yambda-discovery-ranking-two-tower-base / sol-medium-0-153-4-retriever-r02-20260924T221413Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-25T00:14:34.201Z 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 · 2026-09-25T00:14:35.782Z ## 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 · 2026-09-25T00:14:35.782Z 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 · 2026-09-25T00:14:35.782Z 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 · 2026-09-25T00:14:35.782Z 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 2026-09-25 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": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "medium", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "medium", "summary": "auto" } ## Step 6 · user · 2026-09-25T00:14:35.801Z # 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 · 2026-09-25T00:14:37.338Z ### Thinking unavailable — no text recorded I’ll first audit the data sizes, schemas, and installed stack, then build a scalable event-sampled two-tower baseline and evaluate it. I’ll keep a valid submission in place early, then spend the remaining time on measured improvements. ### exec · 2026-09-25T00:14:39.737Z { "code": "const r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:14:39.801Z Script completed Wall time 0.1 seconds Output: /app /task/requirements.txt /task/ranking_evaluation.py /task/evaluate_public.py /task/data/validation_eligible_candidates.npz /task/data/candidate_catalog.parquet /task/data/val_likes.parquet /task/data/validation_preference_state.parquet /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/val_undislikes.parquet /task/data/target_users.parquet /task/data/val_dislikes.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/validation_eligible_candidates.npz /task/data/candidate_catalog.parquet /task/data/val_likes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/validation_preference_state.parquet /task/data/train_listens.parquet /task/data/target_users.parquet /task/data/val_unlikes.parquet /task/data/val_undislikes.parquet /task/data/val_dislikes.parquet # 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": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bcebfb5c87d28b996368ae73b5d8", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 158, "reasoning_output_tokens": 21, "total_tokens": 14734 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 158, "reasoning_output_tokens": 21, "total_tokens": 14734 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 158, "reasoning_output_tokens": 21, "total_tokens": 14734 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d99971f4-7f5c-4673-98bf-526d3e11e5be", "process_id": "19497", "command": [ "/bin/sh", "-lc", "pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3829 }, "formatted_output": "/app\n/task/requirements.txt\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } ## Step 8 · agent · 2026-09-25T00:14:41.123Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:14:44.519Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\\nfor f in sorted(os.listdir('/task/data')):\\n p='/task/data/'+f\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(p)\\n print(f, pf.metadata.num_rows, pf.schema_arrow)\\n elif f.endswith('.npz'):\\n z=np.load(p)\\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\\nPY\\nsed -n '1,260p' /task/ranking_evaluation.py\\nsed -n '1,220p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:14:44.582Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' """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() ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5c002e10-0f07-4c88-be7d-ee2ebc01d1ba", "process_id": "11146", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nfor f in sorted(os.listdir('/task/data')):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, pf.schema_arrow)\n elif f.endswith('.npz'):\n z=np.load(p)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\nPY\nsed -n '1,260p' /task/ranking_evaluation.py\nsed -n '1,220p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nfor f in sorted(os.listdir('/task/data')):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, pf.schema_arrow)\n elif f.endswith('.npz'):\n z=np.load(p)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\nPY\nsed -n '1,260p' /task/ranking_evaluation.py\nsed -n '1,220p' /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\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", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\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", "exit_code": 0, "duration": { "secs": 0, "nanos": 3590 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\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" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bcf0140887d2a254859da8b4fd9e", "usage": { "input_tokens": 15655, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 220, "reasoning_output_tokens": 22, "total_tokens": 15875 }, "turn_token_usage": { "input_tokens": 30231, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 378, "reasoning_output_tokens": 43, "total_tokens": 30609 }, "thread_token_usage": { "input_tokens": 30231, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 378, "reasoning_output_tokens": 43, "total_tokens": 30609 } } ## Step 9 · agent · 2026-09-25T00:14:46.756Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:14:48.457Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3; ls -la /app; env | sort | sed -n '1,120p'; find / -maxdepth 3 -type f -name 'python*' 2>/dev/null | sed -n '1,80p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:14:48.519Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 /usr/bin/python3 /bin/python3 total 12 drwxr-xr-x 3 agent agent 4096 Sep 25 00:10 . drwxr-xr-x 1 root root 4096 Sep 25 00:14 .. drwxr-xr-x 2 agent agent 4096 Sep 25 00:10 solution CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_MANAGED_BY_NPM=1 CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex CODEX_SESSION_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f CODEX_THREAD_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=c4322052594a LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin NVM_CD_FLAGS= NVM_DIR=/home/agent/.nvm NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node PAGER=cat PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/home/agent/.nvm/versions/node/v22.23.3/bin/codex /usr/bin/python3.11 ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c06645b-eeb5-429e-89bc-73ca208246ab", "process_id": "44426", "command": [ "/bin/sh", "-lc", "which -a python python3; ls -la /app; env | sort | sed -n '1,120p'; find / -maxdepth 3 -type f -name 'python*' 2>/dev/null | sed -n '1,80p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which -a python python3; ls -la /app; env | sort | sed -n '1,120p'; find / -maxdepth 3 -type f -name 'python*' 2>/dev/null | sed -n '1,80p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:10 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:14 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:10 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCODEX_THREAD_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4322052594a\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n/usr/bin/python3.11\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:10 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:14 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:10 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCODEX_THREAD_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4322052594a\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n/usr/bin/python3.11\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6370 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:10 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:14 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:10 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCODEX_THREAD_ID=01a0d5e9-f2af-7842-9fd1-1050dbb24f2f\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=c4322052594a\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n/usr/bin/python3.11\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bcf4d25487d29731b514464ff760", "usage": { "input_tokens": 19146, "cached_input_tokens": 15488, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 30, "total_tokens": 19280 }, "turn_token_usage": { "input_tokens": 49377, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 512, "reasoning_output_tokens": 73, "total_tokens": 49889 }, "thread_token_usage": { "input_tokens": 49377, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 512, "reasoning_output_tokens": 73, "total_tokens": 49889 } } ## Step 10 · agent · 2026-09-25T00:14:50.039Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:14:52.394Z { "code": "const r = await tools.exec_command({cmd:\"find / -type d \\\\( -name 'site-packages' -o -name 'dist-packages' \\\\) 2>/dev/null | sed -n '1,100p'; /usr/bin/python3.11 - <<'PY'\\ntry: import pandas; print('pandas',pandas.__version__)\\nexcept Exception as e: print(e)\\ntry: import torch; print('torch',torch.__version__)\\nexcept Exception as e: print(e)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:14:52.853Z Script completed Wall time 0.5 seconds Output: /usr/lib/python3/dist-packages /usr/local/lib/python3.11/dist-packages /usr/local/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages No module named 'pandas' No module named 'torch' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bcf8d1a887d2a980bab5ac9a5310", "usage": { "input_tokens": 19860, "cached_input_tokens": 18944, "cache_write_input_tokens": 0, "output_tokens": 171, "reasoning_output_tokens": 20, "total_tokens": 20031 }, "turn_token_usage": { "input_tokens": 69237, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 683, "reasoning_output_tokens": 93, "total_tokens": 69920 }, "thread_token_usage": { "input_tokens": 69237, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 683, "reasoning_output_tokens": 93, "total_tokens": 69920 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f9003113-1e47-4650-9d21-a98dcfa9e4e3", "process_id": "43627", "command": [ "/bin/sh", "-lc", "find / -type d \\( -name 'site-packages' -o -name 'dist-packages' \\) 2>/dev/null | sed -n '1,100p'; /usr/bin/python3.11 - <<'PY'\ntry: import pandas; print('pandas',pandas.__version__)\nexcept Exception as e: print(e)\ntry: import torch; print('torch',torch.__version__)\nexcept Exception as e: print(e)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d \\( -name 'site-packages' -o -name 'dist-packages' \\) 2>/dev/null | sed -n '1,100p'; /usr/bin/python3.11 - <<'PY'\ntry: import pandas; print('pandas',pandas.__version__)\nexcept Exception as e: print(e)\ntry: import torch; print('torch',torch.__version__)\nexcept Exception as e: print(e)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/lib/python3/dist-packages\n/usr/local/lib/python3.11/dist-packages\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nNo module named 'pandas'\nNo module named 'torch'\n", "stderr": "", "aggregated_output": "/usr/lib/python3/dist-packages\n/usr/local/lib/python3.11/dist-packages\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nNo module named 'pandas'\nNo module named 'torch'\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 290724424 }, "formatted_output": "/usr/lib/python3/dist-packages\n/usr/local/lib/python3.11/dist-packages\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nNo module named 'pandas'\nNo module named 'torch'\n" } } ## Step 11 · agent · 2026-09-25T00:14:53.880Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:14:57.325Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os, torch\\nprint('versions',pd.__version__,torch.__version__)\\nfor f in sorted(os.listdir('/task/data')):\\n p='/task/data/'+f\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(p)\\n print(f, pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\\n elif f.endswith('.npz'):\\n z=np.load(p)\\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:15:01.829Z Script completed Wall time 4.5 seconds Output: versions 2.3.3 2.8.0+cpu candidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')] target_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')] test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} test_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] train_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] train_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] val_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} validation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bcfd09b087d28f9caf7d09961d1a", "usage": { "input_tokens": 20112, "cached_input_tokens": 19712, "cache_write_input_tokens": 0, "output_tokens": 215, "reasoning_output_tokens": 15, "total_tokens": 20327 }, "turn_token_usage": { "input_tokens": 89349, "cached_input_tokens": 79616, "cache_write_input_tokens": 0, "output_tokens": 898, "reasoning_output_tokens": 108, "total_tokens": 90247 }, "thread_token_usage": { "input_tokens": 89349, "cached_input_tokens": 79616, "cache_write_input_tokens": 0, "output_tokens": 898, "reasoning_output_tokens": 108, "total_tokens": 90247 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ca6b856-e0b2-4c7d-ae34-9072f5d178b3", "process_id": "77043", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os, torch\nprint('versions',pd.__version__,torch.__version__)\nfor f in sorted(os.listdir('/task/data')):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\n elif f.endswith('.npz'):\n z=np.load(p)\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os, torch\nprint('versions',pd.__version__,torch.__version__)\nfor f in sorted(os.listdir('/task/data')):\n p='/task/data/'+f\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(p)\n print(f, pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\n elif f.endswith('.npz'):\n z=np.load(p)\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "versions 2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n", "stderr": "", "aggregated_output": "versions 2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 182428669 }, "formatted_output": "versions 2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\ntest_preference_state.parquet 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\nvalidation_preference_state.parquet 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n" } } ## Step 12 · agent · 2026-09-25T00:15:08.213Z ### Thinking unavailable — no text recorded The benchmark is substantial (43.5M training listens, 8,456 target users, 30K candidates), so I’m using streaming/event sampling rather than any user–item matrix. I’m now measuring repeat behavior, candidate coverage, and recency effects—the signals most likely to matter for the seven-day horizon. ### exec · 2026-09-25T00:15:14.676Z { "code": "const r = await tools.exec_command({cmd:\"du -h /task/data/* | sort -h; /opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, pyarrow.compute as pc, numpy as np, pandas as pd\\nD='/task/data/'\\nfor f in ['train_listens.parquet','val_listens.parquet']:\\n pf=pq.ParquetFile(D+f)\\n print(f,'rowgroups',pf.num_row_groups)\\n for col in ['uid','timestamp','item_id','played_ratio_pct']:\\n tab=pf.read(columns=[col])\\n a=tab[col]\\n print(col,'min',pc.min(a).as_py(),'max',pc.max(a).as_py(), 'unique', pc.count_distinct(a).as_py() if col in ('uid','item_id') else '')\\n del tab\\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\\nfor stage in ['validation','test']:\\n z=np.load(D+stage+'_eligible_candidates.npz')\\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\\n print(stage,'eligible counts',np.percentile(cnt,[0,1,10,50,90,99,100]),cnt.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:15:18.522Z Script completed Wall time 3.8 seconds Output: 8.0K /task/data/val_undislikes.parquet 36K /task/data/val_dislikes.parquet 72K /task/data/val_unlikes.parquet 84K /task/data/target_users.parquet 156K /task/data/train_undislikes.parquet 292K /task/data/val_likes.parquet 344K /task/data/candidate_catalog.parquet 840K /task/data/train_dislikes.parquet 1.1M /task/data/test_preference_state.parquet 1.1M /task/data/validation_preference_state.parquet 1.8M /task/data/train_unlikes.parquet 5.2M /task/data/train_likes.parquet 7.3M /task/data/validation_eligible_candidates.npz 7.4M /task/data/test_eligible_candidates.npz 9.5M /task/data/val_listens.parquet 305M /task/data/train_listens.parquet train_listens.parquet rowgroups 42 uid min 100 max 1000000 unique 9238 timestamp min 0 max 24786795 unique item_id min 22 max 9390623 unique 845395 played_ratio_pct min 0 max 159 unique val_listens.parquet rowgroups 2 uid min 100 max 1000000 unique 8196 timestamp min 24788600 max 25393395 unique item_id min 50 max 9390541 unique 191747 played_ratio_pct min 0 max 159 unique item_id positive_listens organic_positive_listens popularity_rank 5862961 26615 20620 1 6901374 25536 17347 2 5635052 24691 17194 3 3542184 24075 17699 4 9378983 24036 18195 5 5463340 22746 17398 6 8213481 22505 17071 7 906358 22238 17183 8 2859641 18380 13704 9 3033749 16682 12267 10 item_id positive_listens organic_positive_listens popularity_rank 1309246 163 138 29998 1634958 163 46 29999 1670524 163 73 30000 validation eligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] 29067.306291390727 test eligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] 29041.3178807947 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bd06098487d28784a3350b2a027d", "usage": { "input_tokens": 21025, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 674, "reasoning_output_tokens": 252, "total_tokens": 21699 }, "turn_token_usage": { "input_tokens": 110374, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 1572, "reasoning_output_tokens": 360, "total_tokens": 111946 }, "thread_token_usage": { "input_tokens": 110374, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 1572, "reasoning_output_tokens": 360, "total_tokens": 111946 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e492709-9fc4-4b7d-86ea-6c60c7c37ab3", "process_id": "58157", "command": [ "/bin/sh", "-lc", "du -h /task/data/* | sort -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pyarrow.compute as pc, numpy as np, pandas as pd\nD='/task/data/'\nfor f in ['train_listens.parquet','val_listens.parquet']:\n pf=pq.ParquetFile(D+f)\n print(f,'rowgroups',pf.num_row_groups)\n for col in ['uid','timestamp','item_id','played_ratio_pct']:\n tab=pf.read(columns=[col])\n a=tab[col]\n print(col,'min',pc.min(a).as_py(),'max',pc.max(a).as_py(), 'unique', pc.count_distinct(a).as_py() if col in ('uid','item_id') else '')\n del tab\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nfor stage in ['validation','test']:\n z=np.load(D+stage+'_eligible_candidates.npz')\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\n print(stage,'eligible counts',np.percentile(cnt,[0,1,10,50,90,99,100]),cnt.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "du -h /task/data/* | sort -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pyarrow.compute as pc, numpy as np, pandas as pd\nD='/task/data/'\nfor f in ['train_listens.parquet','val_listens.parquet']:\n pf=pq.ParquetFile(D+f)\n print(f,'rowgroups',pf.num_row_groups)\n for col in ['uid','timestamp','item_id','played_ratio_pct']:\n tab=pf.read(columns=[col])\n a=tab[col]\n print(col,'min',pc.min(a).as_py(),'max',pc.max(a).as_py(), 'unique', pc.count_distinct(a).as_py() if col in ('uid','item_id') else '')\n del tab\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nfor stage in ['validation','test']:\n z=np.load(D+stage+'_eligible_candidates.npz')\n cnt=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\n print(stage,'eligible counts',np.percentile(cnt,[0,1,10,50,90,99,100]),cnt.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet rowgroups 42\nuid min 100 max 1000000 unique 9238\ntimestamp min 0 max 24786795 unique \nitem_id min 22 max 9390623 unique 845395\nplayed_ratio_pct min 0 max 159 unique \nval_listens.parquet rowgroups 2\nuid min 100 max 1000000 unique 8196\ntimestamp min 24788600 max 25393395 unique \nitem_id min 50 max 9390541 unique 191747\nplayed_ratio_pct min 0 max 159 unique \n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\nvalidation eligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] 29067.306291390727\ntest eligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] 29041.3178807947\n", "stderr": "", "aggregated_output": "train_listens.parquet rowgroups 42\nuid min 100 max 1000000 unique 9238\ntimestamp min 0 max 24786795 unique \nitem_id min 22 max 9390623 unique 845395\nplayed_ratio_pct min 0 max 159 unique \nval_listens.parquet rowgroups 2\nuid min 100 max 1000000 unique 8196\ntimestamp min 24788600 max 25393395 unique \nitem_id min 50 max 9390541 unique 191747\nplayed_ratio_pct min 0 max 159 unique \n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\nvalidation eligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] 29067.306291390727\ntest eligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] 29041.3178807947\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 677755958 }, "formatted_output": "train_listens.parquet rowgroups 42\nuid min 100 max 1000000 unique 9238\ntimestamp min 0 max 24786795 unique \nitem_id min 22 max 9390623 unique 845395\nplayed_ratio_pct min 0 max 159 unique \nval_listens.parquet rowgroups 2\nuid min 100 max 1000000 unique 8196\ntimestamp min 24788600 max 25393395 unique \nitem_id min 50 max 9390541 unique 191747\nplayed_ratio_pct min 0 max 159 unique \n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\nvalidation eligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] 29067.306291390727\ntest eligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] 29041.3178807947\n" } } ## Step 13 · agent · 2026-09-25T00:15:30.881Z ### Thinking unavailable — no text recorded ## Step 14 · agent · 2026-09-25T00:15:41.073Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:15:45.177Z { "code": "const r = await tools.exec_command({cmd:\"free -h; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\nt=pd.read_parquet(D+'target_users.parquet'); print(t.describe().to_string())\\n# public relevant counts, catalog and historical active preference overlaps\\ncat=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\ntu=set(t.uid.astype(int))\\nstrong=ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)&ls.uid.isin(tu)&ls.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\nlikes=lk[(lk.is_organic==1)&lk.uid.isin(tu)&lk.