1855427 } } ### exec · 2026-09-19T00:24:11.010Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":1107,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:24:41.018Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"d3f91b","wall_time_seconds":30.001407605,"session_id":1107,"original_token_count":21} loading compact event streams catalog listens=31,062,540 likes=472,889 seconds=3.8 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd627e98887d19fe8ae2d5296ec7a", "usage": { "input_tokens": 50494, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 50544 }, "turn_token_usage": { "input_tokens": 1883148, "cached_input_tokens": 1834752, "cache_write_input_tokens": 0, "output_tokens": 22823, "reasoning_output_tokens": 8936, "total_tokens": 1905971 }, "thread_token_usage": { "input_tokens": 1883148, "cached_input_tokens": 1834752, "cache_write_input_tokens": 0, "output_tokens": 22823, "reasoning_output_tokens": 8936, "total_tokens": 1905971 } } ### exec · 2026-09-19T00:24:43.881Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":1107,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:25:13.890Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"a06aa0","wall_time_seconds":30.001843837,"session_id":1107,"original_token_count":117} training profiles=44,451 mean_labels=12.70 /app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) epoch=1 profiles=44451 loss=3.32147 epoch=2 profiles=44451 loss=3.21628 epoch=3 profiles=44451 loss=3.13759 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd64936f887d1bf50bf145f9d7b2a", "usage": { "input_tokens": 50623, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 50673 }, "turn_token_usage": { "input_tokens": 1933771, "cached_input_tokens": 1885056, "cache_write_input_tokens": 0, "output_tokens": 22873, "reasoning_output_tokens": 8936, "total_tokens": 1956644 }, "thread_token_usage": { "input_tokens": 1933771, "cached_input_tokens": 1885056, "cache_write_input_tokens": 0, "output_tokens": 22873, "reasoning_output_tokens": 8936, "total_tokens": 1956644 } } ### exec · 2026-09-19T00:25:16.136Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":1107,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:25:46.148Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"a3c64a","wall_time_seconds":30.000939679,"session_id":1107,"original_token_count":45} epoch=4 profiles=44451 loss=3.06808 epoch=5 profiles=44451 loss=3.01779 epoch=6 profiles=44451 loss=2.96824 epoch=7 profiles=44451 loss=2.92828 epoch=8 profiles=44451 loss=2.87665 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd66a18fc87d19f112c71a161ddf8", "usage": { "input_tokens": 50850, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 50900 }, "turn_token_usage": { "input_tokens": 1984621, "cached_input_tokens": 1935488, "cache_write_input_tokens": 0, "output_tokens": 22923, "reasoning_output_tokens": 8936, "total_tokens": 2007544 }, "thread_token_usage": { "input_tokens": 1984621, "cached_input_tokens": 1935488, "cache_write_input_tokens": 0, "output_tokens": 22923, "reasoning_output_tokens": 8936, "total_tokens": 2007544 } } ### exec · 2026-09-19T00:25:50.096Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":1107,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:25:57.412Z Script completed Wall time 7.3 seconds Output: {"chunk_id":"d5352b","wall_time_seconds":7.293814298,"exit_code":0,"original_token_count":38} epoch=9 profiles=44451 loss=2.82458 epoch=10 profiles=44451 loss=2.79112 {"output": "solution/validation_twotower_midneg.parquet", "seconds": 106.6} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd68a6a2087d195a1587c16698388", "usage": { "input_tokens": 51025, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 51075 }, "turn_token_usage": { "input_tokens": 2035646, "cached_input_tokens": 1986176, "cache_write_input_tokens": 0, "output_tokens": 22973, "reasoning_output_tokens": 8936, "total_tokens": 2058619 }, "thread_token_usage": { "input_tokens": 2035646, "cached_input_tokens": 1986176, "cache_write_input_tokens": 0, "output_tokens": 22973, "reasoning_output_tokens": 8936, "total_tokens": 2058619 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3efd884-9f13-4ba0-a768-69bbcae6d897", "process_id": "1107", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_midneg.parquet --epochs 10 --negative-power 0.25 && mv solution/two_tower_validation.pt solution/two_tower_validation_midneg.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_midneg.parquet --epochs 10 --negative-power 0.25 && mv solution/two_tower_validation.pt solution/two_tower_validation_midneg.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32147\nepoch=2 profiles=44451 loss=3.21628\nepoch=3 profiles=44451 loss=3.13759\nepoch=4 profiles=44451 loss=3.06808\nepoch=5 profiles=44451 loss=3.01779\nepoch=6 profiles=44451 loss=2.96824\nepoch=7 profiles=44451 loss=2.92828\nepoch=8 profiles=44451 loss=2.87665\nepoch=9 profiles=44451 loss=2.82458\nepoch=10 profiles=44451 loss=2.79112\n{\"output\": \"solution/validation_twotower_midneg.parquet\", \"seconds\": 106.6}\n", "stderr": "", "aggregated_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32147\nepoch=2 profiles=44451 loss=3.21628\nepoch=3 profiles=44451 loss=3.13759\nepoch=4 profiles=44451 loss=3.06808\nepoch=5 profiles=44451 loss=3.01779\nepoch=6 profiles=44451 loss=2.96824\nepoch=7 profiles=44451 loss=2.92828\nepoch=8 profiles=44451 loss=2.87665\nepoch=9 profiles=44451 loss=2.82458\nepoch=10 profiles=44451 loss=2.79112\n{\"output\": \"solution/validation_twotower_midneg.parquet\", \"seconds\": 106.6}\n", "exit_code": 0, "duration": { "secs": 110, "nanos": 677501344 }, "formatted_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.8\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32147\nepoch=2 profiles=44451 loss=3.21628\nepoch=3 profiles=44451 loss=3.13759\nepoch=4 profiles=44451 loss=3.06808\nepoch=5 profiles=44451 loss=3.01779\nepoch=6 profiles=44451 loss=2.96824\nepoch=7 profiles=44451 loss=2.92828\nepoch=8 profiles=44451 loss=2.87665\nepoch=9 profiles=44451 loss=2.82458\nepoch=10 profiles=44451 loss=2.79112\n{\"output\": \"solution/validation_twotower_midneg.parquet\", \"seconds\": 106.6}\n" } } ## Step 52 · agent · 2026-09-19T00:25:59.191Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:26:01.686Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_midneg.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midcal --mode raw --alphas 3,5,7,10,13,16,20,25,30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:26:02.851Z Script completed Wall time 1.2 seconds Output: SESSION_ID=96967 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6959cb487d18c5ea4bf630ea8eb", "usage": { "input_tokens": 51179, "cached_input_tokens": 50816, "cache_write_input_tokens": 0, "output_tokens": 156, "reasoning_output_tokens": 11, "total_tokens": 51335 }, "turn_token_usage": { "input_tokens": 2086825, "cached_input_tokens": 2036992, "cache_write_input_tokens": 0, "output_tokens": 23129, "reasoning_output_tokens": 8947, "total_tokens": 2109954 }, "thread_token_usage": { "input_tokens": 2086825, "cached_input_tokens": 2036992, "cache_write_input_tokens": 0, "output_tokens": 23129, "reasoning_output_tokens": 8947, "total_tokens": 2109954 } } ### exec · 2026-09-19T00:26:05.302Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":96967,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:26:34.988Z Script completed Wall time 29.7 seconds Output: {"chunk_id":"5d21ed","wall_time_seconds":29.660537994,"exit_code":0,"original_token_count":131} { "model": "solution/validation_twotower_midneg.parquet", "scores": { "quality_ndcg@10": 0.016126, "quality_users": 4263 } } neural score std 13.567561149597168 solution/validation_midcal_a3.parquet solution/validation_midcal_a5.parquet solution/validation_midcal_a7.parquet solution/validation_midcal_a10.parquet solution/validation_midcal_a13.parquet solution/validation_midcal_a16.parquet solution/validation_midcal_a20.parquet solution/validation_midcal_a25.parquet solution/validation_midcal_a30.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd69b17bc87d1b84e67adda51b771", "usage": { "input_tokens": 51363, "cached_input_tokens": 50944, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 51413 }, "turn_token_usage": { "input_tokens": 2138188, "cached_input_tokens": 2087936, "cache_write_input_tokens": 0, "output_tokens": 23179, "reasoning_output_tokens": 8947, "total_tokens": 2161367 }, "thread_token_usage": { "input_tokens": 2138188, "cached_input_tokens": 2087936, "cache_write_input_tokens": 0, "output_tokens": 23179, "reasoning_output_tokens": 8947, "total_tokens": 2161367 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-224a20e9-45cc-4f21-9b28-eae4f777acdd", "process_id": "96967", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_midneg.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midcal --mode raw --alphas 3,5,7,10,13,16,20,25,30" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_twotower_midneg.parquet && /opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midcal --mode raw --alphas 3,5,7,10,13,16,20,25,30" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_twotower_midneg.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016126,\n \"quality_users\": 4263\n }\n}\nneural score std 13.567561149597168\nsolution/validation_midcal_a3.parquet\nsolution/validation_midcal_a5.parquet\nsolution/validation_midcal_a7.parquet\nsolution/validation_midcal_a10.parquet\nsolution/validation_midcal_a13.parquet\nsolution/validation_midcal_a16.parquet\nsolution/validation_midcal_a20.parquet\nsolution/validation_midcal_a25.parquet\nsolution/validation_midcal_a30.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_twotower_midneg.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016126,\n \"quality_users\": 4263\n }\n}\nneural score std 13.567561149597168\nsolution/validation_midcal_a3.parquet\nsolution/validation_midcal_a5.parquet\nsolution/validation_midcal_a7.parquet\nsolution/validation_midcal_a10.parquet\nsolution/validation_midcal_a13.parquet\nsolution/validation_midcal_a16.parquet\nsolution/validation_midcal_a20.parquet\nsolution/validation_midcal_a25.parquet\nsolution/validation_midcal_a30.parquet\n", "exit_code": 0, "duration": { "secs": 33, "nanos": 136076633 }, "formatted_output": "{\n \"model\": \"solution/validation_twotower_midneg.