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nprint('relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict(),'per-user',rel.groupby('uid').size().describe().to_dict())\\nprint('val timestamps',pd.read_parquet(D+'val_listens.parquet',columns=['timestamp']).timestamp.agg(['min','max']).to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:15:47.050Z Script completed Wall time 1.9 seconds Output: /bin/sh: 1: free: not found uid recent_positive_listens history_positive_listens count 8456.000000 8456.000000 8456.000000 mean 502289.108325 1251.076750 3236.440752 std 287953.395912 1414.340904 3697.575286 min 100.000000 20.000000 20.000000 25% 254375.000000 281.000000 630.000000 50% 503850.000000 784.500000 1920.500000 75% 751725.000000 1706.250000 4494.250000 max 1000000.000000 19592.000000 26597.000000 relevant 175729 users 6186 grades {1: 167201, 3: 8528} per-user {'count': 6186.0, 'mean': 28.407533139346913, 'std': 40.76888269394298, 'min': 1.0, '25%': 6.0, '50%': 17.0, '75%': 37.0, 'max': 1745.0} val timestamps {'min': 24788600, 'max': 25393395} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bd16bd0887d2894e34ce01e178b2", "usage": { "input_tokens": 22382, "cached_input_tokens": 20864, "cache_write_input_tokens": 0, "output_tokens": 1387, "reasoning_output_tokens": 996, "total_tokens": 23769 }, "turn_token_usage": { "input_tokens": 132756, "cached_input_tokens": 120448, "cache_write_input_tokens": 0, "output_tokens": 2959, "reasoning_output_tokens": 1356, "total_tokens": 135715 }, "thread_token_usage": { "input_tokens": 132756, "cached_input_tokens": 120448, "cache_write_input_tokens": 0, "output_tokens": 2959, "reasoning_output_tokens": 1356, "total_tokens": 135715 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c6e316fd-3998-4dbc-bc7d-7b72bf9406c1", "process_id": "33739", "command": [ "/bin/sh", "-lc", "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'\nt=pd.read_parquet(D+'target_users.parquet'); print(t.describe().to_string())\n# public relevant counts, catalog and historical active preference overlaps\ncat=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntu=set(t.uid.astype(int))\nstrong=ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)&ls.uid.isin(tu)&ls.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.is_organic==1)&lk.uid.isin(tu)&lk.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict(),'per-user',rel.groupby('uid').size().describe().to_dict())\nprint('val timestamps',pd.read_parquet(D+'val_listens.parquet',columns=['timestamp']).timestamp.agg(['min','max']).to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'\nt=pd.read_parquet(D+'target_users.parquet'); print(t.describe().to_string())\n# public relevant counts, catalog and historical active preference overlaps\ncat=set(pd.read_parquet(D+'candidate_catalog.parquet').item_id.astype(int))\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\ntu=set(t.uid.astype(int))\nstrong=ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)&ls.uid.isin(tu)&ls.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.is_organic==1)&lk.uid.isin(tu)&lk.item_id.isin(cat)][['uid','item_id']].drop_duplicates()\nrel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('relevant',len(rel),'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict(),'per-user',rel.groupby('uid').size().describe().to_dict())\nprint('val timestamps',pd.read_parquet(D+'val_listens.parquet',columns=['timestamp']).timestamp.agg(['min','max']).to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nrelevant 175729 users 6186 grades {1: 167201, 3: 8528} per-user {'count': 6186.0, 'mean': 28.407533139346913, 'std': 40.76888269394298, 'min': 1.0, '25%': 6.0, '50%': 17.0, '75%': 37.0, 'max': 1745.0}\nval timestamps {'min': 24788600, 'max': 25393395}\n", "stderr": "", "aggregated_output": " uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nrelevant 175729 users 6186 grades {1: 167201, 3: 8528} per-user {'count': 6186.0, 'mean': 28.407533139346913, 'std': 40.76888269394298, 'min': 1.0, '25%': 6.0, '50%': 17.0, '75%': 37.0, 'max': 1745.0}\nval timestamps {'min': 24788600, 'max': 25393395}\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 705450758 }, "formatted_output": " uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nrelevant 175729 users 6186 grades {1: 167201, 3: 8528} per-user {'count': 6186.0, 'mean': 28.407533139346913, 'std': 40.76888269394298, 'min': 1.0, '25%': 6.0, '50%': 17.0, '75%': 37.0, 'max': 1745.0}\nval timestamps {'min': 24788600, 'max': 25393395}\n" } } ## Step 15 · agent · 2026-09-25T00:15:49.663Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:15:56.424Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/make_popularity.py\\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+ with np.load(DATA / f\\\"{args.stage}_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].copy()\\n+ items = z[\\\"item_ids\\\"].copy()\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ rows = []\\n+ for uid, bits in zip(uids, packed):\\n+ mask = np.unpackbits(bits, count=len(items), bitorder=\\\"little\\\")\\n+ chosen = items[np.flatnonzero(mask)[:100]]\\n+ rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\\n+ args.output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).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 · 2026-09-25T00:15:56.457Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-65cc5a33-1275-49b9-b890-4f0e990c3e66", "changes": { "/app/solution/make_popularity.py": { "type": "add", "content": "\"\"\"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 with np.load(DATA / f\"{args.stage}_eligible_candidates.npz\") as z:\n uids = z[\"uids\"].copy()\n items = z[\"item_ids\"].copy()\n packed = z[\"packed_eligible\"].copy()\n rows = []\n for uid, bits in zip(uids, packed):\n mask = np.unpackbits(bits, count=len(items), bitorder=\"little\")\n chosen = items[np.flatnonzero(mask)[:100]]\n rows.extend((int(uid), int(item), rank) for rank, item in enumerate(chosen, 1))\n args.output.parent.mkdir(parents=True, exist_ok=True)\n pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).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/make_popularity.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bd334c8887d285798aac586c7473", "usage": { "input_tokens": 24119, "cached_input_tokens": 22144, "cache_write_input_tokens": 0, "output_tokens": 440, "reasoning_output_tokens": 56, "total_tokens": 24559 }, "turn_token_usage": { "input_tokens": 156875, "cached_input_tokens": 142592, "cache_write_input_tokens": 0, "output_tokens": 3399, "reasoning_output_tokens": 1412, "total_tokens": 160274 }, "thread_token_usage": { "input_tokens": 156875, "cached_input_tokens": 142592, "cache_write_input_tokens": 0, "output_tokens": 3399, "reasoning_output_tokens": 1412, "total_tokens": 160274 } } ## Step 16 · agent · 2026-09-25T00:15:57.768Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:15:59.777Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/make_popularity.py --stage validation --output solution/validation_popularity.parquet && /opt/task-venv/bin/python solution/make_popularity.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:16:09.133Z Script completed Wall time 9.4 seconds Output: { "model": "solution/validation_popularity.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bd3cb42c87d28d76a3f77c1660c8", "usage": { "input_tokens": 24583, "cached_input_tokens": 23936, "cache_write_input_tokens": 0, "output_tokens": 136, "reasoning_output_tokens": 16, "total_tokens": 24719 }, "turn_token_usage": { "input_tokens": 181458, "cached_input_tokens": 166528, "cache_write_input_tokens": 0, "output_tokens": 3535, "reasoning_output_tokens": 1428, "total_tokens": 184993 }, "thread_token_usage": { "input_tokens": 181458, "cached_input_tokens": 166528, "cache_write_input_tokens": 0, "output_tokens": 3535, "reasoning_output_tokens": 1428, "total_tokens": 184993 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-54bcdf97-fde4-4823-8c87-8c9e6208cdac", "process_id": "43923", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/make_popularity.py --stage validation --output solution/validation_popularity.parquet && /opt/task-venv/bin/python solution/make_popularity.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_popularity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/make_popularity.py --stage validation --output solution/validation_popularity.parquet && /opt/task-venv/bin/python solution/make_popularity.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_popularity.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 179150681 }, "formatted_output": "{\n \"model\": \"solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 17 · agent · 2026-09-25T00:16:21.081Z ### Thinking unavailable — no text recorded ## Step 18 · agent · 2026-09-25T00:16:24.344Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:16:36.732Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\\nD='/task/data/'\\ntarget=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\\nz=np.load(D+'validation_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,i):\\n r=ur.get(u); c=ic.get(i); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\na=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1)\\nb=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3)\\nrel=pd.concat([a,b]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\\nrel=rel[[elig(int(u),int(i)) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\\nkeys={(int(u)<<32)|int(i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)}\\nlast={k:-1 for k in keys}; cnt={k:0 for k in keys}; org={k:0 for k in keys}\\npf=pq.ParquetFile(D+'train_listens.parquet')\\nfor batch in pf.iter_batches(columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\\n d=batch.to_pandas(); kk=(d.uid.astype('uint64').to_numpy()<=50)\\n for k,t,o in zip(kk[mask],d.timestamp.to_numpy()[mask],d.is_organic.to_numpy()[mask]):\\n k=int(k); last[k]=max(last[k],int(t));cnt[k]+=1;org[k]+=int(o)\\nvals=np.array([last[k] for k in keys]); counts=np.array([cnt[k] for k in keys]); orgs=np.array([org[k] for k in keys])\\nprint('labels',len(keys),'ever',np.mean(vals>=0),'count percentiles',np.percentile(counts,[0,25,50,75,90,99,100]))\\nfor days in [1,7,14,30,60,90,180]: print('within',days,np.mean(vals>=24786800-days*86400))\\nprint('organic ever',np.mean(orgs>0))\\npref=pd.read_parquet(D+'validation_preference_state.parquet'); pref['k']=(pref.uid.astype('uint64')< tuple[np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Return lookup positions and a membership mask without a Python dict.\\\"\\\"\\\"\\n+ p = np.searchsorted(sorted_values, values)\\n+ ok = p < len(sorted_values)\\n+ ok[ok] &= sorted_values[p[ok]] == values[ok]\\n+ return p, ok\\n+\\n+\\n+@dataclass\\n+class Query:\\n+ items: np.ndarray\\n+ weights: np.ndarray\\n+ positives: np.ndarray\\n+ positive_weights: np.ndarray\\n+\\n+\\n+def explicit_by_user(prefix: str) -> dict[int, pd.DataFrame]:\\n+ \\\"\\\"\\\"Load the relatively small explicit-event tables, grouped by listener.\\\"\\\"\\\"\\n+ frames = []\\n+ for kind, value in ((\\\"likes\\\", 2), (\\\"unlikes\\\", -2), (\\\"dislikes\\\", -1), (\\\"undislikes\\\", 1)):\\n+ path = DATA / f\\\"{prefix}_{kind}.parquet\\\"\\n+ if path.exists():\\n+ f = pd.read_parquet(path, columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ f[\\\"action\\\"] = np.int8(value)\\n+ frames.append(f)\\n+ x = pd.concat(frames, ignore_index=True).sort_values([\\\"uid\\\", \\\"timestamp\\\"])\\n+ return {int(uid): g.reset_index(drop=True) for uid, g in x.groupby(\\\"uid\\\", sort=False)}\\n+\\n+\\n+def make_history(\\n+ times: np.ndarray,\\n+ items: np.ndarray,\\n+ organic: np.ndarray,\\n+ ratios: np.ndarray,\\n+ cutoff: int,\\n+ explicit: pd.DataFrame | None,\\n+ max_len: int = HISTORY_LEN,\\n+) -> tuple[np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Encode recent positive events as a bounded, weighted interaction list.\\\"\\\"\\\"\\n+ hi = int(np.searchsorted(times, cutoff, side=\\\"left\\\"))\\n+ lo = max(0, hi - max_len * 5)\\n+ t = times[lo:hi]\\n+ it = items[lo:hi]\\n+ org = organic[lo:hi]\\n+ rat = ratios[lo:hi]\\n+ if len(t):\\n+ # Completion, intent, and a 45-day exponential recency horizon.\\n+ w = (0.55 + np.minimum(rat, 100) / 100.0) * (1.0 + 0.30 * org)\\n+ w *= np.exp(-(cutoff - t) / (45.0 * 86400.0))\\n+ else:\\n+ w = np.empty(0, dtype=np.float32)\\n+\\n+ # Likes are sparse and much stronger than ordinary playback. Reversals are\\n+ # handled by cutoff-specific supplied state at inference; rolling training\\n+ # uses positive like actions prior to the cutoff.\\n+ if explicit is not None and len(explicit):\\n+ e = explicit[(explicit.timestamp < cutoff) & (explicit.action == 2)]\\n+ if len(e):\\n+ e = e.iloc[-64:]\\n+ eit = e.item_idx.to_numpy(np.int64)\\n+ good = eit >= 0\\n+ eit = eit[good]\\n+ et = e.timestamp.to_numpy(np.int64)[good]\\n+ ew = 3.0 * np.exp(-(cutoff - et) / (120.0 * 86400.0))\\n+ it = np.concatenate([it, eit])\\n+ w = np.concatenate([w, ew.astype(np.float32)])\\n+ if len(it) > max_len:\\n+ it, w = it[-max_len:], w[-max_len:]\\n+ if not len(it):\\n+ # Catalog leader as a harmless cold-start token.\\n+ return np.array([0], np.int64), np.array([1.0], np.float32)\\n+ return it.astype(np.int64, copy=False), w.astype(np.float32, copy=False)\\n+\\n+\\n+def process_user(\\n+ uid: int,\\n+ frame: pd.DataFrame,\\n+ item_ids_sorted: np.ndarray,\\n+ original_to_model: np.ndarray,\\n+ explicit: pd.DataFrame | None,\\n+ cutoffs: list[int],\\n+ inference_cutoff: int,\\n+) -> tuple[list[Query], tuple[np.ndarray, np.ndarray]]:\\n+ \\\"\\\"\\\"Build rolling examples for one user from its event stream.\\\"\\\"\\\"\\n+ raw_item = frame.item_id.to_numpy(np.int64)\\n+ sp, ok_item = positions(item_ids_sorted, raw_item)\\n+ model_item = np.full(len(frame), -1, np.int64)\\n+ model_item[ok_item] = original_to_model[sp[ok_item]]\\n+ ratio = frame.played_ratio_pct.to_numpy(np.int16)\\n+ keep = ok_item & (ratio >= 50)\\n+ times = frame.timestamp.to_numpy(np.int64)[keep]\\n+ order = np.argsort(times, kind=\\\"stable\\\")\\n+ times = times[order]\\n+ model_item = model_item[keep][order]\\n+ org = frame.is_organic.to_numpy(np.float32)[keep][order]\\n+ ratio = ratio[keep][order].astype(np.float32)\\n+\\n+ if explicit is not None:\\n+ ep, eok = positions(item_ids_sorted, explicit.item_id.to_numpy(np.int64))\\n+ explicit = explicit.copy()\\n+ explicit[\\\"item_idx\\\"] = -1\\n+ explicit.loc[eok, \\\"item_idx\\\"] = original_to_model[ep[eok]]\\n+\\n+ queries: list[Query] = []\\n+ for cutoff in cutoffs:\\n+ hist_i, hist_w = make_history(times, model_item, org, ratio, cutoff, explicit)\\n+ start, end = cutoff + GAP, cutoff + GAP + WEEK\\n+ label_mask = (times >= start) & (times < end) & (org == 1) & (ratio >= 80)\\n+ pos = model_item[label_mask]\\n+ if len(pos):\\n+ pos, counts = np.unique(pos, return_counts=True)\\n+ pw = np.sqrt(counts).astype(np.float32)\\n+ else:\\n+ pos, pw = np.empty(0, np.int64), np.empty(0, np.float32)\\n+ if explicit is not None:\\n+ el = explicit[(explicit.timestamp >= start) & (explicit.timestamp < end)\\n+ & (explicit.action == 2) & (explicit.is_organic == 1)\\n+ & (explicit.item_idx >= 0)]\\n+ if len(el):\\n+ likes = np.unique(el.item_idx.to_numpy(np.int64))\\n+ # Grade-3 gain is seven versus one; sqrt tempering is more stable.\\n+ pos = np.concatenate([pos, likes])\\n+ pw = np.concatenate([pw, np.full(len(likes), math.sqrt(7), np.float32)])\\n+ if len(pos):\\n+ # Merge tracks that had both a listen and like, retaining larger weight.\\n+ merged: dict[int, float] = {}\\n+ for p, w in zip(pos, pw):\\n+ merged[int(p)] = max(merged.get(int(p), 0.0), float(w))\\n+ queries.append(Query(hist_i, hist_w,\\n+ np.fromiter(merged, np.int64),\\n+ np.fromiter(merged.values(), np.float32)))\\n+ inference = make_history(times, model_item, org, ratio, inference_cutoff, explicit)\\n+ return queries, inference\\n+\\n+\\n+def extract(\\n+ stage: str, snapshots: int\\n+) -> tuple[list[Query], np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n+ \\\"\\\"\\\"Stream logs user-by-user and create examples plus serving histories.\\\"\\\"\\\"\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ # Model order is popularity order; sorted arrays are only for fast lookup.\\n+ item_ids = catalog.item_id.to_numpy(np.int64)\\n+ item_sort = np.argsort(item_ids)\\n+ item_ids_sorted = item_ids[item_sort]\\n+ original_to_model = item_sort.astype(np.int64)\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ target_set = set(map(int, targets))\\n+\\n+ exp_train = explicit_by_user(\\\"train\\\")\\n+ cutoffs = [TRAIN_END - (j + 1) * WEEK - GAP for j in range(snapshots)]\\n+ inference_cutoff = TRAIN_END if stage == \\\"validation\\\" else TRAIN_END + WEEK + GAP\\n+ all_queries: list[Query] = []\\n+ histories: dict[int, tuple[np.ndarray, np.ndarray]] = {}\\n+\\n+ # Train parquet is sorted by uid. Carry the last user across batch boundaries.\\n+ pf = pq.ParquetFile(DATA / \\\"train_listens.parquet\\\")\\n+ carry: pd.DataFrame | None = None\\n+\\n+ def consume(g: pd.DataFrame) -> None:\\n+ uid = int(g.uid.iloc[0])\\n+ q, h = process_user(uid, g, item_ids_sorted, original_to_model,\\n+ exp_train.get(uid), cutoffs, TRAIN_END)\\n+ all_queries.extend(q)\\n+ if uid in target_set:\\n+ histories[uid] = h\\n+\\n+ for batch in pf.iter_batches(columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ batch_size=1_000_000):\\n+ f = batch.to_pandas()\\n+ if carry is not None:\\n+ f = pd.concat([carry, f], ignore_index=True)\\n+ last_uid = int(f.uid.iloc[-1])\\n+ carry = f[f.uid == last_uid].copy()\\n+ done = f[f.uid != last_uid]\\n+ for _, g in done.groupby(\\\"uid\\\", sort=False):\\n+ consume(g)\\n+ if carry is not None:\\n+ consume(carry)\\n+\\n+ if stage == \\\"test\\\":\\n+ # Validation outcomes become both an additional training snapshot and\\n+ # observed user history for the hidden test cutoff.\\n+ exp_val = explicit_by_user(\\\"val\\\")\\n+ v = pd.read_parquet(DATA / \\\"val_listens.parquet\\\")\\n+ for uid, vg in v.groupby(\\\"uid\\\", sort=False):\\n+ uid = int(uid)\\n+ # Reconstruct a compact old-history frame from model-index histories\\n+ # is lossy, so merge the recent validation stream into the existing\\n+ # serving list directly and add the true validation training target.\\n+ raw = vg.item_id.to_numpy(np.int64)\\n+ sp, ok = positions(item_ids_sorted, raw)\\n+ mi = np.full(len(vg), -1, np.int64); mi[ok] = original_to_model[sp[ok]]\\n+ rat = vg.played_ratio_pct.to_numpy(np.int16)\\n+ keep = ok & (rat >= 50)\\n+ vi = mi[keep]; vt = vg.timestamp.to_numpy(np.int64)[keep]\\n+ vo = vg.is_organic.to_numpy(np.float32)[keep]\\n+ vr = rat[keep].astype(np.float32)\\n+ order = np.argsort(vt, kind=\\\"stable\\\"); vi, vt, vo, vr = vi[order], vt[order], vo[order], vr[order]\\n+ old_i, old_w = histories.get(uid, (np.array([0]), np.array([1.0], np.float32)))\\n+ vw = (0.55 + np.minimum(vr, 100) / 100.0) * (1.0 + .30 * vo)\\n+ vw *= np.exp(-(inference_cutoff - vt) / (45.0 * 86400.0))\\n+ ni = np.concatenate([old_i, vi]); nw = np.concatenate([old_w * 0.85, vw])\\n+ histories[uid] = (ni[-HISTORY_LEN:].astype(np.int64), nw[-HISTORY_LEN:].astype(np.float32))\\n+ # Validation-window outcome query with the genuine pre-window history.\\n+ posmask = (vo == 1) & (vr >= 80)\\n+ pos = vi[posmask]\\n+ pw = np.ones(len(pos), np.float32)\\n+ ev = exp_val.get(uid)\\n+ if ev is not None:\\n+ ep, eok = positions(item_ids_sorted, ev.item_id.to_numpy(np.int64))\\n+ likes = original_to_model[ep[eok & (ev.action.to_numpy() == 2) & (ev.is_organic.to_numpy() == 1)]]\\n+ pos = np.concatenate([pos, likes]); pw = np.concatenate([pw, np.full(len(likes), math.sqrt(7), np.float32)])\\n+ if len(pos) and uid in histories:\\n+ up, inv = np.unique(pos, return_inverse=True)\\n+ uw = np.zeros(len(up), np.float32)\\n+ np.maximum.at(uw, inv, pw)\\n+ all_queries.append(Query(old_i, old_w, up, uw))\\n+\\n+ # Apply authoritative cutoff preference state to serving histories.\\n+ pref = pd.read_parquet(DATA / f\\\"{stage}_preference_state.parquet\\\")\\n+ liked = pref[pref.state == \\\"liked\\\"]\\n+ for uid, g in liked.groupby(\\\"uid\\\", sort=False):\\n+ uid = int(uid)\\n+ if uid not in histories:\\n+ continue\\n+ sp, ok = positions(item_ids_sorted, g.item_id.to_numpy(np.int64))\\n+ li = original_to_model[sp[ok]]\\n+ oi, ow = histories[uid]\\n+ histories[uid] = (np.concatenate([oi, li])[-HISTORY_LEN:],\\n+ np.concatenate([ow, np.full(len(li), 3.0, np.float32)])[-HISTORY_LEN:])\\n+\\n+ hist_i = np.zeros((len(targets), HISTORY_LEN), np.int64)\\n+ hist_w = np.zeros((len(targets), HISTORY_LEN), np.float32)\\n+ for r, uid in enumerate(targets):\\n+ ii, ww = histories.get(int(uid), (np.array([0]), np.array([1.0], np.float32)))\\n+ n = min(len(ii), HISTORY_LEN); hist_i[r, -n:] = ii[-n:]; hist_w[r, -n:] = ww[-n:]\\n+ pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\\n+ pop = (pop - pop.mean()) / pop.std()\\n+ return all_queries, hist_i, hist_w, targets, item_ids, pop\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, initial: torch.Tensor):\\n+ super().__init__()\\n+ self.history_embedding = nn.Embedding(n_items, dim)\\n+ self.history_embedding.weight.data.copy_(initial)\\n+ self.mlp = nn.Sequential(nn.Linear(dim + 2, dim * 2), nn.ReLU(), nn.Linear(dim * 2, dim))\\n+ nn.init.zeros_(self.mlp[-1].weight); nn.init.zeros_(self.mlp[-1].bias)\\n+ self.global_bias_coordinate = nn.Parameter(torch.tensor(0.35))\\n+\\n+ def forward(self, items: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\\n+ denom = weights.sum(1, keepdim=True).clamp_min(1e-6)\\n+ base = (self.history_embedding(items) * weights.unsqueeze(-1)).sum(1) / denom\\n+ features = torch.cat([base, torch.log1p(denom) / 5.0,\\n+ (weights > 0).sum(1, keepdim=True).float() / HISTORY_LEN], 1)\\n+ latent = F.normalize(base + 0.2 * self.mlp(features), dim=1)\\n+ return torch.cat([latent, self.global_bias_coordinate.expand(len(items), 1)], 1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, initial: torch.Tensor, popularity: torch.Tensor):\\n+ super().__init__()\\n+ self.item_embedding = nn.Embedding(n_items, dim)\\n+ self.item_embedding.weight.data.copy_(initial)\\n+ self.register_buffer(\\\"popularity\\\", popularity[:, None])\\n+ self.mlp = nn.Sequential(nn.Linear(dim + 1, dim * 2), nn.ReLU(), nn.Linear(dim * 2, dim))\\n+ nn.init.zeros_(self.mlp[-1].weight); nn.init.zeros_(self.mlp[-1].bias)\\n+ self.popularity_scale = nn.Parameter(torch.tensor(0.12))\\n+\\n+ def forward(self, item: torch.Tensor) -> torch.Tensor:\\n+ base = self.item_embedding(item)\\n+ feat = torch.cat([base, self.popularity[item]], 1)\\n+ latent = F.normalize(base + 0.2 * self.mlp(feat), dim=1)\\n+ bias = self.popularity_scale * self.popularity[item]\\n+ return torch.cat([latent, bias], 1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, popularity: np.ndarray, seed: int):\\n+ super().__init__()\\n+ gen = torch.Generator().manual_seed(seed)\\n+ initial = F.normalize(torch.randn(n_items, LATENT, generator=gen), dim=1)\\n+ pop = torch.from_numpy(popularity)\\n+ self.user_tower = UserTower(n_items, LATENT, initial)\\n+ self.item_tower = ItemTower(n_items, LATENT, initial.clone(), pop)\\n+\\n+\\n+def padded_queries(queries: list[Query]) -> tuple[np.ndarray, np.ndarray]:\\n+ hi = np.zeros((len(queries), HISTORY_LEN), np.int64)\\n+ hw = np.zeros((len(queries), HISTORY_LEN), np.float32)\\n+ for r, q in enumerate(queries):\\n+ n = min(len(q.items), HISTORY_LEN); hi[r, -n:] = q.items[-n:]; hw[r, -n:] = q.weights[-n:]\\n+ return hi, hw\\n+\\n+\\n+def train_model(queries: list[Query], popularity: np.ndarray, seed: int, epochs: int) -> TwoTower:\\n+ torch.manual_seed(seed); np.random.seed(seed); random.seed(seed)\\n+ torch.set_num_threads(min(16, torch.get_num_threads()))\\n+ model = TwoTower(len(popularity), popularity, seed)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ hi, hw = padded_queries(queries)\\n+ rng = np.random.default_rng(seed)\\n+ batch_size, negatives = 192, 24\\n+ # Mixture of uniform and popularity-skewed negatives.\\n+ rank_prob = 1.0 / np.sqrt(np.arange(1, len(popularity) + 1, dtype=np.float64))\\n+ rank_prob /= rank_prob.sum()\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(len(queries)); losses = []\\n+ model.train()\\n+ for start in range(0, len(order), batch_size):\\n+ ix = order[start:start + batch_size]\\n+ if len(ix) < 2: continue\\n+ pos = np.array([rng.choice(queries[j].positives, p=queries[j].positive_weights / queries[j].positive_weights.sum()) for j in ix])\\n+ neg = rng.integers(0, len(popularity), size=(len(ix), negatives), dtype=np.int64)\\n+ # Half hard-by-popularity samples preserve the strong catalog prior.\\n+ neg[:, :negatives // 2] = rng.choice(len(popularity), size=(len(ix), negatives // 2), p=rank_prob)\\n+ ti = torch.from_numpy(hi[ix]); tw = torch.from_numpy(hw[ix])\\n+ tp = torch.from_numpy(pos); tn = torch.from_numpy(neg)\\n+ u = model.user_tower(ti, tw)\\n+ ps = (u * model.item_tower(tp)).sum(1)\\n+ nv = model.item_tower(tn.reshape(-1)).reshape(len(ix), negatives, -1)\\n+ ns = (u[:, None, :] * nv).sum(2)\\n+ loss = -F.logsigmoid((ps[:, None] - ns) * 5.0).mean()\\n+ opt.zero_grad(set_to_none=True); loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0); opt.step()\\n+ losses.append(float(loss.detach()))\\n+ print(f\\\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.5f}\\\", flush=True)\\n+ return model\\n+\\n+\\n+@torch.no_grad()\\n+def rank(model: TwoTower, hist_i: np.ndarray, hist_w: np.ndarray, stage: str,\\n+ uids: np.ndarray, item_ids: np.ndarray, output: Path) -> None:\\n+ model.eval()\\n+ all_items = torch.arange(len(item_ids))\\n+ item_vec = model.item_tower(all_items).T.contiguous()\\n+ with np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ euids, eitems, packed = z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(euids.astype(np.int64), uids)\\n+ # Eligibility columns normally match catalog order, but map defensively.\\n+ model_of_elig = {int(x): i for i, x in enumerate(item_ids)}\\n+ emap = np.array([model_of_elig[int(x)] for x in eitems], np.int64)\\n+ rows = []\\n+ block = 128\\n+ for lo in range(0, len(uids), block):\\n+ hi = min(lo + block, len(uids))\\n+ uv = model.user_tower(torch.from_numpy(hist_i[lo:hi]), torch.from_numpy(hist_w[lo:hi]))\\n+ scores = uv @ item_vec\\n+ eligibility = np.unpackbits(packed[lo:hi], axis=1, count=len(eitems), bitorder=\\\"little\\\").astype(bool)\\n+ # Convert eligibility ordering to model ordering.\\n+ allowed = np.zeros_like(eligibility); allowed[:, emap] = eligibility\\n+ scores[~torch.from_numpy(allowed)] = -torch.inf\\n+ top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\\n+ for r, choices in enumerate(top):\\n+ rows.extend((int(uids[lo + r]), int(item_ids[j]), k) for k, j in enumerate(choices, 1))\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(output, index=False)\\n+\\n+\\n+def main() -> None:\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ p.add_argument(\\\"--epochs\\\", type=int, default=8)\\n+ p.add_argument(\\\"--snapshots\\\", type=int, default=6)\\n+ p.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ p.add_argument(\\\"--output\\\", type=Path)\\n+ args = p.parse_args()\\n+ output = args.output or OUT / (\\\"validation.parquet\\\" if args.stage == \\\"validation\\\" else \\\"submission.parquet\\\")\\n+ q, hi, hw, uids, items, pop = extract(args.stage, args.snapshots)\\n+ print(f\\\"extracted {len(q)} rolling query examples\\\", flush=True)\\n+ model = train_model(q, pop, args.seed, args.epochs)\\n+ rank(model, hi, hw, args.stage, uids, items, output)\\n+ torch.save({\\\"state_dict\\\": model.state_dict(), \\\"items\\\": items, \\\"popularity\\\": pop,\\n+ \\\"architecture\\\": {\\\"history_len\\\": HISTORY_LEN, \\\"latent\\\": LATENT}}, OUT / f\\\"model_{args.stage}.pt\\\")\\n+ (OUT / f\\\"run_{args.stage}.json\\\").write_text(json.dumps(vars(args), default=str, indent=2) + \\\"\\\\n\\\")\\n+ print(f\\\"wrote {output}\\\", flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:20:05.640Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b9c6a479-557a-4cba-93f6-041ba7d9195b", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "\"\"\"Train and apply a scalable CPU PyTorch history/item two-tower ranker.\n\nTraining consumes event examples and sampled negatives. It never constructs a\nuser-item matrix. At serving time ItemTower outputs can be indexed offline;\none UserTower call is followed by maximum-inner-product retrieval.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nWEEK = 7 * 24 * 3600\nGAP = 1800\nHISTORY_LEN = 192\nLATENT = 96\n\n\ndef positions(sorted_values: np.ndarray, values: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Return lookup positions and a membership mask without a Python dict.\"\"\"\n p = np.searchsorted(sorted_values, values)\n ok = p < len(sorted_values)\n ok[ok] &= sorted_values[p[ok]] == values[ok]\n return p, ok\n\n\n@dataclass\nclass Query:\n items: np.ndarray\n weights: np.ndarray\n positives: np.ndarray\n positive_weights: np.ndarray\n\n\ndef explicit_by_user(prefix: str) -> dict[int, pd.DataFrame]:\n \"\"\"Load the relatively small explicit-event tables, grouped by listener.\"\"\"\n frames = []\n for kind, value in ((\"likes\", 2), (\"unlikes\", -2), (\"dislikes\", -1), (\"undislikes\", 1)):\n path = DATA / f\"{prefix}_{kind}.parquet\"\n if path.exists():\n f = pd.read_parquet(path, columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n f[\"action\"] = np.int8(value)\n frames.append(f)\n x = pd.concat(frames, ignore_index=True).sort_values([\"uid\", \"timestamp\"])\n return {int(uid): g.reset_index(drop=True) for uid, g in x.groupby(\"uid\", sort=False)}\n\n\ndef make_history(\n times: np.ndarray,\n items: np.ndarray,\n organic: np.ndarray,\n ratios: np.ndarray,\n cutoff: int,\n explicit: pd.DataFrame | None,\n max_len: int = HISTORY_LEN,\n) -> tuple[np.ndarray, np.ndarray]:\n \"\"\"Encode recent positive events as a bounded, weighted interaction list.\"\"\"\n hi = int(np.searchsorted(times, cutoff, side=\"left\"))\n lo = max(0, hi - max_len * 5)\n t = times[lo:hi]\n it = items[lo:hi]\n org = organic[lo:hi]\n rat = ratios[lo:hi]\n if len(t):\n # Completion, intent, and a 45-day exponential recency horizon.\n w = (0.55 + np.minimum(rat, 100) / 100.0) * (1.0 + 0.30 * org)\n w *= np.exp(-(cutoff - t) / (45.0 * 86400.0))\n else:\n w = np.empty(0, dtype=np.float32)\n\n # Likes are sparse and much stronger than ordinary playback. Reversals are\n # handled by cutoff-specific supplied state at inference; rolling training\n # uses positive like actions prior to the cutoff.\n if explicit is not None and len(explicit):\n e = explicit[(explicit.timestamp < cutoff) & (explicit.action == 2)]\n if len(e):\n e = e.iloc[-64:]\n eit = e.item_idx.to_numpy(np.int64)\n good = eit >= 0\n eit = eit[good]\n et = e.timestamp.to_numpy(np.int64)[good]\n ew = 3.0 * np.exp(-(cutoff - et) / (120.0 * 86400.0))\n it = np.concatenate([it, eit])\n w = np.concatenate([w, ew.astype(np.float32)])\n if len(it) > max_len:\n it, w = it[-max_len:], w[-max_len:]\n if not len(it):\n # Catalog leader as a harmless cold-start token.\n return np.array([0], np.int64), np.array([1.0], np.float32)\n return it.astype(np.int64, copy=False), w.astype(np.float32, copy=False)\n\n\ndef process_user(\n uid: int,\n frame: pd.DataFrame,\n item_ids_sorted: np.ndarray,\n original_to_model: np.ndarray,\n explicit: pd.DataFrame | None,\n cutoffs: list[int],\n inference_cutoff: int,\n) -> tuple[list[Query], tuple[np.ndarray, np.ndarray]]:\n \"\"\"Build rolling examples for one user from its event stream.\"\"\"\n raw_item = frame.item_id.to_numpy(np.int64)\n sp, ok_item = positions(item_ids_sorted, raw_item)\n model_item = np.full(len(frame), -1, np.int64)\n model_item[ok_item] = original_to_model[sp[ok_item]]\n ratio = frame.played_ratio_pct.to_numpy(np.int16)\n keep = ok_item & (ratio >= 50)\n times = frame.timestamp.to_numpy(np.int64)[keep]\n order = np.argsort(times, kind=\"stable\")\n times = times[order]\n model_item = model_item[keep][order]\n org = frame.is_organic.to_numpy(np.float32)[keep][order]\n ratio = ratio[keep][order].astype(np.float32)\n\n if explicit is not None:\n ep, eok = positions(item_ids_sorted, explicit.item_id.to_numpy(np.int64))\n explicit = explicit.copy()\n explicit[\"item_idx\"] = -1\n explicit.loc[eok, \"item_idx\"] = original_to_model[ep[eok]]\n\n queries: list[Query] = []\n for cutoff in cutoffs:\n hist_i, hist_w = make_history(times, model_item, org, ratio, cutoff, explicit)\n start, end = cutoff + GAP, cutoff + GAP + WEEK\n label_mask = (times >= start) & (times < end) & (org == 1) & (ratio >= 80)\n pos = model_item[label_mask]\n if len(pos):\n pos, counts = np.unique(pos, return_counts=True)\n pw = np.sqrt(counts).astype(np.float32)\n else:\n pos, pw = np.empty(0, np.int64), np.empty(0, np.float32)\n if explicit is not None:\n el = explicit[(explicit.timestamp >= start) & (explicit.timestamp < end)\n & (explicit.action == 2) & (explicit.is_organic == 1)\n & (explicit.item_idx >= 0)]\n if len(el):\n likes = np.unique(el.item_idx.to_numpy(np.int64))\n # Grade-3 gain is seven versus one; sqrt tempering is more stable.\n pos = np.concatenate([pos, likes])\n pw = np.concatenate([pw, np.full(len(likes), math.sqrt(7), np.float32)])\n if len(pos):\n # Merge tracks that had both a listen and like, retaining larger weight.\n merged: dict[int, float] = {}\n for p, w in zip(pos, pw):\n merged[int(p)] = max(merged.get(int(p), 0.0), float(w))\n queries.append(Query(hist_i, hist_w,\n np.fromiter(merged, np.int64),\n np.fromiter(merged.values(), np.float32)))\n inference = make_history(times, model_item, org, ratio, inference_cutoff, explicit)\n return queries, inference\n\n\ndef extract(\n stage: str, snapshots: int\n) -> tuple[list[Query], np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Stream logs user-by-user and create examples plus serving histories.\"\"\"\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n # Model order is popularity order; sorted arrays are only for fast lookup.\n item_ids = catalog.item_id.to_numpy(np.int64)\n item_sort = np.argsort(item_ids)\n item_ids_sorted = item_ids[item_sort]\n original_to_model = item_sort.astype(np.int64)\n targets = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n target_set = set(map(int, targets))\n\n exp_train = explicit_by_user(\"train\")\n cutoffs = [TRAIN_END - (j + 1) * WEEK - GAP for j in range(snapshots)]\n inference_cutoff = TRAIN_END if stage == \"validation\" else TRAIN_END + WEEK + GAP\n all_queries: list[Query] = []\n histories: dict[int, tuple[np.ndarray, np.ndarray]] = {}\n\n # Train parquet is sorted by uid. Carry the last user across batch boundaries.\n pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n carry: pd.DataFrame | None = None\n\n def consume(g: pd.DataFrame) -> None:\n uid = int(g.uid.iloc[0])\n q, h = process_user(uid, g, item_ids_sorted, original_to_model,\n exp_train.get(uid), cutoffs, TRAIN_END)\n all_queries.extend(q)\n if uid in target_set:\n histories[uid] = h\n\n for batch in pf.iter_batches(columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n batch_size=1_000_000):\n f = batch.to_pandas()\n if carry is not None:\n f = pd.concat([carry, f], ignore_index=True)\n last_uid = int(f.uid.iloc[-1])\n carry = f[f.uid == last_uid].copy()\n done = f[f.uid != last_uid]\n for _, g in done.groupby(\"uid\", sort=False):\n consume(g)\n if carry is not None:\n consume(carry)\n\n if stage == \"test\":\n # Validation outcomes become both an additional training snapshot and\n # observed user history for the hidden test cutoff.\n exp_val = explicit_by_user(\"val\")\n v = pd.read_parquet(DATA / \"val_listens.parquet\")\n for uid, vg in v.groupby(\"uid\", sort=False):\n uid = int(uid)\n # Reconstruct a compact old-history frame from model-index histories\n # is lossy, so merge the recent validation stream into the existing\n # serving list directly and add the true validation training target.\n raw = vg.item_id.to_numpy(np.int64)\n sp, ok = positions(item_ids_sorted, raw)\n mi = np.full(len(vg), -1, np.int64); mi[ok] = original_to_model[sp[ok]]\n rat = vg.played_ratio_pct.to_numpy(np.int16)\n keep = ok & (rat >= 50)\n vi = mi[keep]; vt = vg.timestamp.to_numpy(np.int64)[keep]\n vo = vg.is_organic.to_numpy(np.float32)[keep]\n vr = rat[keep].astype(np.float32)\n order = np.argsort(vt, kind=\"stable\"); vi, vt, vo, vr = vi[order], vt[order], vo[order], vr[order]\n old_i, old_w = histories.get(uid, (np.array([0]), np.array([1.0], np.float32)))\n vw = (0.55 + np.minimum(vr, 100) / 100.0) * (1.0 + .30 * vo)\n vw *= np.exp(-(inference_cutoff - vt) / (45.0 * 86400.0))\n ni = np.concatenate([old_i, vi]); nw = np.concatenate([old_w * 0.85, vw])\n histories[uid] = (ni[-HISTORY_LEN:].astype(np.int64), nw[-HISTORY_LEN:].astype(np.float32))\n # Validation-window outcome query with the genuine pre-window history.\n posmask = (vo == 1) & (vr >= 80)\n pos = vi[posmask]\n pw = np.ones(len(pos), np.float32)\n ev = exp_val.get(uid)\n if ev is not None:\n ep, eok = positions(item_ids_sorted, ev.item_id.to_numpy(np.int64))\n likes = original_to_model[ep[eok & (ev.action.to_numpy() == 2) & (ev.is_organic.to_numpy() == 1)]]\n pos = np.concatenate([pos, likes]); pw = np.concatenate([pw, np.full(len(likes), math.sqrt(7), np.float32)])\n if len(pos) and uid in histories:\n up, inv = np.unique(pos, return_inverse=True)\n uw = np.zeros(len(up), np.float32)\n np.maximum.at(uw, inv, pw)\n all_queries.append(Query(old_i, old_w, up, uw))\n\n # Apply authoritative cutoff preference state to serving histories.\n pref = pd.read_parquet(DATA / f\"{stage}_preference_state.parquet\")\n liked = pref[pref.state == \"liked\"]\n for uid, g in liked.groupby(\"uid\", sort=False):\n uid = int(uid)\n if uid not in histories:\n continue\n sp, ok = positions(item_ids_sorted, g.item_id.to_numpy(np.int64))\n li = original_to_model[sp[ok]]\n oi, ow = histories[uid]\n histories[uid] = (np.concatenate([oi, li])[-HISTORY_LEN:],\n np.concatenate([ow, np.full(len(li), 3.0, np.float32)])[-HISTORY_LEN:])\n\n hist_i = np.zeros((len(targets), HISTORY_LEN), np.int64)\n hist_w = np.zeros((len(targets), HISTORY_LEN), np.float32)\n for r, uid in enumerate(targets):\n ii, ww = histories.get(int(uid), (np.array([0]), np.array([1.0], np.float32)))\n n = min(len(ii), HISTORY_LEN); hist_i[r, -n:] = ii[-n:]; hist_w[r, -n:] = ww[-n:]\n pop = np.log1p(catalog.positive_listens.to_numpy(np.float32))\n pop = (pop - pop.mean()) / pop.std()\n return all_queries, hist_i, hist_w, targets, item_ids, pop\n\n\nclass UserTower(nn.Module):\n def __init__(self, n_items: int, dim: int, initial: torch.Tensor):\n super().__init__()\n self.history_embedding = nn.Embedding(n_items, dim)\n self.history_embedding.weight.data.copy_(initial)\n self.mlp = nn.Sequential(nn.Linear(dim + 2, dim * 2), nn.ReLU(), nn.Linear(dim * 2, dim))\n nn.init.zeros_(self.mlp[-1].weight); nn.init.zeros_(self.mlp[-1].bias)\n self.global_bias_coordinate = nn.Parameter(torch.tensor(0.35))\n\n def forward(self, items: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:\n denom = weights.sum(1, keepdim=True).clamp_min(1e-6)\n base = (self.history_embedding(items) * weights.unsqueeze(-1)).sum(1) / denom\n features = torch.cat([base, torch.log1p(denom) / 5.0,\n (weights > 0).sum(1, keepdim=True).float() / HISTORY_LEN], 1)\n latent = F.normalize(base + 0.2 * self.mlp(features), dim=1)\n return torch.cat([latent, self.global_bias_coordinate.expand(len(items), 1)], 1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, n_items: int, dim: int, initial: torch.Tensor, popularity: torch.Tensor):\n super().__init__()\n self.item_embedding = nn.Embedding(n_items, dim)\n self.item_embedding.weight.data.copy_(initial)\n self.register_buffer(\"popularity\", popularity[:, None])\n self.mlp = nn.Sequential(nn.Linear(dim + 1, dim * 2), nn.ReLU(), nn.Linear(dim * 2, dim))\n nn.init.zeros_(self.mlp[-1].weight); nn.init.zeros_(self.mlp[-1].bias)\n self.popularity_scale = nn.Parameter(torch.tensor(0.12))\n\n def forward(self, item: torch.Tensor) -> torch.Tensor:\n base = self.item_embedding(item)\n feat = torch.cat([base, self.popularity[item]], 1)\n latent = F.normalize(base + 0.2 * self.mlp(feat), dim=1)\n bias = self.popularity_scale * self.popularity[item]\n return torch.cat([latent, bias], 1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self, n_items: int, popularity: np.ndarray, seed: int):\n super().__init__()\n gen = torch.Generator().manual_seed(seed)\n initial = F.normalize(torch.randn(n_items, LATENT, generator=gen), dim=1)\n pop = torch.from_numpy(popularity)\n self.user_tower = UserTower(n_items, LATENT, initial)\n self.item_tower = ItemTower(n_items, LATENT, initial.clone(), pop)\n\n\ndef padded_queries(queries: list[Query]) -> tuple[np.ndarray, np.ndarray]:\n hi = np.zeros((len(queries), HISTORY_LEN), np.int64)\n hw = np.zeros((len(queries), HISTORY_LEN), np.float32)\n for r, q in enumerate(queries):\n n = min(len(q.items), HISTORY_LEN); hi[r, -n:] = q.items[-n:]; hw[r, -n:] = q.weights[-n:]\n return hi, hw\n\n\ndef train_model(queries: list[Query], popularity: np.ndarray, seed: int, epochs: int) -> TwoTower:\n torch.manual_seed(seed); np.random.seed(seed); random.seed(seed)\n torch.set_num_threads(min(16, torch.get_num_threads()))\n model = TwoTower(len(popularity), popularity, seed)\n opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n hi, hw = padded_queries(queries)\n rng = np.random.default_rng(seed)\n batch_size, negatives = 192, 24\n # Mixture of uniform and popularity-skewed negatives.\n rank_prob = 1.0 / np.sqrt(np.arange(1, len(popularity) + 1, dtype=np.float64))\n rank_prob /= rank_prob.sum()\n for epoch in range(epochs):\n order = rng.permutation(len(queries)); losses = []\n model.train()\n for start in range(0, len(order), batch_size):\n ix = order[start:start + batch_size]\n if len(ix) < 2: continue\n pos = np.array([rng.choice(queries[j].positives, p=queries[j].positive_weights / queries[j].positive_weights.sum()) for j in ix])\n neg = rng.integers(0, len(popularity), size=(len(ix), negatives), dtype=np.int64)\n # Half hard-by-popularity samples preserve the strong catalog prior.\n neg[:, :negatives // 2] = rng.choice(len(popularity), size=(len(ix), negatives // 2), p=rank_prob)\n ti = torch.from_numpy(hi[ix]); tw = torch.from_numpy(hw[ix])\n tp = torch.from_numpy(pos); tn = torch.from_numpy(neg)\n u = model.user_tower(ti, tw)\n ps = (u * model.item_tower(tp)).sum(1)\n nv = model.item_tower(tn.reshape(-1)).reshape(len(ix), negatives, -1)\n ns = (u[:, None, :] * nv).sum(2)\n loss = -F.logsigmoid((ps[:, None] - ns) * 5.0).mean()\n opt.zero_grad(set_to_none=True); loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0); opt.step()\n losses.append(float(loss.detach()))\n print(f\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.5f}\", flush=True)\n return model\n\n\n@torch.no_grad()\ndef rank(model: TwoTower, hist_i: np.ndarray, hist_w: np.ndarray, stage: str,\n uids: np.ndarray, item_ids: np.ndarray, output: Path) -> None:\n model.eval()\n all_items = torch.arange(len(item_ids))\n item_vec = model.item_tower(all_items).T.contiguous()\n with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n euids, eitems, packed = z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n assert np.array_equal(euids.astype(np.int64), uids)\n # Eligibility columns normally match catalog order, but map defensively.\n model_of_elig = {int(x): i for i, x in enumerate(item_ids)}\n emap = np.array([model_of_elig[int(x)] for x in eitems], np.int64)\n rows = []\n block = 128\n for lo in range(0, len(uids), block):\n hi = min(lo + block, len(uids))\n uv = model.user_tower(torch.from_numpy(hist_i[lo:hi]), torch.from_numpy(hist_w[lo:hi]))\n scores = uv @ item_vec\n eligibility = np.unpackbits(packed[lo:hi], axis=1, count=len(eitems), bitorder=\"little\").astype(bool)\n # Convert eligibility ordering to model ordering.\n allowed = np.zeros_like(eligibility); allowed[:, emap] = eligibility\n scores[~torch.from_numpy(allowed)] = -torch.inf\n top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\n for r, choices in enumerate(top):\n rows.extend((int(uids[lo + r]), int(item_ids[j]), k) for k, j in enumerate(choices, 1))\n pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(output, index=False)\n\n\ndef main() -> None:\n p = argparse.ArgumentParser()\n p.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n p.add_argument(\"--epochs\", type=int, default=8)\n p.add_argument(\"--snapshots\", type=int, default=6)\n p.add_argument(\"--seed\", type=int, default=20260925)\n p.add_argument(\"--output\", type=Path)\n args = p.parse_args()\n output = args.output or OUT / (\"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\")\n q, hi, hw, uids, items, pop = extract(args.stage, args.snapshots)\n print(f\"extracted {len(q)} rolling query examples\", flush=True)\n model = train_model(q, pop, args.seed, args.epochs)\n rank(model, hi, hw, args.stage, uids, items, output)\n torch.save({\"state_dict\": model.state_dict(), \"items\": items, \"popularity\": pop,\n \"architecture\": {\"history_len\": HISTORY_LEN, \"latent\": LATENT}}, OUT / f\"model_{args.stage}.pt\")\n (OUT / f\"run_{args.stage}.json\").write_text(json.dumps(vars(args), default=str, indent=2) + \"\\n\")\n print(f\"wrote {output}\", flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bd9d5db487d2ba96ea9057df5547", "usage": { "input_tokens": 27557, "cached_input_tokens": 27008, "cache_write_input_tokens": 0, "output_tokens": 8387, "reasoning_output_tokens": 2236, "total_tokens": 35944 }, "turn_token_usage": { "input_tokens": 314347, "cached_input_tokens": 295552, "cache_write_input_tokens": 0, "output_tokens": 14430, "reasoning_output_tokens": 4931, "total_tokens": 328777 }, "thread_token_usage": { "input_tokens": 314347, "cached_input_tokens": 295552, "cache_write_input_tokens": 0, "output_tokens": 14430, "reasoning_output_tokens": 4931, "total_tokens": 328777 } } ## Step 27 · agent · 2026-09-25T00:20:11.796Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:20:12.526Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 6 --snapshots 6 --output solution/validation_tower_v1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:20:13.691Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b5aee6","wall_time_seconds":1.001210022,"session_id":88565,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5be361a6c87d296a1446a3011e837", "usage": { "input_tokens": 35968, "cached_input_tokens": 27392, "cache_write_input_tokens": 0, "output_tokens": 309, "reasoning_output_tokens": 225, "total_tokens": 36277 }, "turn_token_usage": { "input_tokens": 350315, "cached_input_tokens": 322944, "cache_write_input_tokens": 0, "output_tokens": 14739, "reasoning_output_tokens": 5156, "total_tokens": 365054 }, "thread_token_usage": { "input_tokens": 350315, "cached_input_tokens": 322944, "cache_write_input_tokens": 0, "output_tokens": 14739, "reasoning_output_tokens": 5156, "total_tokens": 365054 } } ## Step 28 · agent · 2026-09-25T00:20:15.365Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:20:16.062Z { "code": "const r = await tools.write_stdin({session_id:88565,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:20:46.070Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5be3de0a487d288d6ee105a057aa7", "usage": { "input_tokens": 36336, "cached_input_tokens": 35840, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 36394 }, "turn_token_usage": { "input_tokens": 386651, "cached_input_tokens": 358784, "cache_write_input_tokens": 0, "output_tokens": 14797, "reasoning_output_tokens": 5167, "total_tokens": 401448 }, "thread_token_usage": { "input_tokens": 386651, "cached_input_tokens": 358784, "cache_write_input_tokens": 0, "output_tokens": 14797, "reasoning_output_tokens": 5167, "total_tokens": 401448 } } ## Step 29 · agent · 2026-09-25T00:20:48.103Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:20:48.781Z { "code": "const r = await tools.write_stdin({session_id:88565,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:21:18.789Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5be5e4ca887d2a4273c91097a6a4c", "usage": { "input_tokens": 36417, "cached_input_tokens": 36096, "cache_write_input_tokens": 0, "output_tokens": 63, "reasoning_output_tokens": 16, "total_tokens": 36480 }, "turn_token_usage": { "input_tokens": 423068, "cached_input_tokens": 394880, "cache_write_input_tokens": 0, "output_tokens": 14860, "reasoning_output_tokens": 5183, "total_tokens": 437928 }, "thread_token_usage": { "input_tokens": 423068, "cached_input_tokens": 394880, "cache_write_input_tokens": 0, "output_tokens": 14860, "reasoning_output_tokens": 5183, "total_tokens": 437928 } } ## Step 30 · agent · 2026-09-25T00:21:21.275Z ### Thinking unavailable — no text recorded The first full extraction is still streaming cleanly through the 43.5M listens; the valid popularity fallback remains untouched. One important modeling detail is in place: rolling labels are created strictly after each simulated cutoff and gap, so the validation experiment has no future-history leakage. ### exec · 2026-09-25T00:21:23.148Z { "code": "const r = await tools.write_stdin({session_id:88565,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:21:53.157Z Script completed Wall time 30.0 seconds Output: extracted 34460 rolling query examples epoch 1/6 loss=0.52108 epoch 2/6 loss=0.42787 epoch 3/6 loss=0.37266 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5be7efd5087d2a6278133eadf7d8f", "usage": { "input_tokens": 36503, "cached_input_tokens": 36224, "cache_write_input_tokens": 0, "output_tokens": 116, "reasoning_output_tokens": 7, "total_tokens": 36619 }, "turn_token_usage": { "input_tokens": 459571, "cached_input_tokens": 431104, "cache_write_input_tokens": 0, "output_tokens": 14976, "reasoning_output_tokens": 5190, "total_tokens": 474547 }, "thread_token_usage": { "input_tokens": 459571, "cached_input_tokens": 431104, "cache_write_input_tokens": 0, "output_tokens": 14976, "reasoning_output_tokens": 5190, "total_tokens": 474547 } } ## Step 31 · agent · 2026-09-25T00:21:54.581Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:21:55.251Z { "code": "const r = await tools.write_stdin({session_id:88565,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:22:11.473Z Script completed Wall time 16.2 seconds Output: epoch 4/6 loss=0.33167 epoch 5/6 loss=0.29763 epoch 6/6 loss=0.27176 wrote solution/validation_tower_v1.