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016126,\n \"quality_users\": 4263\n }\n}\nneural score std 13.567561149597168\nsolution/validation_midcal_a3.parquet\nsolution/validation_midcal_a5.parquet\nsolution/validation_midcal_a7.parquet\nsolution/validation_midcal_a10.parquet\nsolution/validation_midcal_a13.parquet\nsolution/validation_midcal_a16.parquet\nsolution/validation_midcal_a20.parquet\nsolution/validation_midcal_a25.parquet\nsolution/validation_midcal_a30.parquet\n" } } ## Step 53 · agent · 2026-09-19T00:26:36.509Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:26:38.406Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in solution/validation_midcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:26:39.571Z Script completed Wall time 1.2 seconds Output: SESSION_ID=77066 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6bb4aec87d1b0c76d08c88e9454", "usage": { "input_tokens": 51616, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 6, "total_tokens": 51734 }, "turn_token_usage": { "input_tokens": 2189804, "cached_input_tokens": 2139136, "cache_write_input_tokens": 0, "output_tokens": 23297, "reasoning_output_tokens": 8953, "total_tokens": 2213101 }, "thread_token_usage": { "input_tokens": 2189804, "cached_input_tokens": 2139136, "cache_write_input_tokens": 0, "output_tokens": 23297, "reasoning_output_tokens": 8953, "total_tokens": 2213101 } } ### exec · 2026-09-19T00:26:42.822Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":77066,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:27:08.609Z Script completed Wall time 25.8 seconds Output: {"chunk_id":"352c76","wall_time_seconds":25.778144936,"exit_code":0,"original_token_count":272} { "model": "solution/validation_midcal_a10.parquet", "scores": { "quality_ndcg@10": 0.032947, "quality_users": 4263 } } { "model": "solution/validation_midcal_a13.parquet", "scores": { "quality_ndcg@10": 0.032756, "quality_users": 4263 } } { "model": "solution/validation_midcal_a16.parquet", "scores": { "quality_ndcg@10": 0.032549, "quality_users": 4263 } } { "model": "solution/validation_midcal_a20.parquet", "scores": { "quality_ndcg@10": 0.032281, "quality_users": 4263 } } { "model": "solution/validation_midcal_a25.parquet", "scores": { "quality_ndcg@10": 0.032051, "quality_users": 4263 } } { "model": "solution/validation_midcal_a3.parquet", "scores": { "quality_ndcg@10": 0.028892, "quality_users": 4263 } } { "model": "solution/validation_midcal_a30.parquet", "scores": { "quality_ndcg@10": 0.031834, "quality_users": 4263 } } { "model": "solution/validation_midcal_a5.parquet", "scores": { "quality_ndcg@10": 0.031591, "quality_users": 4263 } } { "model": "solution/validation_midcal_a7.parquet", "scores": { "quality_ndcg@10": 0.031962, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6c0ae3087d1a0555c1ff1edb5de", "usage": { "input_tokens": 51762, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 51812 }, "turn_token_usage": { "input_tokens": 2241566, "cached_input_tokens": 2190592, "cache_write_input_tokens": 0, "output_tokens": 23347, "reasoning_output_tokens": 8953, "total_tokens": 2264913 }, "thread_token_usage": { "input_tokens": 2241566, "cached_input_tokens": 2190592, "cache_write_input_tokens": 0, "output_tokens": 23347, "reasoning_output_tokens": 8953, "total_tokens": 2264913 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f86de9b-8858-4948-a51c-9dd456ce2dee", "process_id": "77066", "command": [ "/bin/sh", "-lc", "for f in solution/validation_midcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_midcal_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{ \"model\": \"solution/validation_midcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032756, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a16.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032549, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032281, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a25.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032051, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028892, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031834, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031591, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031962, \"quality_users\": 4263 } } \n", "stderr": "", "aggregated_output": "{ \"model\": \"solution/validation_midcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032756, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a16.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032549, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032281, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a25.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032051, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028892, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031834, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031591, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031962, \"quality_users\": 4263 } } \n", "exit_code": 0, "duration": { "secs": 30, "nanos": 37152122 }, "formatted_output": "{ \"model\": \"solution/validation_midcal_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032756, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a16.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032549, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a20.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032281, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a25.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032051, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028892, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a30.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031834, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a5.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031591, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midcal_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031962, \"quality_users\": 4263 } } \n" } } ## Step 54 · agent · 2026-09-19T00:27:11.135Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:27:13.013Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midfine --mode raw --alphas 8,9,10,11,12\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:27:14.179Z Script completed Wall time 1.2 seconds Output: SESSION_ID=66838 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6dd101c87d1a8d72e206c6f694f", "usage": { "input_tokens": 52241, "cached_input_tokens": 51584, "cache_write_input_tokens": 0, "output_tokens": 169, "reasoning_output_tokens": 57, "total_tokens": 52410 }, "turn_token_usage": { "input_tokens": 2293807, "cached_input_tokens": 2242176, "cache_write_input_tokens": 0, "output_tokens": 23516, "reasoning_output_tokens": 9010, "total_tokens": 2317323 }, "thread_token_usage": { "input_tokens": 2293807, "cached_input_tokens": 2242176, "cache_write_input_tokens": 0, "output_tokens": 23516, "reasoning_output_tokens": 9010, "total_tokens": 2317323 } } ### exec · 2026-09-19T00:27:16.323Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":66838,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:27:34.330Z Script completed Wall time 18.0 seconds Output: {"chunk_id":"bb6fc4","wall_time_seconds":17.997953012,"exit_code":0,"original_token_count":59} neural score std 13.567561149597168 solution/validation_midfine_a8.parquet solution/validation_midfine_a9.parquet solution/validation_midfine_a10.parquet solution/validation_midfine_a11.parquet solution/validation_midfine_a12.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6e2590c87d18aa12d9a8ba3d60c", "usage": { "input_tokens": 52438, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 52488 }, "turn_token_usage": { "input_tokens": 2346245, "cached_input_tokens": 2294272, "cache_write_input_tokens": 0, "output_tokens": 23566, "reasoning_output_tokens": 9010, "total_tokens": 2369811 }, "thread_token_usage": { "input_tokens": 2346245, "cached_input_tokens": 2294272, "cache_write_input_tokens": 0, "output_tokens": 23566, "reasoning_output_tokens": 9010, "total_tokens": 2369811 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-302192d9-e384-49ec-8a57-2fdc49cece05", "process_id": "66838", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midfine --mode raw --alphas 8,9,10,11,12" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt --prefix solution/validation_midfine --mode raw --alphas 8,9,10,11,12" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "neural score std 13.567561149597168\nsolution/validation_midfine_a8.parquet\nsolution/validation_midfine_a9.parquet\nsolution/validation_midfine_a10.parquet\nsolution/validation_midfine_a11.parquet\nsolution/validation_midfine_a12.parquet\n", "stderr": "", "aggregated_output": "neural score std 13.567561149597168\nsolution/validation_midfine_a8.parquet\nsolution/validation_midfine_a9.parquet\nsolution/validation_midfine_a10.parquet\nsolution/validation_midfine_a11.parquet\nsolution/validation_midfine_a12.parquet\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 150195309 }, "formatted_output": "neural score std 13.567561149597168\nsolution/validation_midfine_a8.parquet\nsolution/validation_midfine_a9.parquet\nsolution/validation_midfine_a10.parquet\nsolution/validation_midfine_a11.parquet\nsolution/validation_midfine_a12.parquet\n" } } ### exec · 2026-09-19T00:27:40.077Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in solution/validation_midfine_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:27:41.242Z Script completed Wall time 1.2 seconds Output: SESSION_ID=51963 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6f6870087d1bf08f33860e12391", "usage": { "input_tokens": 52604, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 110, "reasoning_output_tokens": 0, "total_tokens": 52714 }, "turn_token_usage": { "input_tokens": 2398849, "cached_input_tokens": 2346496, "cache_write_input_tokens": 0, "output_tokens": 23676, "reasoning_output_tokens": 9010, "total_tokens": 2422525 }, "thread_token_usage": { "input_tokens": 2398849, "cached_input_tokens": 2346496, "cache_write_input_tokens": 0, "output_tokens": 23676, "reasoning_output_tokens": 9010, "total_tokens": 2422525 } } ### exec · 2026-09-19T00:27:43.993Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":51963,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":4000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:27:56.835Z Script completed Wall time 12.8 seconds Output: {"chunk_id":"c15bb6","wall_time_seconds":12.833585587,"exit_code":0,"original_token_count":152} { "model": "solution/validation_midfine_a10.parquet", "scores": { "quality_ndcg@10": 0.032947, "quality_users": 4263 } } { "model": "solution/validation_midfine_a11.parquet", "scores": { "quality_ndcg@10": 