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bea1715887d2995bcaa05981bae7", "usage": { "input_tokens": 36687, "cached_input_tokens": 36352, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 36744 }, "turn_token_usage": { "input_tokens": 496258, "cached_input_tokens": 467456, "cache_write_input_tokens": 0, "output_tokens": 15033, "reasoning_output_tokens": 5200, "total_tokens": 511291 }, "thread_token_usage": { "input_tokens": 496258, "cached_input_tokens": 467456, "cache_write_input_tokens": 0, "output_tokens": 15033, "reasoning_output_tokens": 5200, "total_tokens": 511291 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-57904e35-40a1-4158-a3be-d52fdc33a3b0", "process_id": "88565", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 6 --snapshots 6 --output solution/validation_tower_v1.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 6 --snapshots 6 --output solution/validation_tower_v1.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 34460 rolling query examples\nepoch 1/6 loss=0.52108\nepoch 2/6 loss=0.42787\nepoch 3/6 loss=0.37266\nepoch 4/6 loss=0.33167\nepoch 5/6 loss=0.29763\nepoch 6/6 loss=0.27176\nwrote solution/validation_tower_v1.parquet\n", "stderr": "", "aggregated_output": "extracted 34460 rolling query examples\nepoch 1/6 loss=0.52108\nepoch 2/6 loss=0.42787\nepoch 3/6 loss=0.37266\nepoch 4/6 loss=0.33167\nepoch 5/6 loss=0.29763\nepoch 6/6 loss=0.27176\nwrote solution/validation_tower_v1.parquet\n", "exit_code": 0, "duration": { "secs": 118, "nanos": 738599197 }, "formatted_output": "extracted 34460 rolling query examples\nepoch 1/6 loss=0.52108\nepoch 2/6 loss=0.42787\nepoch 3/6 loss=0.37266\nepoch 4/6 loss=0.33167\nepoch 5/6 loss=0.29763\nepoch 6/6 loss=0.27176\nwrote solution/validation_tower_v1.parquet\n" } } ## Step 32 · agent · 2026-09-25T00:22:12.590Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:22:13.699Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:22:17.103Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_tower_v1.parquet", "scores": { "quality_ndcg@10": 0.018005, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5beb3a75887d29d24c6b7be75a72c", "usage": { "input_tokens": 36815, "cached_input_tokens": 36480, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 6, "total_tokens": 36891 }, "turn_token_usage": { "input_tokens": 533073, "cached_input_tokens": 503936, "cache_write_input_tokens": 0, "output_tokens": 15109, "reasoning_output_tokens": 5206, "total_tokens": 548182 }, "thread_token_usage": { "input_tokens": 533073, "cached_input_tokens": 503936, "cache_write_input_tokens": 0, "output_tokens": 15109, "reasoning_output_tokens": 5206, "total_tokens": 548182 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c044be0f-2282-482e-a0aa-5e1ca834ceeb", "process_id": "12785", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v1.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v1.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_tower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018005,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_tower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018005,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 217148621 }, "formatted_output": "{\n \"model\": \"solution/validation_tower_v1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018005,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 33 · agent · 2026-09-25T00:22:25.674Z ### Thinking unavailable — no text recorded The first neural ranker reaches 0.018005, a 68% lift over popularity. I’m checking calibration next—especially the balance between personalized history similarity and the catalog popularity coordinate—because that can be tuned without changing the serving architecture or retraining. ### exec · 2026-09-25T00:22:29.866Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport torch\\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\\nfor k,v in x['state_dict'].items():\\n if 'scale' in k or 'coordinate' in k: print(k,v)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:22:32.888Z Script completed Wall time 3.0 seconds Output: user_tower.global_bias_coordinate tensor(0.3512) item_tower.popularity_scale tensor(0.1287) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5beb94a9087d28765d4d6f07ad36e", "usage": { "input_tokens": 36961, "cached_input_tokens": 36608, "cache_write_input_tokens": 0, "output_tokens": 488, "reasoning_output_tokens": 316, "total_tokens": 37449 }, "turn_token_usage": { "input_tokens": 570034, "cached_input_tokens": 540544, "cache_write_input_tokens": 0, "output_tokens": 15597, "reasoning_output_tokens": 5522, "total_tokens": 585631 }, "thread_token_usage": { "input_tokens": 570034, "cached_input_tokens": 540544, "cache_write_input_tokens": 0, "output_tokens": 15597, "reasoning_output_tokens": 5522, "total_tokens": 585631 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-309a0fcf-747d-4ca2-becc-adc4733e1e60", "process_id": "38461", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport torch\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nfor k,v in x['state_dict'].items():\n if 'scale' in k or 'coordinate' in k: print(k,v)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport torch\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nfor k,v in x['state_dict'].items():\n if 'scale' in k or 'coordinate' in k: print(k,v)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "user_tower.global_bias_coordinate tensor(0.3512)\nitem_tower.popularity_scale tensor(0.1287)\n", "stderr": "", "aggregated_output": "user_tower.global_bias_coordinate tensor(0.3512)\nitem_tower.popularity_scale tensor(0.1287)\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 854190356 }, "formatted_output": "user_tower.global_bias_coordinate tensor(0.3512)\nitem_tower.popularity_scale tensor(0.1287)\n" } } ## Step 34 · agent · 2026-09-25T00:22:34.820Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:22:38.996Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nfrom pathlib import Path\\nsys.path.insert(0,'/app/solution')\\nimport train_ranker as tr\\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\\nfor s in [0.0,0.05,0.1,0.18,0.3,0.5,0.8]:\\n m=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.item_tower.popularity_scale.data.fill_(s)\\n tr.rank(m,hi,hw,'validation',uids,items,Path(f'/app/solution/validation_scale_{s}.parquet'))\\n print('done',s,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:22:40.162Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e0ece7","wall_time_seconds":1.001202741,"session_id":93219,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bec9149c87d2a94e22da1ff733d0", "usage": { "input_tokens": 37498, "cached_input_tokens": 36736, "cache_write_input_tokens": 0, "output_tokens": 297, "reasoning_output_tokens": 57, "total_tokens": 37795 }, "turn_token_usage": { "input_tokens": 607532, "cached_input_tokens": 577280, "cache_write_input_tokens": 0, "output_tokens": 15894, "reasoning_output_tokens": 5579, "total_tokens": 623426 }, "thread_token_usage": { "input_tokens": 607532, "cached_input_tokens": 577280, "cache_write_input_tokens": 0, "output_tokens": 15894, "reasoning_output_tokens": 5579, "total_tokens": 623426 } } ## Step 35 · agent · 2026-09-25T00:22:42.136Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:22:43.288Z { "code": "const r = await tools.write_stdin({session_id:93219,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:23:13.297Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bed06b0087d2a9d666ac610a62b4", "usage": { "input_tokens": 37853, "cached_input_tokens": 37376, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 37909 }, "turn_token_usage": { "input_tokens": 645385, "cached_input_tokens": 614656, "cache_write_input_tokens": 0, "output_tokens": 15950, "reasoning_output_tokens": 5588, "total_tokens": 661335 }, "thread_token_usage": { "input_tokens": 645385, "cached_input_tokens": 614656, "cache_write_input_tokens": 0, "output_tokens": 15950, "reasoning_output_tokens": 5588, "total_tokens": 661335 } } ### exec · 2026-09-25T00:23:14.895Z { "code": "const r = await tools.write_stdin({session_id:93219,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:23:44.904Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bef1868087d2a60d55113d0fcdc5", "usage": { "input_tokens": 37932, "cached_input_tokens": 37632, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 37977 }, "turn_token_usage": { "input_tokens": 683317, "cached_input_tokens": 652288, "cache_write_input_tokens": 0, "output_tokens": 15995, "reasoning_output_tokens": 5588, "total_tokens": 699312 }, "thread_token_usage": { "input_tokens": 683317, "cached_input_tokens": 652288, "cache_write_input_tokens": 0, "output_tokens": 15995, "reasoning_output_tokens": 5588, "total_tokens": 699312 } } ### exec · 2026-09-25T00:23:48.576Z { "code": "const r = await tools.write_stdin({session_id:93219,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:24:08.879Z Script completed Wall time 20.3 seconds Output: done 0.0 done 0.05 done 0.1 done 0.18 done 0.3 done 0.5 done 0.8 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf1118b487d29794866ed315a2bc", "usage": { "input_tokens": 38000, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 38045 }, "turn_token_usage": { "input_tokens": 721317, "cached_input_tokens": 690048, "cache_write_input_tokens": 0, "output_tokens": 16040, "reasoning_output_tokens": 5588, "total_tokens": 737357 }, "thread_token_usage": { "input_tokens": 721317, "cached_input_tokens": 690048, "cache_write_input_tokens": 0, "output_tokens": 16040, "reasoning_output_tokens": 5588, "total_tokens": 737357 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5cd7674e-bdf1-4a81-9669-f326f8ee79f1", "process_id": "93219", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport train_ranker as tr\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nfor s in [0.0,0.05,0.1,0.18,0.3,0.5,0.8]:\n m=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.item_tower.popularity_scale.data.fill_(s)\n tr.rank(m,hi,hw,'validation',uids,items,Path(f'/app/solution/validation_scale_{s}.parquet'))\n print('done',s,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution')\nimport train_ranker as tr\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nfor s in [0.0,0.05,0.1,0.18,0.3,0.5,0.8]:\n m=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.item_tower.popularity_scale.data.fill_(s)\n tr.rank(m,hi,hw,'validation',uids,items,Path(f'/app/solution/validation_scale_{s}.parquet'))\n print('done',s,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "done 0.0\ndone 0.05\ndone 0.1\ndone 0.18\ndone 0.3\ndone 0.5\ndone 0.8\n", "stderr": "", "aggregated_output": "done 0.0\ndone 0.05\ndone 0.1\ndone 0.18\ndone 0.3\ndone 0.5\ndone 0.8\n", "exit_code": 0, "duration": { "secs": 89, "nanos": 716260363 }, "formatted_output": "done 0.0\ndone 0.05\ndone 0.1\ndone 0.18\ndone 0.3\ndone 0.5\ndone 0.8\n" } } ## Step 36 · agent · 2026-09-25T00:24:10.790Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:24:12.166Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_scale_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:24:35.918Z Script completed Wall time 23.7 seconds Output: { "model": "solution/validation_scale_0.0.parquet", "scores": { "quality_ndcg@10": 0.016336, "quality_users": 4263 } } { "model": "solution/validation_scale_0.05.parquet", "scores": { "quality_ndcg@10": 0.017009, "quality_users": 4263 } } { "model": "solution/validation_scale_0.1.parquet", "scores": { "quality_ndcg@10": 0.017736, "quality_users": 4263 } } { "model": "solution/validation_scale_0.18.parquet", "scores": { "quality_ndcg@10": 0.01795, "quality_users": 4263 } } { "model": "solution/validation_scale_0.3.parquet", "scores": { "quality_ndcg@10": 0.017283, "quality_users": 4263 } } { "model": "solution/validation_scale_0.5.parquet", "scores": { "quality_ndcg@10": 0.017303, "quality_users": 4263 } } { "model": "solution/validation_scale_0.8.parquet", "scores": { "quality_ndcg@10": 0.016732, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf290b6087d2aa9d51906d9db0bd", "usage": { "input_tokens": 38110, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 11, "total_tokens": 38200 }, "turn_token_usage": { "input_tokens": 759427, "cached_input_tokens": 727808, "cache_write_input_tokens": 0, "output_tokens": 16130, "reasoning_output_tokens": 5599, "total_tokens": 775557 }, "thread_token_usage": { "input_tokens": 759427, "cached_input_tokens": 727808, "cache_write_input_tokens": 0, "output_tokens": 16130, "reasoning_output_tokens": 5599, "total_tokens": 775557 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d6a939fa-60a7-495b-8d6c-5b135e7a97ed", "process_id": "48193", "command": [ "/bin/sh", "-lc", "for f in solution/validation_scale_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_scale_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_scale_0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016336,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017009,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017736,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.18.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01795,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017283,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017303,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016732,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_scale_0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016336,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017009,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017736,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.18.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01795,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017283,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017303,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016732,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 583267332 }, "formatted_output": "{\n \"model\": \"solution/validation_scale_0.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016336,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.05.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017009,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017736,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.18.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01795,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017283,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017303,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_scale_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016732,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 37 · agent · 2026-09-25T00:24:48.179Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-25T00:24:50.889Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:25:01.569Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\\nnp.savez_compressed('/app/solution/validation_features.npz',hi=hi,hw=hw,uids=uids,items=items,pop=pop)\\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\\nm=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.eval()\\nwith torch.no_grad():\\n iv=m.item_tower(torch.arange(len(items)))\\n # Independent fixed random-sign projection for repeat matching.\\n gen=torch.Generator().manual_seed(9173)\\n hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\\n # Weighted history sketch, normalized only by sqrt(sum weight^2), preserving repetition.\\n huser=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)\\n huser=huser/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\\n z=np.load('/task/data/validation_eligible_candidates.npz'); packed=z['packed_eligible'];eitems=z['item_ids']; mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in eitems])\\n for alpha in [0.015,0.03,0.06,0.1,0.16,0.25]:\\n rows=[]\\n for lo in range(0,len(uids),128):\\n hh=min(lo+128,len(uids))\\n uv=m.user_tower(torch.from_numpy(hi[lo:hh]),torch.from_numpy(hw[lo:hh]))\\n s=uv@iv.T + alpha*(huser[lo:hh]@hv.T)\\n elig=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(elig);allow[:,emap]=elig\\n s[~torch.from_numpy(allow)]=-torch.inf\\n top=torch.topk(s,100,dim=1).indices.numpy()\\n for r,js in enumerate(top): rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'/app/solution/validation_hash_{alpha}.parquet',index=False)\\n print('done',alpha,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:25:02.735Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"dd1bce","wall_time_seconds":1.00104608,"session_id":31964,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf441d1887d2b6c11eca24374b06", "usage": { "input_tokens": 38559, "cached_input_tokens": 37888, "cache_write_input_tokens": 0, "output_tokens": 1330, "reasoning_output_tokens": 657, "total_tokens": 39889 }, "turn_token_usage": { "input_tokens": 797986, "cached_input_tokens": 765696, "cache_write_input_tokens": 0, "output_tokens": 17460, "reasoning_output_tokens": 6256, "total_tokens": 815446 }, "thread_token_usage": { "input_tokens": 797986, "cached_input_tokens": 765696, "cache_write_input_tokens": 0, "output_tokens": 17460, "reasoning_output_tokens": 6256, "total_tokens": 815446 } } ## Step 39 · agent · 2026-09-25T00:25:03.771Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:25:04.453Z { "code": "const r = await tools.write_stdin({session_id:31964,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:25:34.463Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf5eedf887d29cf41ca2b4b85174", "usage": { "input_tokens": 39947, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 40000 }, "turn_token_usage": { "input_tokens": 837933, "cached_input_tokens": 804096, "cache_write_input_tokens": 0, "output_tokens": 17513, "reasoning_output_tokens": 6262, "total_tokens": 855446 }, "thread_token_usage": { "input_tokens": 837933, "cached_input_tokens": 804096, "cache_write_input_tokens": 0, "output_tokens": 17513, "reasoning_output_tokens": 6262, "total_tokens": 855446 } } ### exec · 2026-09-25T00:25:36.876Z { "code": "const r = await tools.write_stdin({session_id:31964,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:26:06.885Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf7edcd487d28378528e5ff57ae8", "usage": { "input_tokens": 40023, "cached_input_tokens": 39808, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 40068 }, "turn_token_usage": { "input_tokens": 877956, "cached_input_tokens": 843904, "cache_write_input_tokens": 0, "output_tokens": 17558, "reasoning_output_tokens": 6262, "total_tokens": 895514 }, "thread_token_usage": { "input_tokens": 877956, "cached_input_tokens": 843904, "cache_write_input_tokens": 0, "output_tokens": 17558, "reasoning_output_tokens": 6262, "total_tokens": 895514 } } ### exec · 2026-09-25T00:26:10.662Z { "code": "const r = await tools.write_stdin({session_id:31964,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:26:34.068Z Script completed Wall time 23.4 seconds Output: done 0.015 done 0.03 done 0.06 done 0.1 done 0.16 done 0.25 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bf9f17bc87d294cee5f4880ffd21", "usage": { "input_tokens": 40091, "cached_input_tokens": 39808, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 40136 }, "turn_token_usage": { "input_tokens": 918047, "cached_input_tokens": 883712, "cache_write_input_tokens": 0, "output_tokens": 17603, "reasoning_output_tokens": 6262, "total_tokens": 935650 }, "thread_token_usage": { "input_tokens": 918047, "cached_input_tokens": 883712, "cache_write_input_tokens": 0, "output_tokens": 17603, "reasoning_output_tokens": 6262, "total_tokens": 935650 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-99baefc4-0c56-44d3-8ffb-3d2d662fad93", "process_id": "31964", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\nnp.savez_compressed('/app/solution/validation_features.npz',hi=hi,hw=hw,uids=uids,items=items,pop=pop)\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nm=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.eval()\nwith torch.no_grad():\n iv=m.item_tower(torch.arange(len(items)))\n # Independent fixed random-sign projection for repeat matching.\n gen=torch.Generator().manual_seed(9173)\n hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\n # Weighted history sketch, normalized only by sqrt(sum weight^2), preserving repetition.