0.032819, "quality_users": 4263 } } { "model": "solution/validation_midfine_a12.parquet", "scores": { "quality_ndcg@10": 0.032732, "quality_users": 4263 } } { "model": "solution/validation_midfine_a8.parquet", "scores": { "quality_ndcg@10": 0.032405, "quality_users": 4263 } } { "model": "solution/validation_midfine_a9.parquet", "scores": { "quality_ndcg@10": 0.032823, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd6fd6f6487d1956d24b0437ebd8f", "usage": { "input_tokens": 52742, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 52792 }, "turn_token_usage": { "input_tokens": 2451591, "cached_input_tokens": 2398976, "cache_write_input_tokens": 0, "output_tokens": 23726, "reasoning_output_tokens": 9010, "total_tokens": 2475317 }, "thread_token_usage": { "input_tokens": 2451591, "cached_input_tokens": 2398976, "cache_write_input_tokens": 0, "output_tokens": 23726, "reasoning_output_tokens": 9010, "total_tokens": 2475317 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c94208b1-ef68-4de5-a3b7-980b4f98aaeb", "process_id": "51963", "command": [ "/bin/sh", "-lc", "for f in solution/validation_midfine_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_midfine_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{ \"model\": \"solution/validation_midfine_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032819, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032732, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032823, \"quality_users\": 4263 } } \n", "stderr": "", "aggregated_output": "{ \"model\": \"solution/validation_midfine_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032819, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032732, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032823, \"quality_users\": 4263 } } \n", "exit_code": 0, "duration": { "secs": 16, "nanos": 592636364 }, "formatted_output": "{ \"model\": \"solution/validation_midfine_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032947, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032819, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032732, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_midfine_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.032823, \"quality_users\": 4263 } } \n" } } ## Step 55 · agent · 2026-09-19T00:27:59.539Z ### Thinking unavailable — no text recorded The intermediate negative sampler is the clear winner: combined with the separable trend coordinate it scores 0.032947 NDCG@10, versus 0.031211 for trend alone and 0.023610 for the initial neural model. I’ve selected its 0.25 sampling exponent and popularity weight 10, and I’m now doing the required final refit with validation events treated as observed history/training outcomes. ### exec · 2026-09-19T00:28:02.915Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/train_two_tower.py --stage test --output solution/submission_neural_only.parquet --epochs 10 --negative-power 0.25\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:28:04.080Z Script completed Wall time 1.2 seconds Output: SESSION_ID=99671 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd70d032087d1bd0a891f39e29dca", "usage": { "input_tokens": 53054, "cached_input_tokens": 52608, "cache_write_input_tokens": 0, "output_tokens": 273, "reasoning_output_tokens": 80, "total_tokens": 53327 }, "turn_token_usage": { "input_tokens": 2504645, "cached_input_tokens": 2451584, "cache_write_input_tokens": 0, "output_tokens": 23999, "reasoning_output_tokens": 9090, "total_tokens": 2528644 }, "thread_token_usage": { "input_tokens": 2504645, "cached_input_tokens": 2451584, "cache_write_input_tokens": 0, "output_tokens": 23999, "reasoning_output_tokens": 9090, "total_tokens": 2528644 } } ### exec · 2026-09-19T00:28:08.736Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":99671,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:28:38.744Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"a4aed8","wall_time_seconds":30.001728071,"session_id":99671,"original_token_count":21} loading compact event streams catalog listens=32,061,602 likes=491,196 seconds=3.9 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7144bd087d1a3c3a922ecb0e663", "usage": { "input_tokens": 53355, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 53405 }, "turn_token_usage": { "input_tokens": 2558000, "cached_input_tokens": 2504448, "cache_write_input_tokens": 0, "output_tokens": 24049, "reasoning_output_tokens": 9090, "total_tokens": 2582049 }, "thread_token_usage": { "input_tokens": 2558000, "cached_input_tokens": 2504448, "cache_write_input_tokens": 0, "output_tokens": 24049, "reasoning_output_tokens": 9090, "total_tokens": 2582049 } } ### exec · 2026-09-19T00:28:40.717Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":99671,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:29:10.725Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"846e37","wall_time_seconds":30.001179372,"session_id":99671,"original_token_count":117} training profiles=45,500 mean_labels=12.71 /app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) epoch=1 profiles=45500 loss=3.32542 epoch=2 profiles=45500 loss=3.22031 epoch=3 profiles=45500 loss=3.13731 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd736f02887d1aed4818ccf4f1dcc", "usage": { "input_tokens": 53483, "cached_input_tokens": 53120, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 53533 }, "turn_token_usage": { "input_tokens": 2611483, "cached_input_tokens": 2557568, "cache_write_input_tokens": 0, "output_tokens": 24099, "reasoning_output_tokens": 9090, "total_tokens": 2635582 }, "thread_token_usage": { "input_tokens": 2611483, "cached_input_tokens": 2557568, "cache_write_input_tokens": 0, "output_tokens": 24099, "reasoning_output_tokens": 9090, "total_tokens": 2635582 } } ### exec · 2026-09-19T00:29:13.866Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":99671,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:29:43.879Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"35c37f","wall_time_seconds":30.001186009,"session_id":99671,"original_token_count":45} epoch=4 profiles=45500 loss=3.08061 epoch=5 profiles=45500 loss=3.02438 epoch=6 profiles=45500 loss=2.97045 epoch=7 profiles=45500 loss=2.92638 epoch=8 profiles=45500 loss=2.88205 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd756e7c887d18d71e9a76d57572c", "usage": { "input_tokens": 53709, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 53759 }, "turn_token_usage": { "input_tokens": 2665192, "cached_input_tokens": 2610816, "cache_write_input_tokens": 0, "output_tokens": 24149, "reasoning_output_tokens": 9090, "total_tokens": 2689341 }, "thread_token_usage": { "input_tokens": 2665192, "cached_input_tokens": 2610816, "cache_write_input_tokens": 0, "output_tokens": 24149, "reasoning_output_tokens": 9090, "total_tokens": 2689341 } } ### exec · 2026-09-19T00:29:45.824Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":99671,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:29:54.834Z Script completed Wall time 9.0 seconds Output: {"chunk_id":"cb414e","wall_time_seconds":8.997676699,"exit_code":0,"original_token_count":37} epoch=9 profiles=45500 loss=2.83063 epoch=10 profiles=45500 loss=2.78332 {"output": "solution/submission_neural_only.parquet", "seconds": 107.6} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd778147087d18a1d65b5793461db", "usage": { "input_tokens": 53883, "cached_input_tokens": 53504, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 53933 }, "turn_token_usage": { "input_tokens": 2719075, "cached_input_tokens": 2664320, "cache_write_input_tokens": 0, "output_tokens": 24199, "reasoning_output_tokens": 9090, "total_tokens": 2743274 }, "thread_token_usage": { "input_tokens": 2719075, "cached_input_tokens": 2664320, "cache_write_input_tokens": 0, "output_tokens": 24199, "reasoning_output_tokens": 9090, "total_tokens": 2743274 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73453a65-a7ed-414f-8abb-75f34f0ba28e", "process_id": "99671", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage test --output solution/submission_neural_only.parquet --epochs 10 --negative-power 0.25" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage test --output solution/submission_neural_only.parquet --epochs 10 --negative-power 0.25" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading compact event streams\ncatalog listens=32,061,602 likes=491,196 seconds=3.9\ntraining profiles=45,500 mean_labels=12.71\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=45500 loss=3.32542\nepoch=2 profiles=45500 loss=3.22031\nepoch=3 profiles=45500 loss=3.13731\nepoch=4 profiles=45500 loss=3.08061\nepoch=5 profiles=45500 loss=3.02438\nepoch=6 profiles=45500 loss=2.97045\nepoch=7 profiles=45500 loss=2.92638\nepoch=8 profiles=45500 loss=2.88205\nepoch=9 profiles=45500 loss=2.83063\nepoch=10 profiles=45500 loss=2.78332\n{\"output\": \"solution/submission_neural_only.parquet\", \"seconds\": 107.6}\n", "stderr": "", "aggregated_output": "loading compact event streams\ncatalog listens=32,061,602 likes=491,196 seconds=3.9\ntraining profiles=45,500 mean_labels=12.71\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=45500 loss=3.32542\nepoch=2 profiles=45500 loss=3.22031\nepoch=3 profiles=45500 loss=3.13731\nepoch=4 profiles=45500 loss=3.08061\nepoch=5 profiles=45500 loss=3.02438\nepoch=6 profiles=45500 loss=2.97045\nepoch=7 profiles=45500 loss=2.92638\nepoch=8 profiles=45500 loss=2.88205\nepoch=9 profiles=45500 loss=2.83063\nepoch=10 profiles=45500 loss=2.78332\n{\"output\": \"solution/submission_neural_only.parquet\", \"seconds\": 107.6}\n", "exit_code": 0, "duration": { "secs": 111, "nanos": 753326310 }, "formatted_output": "loading compact event streams\ncatalog listens=32,061,602 likes=491,196 seconds=3.9\ntraining profiles=45,500 mean_labels=12.71\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=45500 loss=3.32542\nepoch=2 profiles=45500 loss=3.22031\nepoch=3 profiles=45500 loss=3.13731\nepoch=4 profiles=45500 loss=3.08061\nepoch=5 profiles=45500 loss=3.02438\nepoch=6 profiles=45500 loss=2.97045\nepoch=7 profiles=45500 loss=2.92638\nepoch=8 profiles=45500 loss=2.88205\nepoch=9 profiles=45500 loss=2.83063\nepoch=10 profiles=45500 loss=2.78332\n{\"output\": \"solution/submission_neural_only.parquet\", \"seconds\": 107.6}\n" } } ## Step 56 · agent · 2026-09-19T00:29:56.736Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:29:58.676Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_final --mode raw --alphas 10 && cp solution/submission_final_a10.parquet solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:29:59.842Z Script completed Wall time 1.2 seconds Output: SESSION_ID=39432 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd78302bc87d198d922fe07006300", "usage": { "input_tokens": 54034, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 150, "reasoning_output_tokens": 34, "total_tokens": 54184 }, "turn_token_usage": { "input_tokens": 2773109, "cached_input_tokens": 2718080, "cache_write_input_tokens": 0, "output_tokens": 24349, "reasoning_output_tokens": 9124, "total_tokens": 2797458 }, "thread_token_usage": { "input_tokens": 2773109, "cached_input_tokens": 2718080, "cache_write_input_tokens": 0, "output_tokens": 24349, "reasoning_output_tokens": 9124, "total_tokens": 2797458 } } ### exec · 2026-09-19T00:30:02.438Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":39432,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:30:11.422Z Script completed Wall time 9.0 seconds Output: {"chunk_id":"18a1b6","wall_time_seconds":8.975419319,"exit_code":0,"original_token_count":19} neural score std 13.082048416137695 solution/submission_final_a10.