\n huser=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)\n huser=huser/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\n z=np.load('/task/data/validation_eligible_candidates.npz'); packed=z['packed_eligible'];eitems=z['item_ids']; mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in eitems])\n for alpha in [0.015,0.03,0.06,0.1,0.16,0.25]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids))\n uv=m.user_tower(torch.from_numpy(hi[lo:hh]),torch.from_numpy(hw[lo:hh]))\n s=uv@iv.T + alpha*(huser[lo:hh]@hv.T)\n elig=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(elig);allow[:,emap]=elig\n s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top): rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'/app/solution/validation_hash_{alpha}.parquet',index=False)\n print('done',alpha,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nq,hi,hw,uids,items,pop=tr.extract('validation',6)\nnp.savez_compressed('/app/solution/validation_features.npz',hi=hi,hw=hw,uids=uids,items=items,pop=pop)\nx=torch.load('/app/solution/model_validation.pt',weights_only=False)\nm=tr.TwoTower(len(pop),pop,20260925);m.load_state_dict(x['state_dict']);m.eval()\nwith torch.no_grad():\n iv=m.item_tower(torch.arange(len(items)))\n # Independent fixed random-sign projection for repeat matching.\n gen=torch.Generator().manual_seed(9173)\n hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\n # Weighted history sketch, normalized only by sqrt(sum weight^2), preserving repetition.\n huser=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)\n huser=huser/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\n z=np.load('/task/data/validation_eligible_candidates.npz'); packed=z['packed_eligible'];eitems=z['item_ids']; mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in eitems])\n for alpha in [0.015,0.03,0.06,0.1,0.16,0.25]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids))\n uv=m.user_tower(torch.from_numpy(hi[lo:hh]),torch.from_numpy(hw[lo:hh]))\n s=uv@iv.T + alpha*(huser[lo:hh]@hv.T)\n elig=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(elig);allow[:,emap]=elig\n s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top): rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(f'/app/solution/validation_hash_{alpha}.parquet',index=False)\n print('done',alpha,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "done 0.015\ndone 0.03\ndone 0.06\ndone 0.1\ndone 0.16\ndone 0.25\n", "stderr": "", "aggregated_output": "done 0.015\ndone 0.03\ndone 0.06\ndone 0.1\ndone 0.16\ndone 0.25\n", "exit_code": 0, "duration": { "secs": 92, "nanos": 328224763 }, "formatted_output": "done 0.015\ndone 0.03\ndone 0.06\ndone 0.1\ndone 0.16\ndone 0.25\n" } } ## Step 40 · agent · 2026-09-25T00:26:35.071Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:26:36.333Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_hash_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:26:56.828Z Script completed Wall time 20.5 seconds Output: { "model": "solution/validation_hash_0.015.parquet", "scores": { "quality_ndcg@10": 0.018045, "quality_users": 4263 } } { "model": "solution/validation_hash_0.03.parquet", "scores": { "quality_ndcg@10": 0.01821, "quality_users": 4263 } } { "model": "solution/validation_hash_0.06.parquet", "scores": { "quality_ndcg@10": 0.018188, "quality_users": 4263 } } { "model": "solution/validation_hash_0.1.parquet", "scores": { "quality_ndcg@10": 0.017888, "quality_users": 4263 } } { "model": "solution/validation_hash_0.16.parquet", "scores": { "quality_ndcg@10": 0.01755, "quality_users": 4263 } } { "model": "solution/validation_hash_0.25.parquet", "scores": { "quality_ndcg@10": 0.017143, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bfba3fac87d29398e9dca793e675", "usage": { "input_tokens": 40195, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 6, "total_tokens": 40280 }, "turn_token_usage": { "input_tokens": 958242, "cached_input_tokens": 923648, "cache_write_input_tokens": 0, "output_tokens": 17688, "reasoning_output_tokens": 6268, "total_tokens": 975930 }, "thread_token_usage": { "input_tokens": 958242, "cached_input_tokens": 923648, "cache_write_input_tokens": 0, "output_tokens": 17688, "reasoning_output_tokens": 6268, "total_tokens": 975930 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8a2f9c50-cf9a-4c7d-81ae-4d8049bef824", "process_id": "83278", "command": [ "/bin/sh", "-lc", "for f in solution/validation_hash_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_hash_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_hash_0.015.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018045,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01821,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.06.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018188,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017888,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.16.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01755,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017143,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_hash_0.015.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018045,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01821,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.06.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018188,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017888,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.16.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01755,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017143,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 304156879 }, "formatted_output": "{\n \"model\": \"solution/validation_hash_0.015.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018045,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01821,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.06.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018188,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017888,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.16.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01755,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_hash_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017143,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 41 · agent · 2026-09-25T00:27:01.726Z ### Thinking unavailable — no text recorded Calibration peaks close to the learned setting, and a protected low-dimensional repeat sketch adds a smaller lift to 0.018210. I’m testing a shorter rolling horizon now; the current six-week objective may be averaging away short-term taste that matters in the next seven days. If complementary, the models can still be served as one exact dot product by concatenating their tower vectors. ### exec · 2026-09-25T00:27:04.918Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_validation.pt solution/model_validation_v1.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 8 --snapshots 3 --seed 20260926 --output solution/validation_tower_v2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:27:06.084Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a06831","wall_time_seconds":1.001072432,"session_id":48307,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bfd1075c87d28be663db88f002cb", "usage": { "input_tokens": 40591, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 382, "reasoning_output_tokens": 197, "total_tokens": 40973 }, "turn_token_usage": { "input_tokens": 998833, "cached_input_tokens": 963712, "cache_write_input_tokens": 0, "output_tokens": 18070, "reasoning_output_tokens": 6465, "total_tokens": 1016903 }, "thread_token_usage": { "input_tokens": 998833, "cached_input_tokens": 963712, "cache_write_input_tokens": 0, "output_tokens": 18070, "reasoning_output_tokens": 6465, "total_tokens": 1016903 } } ## Step 42 · agent · 2026-09-25T00:27:07.318Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:27:08.034Z { "code": "const r = await tools.write_stdin({session_id:48307,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:27:38.043Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bfda642c87d2923a87e6c0a34a8b", "usage": { "input_tokens": 41030, "cached_input_tokens": 40448, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 41083 }, "turn_token_usage": { "input_tokens": 1039863, "cached_input_tokens": 1004160, "cache_write_input_tokens": 0, "output_tokens": 18123, "reasoning_output_tokens": 6471, "total_tokens": 1057986 }, "thread_token_usage": { "input_tokens": 1039863, "cached_input_tokens": 1004160, "cache_write_input_tokens": 0, "output_tokens": 18123, "reasoning_output_tokens": 6471, "total_tokens": 1057986 } } ### exec · 2026-09-25T00:27:40.495Z { "code": "const r = await tools.write_stdin({session_id:48307,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:28:10.504Z Script completed Wall time 30.0 seconds Output: extracted 17735 rolling query examples epoch 1/8 loss=0.53229 epoch 2/8 loss=0.44463 epoch 3/8 loss=0.39781 epoch 4/8 loss=0.37379 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5bffa392487d2b00439b4506e8913", "usage": { "input_tokens": 41106, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 41151 }, "turn_token_usage": { "input_tokens": 1080969, "cached_input_tokens": 1044992, "cache_write_input_tokens": 0, "output_tokens": 18168, "reasoning_output_tokens": 6471, "total_tokens": 1099137 }, "thread_token_usage": { "input_tokens": 1080969, "cached_input_tokens": 1044992, "cache_write_input_tokens": 0, "output_tokens": 18168, "reasoning_output_tokens": 6471, "total_tokens": 1099137 } } ### exec · 2026-09-25T00:28:12.263Z { "code": "const r = await tools.write_stdin({session_id:48307,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:28:27.219Z Script completed Wall time 15.0 seconds Output: epoch 5/8 loss=0.33912 epoch 6/8 loss=0.31997 epoch 7/8 loss=0.29648 epoch 8/8 loss=0.27861 wrote solution/validation_tower_v2.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c01ac45887d2998ccb5595035b3f", "usage": { "input_tokens": 41231, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 41276 }, "turn_token_usage": { "input_tokens": 1122200, "cached_input_tokens": 1085952, "cache_write_input_tokens": 0, "output_tokens": 18213, "reasoning_output_tokens": 6471, "total_tokens": 1140413 }, "thread_token_usage": { "input_tokens": 1122200, "cached_input_tokens": 1085952, "cache_write_input_tokens": 0, "output_tokens": 18213, "reasoning_output_tokens": 6471, "total_tokens": 1140413 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c78325f2-8989-47ed-afbb-ef17536588e4", "process_id": "48307", "command": [ "/bin/sh", "-lc", "cp solution/model_validation.pt solution/model_validation_v1.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 8 --snapshots 3 --seed 20260926 --output solution/validation_tower_v2.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_validation.pt solution/model_validation_v1.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 8 --snapshots 3 --seed 20260926 --output solution/validation_tower_v2.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 17735 rolling query examples\nepoch 1/8 loss=0.53229\nepoch 2/8 loss=0.44463\nepoch 3/8 loss=0.39781\nepoch 4/8 loss=0.37379\nepoch 5/8 loss=0.33912\nepoch 6/8 loss=0.31997\nepoch 7/8 loss=0.29648\nepoch 8/8 loss=0.27861\nwrote solution/validation_tower_v2.parquet\n", "stderr": "", "aggregated_output": "extracted 17735 rolling query examples\nepoch 1/8 loss=0.53229\nepoch 2/8 loss=0.44463\nepoch 3/8 loss=0.39781\nepoch 4/8 loss=0.37379\nepoch 5/8 loss=0.33912\nepoch 6/8 loss=0.31997\nepoch 7/8 loss=0.29648\nepoch 8/8 loss=0.27861\nwrote solution/validation_tower_v2.parquet\n", "exit_code": 0, "duration": { "secs": 82, "nanos": 134345070 }, "formatted_output": "extracted 17735 rolling query examples\nepoch 1/8 loss=0.53229\nepoch 2/8 loss=0.44463\nepoch 3/8 loss=0.39781\nepoch 4/8 loss=0.37379\nepoch 5/8 loss=0.33912\nepoch 6/8 loss=0.31997\nepoch 7/8 loss=0.29648\nepoch 8/8 loss=0.27861\nwrote solution/validation_tower_v2.parquet\n" } } ## Step 43 · agent · 2026-09-25T00:28:28.192Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:29.493Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_validation.pt solution/model_validation_v2.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:28:32.974Z Script completed Wall time 3.5 seconds Output: { "model": "solution/validation_tower_v2.parquet", "scores": { "quality_ndcg@10": 0.020348, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c02b62d087d2ac75a127aa553d07", "usage": { "input_tokens": 41359, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 88, "reasoning_output_tokens": 6, "total_tokens": 41447 }, "turn_token_usage": { "input_tokens": 1163559, "cached_input_tokens": 1127040, "cache_write_input_tokens": 0, "output_tokens": 18301, "reasoning_output_tokens": 6477, "total_tokens": 1181860 }, "thread_token_usage": { "input_tokens": 1163559, "cached_input_tokens": 1127040, "cache_write_input_tokens": 0, "output_tokens": 18301, "reasoning_output_tokens": 6477, "total_tokens": 1181860 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8367c66b-1c1d-4190-8021-c7b361c066b4", "process_id": "78136", "command": [ "/bin/sh", "-lc", "cp solution/model_validation.pt solution/model_validation_v2.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v2.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_validation.pt solution/model_validation_v2.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v2.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_tower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020348,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_tower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020348,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 311848762 }, "formatted_output": "{\n \"model\": \"solution/validation_tower_v2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020348,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 44 · agent · 2026-09-25T00:28:35.427Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:48.015Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch,numpy as np,pandas as pd\\nsys.path.insert(0,'/app/solution');import train_ranker as tr\\nf=np.load('/app/solution/validation_features.npz');hi=f['hi'];hw=f['hw'];uids=f['uids'];items=f['items'];pop=f['pop']\\ndef load(path,seed):\\n x=torch.load(path,weights_only=False);m=tr.TwoTower(len(pop),pop,seed);m.load_state_dict(x['state_dict']);m.eval();return m\\nm1=load('/app/solution/model_validation_v1.pt',20260925);m2=load('/app/solution/model_validation_v2.pt',20260926)\\nwith torch.no_grad():\\n i1=m1.item_tower(torch.arange(len(items)));i2=m2.item_tower(torch.arange(len(items)))\\n gen=torch.Generator().manual_seed(9173);hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\\n hu=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\\n z=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];ei=z['item_ids'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in ei])\\n for beta,alpha in [(0.15,0),(0.3,0),(0.5,0),(0.75,0),(1,0),(0.3,.03),(0.5,.03),(0.75,.03)]:\\n rows=[]\\n for lo in range(0,len(uids),128):\\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\\n s=m2.user_tower(a,b)@i2.T + beta*(m1.user_tower(a,b)@i1.T)\\n if alpha:s+=alpha*(hu[lo:hh]@hv.T)\\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\\n top=torch.topk(s,100,dim=1).indices.numpy()\\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\\n out=f'/app/solution/validation_blend_{beta}_{alpha}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:28:49.182Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"14651e","wall_time_seconds":1.001733421,"session_id":84432,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c031285c87d29663fb48cd5e3362", "usage": { "input_tokens": 41517, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 800, "reasoning_output_tokens": 91, "total_tokens": 42317 }, "turn_token_usage": { "input_tokens": 1205076, "cached_input_tokens": 1168256, "cache_write_input_tokens": 0, "output_tokens": 19101, "reasoning_output_tokens": 6568, "total_tokens": 1224177 }, "thread_token_usage": { "input_tokens": 1205076, "cached_input_tokens": 1168256, "cache_write_input_tokens": 0, "output_tokens": 19101, "reasoning_output_tokens": 6568, "total_tokens": 1224177 } } ## Step 45 · agent · 2026-09-25T00:28:50.364Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:51.039Z { "code": "const r = await tools.write_stdin({session_id:84432,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:29:17.120Z Script completed Wall time 26.1 seconds Output: /app/solution/validation_blend_0.15_0.parquet /app/solution/validation_blend_0.3_0.parquet /app/solution/validation_blend_0.5_0.parquet /app/solution/validation_blend_0.75_0.parquet /app/solution/validation_blend_1_0.parquet /app/solution/validation_blend_0.3_0.03.parquet /app/solution/validation_blend_0.5_0.03.parquet /app/solution/validation_blend_0.75_0.03.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0415d2487d29ae49a79c1db95fb", "usage": { "input_tokens": 42374, "cached_input_tokens": 41344, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 42427 }, "turn_token_usage": { "input_tokens": 1247450, "cached_input_tokens": 1209600, "cache_write_input_tokens": 0, "output_tokens": 19154, "reasoning_output_tokens": 6574, "total_tokens": 1266604 }, "thread_token_usage": { "input_tokens": 1247450, "cached_input_tokens": 1209600, "cache_write_input_tokens": 0, "output_tokens": 19154, "reasoning_output_tokens": 6574, "total_tokens": 1266604 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae4ffd46-2469-466b-8819-1f7297edebba", "process_id": "84432", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi=f['hi'];hw=f['hw'];uids=f['uids'];items=f['items'];pop=f['pop']\ndef load(path,seed):\n x=torch.load(path,weights_only=False);m=tr.TwoTower(len(pop),pop,seed);m.load_state_dict(x['state_dict']);m.eval();return m\nm1=load('/app/solution/model_validation_v1.pt',20260925);m2=load('/app/solution/model_validation_v2.pt',20260926)\nwith torch.no_grad():\n i1=m1.item_tower(torch.arange(len(items)));i2=m2.item_tower(torch.arange(len(items)))\n gen=torch.Generator().manual_seed(9173);hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\n hu=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\n z=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];ei=z['item_ids'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in ei])\n for beta,alpha in [(0.15,0),(0.3,0),(0.5,0),(0.75,0),(1,0),(0.3,.03),(0.5,.03),(0.75,.03)]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n s=m2.user_tower(a,b)@i2.T + beta*(m1.user_tower(a,b)@i1.T)\n if alpha:s+=alpha*(hu[lo:hh]@hv.T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend_{beta}_{alpha}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi=f['hi'];hw=f['hw'];uids=f['uids'];items=f['items'];pop=f['pop']\ndef load(path,seed):\n x=torch.load(path,weights_only=False);m=tr.TwoTower(len(pop),pop,seed);m.load_state_dict(x['state_dict']);m.eval();return m\nm1=load('/app/solution/model_validation_v1.pt',20260925);m2=load('/app/solution/model_validation_v2.pt',20260926)\nwith torch.no_grad():\n i1=m1.item_tower(torch.arange(len(items)));i2=m2.item_tower(torch.arange(len(items)))\n gen=torch.Generator().manual_seed(9173);hv=torch.randint(0,2,(len(items),256),generator=gen,dtype=torch.float32).mul_(2).sub_(1).div_(16)\n hu=(hv[torch.from_numpy(hi)]*torch.from_numpy(hw)[:,:,None]).sum(1)/(torch.from_numpy(hw).square().sum(1,keepdim=True).sqrt().clamp_min(1e-6))\n z=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];ei=z['item_ids'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in ei])\n for beta,alpha in [(0.15,0),(0.3,0),(0.5,0),(0.75,0),(1,0),(0.3,.03),(0.5,.03),(0.75,.03)]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n s=m2.user_tower(a,b)@i2.T + beta*(m1.user_tower(a,b)@i1.T)\n if alpha:s+=alpha*(hu[lo:hh]@hv.T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend_{beta}_{alpha}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/validation_blend_0.15_0.parquet\n/app/solution/validation_blend_0.3_0.parquet\n/app/solution/validation_blend_0.5_0.parquet\n/app/solution/validation_blend_0.75_0.parquet\n/app/solution/validation_blend_1_0.parquet\n/app/solution/validation_blend_0.3_0.03.parquet\n/app/solution/validation_blend_0.5_0.03.parquet\n/app/solution/validation_blend_0.75_0.03.parquet\n", "stderr": "", "aggregated_output": "/app/solution/validation_blend_0.15_0.parquet\n/app/solution/validation_blend_0.3_0.parquet\n/app/solution/validation_blend_0.5_0.parquet\n/app/solution/validation_blend_0.75_0.parquet\n/app/solution/validation_blend_1_0.parquet\n/app/solution/validation_blend_0.3_0.03.parquet\n/app/solution/validation_blend_0.5_0.03.parquet\n/app/solution/validation_blend_0.75_0.03.parquet\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 937782334 }, "formatted_output": "/app/solution/validation_blend_0.15_0.parquet\n/app/solution/validation_blend_0.3_0.parquet\n/app/solution/validation_blend_0.5_0.parquet\n/app/solution/validation_blend_0.75_0.parquet\n/app/solution/validation_blend_1_0.parquet\n/app/solution/validation_blend_0.3_0.03.parquet\n/app/solution/validation_blend_0.5_0.03.parquet\n/app/solution/validation_blend_0.75_0.03.parquet\n" } } ## Step 46 · agent · 2026-09-25T00:29:18.449Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:29:19.735Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:29:46.969Z Script completed Wall time 27.2 seconds Output: { "model": "solution/validation_blend_0.15_0.parquet", "scores": { "quality_ndcg@10": 0.023319, "quality_users": 4263 } } { "model": "solution/validation_blend_0.3_0.03.parquet", "scores": { "quality_ndcg@10": 0.024158, "quality_users": 4263 } } { "model": "solution/validation_blend_0.3_0.parquet", "scores": { "quality_ndcg@10": 0.024378, "quality_users": 4263 } } { "model": "solution/validation_blend_0.5_0.03.parquet", "scores": { "quality_ndcg@10": 0.025173, "quality_users": 4263 } } { "model": "solution/validation_blend_0.5_0.parquet", "scores": { "quality_ndcg@10": 0.025163, "quality_users": 4263 } } { "model": "solution/validation_blend_0.75_0.03.parquet", "scores": { "quality_ndcg@10": 0.025314, "quality_users": 4263 } } { "model": "solution/validation_blend_0.75_0.parquet", "scores": { "quality_ndcg@10": 0.02549, "quality_users": 4263 } } { "model": "solution/validation_blend_1_0.parquet", "scores": { "quality_ndcg@10": 0.025244, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c05d4af087d2b3498351b64cae9e", "usage": { "input_tokens": 42582, "cached_input_tokens": 42240, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 6, "total_tokens": 42668 }, "turn_token_usage": { "input_tokens": 1290032, "cached_input_tokens": 1251840, "cache_write_input_tokens": 0, "output_tokens": 19240, "reasoning_output_tokens": 6580, "total_tokens": 1309272 }, "thread_token_usage": { "input_tokens": 1290032, "cached_input_tokens": 1251840, "cache_write_input_tokens": 0, "output_tokens": 19240, "reasoning_output_tokens": 6580, "total_tokens": 1309272 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7e06b334-53d3-486f-925a-6d324918e76e", "process_id": "19534", "command": [ "/bin/sh", "-lc", "for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_blend_0.15_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023319,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024378,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025173,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025163,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02549,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_1_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025244,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_blend_0.15_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023319,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024378,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025173,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025163,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02549,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_1_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025244,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 27, "nanos": 66210404 }, "formatted_output": "{\n \"model\": \"solution/validation_blend_0.15_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023319,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024158,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.3_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024378,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025173,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.5_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025163,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.03.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025314,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_0.75_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02549,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend_1_0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025244,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 47 · agent · 2026-09-25T00:29:49.363Z ### Thinking unavailable — no text recorded The shorter-horizon model alone scores 0.020348. Concatenating its vectors with the six-week model (a single wider two-tower dot product, not a request-time reranker) raises validation to 0.025490. I’m adding one final complementary model focused only on the immediately preceding pseudo-window, then I’ll refit the selected ensemble on train+validation and lock the hidden-test submission. ### exec · 2026-09-25T00:29:52.551Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --snapshots 1 --seed 20260927 --output solution/validation_tower_v3.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:29:53.717Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"49e154","wall_time_seconds":1.000849062,"session_id":62077,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c07b28f887d2bc58057719721b65", "usage": { "input_tokens": 43103, "cached_input_tokens": 42368, "cache_write_input_tokens": 0, "output_tokens": 250, "reasoning_output_tokens": 69, "total_tokens": 43353 }, "turn_token_usage": { "input_tokens": 1333135, "cached_input_tokens": 1294208, "cache_write_input_tokens": 0, "output_tokens": 19490, "reasoning_output_tokens": 6649, "total_tokens": 1352625 }, "thread_token_usage": { "input_tokens": 1333135, "cached_input_tokens": 1294208, "cache_write_input_tokens": 0, "output_tokens": 19490, "reasoning_output_tokens": 6649, "total_tokens": 1352625 } } ## Step 48 · agent · 2026-09-25T00:29:55.904Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:29:56.618Z { "code": "const r = await tools.write_stdin({session_id:62077,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:30:26.626Z Script completed Wall time 30.0 seconds Output: extracted 6065 rolling query examples ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c081e2ac87d2af916b83c913f6c7", "usage": { "input_tokens": 43410, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 43463 }, "turn_token_usage": { "input_tokens": 1376545, "cached_input_tokens": 1337088, "cache_write_input_tokens": 0, "output_tokens": 19543, "reasoning_output_tokens": 6655, "total_tokens": 1396088 }, "thread_token_usage": { "input_tokens": 1376545, "cached_input_tokens": 1337088, "cache_write_input_tokens": 0, "output_tokens": 19543, "reasoning_output_tokens": 6655, "total_tokens": 1396088 } } ### exec · 2026-09-25T00:30:28.464Z { "code": "const r = await tools.write_stdin({session_id:62077,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:30:40.046Z Script completed Wall time 11.6 seconds Output: epoch 1/10 loss=0.55516 epoch 2/10 loss=0.47475 epoch 3/10 loss=0.43370 epoch 4/10 loss=0.40027 epoch 5/10 loss=0.37710 epoch 6/10 loss=0.35695 epoch 7/10 loss=0.33625 epoch 8/10 loss=0.32686 epoch 9/10 loss=0.30765 epoch 10/10 loss=0.29898 wrote solution/validation_tower_v3.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0a2cedc87d2aacf1a67d0094b34", "usage": { "input_tokens": 43495, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 43540 }, "turn_token_usage": { "input_tokens": 1420040, "cached_input_tokens": 1380352, "cache_write_input_tokens": 0, "output_tokens": 19588, "reasoning_output_tokens": 6655, "total_tokens": 1439628 }, "thread_token_usage": { "input_tokens": 1420040, "cached_input_tokens": 1380352, "cache_write_input_tokens": 0, "output_tokens": 19588, "reasoning_output_tokens": 6655, "total_tokens": 1439628 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fe768403-b5cb-4c50-898d-4d3f13bea128", "process_id": "62077", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --snapshots 1 --seed 20260927 --output solution/validation_tower_v3.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --snapshots 1 --seed 20260927 --output solution/validation_tower_v3.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 6065 rolling query examples\nepoch 1/10 loss=0.55516\nepoch 2/10 loss=0.47475\nepoch 3/10 loss=0.43370\nepoch 4/10 loss=0.40027\nepoch 5/10 loss=0.37710\nepoch 6/10 loss=0.35695\nepoch 7/10 loss=0.33625\nepoch 8/10 loss=0.32686\nepoch 9/10 loss=0.30765\nepoch 10/10 loss=0.29898\nwrote solution/validation_tower_v3.parquet\n", "stderr": "", "aggregated_output": "extracted 6065 rolling query examples\nepoch 1/10 loss=0.55516\nepoch 2/10 loss=0.47475\nepoch 3/10 loss=0.43370\nepoch 4/10 loss=0.40027\nepoch 5/10 loss=0.37710\nepoch 6/10 loss=0.35695\nepoch 7/10 loss=0.33625\nepoch 8/10 loss=0.32686\nepoch 9/10 loss=0.30765\nepoch 10/10 loss=0.29898\nwrote solution/validation_tower_v3.parquet\n", "exit_code": 0, "duration": { "secs": 47, "nanos": 327908271 }, "formatted_output": "extracted 6065 rolling query examples\nepoch 1/10 loss=0.55516\nepoch 2/10 loss=0.47475\nepoch 3/10 loss=0.43370\nepoch 4/10 loss=0.40027\nepoch 5/10 loss=0.37710\nepoch 6/10 loss=0.35695\nepoch 7/10 loss=0.33625\nepoch 8/10 loss=0.32686\nepoch 9/10 loss=0.30765\nepoch 10/10 loss=0.29898\nwrote solution/validation_tower_v3.parquet\n" } } ## Step 49 · agent · 2026-09-25T00:30:41.170Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:30:42.511Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_validation.pt solution/model_validation_v3.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v3.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:30:45.943Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_tower_v3.parquet", "scores": { "quality_ndcg@10": 0.019, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0b03f0487d28faa54c49afcc893", "usage": { "input_tokens": 43695, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 93, "reasoning_output_tokens": 11, "total_tokens": 43788 }, "turn_token_usage": { "input_tokens": 1463735, "cached_input_tokens": 1423616, "cache_write_input_tokens": 0, "output_tokens": 19681, "reasoning_output_tokens": 6666, "total_tokens": 1483416 }, "thread_token_usage": { "input_tokens": 1463735, "cached_input_tokens": 1423616, "cache_write_input_tokens": 0, "output_tokens": 19681, "reasoning_output_tokens": 6666, "total_tokens": 1483416 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-25b442fc-62ca-479c-a37e-e1f62df0470d", "process_id": "26383", "command": [ "/bin/sh", "-lc", "cp solution/model_validation.pt solution/model_validation_v3.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v3.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_validation.pt solution/model_validation_v3.pt && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_tower_v3.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_tower_v3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_tower_v3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 261967308 }, "formatted_output": "{\n \"model\": \"solution/validation_tower_v3.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 50 · agent · 2026-09-25T00:30:47.407Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:30:57.657Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch,numpy as np,pandas as pd\\nsys.path.insert(0,'/app/solution');import train_ranker as tr\\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\\nms=[ld('/app/solution/model_validation_v1.pt',20260925),ld('/app/solution/model_validation_v2.pt',20260926),ld('/app/solution/model_validation_v3.pt',20260927)]\\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\\nfor gam in [.1,.25,.4,.6,.8]:\\n rows=[]\\n for lo in range(0,len(uids),128):\\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\\n top=torch.topk(s,100,dim=1).indices.numpy()\\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:30:58.830Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f0b81e","wall_time_seconds":1.001408193,"session_id":70479,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0b6216487d2ad485b3437247b72", "usage": { "input_tokens": 43857, "cached_input_tokens": 43520, "cache_write_input_tokens": 0, "output_tokens": 608, "reasoning_output_tokens": 31, "total_tokens": 44465 }, "turn_token_usage": { "input_tokens": 1507592, "cached_input_tokens": 1467136, "cache_write_input_tokens": 0, "output_tokens": 20289, "reasoning_output_tokens": 6697, "total_tokens": 1527881 }, "thread_token_usage": { "input_tokens": 1507592, "cached_input_tokens": 1467136, "cache_write_input_tokens": 0, "output_tokens": 20289, "reasoning_output_tokens": 6697, "total_tokens": 1527881 } } ### exec · 2026-09-25T00:31:02.052Z { "code": "const r = await tools.write_stdin({session_id:70479,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:31:19.639Z Script completed Wall time 17.6 seconds Output: /app/solution/validation_blend3_0.1.parquet /app/solution/validation_blend3_0.25.parquet /app/solution/validation_blend3_0.4.parquet /app/solution/validation_blend3_0.6.parquet /app/solution/validation_blend3_0.8.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0c309c487d2a012cdf7827c3ba0", "usage": { "input_tokens": 44524, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 44569 }, "turn_token_usage": { "input_tokens": 1552116, "cached_input_tokens": 1510784, "cache_write_input_tokens": 0, "output_tokens": 20334, "reasoning_output_tokens": 6697, "total_tokens": 1572450 }, "thread_token_usage": { "input_tokens": 1552116, "cached_input_tokens": 1510784, "cache_write_input_tokens": 0, "output_tokens": 20334, "reasoning_output_tokens": 6697, "total_tokens": 1572450 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-62491a83-45c9-4ea4-8653-c3073172b5b5", "process_id": "70479", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\nms=[ld('/app/solution/model_validation_v1.pt',20260925),ld('/app/solution/model_validation_v2.pt',20260926),ld('/app/solution/model_validation_v3.pt',20260927)]\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\nfor gam in [.1,.25,.4,.6,.8]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\nms=[ld('/app/solution/model_validation_v1.pt',20260925),ld('/app/solution/model_validation_v2.pt',20260926),ld('/app/solution/model_validation_v3.pt',20260927)]\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\nfor gam in [.1,.25,.4,.6,.8]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/validation_blend3_0.1.parquet\n/app/solution/validation_blend3_0.25.parquet\n/app/solution/validation_blend3_0.4.parquet\n/app/solution/validation_blend3_0.6.parquet\n/app/solution/validation_blend3_0.8.parquet\n", "stderr": "", "aggregated_output": "/app/solution/validation_blend3_0.1.parquet\n/app/solution/validation_blend3_0.25.parquet\n/app/solution/validation_blend3_0.4.parquet\n/app/solution/validation_blend3_0.6.parquet\n/app/solution/validation_blend3_0.8.parquet\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 801472448 }, "formatted_output": "/app/solution/validation_blend3_0.1.parquet\n/app/solution/validation_blend3_0.25.parquet\n/app/solution/validation_blend3_0.4.parquet\n/app/solution/validation_blend3_0.6.parquet\n/app/solution/validation_blend3_0.8.parquet\n" } } ### exec · 2026-09-25T00:31:22.085Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_blend3_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:31:39.227Z Script completed Wall time 17.1 seconds Output: { "model": "solution/validation_blend3_0.1.parquet", "scores": { "quality_ndcg@10": 0.026846, "quality_users": 4263 } } { "model": "solution/validation_blend3_0.25.parquet", "scores": { "quality_ndcg@10": 0.028141, "quality_users": 4263 } } { "model": "solution/validation_blend3_0.4.parquet", "scores": { "quality_ndcg@10": 0.029372, "quality_users": 4263 } } { "model": "solution/validation_blend3_0.6.parquet", "scores": { "quality_ndcg@10": 0.030668, "quality_users": 4263 } } { "model": "solution/validation_blend3_0.8.parquet", "scores": { "quality_ndcg@10": 0.031126, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0d7e23087d29087b83c0ed1c904", "usage": { "input_tokens": 44667, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 0, "total_tokens": 44746 }, "turn_token_usage": { "input_tokens": 1596783, "cached_input_tokens": 1555072, "cache_write_input_tokens": 0, "output_tokens": 20413, "reasoning_output_tokens": 6697, "total_tokens": 1617196 }, "thread_token_usage": { "input_tokens": 1596783, "cached_input_tokens": 1555072, "cache_write_input_tokens": 0, "output_tokens": 20413, "reasoning_output_tokens": 6697, "total_tokens": 1617196 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5b149e3-e768-47e8-8690-a2c0e31af724", "process_id": "2504", "command": [ "/bin/sh", "-lc", "for f in solution/validation_blend3_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_blend3_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_blend3_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026846,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028141,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029372,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.6.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030668,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031126,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_blend3_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026846,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028141,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029372,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.6.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030668,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031126,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 940656909 }, "formatted_output": "{\n \"model\": \"solution/validation_blend3_0.1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026846,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.25.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028141,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.4.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029372,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.6.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030668,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_0.8.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031126,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 51 · agent · 2026-09-25T00:31:41.510Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:31:52.679Z { "code": "const r = await tools.exec_command({cmd:\"sed 's/for gam in \\\\[.1,.25,.4,.6,.8\\\\]:/for gam in [.9,1.0,1.2,1.5,2.0]:/' /dev/stdin <<'PY' > /tmp/noop\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch,numpy as np,pandas as pd\\nsys.path.insert(0,'/app/solution');import train_ranker as tr\\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\\nms=[ld(f'/app/solution/model_validation_v{i}.pt',20260924+i) for i in [1,2,3]]\\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\\nfor gam in [.9,1.0,1.2,1.5,2.0]:\\n rows=[]\\n for lo in range(0,len(uids),128):\\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\\n top=torch.topk(s,100,dim=1).indices.numpy()\\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:31:53.852Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"0d2d5e","wall_time_seconds":1.001329717,"session_id":4400,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0eb69c087d289601989f49c40a3", "usage": { "input_tokens": 45019, "cached_input_tokens": 44544, "cache_write_input_tokens": 0, "output_tokens": 674, "reasoning_output_tokens": 45, "total_tokens": 45693 }, "turn_token_usage": { "input_tokens": 1641802, "cached_input_tokens": 1599616, "cache_write_input_tokens": 0, "output_tokens": 21087, "reasoning_output_tokens": 6742, "total_tokens": 1662889 }, "thread_token_usage": { "input_tokens": 1641802, "cached_input_tokens": 1599616, "cache_write_input_tokens": 0, "output_tokens": 21087, "reasoning_output_tokens": 6742, "total_tokens": 1662889 } } ## Step 52 · agent · 2026-09-25T00:31:56.868Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:31:57.631Z { "code": "const r = await tools.write_stdin({session_id:4400,chars:\"\",yield_time_ms:30000,max_output_tokens:3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:32:13.608Z Script completed Wall time 16.0 seconds Output: /app/solution/validation_blend3_0.9.parquet /app/solution/validation_blend3_1.0.parquet /app/solution/validation_blend3_1.2.parquet /app/solution/validation_blend3_1.5.parquet /app/solution/validation_blend3_2.0.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c0fa0d1087d2b809476214e0a08e", "usage": { "input_tokens": 45753, "cached_input_tokens": 44800, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 28, "total_tokens": 45828 }, "turn_token_usage": { "input_tokens": 1687555, "cached_input_tokens": 1644416, "cache_write_input_tokens": 0, "output_tokens": 21162, "reasoning_output_tokens": 6770, "total_tokens": 1708717 }, "thread_token_usage": { "input_tokens": 1687555, "cached_input_tokens": 1644416, "cache_write_input_tokens": 0, "output_tokens": 21162, "reasoning_output_tokens": 6770, "total_tokens": 1708717 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a38da3c2-b611-43a4-946d-f6d709009449", "process_id": "4400", "command": [ "/bin/sh", "-lc", "sed 's/for gam in \\[.1,.25,.4,.6,.8\\]:/for gam in [.9,1.0,1.2,1.5,2.0]:/' /dev/stdin <<'PY' > /tmp/noop\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\nms=[ld(f'/app/solution/model_validation_v{i}.pt',20260924+i) for i in [1,2,3]]\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\nfor gam in [.9,1.0,1.2,1.5,2.0]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed 's/for gam in \\[.1,.25,.4,.6,.8\\]:/for gam in [.9,1.0,1.2,1.5,2.0]:/' /dev/stdin <<'PY' > /tmp/noop\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport sys,torch,numpy as np,pandas as pd\nsys.path.insert(0,'/app/solution');import train_ranker as tr\nf=np.load('/app/solution/validation_features.npz');hi,hw,uids,items,pop=[f[k] for k in ['hi','hw','uids','items','pop']]\ndef ld(p,s):m=tr.TwoTower(len(pop),pop,s);m.load_state_dict(torch.load(p,weights_only=False)['state_dict']);m.eval();return m\nms=[ld(f'/app/solution/model_validation_v{i}.pt',20260924+i) for i in [1,2,3]]\nwith torch.no_grad():ivs=[m.item_tower(torch.arange(len(items))) for m in ms]\nz=np.load('/task/data/validation_eligible_candidates.npz');packed=z['packed_eligible'];mp={int(v):i for i,v in enumerate(items)};emap=np.array([mp[int(v)] for v in z['item_ids']])\nfor gam in [.9,1.0,1.2,1.5,2.0]:\n rows=[]\n for lo in range(0,len(uids),128):\n hh=min(lo+128,len(uids));a=torch.from_numpy(hi[lo:hh]);b=torch.from_numpy(hw[lo:hh])\n with torch.no_grad():s=ms[1].user_tower(a,b)@ivs[1].T+.75*(ms[0].user_tower(a,b)@ivs[0].T)+gam*(ms[2].user_tower(a,b)@ivs[2].T)\n e=np.unpackbits(packed[lo:hh],axis=1,count=len(items),bitorder='little').astype(bool);allow=np.zeros_like(e);allow[:,emap]=e;s[~torch.from_numpy(allow)]=-torch.inf\n top=torch.topk(s,100,dim=1).indices.numpy()\n for r,js in enumerate(top):rows += [(int(uids[lo+r]),int(items[j]),k) for k,j in enumerate(js,1)]\n out=f'/app/solution/validation_blend3_{gam}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(out,index=False);print(out,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/validation_blend3_0.9.parquet\n/app/solution/validation_blend3_1.0.parquet\n/app/solution/validation_blend3_1.2.parquet\n/app/solution/validation_blend3_1.5.parquet\n/app/solution/validation_blend3_2.0.parquet\n", "stderr": "", "aggregated_output": "/app/solution/validation_blend3_0.9.parquet\n/app/solution/validation_blend3_1.0.parquet\n/app/solution/validation_blend3_1.2.parquet\n/app/solution/validation_blend3_1.5.parquet\n/app/solution/validation_blend3_2.0.parquet\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 749179462 }, "formatted_output": "/app/solution/validation_blend3_0.9.parquet\n/app/solution/validation_blend3_1.0.parquet\n/app/solution/validation_blend3_1.2.parquet\n/app/solution/validation_blend3_1.5.parquet\n/app/solution/validation_blend3_2.0.parquet\n" } } ### exec · 2026-09-25T00:32:17.889Z { "code": "const r = await tools.exec_command({cmd:\"for f in solution/validation_blend3_0.9.parquet solution/validation_blend3_1.0.parquet solution/validation_blend3_1.2.parquet solution/validation_blend3_1.5.parquet solution/validation_blend3_2.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:32:35.041Z Script completed Wall time 17.1 seconds Output: { "model": "solution/validation_blend3_0.9.parquet", "scores": { "quality_ndcg@10": 0.03138, "quality_users": 4263 } } { "model": "solution/validation_blend3_1.0.parquet", "scores": { "quality_ndcg@10": 0.031358, "quality_users": 4263 } } { "model": "solution/validation_blend3_1.2.parquet", "scores": { "quality_ndcg@10": 0.031714, "quality_users": 4263 } } { "model": "solution/validation_blend3_1.5.parquet", "scores": { "quality_ndcg@10": 0.031779, "quality_users": 4263 } } { "model": "solution/validation_blend3_2.0.parquet", "scores": { "quality_ndcg@10": 0.031155, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c10eb0bc87d2b1fd3b3173b46bb6", "usage": { "input_tokens": 45926, "cached_input_tokens": 45568, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 0, "total_tokens": 46055 }, "turn_token_usage": { "input_tokens": 1733481, "cached_input_tokens": 1689984, "cache_write_input_tokens": 0, "output_tokens": 21291, "reasoning_output_tokens": 6770, "total_tokens": 1754772 }, "thread_token_usage": { "input_tokens": 1733481, "cached_input_tokens": 1689984, "cache_write_input_tokens": 0, "output_tokens": 21291, "reasoning_output_tokens": 6770, "total_tokens": 1754772 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7e9dbb4f-4cf3-4b37-9d42-9a19ac9683fc", "process_id": "74947", "command": [ "/bin/sh", "-lc", "for f in solution/validation_blend3_0.9.parquet solution/validation_blend3_1.0.parquet solution/validation_blend3_1.2.parquet solution/validation_blend3_1.5.parquet solution/validation_blend3_2.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in solution/validation_blend3_0.9.parquet solution/validation_blend3_1.0.parquet solution/validation_blend3_1.2.parquet solution/validation_blend3_1.5.parquet solution/validation_blend3_2.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_blend3_0.9.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03138,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031358,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031714,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031155,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_blend3_0.9.