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7880fe887d1bee05ee5f0a4963f", "usage": { "input_tokens": 54212, "cached_input_tokens": 53888, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 54262 }, "turn_token_usage": { "input_tokens": 2827321, "cached_input_tokens": 2771968, "cache_write_input_tokens": 0, "output_tokens": 24399, "reasoning_output_tokens": 9124, "total_tokens": 2851720 }, "thread_token_usage": { "input_tokens": 2827321, "cached_input_tokens": 2771968, "cache_write_input_tokens": 0, "output_tokens": 24399, "reasoning_output_tokens": 9124, "total_tokens": 2851720 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0bdd020f-a9a4-439d-9d97-2f906520eec2", "process_id": "39432", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_final --mode raw --alphas 10 && cp solution/submission_final_a10.parquet solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_final --mode raw --alphas 10 && cp solution/submission_final_a10.parquet solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "neural score std 13.082048416137695\nsolution/submission_final_a10.parquet\n", "stderr": "", "aggregated_output": "neural score std 13.082048416137695\nsolution/submission_final_a10.parquet\n", "exit_code": 0, "duration": { "secs": 12, "nanos": 579748868 }, "formatted_output": "neural score std 13.082048416137695\nsolution/submission_final_a10.parquet\n" } } ## Step 57 · agent · 2026-09-19T00:30:24.568Z ### Thinking unavailable — no text recorded ## Step 58 · agent · 2026-09-19T00:30:27.451Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:30:38.468Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate_ranker.py\\n@@\\n- ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\\n- ap.add_argument('--checkpoint',type=Path,required=True); ap.add_argument('--prefix',type=Path,required=True)\\n+ ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\\n+ ap.add_argument('--checkpoint',type=Path,nargs='+',required=True); ap.add_argument('--prefix',type=Path,required=True)\\n@@\\n- ck=torch.load(a.checkpoint,map_location='cpu',weights_only=True)\\n- model=TwoTower(len(items),pf.shape[1]); model.user_tower.load_state_dict(ck['user_tower']); model.item_tower.load_state_dict(ck['item_tower']); model.eval()\\n hs,rs,uf=make_inference_profiles(a.stage,ev,len(uids))\\n+ all_user=[]; all_item=[]\\n with torch.inference_mode():\\n h=torch.from_numpy(pad_bags(hs,PAD_HISTORY,len(items))); r=torch.from_numpy(pad_bags(rs,PAD_RECENT,len(items)))\\n- user=[]\\n- for s in range(0,len(uids),256): user.append(model.user_tower(h[s:s+256],r[s:s+256],torch.from_numpy(uf[s:s+256])))\\n- user=torch.cat(user); ivec=model.item_tower(torch.arange(len(items)),torch.from_numpy(pf.astype(np.float32)))\\n- # Unit vectors give the extra coordinate one stable scale for all users.\\n- if a.mode == 'cosine':\\n- user=torch.nn.functional.normalize(user,dim=1); ivec=torch.nn.functional.normalize(ivec,dim=1)\\n+ for checkpoint in a.checkpoint:\\n+ ck=torch.load(checkpoint,map_location='cpu',weights_only=True)\\n+ model=TwoTower(len(items),pf.shape[1]); model.user_tower.load_state_dict(ck['user_tower']); model.item_tower.load_state_dict(ck['item_tower']); model.eval()\\n+ user=[]\\n+ for s in range(0,len(uids),256): user.append(model.user_tower(h[s:s+256],r[s:s+256],torch.from_numpy(uf[s:s+256])))\\n+ user=torch.cat(user); ivec=model.item_tower(torch.arange(len(items)),torch.from_numpy(pf.astype(np.float32)))\\n+ if a.mode == 'cosine':\\n+ user=torch.nn.functional.normalize(user,dim=1); ivec=torch.nn.functional.normalize(ivec,dim=1)\\n+ all_user.append(user); all_item.append(ivec)\\n+ # Concatenation makes an ensemble itself an exact dot-product tower.\\n+ scale=math.sqrt(len(all_user))\\n+ user=torch.cat(all_user,dim=1)/scale; ivec=torch.cat(all_item,dim=1)/scale\\n@@\\n- score=neural+alpha*trend[None,:]; score[~elig]=-np.inf\\n+ # Algebraically this is dot([user, 1], [item, alpha * trend]).\\n+ # Thus the calibrated production score remains exact MIPS.\\n+ score=neural+alpha*trend[None,:]; score[~elig]=-np.inf\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:30:38.560Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-83a6bd98-5fdc-4c76-94dc-81b762e7497a", "changes": { "/app/solution/calibrate_ranker.py": { "type": "update", "unified_diff": "@@ -15,3 +15,3 @@\n ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],required=True)\n- ap.add_argument('--checkpoint',type=Path,required=True); ap.add_argument('--prefix',type=Path,required=True)\n+ ap.add_argument('--checkpoint',type=Path,nargs='+',required=True); ap.add_argument('--prefix',type=Path,required=True)\n ap.add_argument('--alphas',default='0,0.1,0.25,0.5,0.75,1,1.5,2,3')\n@@ -41,13 +41,18 @@\n pf=np.log1p(np.stack(cols,1).astype(np.float32)); pf=(pf-pf.mean(0))/(pf.std(0)+1e-5)\n- ck=torch.load(a.checkpoint,map_location='cpu',weights_only=True)\n- model=TwoTower(len(items),pf.shape[1]); model.user_tower.load_state_dict(ck['user_tower']); model.item_tower.load_state_dict(ck['item_tower']); model.eval()\n hs,rs,uf=make_inference_profiles(a.stage,ev,len(uids))\n+ all_user=[]; all_item=[]\n with torch.inference_mode():\n h=torch.from_numpy(pad_bags(hs,PAD_HISTORY,len(items))); r=torch.from_numpy(pad_bags(rs,PAD_RECENT,len(items)))\n- user=[]\n- for s in range(0,len(uids),256): user.append(model.user_tower(h[s:s+256],r[s:s+256],torch.from_numpy(uf[s:s+256])))\n- user=torch.cat(user); ivec=model.item_tower(torch.arange(len(items)),torch.from_numpy(pf.astype(np.float32)))\n- # Unit vectors give the extra coordinate one stable scale for all users.\n- if a.mode == 'cosine':\n- user=torch.nn.functional.normalize(user,dim=1); ivec=torch.nn.functional.normalize(ivec,dim=1)\n+ for checkpoint in a.checkpoint:\n+ ck=torch.load(checkpoint,map_location='cpu',weights_only=True)\n+ model=TwoTower(len(items),pf.shape[1]); model.user_tower.load_state_dict(ck['user_tower']); model.item_tower.load_state_dict(ck['item_tower']); model.eval()\n+ user=[]\n+ for s in range(0,len(uids),256): user.append(model.user_tower(h[s:s+256],r[s:s+256],torch.from_numpy(uf[s:s+256])))\n+ user=torch.cat(user); ivec=model.item_tower(torch.arange(len(items)),torch.from_numpy(pf.astype(np.float32)))\n+ if a.mode == 'cosine':\n+ user=torch.nn.functional.normalize(user,dim=1); ivec=torch.nn.functional.normalize(ivec,dim=1)\n+ all_user.append(user); all_item.append(ivec)\n+ # Concatenation makes an ensemble itself an exact dot-product tower.\n+ scale=math.sqrt(len(all_user))\n+ user=torch.cat(all_user,dim=1)/scale; ivec=torch.cat(all_item,dim=1)/scale\n z=np.load(DATA/f'{a.stage}_eligible_candidates.npz'); packed=z['packed_eligible']\n@@ -60,2 +65,4 @@\n for alpha in alphas:\n+ # Algebraically this is dot([user, 1], [item, alpha * trend]).\n+ # Thus the calibrated production score remains exact MIPS.\n score=neural+alpha*trend[None,:]; score[~elig]=-np.inf\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/calibrate_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd793b4f887d1998f902769ae004e", "usage": { "input_tokens": 54338, "cached_input_tokens": 54016, "cache_write_input_tokens": 0, "output_tokens": 1428, "reasoning_output_tokens": 641, "total_tokens": 55766 }, "turn_token_usage": { "input_tokens": 2881659, "cached_input_tokens": 2825984, "cache_write_input_tokens": 0, "output_tokens": 25827, "reasoning_output_tokens": 9765, "total_tokens": 2907486 }, "thread_token_usage": { "input_tokens": 2881659, "cached_input_tokens": 2825984, "cache_write_input_tokens": 0, "output_tokens": 25827, "reasoning_output_tokens": 9765, "total_tokens": 2907486 } } ## Step 59 · agent · 2026-09-19T00:30:42.135Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:30:42.984Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate_ranker.py\\n@@\\n import argparse\\n+import math\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:30:43.104Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0f462332-c73a-4244-940d-bc14ecf027f7", "changes": { "/app/solution/calibrate_ranker.py": { "type": "update", "unified_diff": "@@ -3,2 +3,3 @@\n import argparse\n+import math\n from pathlib import Path\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/calibrate_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7aedda087d18523d6c569ed1561", "usage": { "input_tokens": 55790, "cached_input_tokens": 54144, "cache_write_input_tokens": 0, "output_tokens": 65, "reasoning_output_tokens": 10, "total_tokens": 55855 }, "turn_token_usage": { "input_tokens": 2937449, "cached_input_tokens": 2880128, "cache_write_input_tokens": 0, "output_tokens": 25892, "reasoning_output_tokens": 9775, "total_tokens": 2963341 }, "thread_token_usage": { "input_tokens": 2937449, "cached_input_tokens": 2880128, "cache_write_input_tokens": 0, "output_tokens": 25892, "reasoning_output_tokens": 9775, "total_tokens": 2963341 } } ## Step 60 · agent · 2026-09-19T00:30:51.116Z ### Thinking unavailable — no text recorded The selected model is already refit and a valid final submission is saved. I’m using the remaining budget for a low-risk variance check: a second random initialization. If averaging the two validation towers helps, the final score remains a single exact dot product by concatenating their vectors; if it does not, the saved single-model submission stays in place. ### exec · 2026-09-19T00:30:51.137Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_seed2.parquet --epochs 10 --negative-power 0.25 --seed 73 && mv solution/two_tower_validation.pt solution/two_tower_validation_seed2.pt\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:30:52.307Z Script completed Wall time 1.2 seconds Output: SESSION_ID=19989 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7b3699487d1a6b774e4382baa5e", "usage": { "input_tokens": 55879, "cached_input_tokens": 55552, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 8, "total_tokens": 56087 }, "turn_token_usage": { "input_tokens": 2993328, "cached_input_tokens": 2935680, "cache_write_input_tokens": 0, "output_tokens": 26100, "reasoning_output_tokens": 9783, "total_tokens": 3019428 }, "thread_token_usage": { "input_tokens": 2993328, "cached_input_tokens": 2935680, "cache_write_input_tokens": 0, "output_tokens": 26100, "reasoning_output_tokens": 9783, "total_tokens": 3019428 } } ### exec · 2026-09-19T00:30:55.038Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":19989,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:31:25.046Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"08268f","wall_time_seconds":30.000959111,"session_id":19989,"original_token_count":21} loading compact event streams catalog listens=31,062,540 likes=472,889 seconds=3.7 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7bc7d0c87d19abc460474c53305", "usage": { "input_tokens": 56115, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 56165 }, "turn_token_usage": { "input_tokens": 3049443, "cached_input_tokens": 2946816, "cache_write_input_tokens": 0, "output_tokens": 26150, "reasoning_output_tokens": 9783, "total_tokens": 3075593 }, "thread_token_usage": { "input_tokens": 3049443, "cached_input_tokens": 2946816, "cache_write_input_tokens": 0, "output_tokens": 26150, "reasoning_output_tokens": 9783, "total_tokens": 3075593 } } ### exec · 2026-09-19T00:31:27.429Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":19989,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:31:57.439Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"5f8a77","wall_time_seconds":30.001318086,"session_id":19989,"original_token_count":117} training profiles=44,451 mean_labels=12.70 /app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) opt.step(); losses.append(float(loss)) epoch=1 profiles=44451 loss=3.32654 epoch=2 profiles=44451 loss=3.21744 epoch=3 profiles=44451 loss=3.13453 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7dd3b2c87d191d0c7a2e2f55b57", "usage": { "input_tokens": 56242, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 56292 }, "turn_token_usage": { "input_tokens": 3105685, "cached_input_tokens": 3002496, "cache_write_input_tokens": 0, "output_tokens": 26200, "reasoning_output_tokens": 9783, "total_tokens": 3131885 }, "thread_token_usage": { "input_tokens": 3105685, "cached_input_tokens": 3002496, "cache_write_input_tokens": 0, "output_tokens": 26200, "reasoning_output_tokens": 9783, "total_tokens": 3131885 } } ### exec · 2026-09-19T00:32:00.094Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":19989,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:32:30.108Z Script completed Wall time 30.0 seconds Output: {"chunk_id":"7417bd","wall_time_seconds":30.001800127,"session_id":19989,"original_token_count":45} epoch=4 profiles=44451 loss=3.06695 epoch=5 profiles=44451 loss=3.01121 epoch=6 profiles=44451 loss=2.97018 epoch=7 profiles=44451 loss=2.91542 epoch=8 profiles=44451 loss=2.87416 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd7fda15887d1a1a75da811ca1451", "usage": { "input_tokens": 56470, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 56520 }, "turn_token_usage": { "input_tokens": 3162155, "cached_input_tokens": 3058432, "cache_write_input_tokens": 0, "output_tokens": 26250, "reasoning_output_tokens": 9783, "total_tokens": 3188405 }, "thread_token_usage": { "input_tokens": 3162155, "cached_input_tokens": 3058432, "cache_write_input_tokens": 0, "output_tokens": 26250, "reasoning_output_tokens": 9783, "total_tokens": 3188405 } } ### exec · 2026-09-19T00:32:32.546Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":19989,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":5000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:32:40.951Z Script completed Wall time 8.4 seconds Output: {"chunk_id":"4029c0","wall_time_seconds":8.395492911,"exit_code":0,"original_token_count":37} epoch=9 profiles=44451 loss=2.82811 epoch=10 profiles=44451 loss=2.77782 {"output": "solution/validation_twotower_seed2.parquet", "seconds": 105.5} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd81e802487d1a8ad63da2443b899", "usage": { "input_tokens": 56643, "cached_input_tokens": 56320, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 56693 }, "turn_token_usage": { "input_tokens": 3218798, "cached_input_tokens": 3114752, "cache_write_input_tokens": 0, "output_tokens": 26300, "reasoning_output_tokens": 9783, "total_tokens": 3245098 }, "thread_token_usage": { "input_tokens": 3218798, "cached_input_tokens": 3114752, "cache_write_input_tokens": 0, "output_tokens": 26300, "reasoning_output_tokens": 9783, "total_tokens": 3245098 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f2074695-fe92-4ca7-acf3-93caf054d2ec", "process_id": "19989", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_seed2.parquet --epochs 10 --negative-power 0.25 --seed 73 && mv solution/two_tower_validation.pt solution/two_tower_validation_seed2.pt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_twotower_seed2.parquet --epochs 10 --negative-power 0.25 --seed 73 && mv solution/two_tower_validation.pt solution/two_tower_validation_seed2.pt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.7\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32654\nepoch=2 profiles=44451 loss=3.21744\nepoch=3 profiles=44451 loss=3.13453\nepoch=4 profiles=44451 loss=3.06695\nepoch=5 profiles=44451 loss=3.01121\nepoch=6 profiles=44451 loss=2.97018\nepoch=7 profiles=44451 loss=2.91542\nepoch=8 profiles=44451 loss=2.87416\nepoch=9 profiles=44451 loss=2.82811\nepoch=10 profiles=44451 loss=2.77782\n{\"output\": \"solution/validation_twotower_seed2.parquet\", \"seconds\": 105.5}\n", "stderr": "", "aggregated_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.7\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32654\nepoch=2 profiles=44451 loss=3.21744\nepoch=3 profiles=44451 loss=3.13453\nepoch=4 profiles=44451 loss=3.06695\nepoch=5 profiles=44451 loss=3.01121\nepoch=6 profiles=44451 loss=2.97018\nepoch=7 profiles=44451 loss=2.91542\nepoch=8 profiles=44451 loss=2.87416\nepoch=9 profiles=44451 loss=2.82811\nepoch=10 profiles=44451 loss=2.77782\n{\"output\": \"solution/validation_twotower_seed2.parquet\", \"seconds\": 105.5}\n", "exit_code": 0, "duration": { "secs": 109, "nanos": 644003387 }, "formatted_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.7\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:295: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.32654\nepoch=2 profiles=44451 loss=3.21744\nepoch=3 profiles=44451 loss=3.13453\nepoch=4 profiles=44451 loss=3.06695\nepoch=5 profiles=44451 loss=3.01121\nepoch=6 profiles=44451 loss=2.97018\nepoch=7 profiles=44451 loss=2.91542\nepoch=8 profiles=44451 loss=2.87416\nepoch=9 profiles=44451 loss=2.82811\nepoch=10 profiles=44451 loss=2.77782\n{\"output\": \"solution/validation_twotower_seed2.parquet\", \"seconds\": 105.5}\n" } } ## Step 61 · agent · 2026-09-19T00:32:44.036Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:32:46.175Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt solution/two_tower_validation_seed2.pt --prefix solution/validation_ensemble --mode raw --alphas 7,8,9,10,11,12,13\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:32:47.341Z Script completed Wall time 1.2 seconds Output: SESSION_ID=54991 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd829230487d1889da287b9ceb8af", "usage": { "input_tokens": 56797, "cached_input_tokens": 56448, "cache_write_input_tokens": 0, "output_tokens": 150, "reasoning_output_tokens": 25, "total_tokens": 56947 }, "turn_token_usage": { "input_tokens": 3275595, "cached_input_tokens": 3171200, "cache_write_input_tokens": 0, "output_tokens": 26450, "reasoning_output_tokens": 9808, "total_tokens": 3302045 }, "thread_token_usage": { "input_tokens": 3275595, "cached_input_tokens": 3171200, "cache_write_input_tokens": 0, "output_tokens": 26450, "reasoning_output_tokens": 9808, "total_tokens": 3302045 } } ### exec · 2026-09-19T00:32:50.732Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":54991,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:33:11.910Z Script completed Wall time 21.2 seconds Output: {"chunk_id":"e3979d","wall_time_seconds":21.168180706,"exit_code":0,"original_token_count":80} neural score std 13.415834426879883 solution/validation_ensemble_a7.parquet solution/validation_ensemble_a8.parquet solution/validation_ensemble_a9.parquet solution/validation_ensemble_a10.parquet solution/validation_ensemble_a11.parquet solution/validation_ensemble_a12.parquet solution/validation_ensemble_a13.