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03138,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031358,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031714,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031155,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 937012298 }, "formatted_output": "{\n \"model\": \"solution/validation_blend3_0.9.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03138,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031358,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.2.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031714,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_1.5.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"solution/validation_blend3_2.0.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031155,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 53 · agent · 2026-09-25T00:32:46.244Z ### Thinking unavailable — no text recorded ## Step 54 · agent · 2026-09-25T00:32:47.113Z ### Thinking unavailable — no text recorded The one-window model is complementary despite being weaker alone; the selected concatenated tower reaches 0.031779 NDCG@10. I’ve fixed the ensemble weights from validation and am starting the required refit. Each component will now include the public validation outcomes as training labels and validation events/preferences as history for the hidden-test cutoff. ### exec · 2026-09-25T00:33:06.791Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n q, hi, hw, uids, items, pop = extract(args.stage, args.snapshots)\\n print(f\\\"extracted {len(q)} rolling query examples\\\", flush=True)\\n+ # Cached serving inputs let multiple independently seeded towers be\\n+ # concatenated without rescanning the immutable event logs.\\n+ np.savez_compressed(OUT / f\\\"features_{args.stage}.npz\\\",\\n+ hi=hi, hw=hw, uids=uids, items=items, pop=pop)\\n*** Add File: /app/solution/make_ensemble.py\\n+\\\"\\\"\\\"Apply the selected concatenated two-tower ensemble to a cutoff.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+import sys\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+sys.path.insert(0, str(Path(__file__).parent))\\n+import train_ranker as tr\\n+\\n+\\n+def main() -> None:\\n+ p = argparse.ArgumentParser()\\n+ p.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], default=\\\"test\\\")\\n+ p.add_argument(\\\"--output\\\", type=Path, default=Path(\\\"/app/solution/submission.parquet\\\"))\\n+ args = p.parse_args()\\n+ root = Path(\\\"/app/solution\\\")\\n+ f = np.load(root / f\\\"features_{args.stage}.npz\\\")\\n+ hi, hw, uids, items, pop = (f[k] for k in (\\\"hi\\\", \\\"hw\\\", \\\"uids\\\", \\\"items\\\", \\\"pop\\\"))\\n+ seeds = [20260925, 20260926, 20260927]\\n+ # Positive weights are equivalent to concatenating sqrt(weight)-scaled\\n+ # component embeddings, hence the final score remains exactly one dot.\\n+ weights = [0.75, 1.0, 1.5]\\n+ models = []\\n+ for component, seed in enumerate(seeds, 1):\\n+ checkpoint = torch.load(root / f\\\"model_{args.stage}_v{component}.pt\\\", weights_only=False)\\n+ model = tr.TwoTower(len(pop), pop, seed)\\n+ model.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n+ model.eval()\\n+ models.append(model)\\n+ with torch.no_grad():\\n+ item_vectors = [m.item_tower(torch.arange(len(items))) for m in models]\\n+\\n+ z = np.load(f\\\"/task/data/{args.stage}_eligible_candidates.npz\\\")\\n+ packed, eligible_items = z[\\\"packed_eligible\\\"], z[\\\"item_ids\\\"]\\n+ lookup = {int(item): i for i, item in enumerate(items)}\\n+ eligible_to_model = np.array([lookup[int(item)] for item in eligible_items])\\n+ rows = []\\n+ for lo in range(0, len(uids), 128):\\n+ stop = min(lo + 128, len(uids))\\n+ ti, tw = torch.from_numpy(hi[lo:stop]), torch.from_numpy(hw[lo:stop])\\n+ with torch.no_grad():\\n+ scores = sum(w * (m.user_tower(ti, tw) @ iv.T)\\n+ for w, m, iv in zip(weights, models, item_vectors))\\n+ mask = np.unpackbits(packed[lo:stop], axis=1, count=len(items), bitorder=\\\"little\\\").astype(bool)\\n+ allowed = np.zeros_like(mask)\\n+ allowed[:, eligible_to_model] = mask\\n+ scores[~torch.from_numpy(allowed)] = -torch.inf\\n+ top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\\n+ for r, choices in enumerate(top):\\n+ rows.extend((int(uids[lo + r]), int(items[j]), rank)\\n+ for rank, j in enumerate(choices, 1))\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(args.output, index=False)\\n+ print(f\\\"wrote {args.output}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:33:06.890Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-407d51c8-3055-4094-8388-f4b0f8405acd", "changes": { "/app/solution/make_ensemble.py": { "type": "add", "content": "\"\"\"Apply the selected concatenated two-tower ensemble to a cutoff.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path.insert(0, str(Path(__file__).parent))\nimport train_ranker as tr\n\n\ndef main() -> None:\n p = argparse.ArgumentParser()\n p.add_argument(\"--stage\", choices=[\"validation\", \"test\"], default=\"test\")\n p.add_argument(\"--output\", type=Path, default=Path(\"/app/solution/submission.parquet\"))\n args = p.parse_args()\n root = Path(\"/app/solution\")\n f = np.load(root / f\"features_{args.stage}.npz\")\n hi, hw, uids, items, pop = (f[k] for k in (\"hi\", \"hw\", \"uids\", \"items\", \"pop\"))\n seeds = [20260925, 20260926, 20260927]\n # Positive weights are equivalent to concatenating sqrt(weight)-scaled\n # component embeddings, hence the final score remains exactly one dot.\n weights = [0.75, 1.0, 1.5]\n models = []\n for component, seed in enumerate(seeds, 1):\n checkpoint = torch.load(root / f\"model_{args.stage}_v{component}.pt\", weights_only=False)\n model = tr.TwoTower(len(pop), pop, seed)\n model.load_state_dict(checkpoint[\"state_dict\"])\n model.eval()\n models.append(model)\n with torch.no_grad():\n item_vectors = [m.item_tower(torch.arange(len(items))) for m in models]\n\n z = np.load(f\"/task/data/{args.stage}_eligible_candidates.npz\")\n packed, eligible_items = z[\"packed_eligible\"], z[\"item_ids\"]\n lookup = {int(item): i for i, item in enumerate(items)}\n eligible_to_model = np.array([lookup[int(item)] for item in eligible_items])\n rows = []\n for lo in range(0, len(uids), 128):\n stop = min(lo + 128, len(uids))\n ti, tw = torch.from_numpy(hi[lo:stop]), torch.from_numpy(hw[lo:stop])\n with torch.no_grad():\n scores = sum(w * (m.user_tower(ti, tw) @ iv.T)\n for w, m, iv in zip(weights, models, item_vectors))\n mask = np.unpackbits(packed[lo:stop], axis=1, count=len(items), bitorder=\"little\").astype(bool)\n allowed = np.zeros_like(mask)\n allowed[:, eligible_to_model] = mask\n scores[~torch.from_numpy(allowed)] = -torch.inf\n top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\n for r, choices in enumerate(top):\n rows.extend((int(uids[lo + r]), int(items[j]), rank)\n for rank, j in enumerate(choices, 1))\n pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(args.output, index=False)\n print(f\"wrote {args.output}\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -400,2 +400,6 @@\n print(f\"extracted {len(q)} rolling query examples\", flush=True)\n+ # Cached serving inputs let multiple independently seeded towers be\n+ # concatenated without rescanning the immutable event logs.\n+ np.savez_compressed(OUT / f\"features_{args.stage}.npz\",\n+ hi=hi, hw=hw, uids=uids, items=items, pop=pop)\n model = train_model(q, pop, args.seed, args.epochs)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/make_ensemble.py\nM /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c12341a887d2a06b99e7bcd3aa90", "usage": { "input_tokens": 46328, "cached_input_tokens": 45696, "cache_write_input_tokens": 0, "output_tokens": 1671, "reasoning_output_tokens": 574, "total_tokens": 47999 }, "turn_token_usage": { "input_tokens": 1779809, "cached_input_tokens": 1735680, "cache_write_input_tokens": 0, "output_tokens": 22962, "reasoning_output_tokens": 7344, "total_tokens": 1802771 }, "thread_token_usage": { "input_tokens": 1779809, "cached_input_tokens": 1735680, "cache_write_input_tokens": 0, "output_tokens": 22962, "reasoning_output_tokens": 7344, "total_tokens": 1802771 } } ## Step 55 · agent · 2026-09-25T00:33:08.289Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:33:09.736Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 6 --snapshots 6 --seed 20260925 --output solution/submission_component1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:33:10.911Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"65948e","wall_time_seconds":1.001249568,"session_id":12580,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c143548487d2900c658d270be178", "usage": { "input_tokens": 48023, "cached_input_tokens": 46208, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 13, "total_tokens": 48124 }, "turn_token_usage": { "input_tokens": 1827832, "cached_input_tokens": 1781888, "cache_write_input_tokens": 0, "output_tokens": 23063, "reasoning_output_tokens": 7357, "total_tokens": 1850895 }, "thread_token_usage": { "input_tokens": 1827832, "cached_input_tokens": 1781888, "cache_write_input_tokens": 0, "output_tokens": 23063, "reasoning_output_tokens": 7357, "total_tokens": 1850895 } } ## Step 56 · agent · 2026-09-25T00:33:12.111Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:33:12.788Z { "code": "const r = await tools.write_stdin({session_id:12580,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:33:42.798Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c147173087d28c3f55ae373da939", "usage": { "input_tokens": 48181, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 48234 }, "turn_token_usage": { "input_tokens": 1876013, "cached_input_tokens": 1829760, "cache_write_input_tokens": 0, "output_tokens": 23116, "reasoning_output_tokens": 7363, "total_tokens": 1899129 }, "thread_token_usage": { "input_tokens": 1876013, "cached_input_tokens": 1829760, "cache_write_input_tokens": 0, "output_tokens": 23116, "reasoning_output_tokens": 7363, "total_tokens": 1899129 } } ### exec · 2026-09-25T00:33:44.424Z { "code": "const r = await tools.write_stdin({session_id:12580,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:34:14.433Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c1670a1887d285d6f31c32c8aa11", "usage": { "input_tokens": 48257, "cached_input_tokens": 48000, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 48302 }, "turn_token_usage": { "input_tokens": 1924270, "cached_input_tokens": 1877760, "cache_write_input_tokens": 0, "output_tokens": 23161, "reasoning_output_tokens": 7363, "total_tokens": 1947431 }, "thread_token_usage": { "input_tokens": 1924270, "cached_input_tokens": 1877760, "cache_write_input_tokens": 0, "output_tokens": 23161, "reasoning_output_tokens": 7363, "total_tokens": 1947431 } } ### exec · 2026-09-25T00:34:16.042Z { "code": "const r = await tools.write_stdin({session_id:12580,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:34:46.051Z Script completed Wall time 30.0 seconds Output: extracted 40851 rolling query examples epoch 1/6 loss=0.52085 epoch 2/6 loss=0.42139 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c186b41487d28aae9c3cdcfa3c8e", "usage": { "input_tokens": 48325, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 48370 }, "turn_token_usage": { "input_tokens": 1972595, "cached_input_tokens": 1925888, "cache_write_input_tokens": 0, "output_tokens": 23206, "reasoning_output_tokens": 7363, "total_tokens": 1995801 }, "thread_token_usage": { "input_tokens": 1972595, "cached_input_tokens": 1925888, "cache_write_input_tokens": 0, "output_tokens": 23206, "reasoning_output_tokens": 7363, "total_tokens": 1995801 } } ## Step 57 · agent · 2026-09-25T00:34:47.180Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:34:47.876Z { "code": "const r = await tools.write_stdin({session_id:12580,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:35:16.949Z Script completed Wall time 29.1 seconds Output: epoch 3/6 loss=0.36333 epoch 4/6 loss=0.31777 epoch 5/6 loss=0.28774 epoch 6/6 loss=0.26440 wrote solution/submission_component1.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c1a63ac887d29490eee4c67e7444", "usage": { "input_tokens": 48426, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 48484 }, "turn_token_usage": { "input_tokens": 2021021, "cached_input_tokens": 1974016, "cache_write_input_tokens": 0, "output_tokens": 23264, "reasoning_output_tokens": 7374, "total_tokens": 2044285 }, "thread_token_usage": { "input_tokens": 2021021, "cached_input_tokens": 1974016, "cache_write_input_tokens": 0, "output_tokens": 23264, "reasoning_output_tokens": 7374, "total_tokens": 2044285 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4098d4c9-dce7-4a36-803a-c102d36f08d3", "process_id": "12580", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 6 --snapshots 6 --seed 20260925 --output solution/submission_component1.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 6 --snapshots 6 --seed 20260925 --output solution/submission_component1.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 40851 rolling query examples\nepoch 1/6 loss=0.52085\nepoch 2/6 loss=0.42139\nepoch 3/6 loss=0.36333\nepoch 4/6 loss=0.31777\nepoch 5/6 loss=0.28774\nepoch 6/6 loss=0.26440\nwrote solution/submission_component1.parquet\n", "stderr": "", "aggregated_output": "extracted 40851 rolling query examples\nepoch 1/6 loss=0.52085\nepoch 2/6 loss=0.42139\nepoch 3/6 loss=0.36333\nepoch 4/6 loss=0.31777\nepoch 5/6 loss=0.28774\nepoch 6/6 loss=0.26440\nwrote solution/submission_component1.parquet\n", "exit_code": 0, "duration": { "secs": 127, "nanos": 35030992 }, "formatted_output": "extracted 40851 rolling query examples\nepoch 1/6 loss=0.52085\nepoch 2/6 loss=0.42139\nepoch 3/6 loss=0.36333\nepoch 4/6 loss=0.31777\nepoch 5/6 loss=0.28774\nepoch 6/6 loss=0.26440\nwrote solution/submission_component1.parquet\n" } } ## Step 58 · agent · 2026-09-25T00:35:18.024Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:19.655Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_test.pt solution/model_test_v1.pt\\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 8 --snapshots 3 --seed 20260926 --output solution/submission_component2.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:35:20.821Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"21cd2a","wall_time_seconds":1.000942295,"session_id":37078,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c1c51e7487d295d8fa13f21df67c", "usage": { "input_tokens": 48565, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 111, "reasoning_output_tokens": 11, "total_tokens": 48676 }, "turn_token_usage": { "input_tokens": 2069586, "cached_input_tokens": 2022272, "cache_write_input_tokens": 0, "output_tokens": 23375, "reasoning_output_tokens": 7385, "total_tokens": 2092961 }, "thread_token_usage": { "input_tokens": 2069586, "cached_input_tokens": 2022272, "cache_write_input_tokens": 0, "output_tokens": 23375, "reasoning_output_tokens": 7385, "total_tokens": 2092961 } } ## Step 59 · agent · 2026-09-25T00:35:21.951Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:22.626Z { "code": "const r = await tools.write_stdin({session_id:37078,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:35:52.634Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c1c919f087d2aa6c6f98ce44dc58", "usage": { "input_tokens": 48734, "cached_input_tokens": 48384, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 48787 }, "turn_token_usage": { "input_tokens": 2118320, "cached_input_tokens": 2070656, "cache_write_input_tokens": 0, "output_tokens": 23428, "reasoning_output_tokens": 7391, "total_tokens": 2141748 }, "thread_token_usage": { "input_tokens": 2118320, "cached_input_tokens": 2070656, "cache_write_input_tokens": 0, "output_tokens": 23428, "reasoning_output_tokens": 7391, "total_tokens": 2141748 } } ### exec · 2026-09-25T00:35:54.259Z { "code": "const r = await tools.write_stdin({session_id:37078,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:36:24.267Z Script completed Wall time 30.0 seconds Output: extracted 24126 rolling query examples epoch 1/8 loss=0.53185 epoch 2/8 loss=0.43622 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c1e8d6b087d29dd1a5474c80668e", "usage": { "input_tokens": 48810, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 48855 }, "turn_token_usage": { "input_tokens": 2167130, "cached_input_tokens": 2119168, "cache_write_input_tokens": 0, "output_tokens": 23473, "reasoning_output_tokens": 7391, "total_tokens": 2190603 }, "thread_token_usage": { "input_tokens": 2167130, "cached_input_tokens": 2119168, "cache_write_input_tokens": 0, "output_tokens": 23473, "reasoning_output_tokens": 7391, "total_tokens": 2190603 } } ### exec · 2026-09-25T00:36:25.735Z { "code": "const r = await tools.write_stdin({session_id:37078,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:36:54.238Z Script completed Wall time 28.5 seconds Output: epoch 3/8 loss=0.38511 epoch 4/8 loss=0.35241 epoch 5/8 loss=0.32125 epoch 6/8 loss=0.29663 epoch 7/8 loss=0.27799 epoch 8/8 loss=0.26036 wrote solution/submission_component2.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c20875f487d29b3ae2ba3b32bcf3", "usage": { "input_tokens": 48911, "cached_input_tokens": 48640, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 48956 }, "turn_token_usage": { "input_tokens": 2216041, "cached_input_tokens": 2167808, "cache_write_input_tokens": 0, "output_tokens": 23518, "reasoning_output_tokens": 7391, "total_tokens": 2239559 }, "thread_token_usage": { "input_tokens": 2216041, "cached_input_tokens": 2167808, "cache_write_input_tokens": 0, "output_tokens": 23518, "reasoning_output_tokens": 7391, "total_tokens": 2239559 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b7c3b701-1056-4a35-8a4c-5c7abfa9872b", "process_id": "37078", "command": [ "/bin/sh", "-lc", "cp solution/model_test.pt solution/model_test_v1.pt\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 8 --snapshots 3 --seed 20260926 --output solution/submission_component2.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_test.pt solution/model_test_v1.pt\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 8 --snapshots 3 --seed 20260926 --output solution/submission_component2.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 24126 rolling query examples\nepoch 1/8 loss=0.53185\nepoch 2/8 loss=0.43622\nepoch 3/8 loss=0.38511\nepoch 4/8 loss=0.35241\nepoch 5/8 loss=0.32125\nepoch 6/8 loss=0.29663\nepoch 7/8 loss=0.27799\nepoch 8/8 loss=0.26036\nwrote solution/submission_component2.parquet\n", "stderr": "", "aggregated_output": "extracted 24126 rolling query examples\nepoch 1/8 loss=0.53185\nepoch 2/8 loss=0.43622\nepoch 3/8 loss=0.38511\nepoch 4/8 loss=0.35241\nepoch 5/8 loss=0.32125\nepoch 6/8 loss=0.29663\nepoch 7/8 loss=0.27799\nepoch 8/8 loss=0.26036\nwrote solution/submission_component2.parquet\n", "exit_code": 0, "duration": { "secs": 94, "nanos": 416068815 }, "formatted_output": "extracted 24126 rolling query examples\nepoch 1/8 loss=0.53185\nepoch 2/8 loss=0.43622\nepoch 3/8 loss=0.38511\nepoch 4/8 loss=0.35241\nepoch 5/8 loss=0.32125\nepoch 6/8 loss=0.29663\nepoch 7/8 loss=0.27799\nepoch 8/8 loss=0.26036\nwrote solution/submission_component2.parquet\n" } } ## Step 60 · agent · 2026-09-25T00:36:56.137Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:36:57.776Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_test.pt solution/model_test_v2.pt\\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 10 --snapshots 1 --seed 20260927 --output solution/submission_component3.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000}); text(JSON.stringify(r));\n" } ### Tool output · 2026-09-25T00:36:58.941Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e12463","wall_time_seconds":1.000878916,"session_id":15886,"original_token_count":0,"output":""} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c2266ca887d2b72d4a7023caa9a6", "usage": { "input_tokens": 49061, "cached_input_tokens": 48768, "cache_write_input_tokens": 0, "output_tokens": 109, "reasoning_output_tokens": 9, "total_tokens": 49170 }, "turn_token_usage": { "input_tokens": 2265102, "cached_input_tokens": 2216576, "cache_write_input_tokens": 0, "output_tokens": 23627, "reasoning_output_tokens": 7400, "total_tokens": 2288729 }, "thread_token_usage": { "input_tokens": 2265102, "cached_input_tokens": 2216576, "cache_write_input_tokens": 0, "output_tokens": 23627, "reasoning_output_tokens": 7400, "total_tokens": 2288729 } } ### exec · 2026-09-25T00:37:00.534Z { "code": "const r = await tools.write_stdin({session_id:15886,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:37:30.542Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c22b273487d2b9cb52c6e160d662", "usage": { "input_tokens": 49227, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 49272 }, "turn_token_usage": { "input_tokens": 2314329, "cached_input_tokens": 2265472, "cache_write_input_tokens": 0, "output_tokens": 23672, "reasoning_output_tokens": 7400, "total_tokens": 2338001 }, "thread_token_usage": { "input_tokens": 2314329, "cached_input_tokens": 2265472, "cache_write_input_tokens": 0, "output_tokens": 23672, "reasoning_output_tokens": 7400, "total_tokens": 2338001 } } ### exec · 2026-09-25T00:37:32.313Z { "code": "const r = await tools.write_stdin({session_id:15886,chars:\"\",yield_time_ms:30000,max_output_tokens:5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:38:02.322Z Script completed Wall time 30.0 seconds Output: extracted 12456 rolling query examples epoch 1/10 loss=0.55005 epoch 2/10 loss=0.46191 epoch 3/10 loss=0.41470 epoch 4/10 loss=0.38292 epoch 5/10 loss=0.35341 epoch 6/10 loss=0.33619 epoch 7/10 loss=0.31832 epoch 8/10 loss=0.29961 epoch 9/10 loss=0.28526 epoch 10/10 loss=0.27913 wrote solution/submission_component3.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c24ac73087d2b5c1157ec6d19edb", "usage": { "input_tokens": 49295, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 49340 }, "turn_token_usage": { "input_tokens": 2363624, "cached_input_tokens": 2314496, "cache_write_input_tokens": 0, "output_tokens": 23717, "reasoning_output_tokens": 7400, "total_tokens": 2387341 }, "thread_token_usage": { "input_tokens": 2363624, "cached_input_tokens": 2314496, "cache_write_input_tokens": 0, "output_tokens": 23717, "reasoning_output_tokens": 7400, "total_tokens": 2387341 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-29d54520-927c-42ae-bd23-de9952bf26f1", "process_id": "15886", "command": [ "/bin/sh", "-lc", "cp solution/model_test.pt solution/model_test_v2.pt\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 10 --snapshots 1 --seed 20260927 --output solution/submission_component3.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_test.pt solution/model_test_v2.pt\n/opt/task-venv/bin/python solution/train_ranker.py --stage test --epochs 10 --snapshots 1 --seed 20260927 --output solution/submission_component3.