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd82f893487d1b7d4d503d341b14c", "usage": { "input_tokens": 56975, "cached_input_tokens": 56576, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 57025 }, "turn_token_usage": { "input_tokens": 3332570, "cached_input_tokens": 3227776, "cache_write_input_tokens": 0, "output_tokens": 26500, "reasoning_output_tokens": 9808, "total_tokens": 3359070 }, "thread_token_usage": { "input_tokens": 3332570, "cached_input_tokens": 3227776, "cache_write_input_tokens": 0, "output_tokens": 26500, "reasoning_output_tokens": 9808, "total_tokens": 3359070 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-767db59e-7c06-4a90-aad9-ec7237dce200", "process_id": "54991", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt solution/two_tower_validation_seed2.pt --prefix solution/validation_ensemble --mode raw --alphas 7,8,9,10,11,12,13" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage validation --checkpoint solution/two_tower_validation_midneg.pt solution/two_tower_validation_seed2.pt --prefix solution/validation_ensemble --mode raw --alphas 7,8,9,10,11,12,13" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "neural score std 13.415834426879883\nsolution/validation_ensemble_a7.parquet\nsolution/validation_ensemble_a8.parquet\nsolution/validation_ensemble_a9.parquet\nsolution/validation_ensemble_a10.parquet\nsolution/validation_ensemble_a11.parquet\nsolution/validation_ensemble_a12.parquet\nsolution/validation_ensemble_a13.parquet\n", "stderr": "", "aggregated_output": "neural score std 13.415834426879883\nsolution/validation_ensemble_a7.parquet\nsolution/validation_ensemble_a8.parquet\nsolution/validation_ensemble_a9.parquet\nsolution/validation_ensemble_a10.parquet\nsolution/validation_ensemble_a11.parquet\nsolution/validation_ensemble_a12.parquet\nsolution/validation_ensemble_a13.parquet\n", "exit_code": 0, "duration": { "secs": 25, "nanos": 568195897 }, "formatted_output": "neural score std 13.415834426879883\nsolution/validation_ensemble_a7.parquet\nsolution/validation_ensemble_a8.parquet\nsolution/validation_ensemble_a9.parquet\nsolution/validation_ensemble_a10.parquet\nsolution/validation_ensemble_a11.parquet\nsolution/validation_ensemble_a12.parquet\nsolution/validation_ensemble_a13.parquet\n" } } ## Step 62 · agent · 2026-09-19T00:33:13.960Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:33:15.843Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\" | tr '\\\\n' ' ' | sed 's/ */ /g'; echo; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:33:17.009Z Script completed Wall time 1.2 seconds Output: SESSION_ID=74209 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd848183087d184135001ee3d0d7b", "usage": { "input_tokens": 57161, "cached_input_tokens": 56832, "cache_write_input_tokens": 0, "output_tokens": 118, "reasoning_output_tokens": 6, "total_tokens": 57279 }, "turn_token_usage": { "input_tokens": 3389731, "cached_input_tokens": 3284608, "cache_write_input_tokens": 0, "output_tokens": 26618, "reasoning_output_tokens": 9814, "total_tokens": 3416349 }, "thread_token_usage": { "input_tokens": 3389731, "cached_input_tokens": 3284608, "cache_write_input_tokens": 0, "output_tokens": 26618, "reasoning_output_tokens": 9814, "total_tokens": 3416349 } } ### exec · 2026-09-19T00:33:20.272Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":74209,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":4000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:33:39.264Z Script completed Wall time 19.0 seconds Output: {"chunk_id":"ceadff","wall_time_seconds":18.983326262,"exit_code":0,"original_token_count":215} { "model": "solution/validation_ensemble_a10.parquet", "scores": { "quality_ndcg@10": 0.031039, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a11.parquet", "scores": { "quality_ndcg@10": 0.030845, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a12.parquet", "scores": { "quality_ndcg@10": 0.030813, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a13.parquet", "scores": { "quality_ndcg@10": 0.030724, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a7.parquet", "scores": { "quality_ndcg@10": 0.030359, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a8.parquet", "scores": { "quality_ndcg@10": 0.030584, "quality_users": 4263 } } { "model": "solution/validation_ensemble_a9.parquet", "scores": { "quality_ndcg@10": 0.030829, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd84d2ed087d19b8aace72f8de0a9", "usage": { "input_tokens": 57307, "cached_input_tokens": 56960, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 57357 }, "turn_token_usage": { "input_tokens": 3447038, "cached_input_tokens": 3341568, "cache_write_input_tokens": 0, "output_tokens": 26668, "reasoning_output_tokens": 9814, "total_tokens": 3473706 }, "thread_token_usage": { "input_tokens": 3447038, "cached_input_tokens": 3341568, "cache_write_input_tokens": 0, "output_tokens": 26668, "reasoning_output_tokens": 9814, "total_tokens": 3473706 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bc0d78cc-ef31-470b-9a56-f0b2429753e9", "process_id": "74209", "command": [ "/bin/sh", "-lc", "for f in solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_ensemble_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/ */ /g'; echo; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{ \"model\": \"solution/validation_ensemble_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031039, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030845, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030813, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030724, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030359, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030584, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030829, \"quality_users\": 4263 } } \n", "stderr": "", "aggregated_output": "{ \"model\": \"solution/validation_ensemble_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031039, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030845, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030813, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030724, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030359, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030584, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030829, \"quality_users\": 4263 } } \n", "exit_code": 0, "duration": { "secs": 23, "nanos": 254551302 }, "formatted_output": "{ \"model\": \"solution/validation_ensemble_a10.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031039, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a11.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030845, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a12.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030813, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a13.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030724, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a7.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030359, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a8.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030584, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_ensemble_a9.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030829, \"quality_users\": 4263 } } \n" } } ## Step 63 · agent · 2026-09-19T00:33:47.281Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:33:55.006Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/calibrate_ranker.py\\n@@\\n z=np.load(DATA/f'{a.stage}_eligible_candidates.npz'); packed=z['packed_eligible']\\n alphas=[float(x) for x in a.alphas.split(',')]\\n outputs={alpha:[] for alpha in alphas}\\n+ # These are the actual production retrieval vectors. Their final scalar\\n+ # coordinate carries the cutoff-correct global discovery trend.\\n+ user_extended=np.column_stack([user.numpy(),np.ones(len(user),dtype=np.float32)])\\n+ item_extended={alpha:np.column_stack([ivec.numpy(),(alpha*trend).astype(np.float32)])\\n+ for alpha in alphas}\\n for s in range(0,len(uids),128):\\n- e=min(len(uids),s+128); neural=(user[s:e]@ivec.T).numpy()\\n+ e=min(len(uids),s+128)\\n elig=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder='little').astype(bool)\\n- if s == 0: print('neural score std',float(neural.std()),flush=True)\\n+ if s == 0: print('neural score std',float((user[s:e]@ivec.T).std()),flush=True)\\n for alpha in alphas:\\n- # Algebraically this is dot([user, 1], [item, alpha * trend]).\\n- # Thus the calibrated production score remains exact MIPS.\\n- score=neural+alpha*trend[None,:]; score[~elig]=-np.inf\\n+ # Exact MIPS: there are no cross features or post-dot-product terms.\\n+ score=user_extended[s:e]@item_extended[alpha].T; score[~elig]=-np.inf\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:33:55.130Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4dd31f32-b9f3-43bf-98fa-bfabae67784c", "changes": { "/app/solution/calibrate_ranker.py": { "type": "update", "unified_diff": "@@ -61,10 +61,14 @@\n outputs={alpha:[] for alpha in alphas}\n+ # These are the actual production retrieval vectors. Their final scalar\n+ # coordinate carries the cutoff-correct global discovery trend.\n+ user_extended=np.column_stack([user.numpy(),np.ones(len(user),dtype=np.float32)])\n+ item_extended={alpha:np.column_stack([ivec.numpy(),(alpha*trend).astype(np.float32)])\n+ for alpha in alphas}\n for s in range(0,len(uids),128):\n- e=min(len(uids),s+128); neural=(user[s:e]@ivec.T).numpy()\n+ e=min(len(uids),s+128)\n elig=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder='little').astype(bool)\n- if s == 0: print('neural score std',float(neural.std()),flush=True)\n+ if s == 0: print('neural score std',float((user[s:e]@ivec.T).std()),flush=True)\n for alpha in alphas:\n- # Algebraically this is dot([user, 1], [item, alpha * trend]).\n- # Thus the calibrated production score remains exact MIPS.\n- score=neural+alpha*trend[None,:]; score[~elig]=-np.inf\n+ # Exact MIPS: there are no cross features or post-dot-product terms.\n+ score=user_extended[s:e]@item_extended[alpha].T; score[~elig]=-np.inf\n ix=np.argpartition(score,-100,axis=1)[:,-100:]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/calibrate_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd863743c87d1aac11820779c2c3d", "usage": { "input_tokens": 57702, "cached_input_tokens": 57088, "cache_write_input_tokens": 0, "output_tokens": 822, "reasoning_output_tokens": 384, "total_tokens": 58524 }, "turn_token_usage": { "input_tokens": 3504740, "cached_input_tokens": 3398656, "cache_write_input_tokens": 0, "output_tokens": 27490, "reasoning_output_tokens": 10198, "total_tokens": 3532230 }, "thread_token_usage": { "input_tokens": 3504740, "cached_input_tokens": 3398656, "cache_write_input_tokens": 0, "output_tokens": 27490, "reasoning_output_tokens": 10198, "total_tokens": 3532230 } } ## Step 64 · agent · 2026-09-19T00:33:57.283Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:33:59.340Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_exact --mode raw --alphas 10 && cp solution/submission_exact_a10.parquet solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:34:00.506Z Script completed Wall time 1.2 seconds Output: SESSION_ID=60913 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd8736af487d1a6586b175830d35e", "usage": { "input_tokens": 58548, "cached_input_tokens": 57472, "cache_write_input_tokens": 0, "output_tokens": 141, "reasoning_output_tokens": 25, "total_tokens": 58689 }, "turn_token_usage": { "input_tokens": 3563288, "cached_input_tokens": 3456128, "cache_write_input_tokens": 0, "output_tokens": 27631, "reasoning_output_tokens": 10223, "total_tokens": 3590919 }, "thread_token_usage": { "input_tokens": 3563288, "cached_input_tokens": 3456128, "cache_write_input_tokens": 0, "output_tokens": 27631, "reasoning_output_tokens": 10223, "total_tokens": 3590919 } } ### exec · 2026-09-19T00:34:02.869Z { "code": "let{output,...rest}=await tools.write_stdin({\"session_id\":60913,\"yield_time_ms\":30000,\"chars\":\"\",\"max_output_tokens\":3000});text(rest);text(output);\n" } ### Tool output · 2026-09-19T00:34:12.232Z Script completed Wall time 9.3 seconds Output: {"chunk_id":"a23e44","wall_time_seconds":9.328491167,"exit_code":0,"original_token_count":19} neural score std 13.082048416137695 solution/submission_exact_a10.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd878b10487d19ebb8a617a66b8b6", "usage": { "input_tokens": 58717, "cached_input_tokens": 58368, "cache_write_input_tokens": 0, "output_tokens": 50, "reasoning_output_tokens": 0, "total_tokens": 58767 }, "turn_token_usage": { "input_tokens": 3622005, "cached_input_tokens": 3514496, "cache_write_input_tokens": 0, "output_tokens": 27681, "reasoning_output_tokens": 10223, "total_tokens": 3649686 }, "thread_token_usage": { "input_tokens": 3622005, "cached_input_tokens": 3514496, "cache_write_input_tokens": 0, "output_tokens": 27681, "reasoning_output_tokens": 10223, "total_tokens": 3649686 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d1563ca2-d1da-4eff-9a43-559909253fb3", "process_id": "60913", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_exact --mode raw --alphas 10 && cp solution/submission_exact_a10.parquet solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/calibrate_ranker.py --stage test --checkpoint solution/two_tower_test.pt --prefix solution/submission_exact --mode raw --alphas 10 && cp solution/submission_exact_a10.parquet solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "neural score std 13.082048416137695\nsolution/submission_exact_a10.parquet\n", "stderr": "", "aggregated_output": "neural score std 13.082048416137695\nsolution/submission_exact_a10.parquet\n", "exit_code": 0, "duration": { "secs": 12, "nanos": 699904815 }, "formatted_output": "neural score std 13.082048416137695\nsolution/submission_exact_a10.parquet\n" } } ## Step 65 · agent · 2026-09-19T00:34:22.594Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:34:35.026Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/run_final.sh\\n+#!/bin/sh\\n+set -eu\\n+cd /app\\n+/opt/task-venv/bin/python solution/train_two_tower.py \\\\\\n+ --stage test \\\\\\n+ --output solution/submission_neural_only.parquet \\\\\\n+ --epochs 10 \\\\\\n+ --negative-power 0.25 \\\\\\n+ --seed 20260919\\n+/opt/task-venv/bin/python solution/calibrate_ranker.py \\\\\\n+ --stage test \\\\\\n+ --checkpoint solution/two_tower_test.pt \\\\\\n+ --prefix solution/submission_exact \\\\\\n+ --mode raw \\\\\\n+ --alphas 10\\n+cp solution/submission_exact_a10.parquet solution/submission.parquet\\n+\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+## Model\\n+\\n+The ranker is a CPU PyTorch two-tower model with no user-ID embedding and no\\n+global user-item matrix. The user tower separately embeds (a) the 256 most\\n+frequent positive-history catalog tracks and (b) 96 unique tracks from the\\n+last 60 days, mean-pools each bag, adds four activity features, and maps the\\n+result through an MLP to 64 dimensions. The item tower combines a trainable\\n+item embedding with an MLP over six cutoff-correct item statistics (organic\\n+80%-listen counts at 3/7/14/30 days, seven-day organic likes, and lifetime\\n+organic 80%-listens) and emits a 64-dimensional vector.\\n+\\n+A validation-selected trend coordinate is appended to both outputs: `1` on\\n+the user side and 10 times the standardized sum of log seven-day organic\\n+80%-listen and organic-like counts on the item side. The deployed vectors\\n+therefore have 65 dimensions and the complete relevance score—including the\\n+trend term—is exactly one dot product. Candidate vectors can be computed\\n+offline, and serving computes only one history-based user vector before MIPS.\\n+\\n+## Training\\n+\\n+Training uses twelve rolling, cutoff-correct seven-day pseudo-future windows.\\n+Positives are previously unseen catalog tracks with an organic 80% listen;\\n+organic likes are oversampled fourfold to reflect their larger graded gain.\\n+The loss is a 40-way sampled-softmax cross entropy with negatives sampled from\\n+the empirical future-target distribution raised to power 0.25. Histories are\\n+strictly earlier than each pseudo cutoff. The final fit uses `train_* + val_*`,\\n+so validation events become observed history and the public validation window\\n+becomes the newest training pseudo-future.\\n+\\n+## Validation\\n+\\n+The selected single-tower-seed model plus trend coordinate achieved public\\n+graded **NDCG@10 = 0.032947** over 4,263 scored users. For reference, the\\n+seven-day trend alone scored 0.031211, the original neural model alone scored\\n+0.023610, and lifetime popularity scored 0.010710.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app`, run:\\n+\\n+```sh\\n+sh /app/solution/run_final.sh\\n+```\\n+\\n+This refits on train plus validation and writes\\n+`/app/solution/submission.parquet`.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:34:35.155Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-de3f6485-18c7-499d-953c-2b4aeb39de1c", "changes": { "/app/solution/run_final.sh": { "type": "add", "content": "#!/bin/sh\nset -eu\ncd /app\n/opt/task-venv/bin/python solution/train_two_tower.py \\\n --stage test \\\n --output solution/submission_neural_only.parquet \\\n --epochs 10 \\\n --negative-power 0.25 \\\n --seed 20260919\n/opt/task-venv/bin/python solution/calibrate_ranker.py \\\n --stage test \\\n --checkpoint solution/two_tower_test.pt \\\n --prefix solution/submission_exact \\\n --mode raw \\\n --alphas 10\ncp solution/submission_exact_a10.parquet solution/submission.parquet\n\n" }, "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n## Model\n\nThe ranker is a CPU PyTorch two-tower model with no user-ID embedding and no\nglobal user-item matrix. The user tower separately embeds (a) the 256 most\nfrequent positive-history catalog tracks and (b) 96 unique tracks from the\nlast 60 days, mean-pools each bag, adds four activity features, and maps the\nresult through an MLP to 64 dimensions. The item tower combines a trainable\nitem embedding with an MLP over six cutoff-correct item statistics (organic\n80%-listen counts at 3/7/14/30 days, seven-day organic likes, and lifetime\norganic 80%-listens) and emits a 64-dimensional vector.\n\nA validation-selected trend coordinate is appended to both outputs: `1` on\nthe user side and 10 times the standardized sum of log seven-day organic\n80%-listen and organic-like counts on the item side. The deployed vectors\ntherefore have 65 dimensions and the complete relevance score—including the\ntrend term—is exactly one dot product. Candidate vectors can be computed\noffline, and serving computes only one history-based user vector before MIPS.\n\n## Training\n\nTraining uses twelve rolling, cutoff-correct seven-day pseudo-future windows.\nPositives are previously unseen catalog tracks with an organic 80% listen;\norganic likes are oversampled fourfold to reflect their larger graded gain.\nThe loss is a 40-way sampled-softmax cross entropy with negatives sampled from\nthe empirical future-target distribution raised to power 0.25. Histories are\nstrictly earlier than each pseudo cutoff. The final fit uses `train_* + val_*`,\nso validation events become observed history and the public validation window\nbecomes the newest training pseudo-future.\n\n## Validation\n\nThe selected single-tower-seed model plus trend coordinate achieved public\ngraded **NDCG@10 = 0.032947** over 4,263 scored users. For reference, the\nseven-day trend alone scored 0.031211, the original neural model alone scored\n0.023610, and lifetime popularity scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThis refits on train plus validation and writes\n`/app/solution/submission.parquet`.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/run_final.sh\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd884a29487d1aaaf4129b4331b29", "usage": { "input_tokens": 58842, "cached_input_tokens": 58496, "cache_write_input_tokens": 0, "output_tokens": 1181, "reasoning_output_tokens": 391, "total_tokens": 60023 }, "turn_token_usage": { "input_tokens": 3680847, "cached_input_tokens": 3572992, "cache_write_input_tokens": 0, "output_tokens": 28862, "reasoning_output_tokens": 10614, "total_tokens": 3709709 }, "thread_token_usage": { "input_tokens": 3680847, "cached_input_tokens": 3572992, "cache_write_input_tokens": 0, "output_tokens": 28862, "reasoning_output_tokens": 10614, "total_tokens": 3709709 } } ## Step 66 · agent · 2026-09-19T00:34:38.835Z ### Thinking unavailable — no text recorded The variance check did not help (the two-seed ensemble fell to 0.031039), so I kept the stronger single-seed model. The final artifact has now been regenerated through the explicit extended-vector dot-product path; I’m doing the last schema, rank, duplication, and test-eligibility audit now. ### exec · 2026-09-19T00:34:47.951Z { "code": "const r = await tools.exec_command({\"cmd\":\"chmod +x solution/run_final.sh && /opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nx=validate_ranking(pd.read_parquet(p),targets)\\nz=np.load(D/'test_eligible_candidates.npz')\\nassert np.array_equal(np.sort(x.uid.unique()),np.sort(z['uids'].astype(np.int64)))\\nitem_to_col={int(v):i for i,v in enumerate(z['item_ids'])}; uid_to_row={int(v):i for i,v in enumerate(z['uids'])}\\nvalid=True\\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\\n c=item_to_col.get(int(item),-1); r=uid_to_row[int(uid)]\\n if c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1): valid=False; break\\nassert valid\\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'eligible':valid,'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\\nPY\\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh\\ngit status --short 2>/dev/null || true\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:35:18.118Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd89b99dc87d1ad8c2d45d1ec454e", "usage": { "input_tokens": 60047, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 522, "reasoning_output_tokens": 12, "total_tokens": 60569 }, "turn_token_usage": { "input_tokens": 3740894, "cached_input_tokens": 3631616, "cache_write_input_tokens": 0, "output_tokens": 29384, "reasoning_output_tokens": 10626, "total_tokens": 3770278 }, "thread_token_usage": { "input_tokens": 3740894, "cached_input_tokens": 3631616, "cache_write_input_tokens": 0, "output_tokens": 29384, "reasoning_output_tokens": 10626, "total_tokens": 3770278 } } ## Step 67 · agent · 2026-09-19T00:35:20.704Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:35:27.178Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nx=validate_ranking(pd.read_parquet(p),targets).sort_values(['uid','rank'])\\nz=np.load(D/'test_eligible_candidates.npz')\\nassert np.array_equal(x.uid.unique(),z['uids'].astype(np.int64))\\nitems=z['item_ids'].astype(np.int64); order=np.argsort(items)\\nvals=x.item_id.to_numpy(); loc=np.searchsorted(items[order],vals); cols=order[loc]\\nrows=np.repeat(np.arange(len(z['uids'])),100)\\nelig=((z['packed_eligible'][rows,cols//8] >> (cols%8)) & 1).astype(bool)\\nassert elig.all()\\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'all_test_eligible':bool(elig.all()),'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\\nPY\\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:35:29.317Z Script completed Wall time 2.1 seconds Output: {'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'all_test_eligible': True, 'bytes': 1187911, 'rank_min': 1, 'rank_max': 100} -rw-r--r-- 1 agent agent 2.2K Sep 19 00:34 solution/README.md -rwxr-xr-x 1 agent agent 468 Sep 19 00:34 solution/run_final.sh -rw-r--r-- 1 agent agent 1.2M Sep 19 00:34 solution/submission.parquet -rw-r--r-- 1 agent agent 23M Sep 19 00:29 solution/two_tower_test.pt ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd8c64bf087d1899ca609b32bfa2c", "usage": { "input_tokens": 60592, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 439, "reasoning_output_tokens": 71, "total_tokens": 61031 }, "turn_token_usage": { "input_tokens": 3801486, "cached_input_tokens": 3691520, "cache_write_input_tokens": 0, "output_tokens": 29823, "reasoning_output_tokens": 10697, "total_tokens": 3831309 }, "thread_token_usage": { "input_tokens": 3801486, "cached_input_tokens": 3691520, "cache_write_input_tokens": 0, "output_tokens": 29823, "reasoning_output_tokens": 10697, "total_tokens": 3831309 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-62041697-a7ff-48fb-8378-05de1900b154", "process_id": "10448", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nx=validate_ranking(pd.read_parquet(p),targets).sort_values(['uid','rank'])\nz=np.load(D/'test_eligible_candidates.npz')\nassert np.array_equal(x.uid.unique(),z['uids'].astype(np.int64))\nitems=z['item_ids'].astype(np.int64); order=np.argsort(items)\nvals=x.item_id.to_numpy(); loc=np.searchsorted(items[order],vals); cols=order[loc]\nrows=np.repeat(np.arange(len(z['uids'])),100)\nelig=((z['packed_eligible'][rows,cols//8] >> (cols%8)) & 1).astype(bool)\nassert elig.all()\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'all_test_eligible':bool(elig.all()),'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\nPY\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nx=validate_ranking(pd.read_parquet(p),targets).sort_values(['uid','rank'])\nz=np.load(D/'test_eligible_candidates.npz')\nassert np.array_equal(x.uid.unique(),z['uids'].astype(np.int64))\nitems=z['item_ids'].astype(np.int64); order=np.argsort(items)\nvals=x.item_id.to_numpy(); loc=np.searchsorted(items[order],vals); cols=order[loc]\nrows=np.repeat(np.arange(len(z['uids'])),100)\nelig=((z['packed_eligible'][rows,cols//8] >> (cols%8)) & 1).astype(bool)\nassert elig.all()\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'all_test_eligible':bool(elig.all()),'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\nPY\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'all_test_eligible': True, 'bytes': 1187911, 'rank_min': 1, 'rank_max': 100}\n-rw-r--r-- 1 agent agent 2.2K Sep 19 00:34 solution/README.md\n-rwxr-xr-x 1 agent agent 468 Sep 19 00:34 solution/run_final.sh\n-rw-r--r-- 1 agent agent 1.2M Sep 19 00:34 solution/submission.parquet\n-rw-r--r-- 1 agent agent 23M Sep 19 00:29 solution/two_tower_test.pt\n", "stderr": "", "aggregated_output": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'all_test_eligible': True, 'bytes': 1187911, 'rank_min': 1, 'rank_max': 100}\n-rw-r--r-- 1 agent agent 2.2K Sep 19 00:34 solution/README.md\n-rwxr-xr-x 1 agent agent 468 Sep 19 00:34 solution/run_final.sh\n-rw-r--r-- 1 agent agent 1.2M Sep 19 00:34 solution/submission.parquet\n-rw-r--r-- 1 agent agent 23M Sep 19 00:29 solution/two_tower_test.pt\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 964860097 }, "formatted_output": "{'path': '/app/solution/submission.parquet', 'rows': 845600, 'users': 8456, 'all_test_eligible': True, 'bytes': 1187911, 'rank_min': 1, 'rank_max': 100}\n-rw-r--r-- 1 agent agent 2.2K Sep 19 00:34 solution/README.md\n-rwxr-xr-x 1 agent agent 468 Sep 19 00:34 solution/run_final.sh\n-rw-r--r-- 1 agent agent 1.2M Sep 19 00:34 solution/submission.parquet\n-rw-r--r-- 1 agent agent 23M Sep 19 00:29 solution/two_tower_test.pt\n" } } ## Step 68 · agent · 2026-09-19T00:35:39.566Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:35:42.527Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nfrom pathlib import Path\\nD=Path('/task/data'); c=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy()\\nfor stage in ['validation','test']:\\n z=np.load(D/f'{stage}_eligible_candidates.npz')\\n print(stage,'catalog_order_equal',np.array_equal(c,z['item_ids']))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:35:44.284Z Script completed Wall time 1.8 seconds Output: validation catalog_order_equal True test catalog_order_equal True ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd8d17e3887d198bea69ca3bdaa60", "usage": { "input_tokens": 61219, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 678, "reasoning_output_tokens": 504, "total_tokens": 61897 }, "turn_token_usage": { "input_tokens": 3862705, "cached_input_tokens": 3751936, "cache_write_input_tokens": 0, "output_tokens": 30501, "reasoning_output_tokens": 11201, "total_tokens": 3893206 }, "thread_token_usage": { "input_tokens": 3862705, "cached_input_tokens": 3751936, "cache_write_input_tokens": 0, "output_tokens": 30501, "reasoning_output_tokens": 11201, "total_tokens": 3893206 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3e74e294-075a-40d3-8294-4363161572ba", "process_id": "28280", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nfrom pathlib import Path\nD=Path('/task/data'); c=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,'catalog_order_equal',np.array_equal(c,z['item_ids']))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nfrom pathlib import Path\nD=Path('/task/data'); c=pd.read_parquet(D/'candidate_catalog.parquet').item_id.to_numpy()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,'catalog_order_equal',np.array_equal(c,z['item_ids']))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "validation catalog_order_equal True\ntest catalog_order_equal True\n", "stderr": "", "aggregated_output": "validation catalog_order_equal True\ntest catalog_order_equal True\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 588870400 }, "formatted_output": "validation catalog_order_equal True\ntest catalog_order_equal True\n" } } ## Step 69 · agent · 2026-09-19T00:35:45.841Z ### Thinking unavailable — no text recorded Completed the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training and inference: [train_two_tower.py](/app/solution/train_two_tower.py), [calibrate_ranker.py](/app/solution/calibrate_ranker.py) - Documentation: [README.md](/app/solution/README.md) - Reproduction command: [run_final.sh](/app/solution/run_final.sh) Public validation NDCG@10: **0.032947** Final artifact audit: - 845,600 rows - 8,456 users - Exactly 100 distinct items and ranks 1–100 per user - All submitted items verified against test eligibility - Complete score remains an exact dot product between independently computed user and item vectors - Final model refit on `train_* + val_*` without constructing a global interaction matrix ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "session_id": "01a0b6fa-4a3b-71d2-9307-2579541b8253", "root_turn_id": "01a0b6fa-4a4b-7b80-b90b-21b49bd021c0", "response_id": "resp_0f453480cb8a0b0e016aadd8e089e487d19c1a02dab588df1a", "usage": { "input_tokens": 61932, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 227, "reasoning_output_tokens": 29, "total_tokens": 62159 }, "turn_token_usage": { "input_tokens": 3924637, "cached_input_tokens": 3812992, "cache_write_input_tokens": 0, "output_tokens": 30728, "reasoning_output_tokens": 11230, "total_tokens": 3955365 }, "thread_token_usage": { "input_tokens": 3924637, "cached_input_tokens": 3812992, "cache_write_input_tokens": 0, "output_tokens": 30728, "reasoning_output_tokens": 11230, "total_tokens": 3955365 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-331f4423-5f8e-4e0b-9daa-a0709eb1433e", "process_id": "23351", "command": [ "/bin/sh", "-lc", "chmod +x solution/run_final.sh && /opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nx=validate_ranking(pd.read_parquet(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nassert np.array_equal(np.sort(x.uid.unique()),np.sort(z['uids'].astype(np.int64)))\nitem_to_col={int(v):i for i,v in enumerate(z['item_ids'])}; uid_to_row={int(v):i for i,v in enumerate(z['uids'])}\nvalid=True\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n c=item_to_col.get(int(item),-1); r=uid_to_row[int(uid)]\n if c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1): valid=False; break\nassert valid\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'eligible':valid,'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\nPY\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh\ngit status --short 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "chmod +x solution/run_final.sh && /opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/calibrate_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nx=validate_ranking(pd.read_parquet(p),targets)\nz=np.load(D/'test_eligible_candidates.npz')\nassert np.array_equal(np.sort(x.uid.unique()),np.sort(z['uids'].astype(np.int64)))\nitem_to_col={int(v):i for i,v in enumerate(z['item_ids'])}; uid_to_row={int(v):i for i,v in enumerate(z['uids'])}\nvalid=True\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n c=item_to_col.get(int(item),-1); r=uid_to_row[int(uid)]\n if c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1): valid=False; break\nassert valid\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'eligible':valid,'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\nPY\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh\ngit status --short 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": -1, "duration": { "secs": 61, "nanos": 949381593 }, "formatted_output": "" } }