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "extracted 12456 rolling query examples\nepoch 1/10 loss=0.55005\nepoch 2/10 loss=0.46191\nepoch 3/10 loss=0.41470\nepoch 4/10 loss=0.38292\nepoch 5/10 loss=0.35341\nepoch 6/10 loss=0.33619\nepoch 7/10 loss=0.31832\nepoch 8/10 loss=0.29961\nepoch 9/10 loss=0.28526\nepoch 10/10 loss=0.27913\nwrote solution/submission_component3.parquet\n", "stderr": "", "aggregated_output": "extracted 12456 rolling query examples\nepoch 1/10 loss=0.55005\nepoch 2/10 loss=0.46191\nepoch 3/10 loss=0.41470\nepoch 4/10 loss=0.38292\nepoch 5/10 loss=0.35341\nepoch 6/10 loss=0.33619\nepoch 7/10 loss=0.31832\nepoch 8/10 loss=0.29961\nepoch 9/10 loss=0.28526\nepoch 10/10 loss=0.27913\nwrote solution/submission_component3.parquet\n", "exit_code": 0, "duration": { "secs": 64, "nanos": 731443267 }, "formatted_output": "extracted 12456 rolling query examples\nepoch 1/10 loss=0.55005\nepoch 2/10 loss=0.46191\nepoch 3/10 loss=0.41470\nepoch 4/10 loss=0.38292\nepoch 5/10 loss=0.35341\nepoch 6/10 loss=0.33619\nepoch 7/10 loss=0.31832\nepoch 8/10 loss=0.29961\nepoch 9/10 loss=0.28526\nepoch 10/10 loss=0.27913\nwrote solution/submission_component3.parquet\n" } } ## Step 61 · agent · 2026-09-25T00:38:03.326Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:38:04.672Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_test.pt solution/model_test_v3.pt\\n/opt/task-venv/bin/python solution/make_ensemble.py --stage test --output solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:38:12.787Z Script completed Wall time 8.1 seconds Output: wrote solution/submission.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c26a83d887d2a7be918844b843a2", "usage": { "input_tokens": 49502, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 91, "reasoning_output_tokens": 8, "total_tokens": 49593 }, "turn_token_usage": { "input_tokens": 2413126, "cached_input_tokens": 2363648, "cache_write_input_tokens": 0, "output_tokens": 23808, "reasoning_output_tokens": 7408, "total_tokens": 2436934 }, "thread_token_usage": { "input_tokens": 2413126, "cached_input_tokens": 2363648, "cache_write_input_tokens": 0, "output_tokens": 23808, "reasoning_output_tokens": 7408, "total_tokens": 2436934 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ebd9b9db-8845-4b28-b08b-13867112a9da", "process_id": "68177", "command": [ "/bin/sh", "-lc", "cp solution/model_test.pt solution/model_test_v3.pt\n/opt/task-venv/bin/python solution/make_ensemble.py --stage test --output solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_test.pt solution/model_test_v3.pt\n/opt/task-venv/bin/python solution/make_ensemble.py --stage test --output solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote solution/submission.parquet\n", "stderr": "", "aggregated_output": "wrote solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 944500272 }, "formatted_output": "wrote solution/submission.parquet\n" } } ## Step 62 · agent · 2026-09-25T00:38:21.456Z ### Thinking unavailable — no text recorded The refit is complete: all three towers were retrained with validation outcomes included, and the hidden-test ranking has been written. I’m doing the final contract audit now—exact row counts/ranks, uniqueness, test eligibility, reproducible command, and a clean public-score record in the README. ### exec · 2026-09-25T00:38:28.242Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/validation_features.npz solution/features_validation.npz\\n/opt/task-venv/bin/python solution/make_ensemble.py --stage validation --output solution/validation.parquet\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\\n/opt/task-venv/bin/python - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking,CandidateSet\\nD='/task/data';p='/app/solution/submission.parquet'\\nt=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int));f=validate_ranking(pd.read_parquet(p),t);c=CandidateSet(__import__('pathlib').Path(D+'/test_eligible_candidates.npz'))\\nbad=sum(not c.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\\nprint({'rows':len(f),'users':f.uid.nunique(),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'ineligible':bad,'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:38:42.983Z Script completed Wall time 14.7 seconds Output: wrote solution/validation.parquet { "model": "solution/validation.parquet", "scores": { "quality_ndcg@10": 0.031779, "quality_users": 4263 } } {'rows': 845600, 'users': 8456, 'duplicates': 0, 'ineligible': 0, 'rank_min': 1, 'rank_max': 100} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c275019c87d2bcfa64a6bd973aad", "usage": { "input_tokens": 49624, "cached_input_tokens": 49280, "cache_write_input_tokens": 0, "output_tokens": 797, "reasoning_output_tokens": 417, "total_tokens": 50421 }, "turn_token_usage": { "input_tokens": 2462750, "cached_input_tokens": 2412928, "cache_write_input_tokens": 0, "output_tokens": 24605, "reasoning_output_tokens": 7825, "total_tokens": 2487355 }, "thread_token_usage": { "input_tokens": 2462750, "cached_input_tokens": 2412928, "cache_write_input_tokens": 0, "output_tokens": 24605, "reasoning_output_tokens": 7825, "total_tokens": 2487355 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-16cda8f8-9776-411b-8f81-fd925cc4a2a1", "process_id": "20828", "command": [ "/bin/sh", "-lc", "cp solution/validation_features.npz solution/features_validation.npz\n/opt/task-venv/bin/python solution/make_ensemble.py --stage validation --output solution/validation.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking,CandidateSet\nD='/task/data';p='/app/solution/submission.parquet'\nt=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int));f=validate_ranking(pd.read_parquet(p),t);c=CandidateSet(__import__('pathlib').Path(D+'/test_eligible_candidates.npz'))\nbad=sum(not c.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(f),'users':f.uid.nunique(),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'ineligible':bad,'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/validation_features.npz solution/features_validation.npz\n/opt/task-venv/bin/python solution/make_ensemble.py --stage validation --output solution/validation.parquet\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking,CandidateSet\nD='/task/data';p='/app/solution/submission.parquet'\nt=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int));f=validate_ranking(pd.read_parquet(p),t);c=CandidateSet(__import__('pathlib').Path(D+'/test_eligible_candidates.npz'))\nbad=sum(not c.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(f),'users':f.uid.nunique(),'duplicates':int(f.duplicated(['uid','item_id']).sum()),'ineligible':bad,'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote solution/validation.parquet\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'duplicates': 0, 'ineligible': 0, 'rank_min': 1, 'rank_max': 100}\n", "stderr": "", "aggregated_output": "wrote solution/validation.parquet\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'duplicates': 0, 'ineligible': 0, 'rank_min': 1, 'rank_max': 100}\n", "exit_code": 0, "duration": { "secs": 14, "nanos": 564852107 }, "formatted_output": "wrote solution/validation.parquet\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031779,\n \"quality_users\": 4263\n }\n}\n{'rows': 845600, 'users': 8456, 'duplicates': 0, 'ineligible': 0, 'rank_min': 1, 'rank_max': 100}\n" } } ## Step 63 · agent · 2026-09-25T00:38:45.105Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:38:46.149Z { "code": "const r = await tools.exec_command({cmd:\"du -sh solution; ls -lh solution | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:38:46.303Z Script completed Wall time 0.0 seconds Output: 266M solution total 266M -rw-r--r-- 1 agent agent 7.1M Sep 25 00:37 features_test.npz -rw-r--r-- 1 agent agent 7.1M Sep 25 00:38 features_validation.npz -rw-r--r-- 1 agent agent 2.6K Sep 25 00:33 make_ensemble.py -rw-r--r-- 1 agent agent 1.1K Sep 25 00:15 make_popularity.py -rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:35 model_test_v1.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:36 model_test_v2.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test_v3.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:27 model_validation_v1.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:28 model_validation_v2.pt -rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation_v3.pt -rw-r--r-- 1 agent agent 130 Sep 25 00:38 run_test.json -rw-r--r-- 1 agent agent 134 Sep 25 00:30 run_validation.json -rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:35 submission_component1.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:36 submission_component2.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission_component3.parquet -rw-r--r-- 1 agent agent 20K Sep 25 00:33 train_ranker.py -rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 validation.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.1.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.25.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.4.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.6.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.8.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_0.9.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.2.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.5.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_2.0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.15_0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.3_0.03.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.3_0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.03.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.03.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_1_0.parquet -rw-r--r-- 1 agent agent 7.1M Sep 25 00:26 validation_features.npz -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.015.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.03.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.06.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.1.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.16.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.25.parquet -rw-r--r-- 1 agent agent 712K Sep 25 00:16 validation_popularity.parquet -rw-r--r-- 1 agent agent 1.8M Sep 25 00:23 validation_scale_0.0.parquet -rw-r--r-- 1 agent agent 1.7M Sep 25 00:23 validation_scale_0.05.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.1.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.18.parquet -rw-r--r-- 1 agent agent 1.5M Sep 25 00:24 validation_scale_0.3.parquet -rw-r--r-- 1 agent agent 1.4M Sep 25 00:24 validation_scale_0.5.parquet -rw-r--r-- 1 agent agent 1.3M Sep 25 00:24 validation_scale_0.8.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:22 validation_tower_v1.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_tower_v2.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 00:30 validation_tower_v3.parquet ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-78bb5713-1b31-4a12-9526-e053ff2a6201", "process_id": "67825", "command": [ "/bin/sh", "-lc", "du -sh solution; ls -lh solution | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "du -sh solution", "path": "solution" }, { "type": "list_files", "cmd": "ls -lh solution", "path": "solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "266M\tsolution\ntotal 266M\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:37 features_test.npz\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:38 features_validation.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 00:33 make_ensemble.py\n-rw-r--r-- 1 agent agent 1.1K Sep 25 00:15 make_popularity.py\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:35 model_test_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:36 model_test_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test_v3.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:27 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:28 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation_v3.pt\n-rw-r--r-- 1 agent agent 130 Sep 25 00:38 run_test.json\n-rw-r--r-- 1 agent agent 134 Sep 25 00:30 run_validation.json\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:35 submission_component1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:36 submission_component2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission_component3.parquet\n-rw-r--r-- 1 agent agent 20K Sep 25 00:33 train_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.25.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.4.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.6.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_0.9.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.5.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_2.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.15_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.3_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.3_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_1_0.parquet\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:26 validation_features.npz\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.015.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.06.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.16.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.25.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:16 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 25 00:23 validation_scale_0.0.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:23 validation_scale_0.05.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.18.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 00:24 validation_scale_0.3.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 00:24 validation_scale_0.5.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:24 validation_scale_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:22 validation_tower_v1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_tower_v2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:30 validation_tower_v3.parquet\n", "stderr": "", "aggregated_output": "266M\tsolution\ntotal 266M\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:37 features_test.npz\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:38 features_validation.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 00:33 make_ensemble.py\n-rw-r--r-- 1 agent agent 1.1K Sep 25 00:15 make_popularity.py\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:35 model_test_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:36 model_test_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test_v3.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:27 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:28 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation_v3.pt\n-rw-r--r-- 1 agent agent 130 Sep 25 00:38 run_test.json\n-rw-r--r-- 1 agent agent 134 Sep 25 00:30 run_validation.json\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:35 submission_component1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:36 submission_component2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission_component3.parquet\n-rw-r--r-- 1 agent agent 20K Sep 25 00:33 train_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.25.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.4.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.6.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_0.9.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.5.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_2.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.15_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.3_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.3_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_1_0.parquet\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:26 validation_features.npz\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.015.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.06.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.16.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.25.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:16 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 25 00:23 validation_scale_0.0.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:23 validation_scale_0.05.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.18.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 00:24 validation_scale_0.3.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 00:24 validation_scale_0.5.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:24 validation_scale_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:22 validation_tower_v1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_tower_v2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:30 validation_tower_v3.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3920 }, "formatted_output": "266M\tsolution\ntotal 266M\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:37 features_test.npz\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:38 features_validation.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 00:33 make_ensemble.py\n-rw-r--r-- 1 agent agent 1.1K Sep 25 00:15 make_popularity.py\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:35 model_test_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:36 model_test_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:38 model_test_v3.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:27 model_validation_v1.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:28 model_validation_v2.pt\n-rw-r--r-- 1 agent agent 23M Sep 25 00:30 model_validation_v3.pt\n-rw-r--r-- 1 agent agent 130 Sep 25 00:38 run_test.json\n-rw-r--r-- 1 agent agent 134 Sep 25 00:30 run_validation.json\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:35 submission_component1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:36 submission_component2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 submission_component3.parquet\n-rw-r--r-- 1 agent agent 20K Sep 25 00:33 train_ranker.py\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:38 validation.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.25.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.4.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.6.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:31 validation_blend3_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_0.9.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_1.5.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:32 validation_blend3_2.0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.15_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.3_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_blend_0.3_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.5_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_0.75_0.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:29 validation_blend_1_0.parquet\n-rw-r--r-- 1 agent agent 7.1M Sep 25 00:26 validation_features.npz\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.015.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.03.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.06.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.16.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:26 validation_hash_0.25.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:16 validation_popularity.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 25 00:23 validation_scale_0.0.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:23 validation_scale_0.05.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:24 validation_scale_0.18.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 25 00:24 validation_scale_0.3.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 25 00:24 validation_scale_0.5.parquet\n-rw-r--r-- 1 agent agent 1.3M Sep 25 00:24 validation_scale_0.8.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:22 validation_tower_v1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:28 validation_tower_v2.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 25 00:30 validation_tower_v3.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c29331dc87d2a4a6d7ab992ead8a", "usage": { "input_tokens": 50536, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 132, "reasoning_output_tokens": 66, "total_tokens": 50668 }, "turn_token_usage": { "input_tokens": 2513286, "cached_input_tokens": 2462336, "cache_write_input_tokens": 0, "output_tokens": 24737, "reasoning_output_tokens": 7891, "total_tokens": 2538023 }, "thread_token_usage": { "input_tokens": 2513286, "cached_input_tokens": 2462336, "cache_write_input_tokens": 0, "output_tokens": 24737, "reasoning_output_tokens": 7891, "total_tokens": 2538023 } } ## Step 64 · agent · 2026-09-25T00:38:53.385Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:39:06.220Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made-for-You two-tower ranker\\n+\\n+## Model\\n+\\n+The final model is a 291-dimensional composite two-tower model (three\\n+independently trained 97-dimensional branches concatenated with fixed positive\\n+scales). The **user tower has no user-ID embedding**. It embeds at most 192\\n+recent positive history events, weights them by completion, organic intent,\\n+recency, and active likes, performs weighted pooling, and applies a residual\\n+two-layer ReLU MLP. The separate item tower uses a trainable catalog-item\\n+embedding, standardized log-popularity, and its own residual two-layer ReLU\\n+MLP. Each branch adds one popularity-bias coordinate.\\n+\\n+All item vectors are computed once. Concatenating `sqrt(weight) * vector` for\\n+the three branches means the final score is exactly\\n+`dot(user_vector, item_vector)`. Production serving therefore computes one\\n+composite user vector and performs MIPS against an offline item index. The\\n+benchmark implementation scores items in small blocks only to apply the\\n+provided per-user eligibility masks.\\n+\\n+## Training\\n+\\n+`train_ranker.py` streams event logs in user order and never creates or\\n+factorizes a global user-item interaction matrix. It creates rolling\\n+cutoff/gap/seven-day examples, then optimizes sampled pairwise logistic (BPR)\\n+loss. Organic 80%-complete listens are positives, organic likes receive the\\n+grade-3 gain weight, and negatives are a mixture of uniform and\\n+popularity-skewed catalog samples. The selected branches use 6-, 3-, and\\n+1-window horizons. Selection used only train history and validation outcomes;\\n+the saved test checkpoints were refit with validation events added as observed\\n+training/history data.\\n+\\n+Public validation result for `validation.parquet`:\\n+\\n+```text\\n+graded NDCG@10 = 0.031779 (4,263 scored users)\\n+```\\n+\\n+## Reproduce the final file\\n+\\n+The refitted checkpoints and cached cutoff-correct history tensors are saved in\\n+this directory. Generate the submission with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/make_ensemble.py \\\\\\n+ --stage test --output /app/solution/submission.parquet\\n+```\\n+\\n+To refit from raw logs, run `train_ranker.py` three times with\\n+`(--snapshots, --epochs, --seed)` equal to `(6,6,20260925)`,\\n+`(3,8,20260926)`, and `(1,10,20260927)`, preserve the resulting checkpoints as\\n+`model_test_v1.pt` through `model_test_v3.pt`, then run the command above.\\n+\\n+The final artifact has 845,600 rows for 8,456 users, ranks 1-100 exactly once\\n+per user, no duplicate items, and zero test-ineligible items.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:39:06.314Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7e755c5e-1551-482e-81df-5bad0abbd3a0", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made-for-You two-tower ranker\n\n## Model\n\nThe final model is a 291-dimensional composite two-tower model (three\nindependently trained 97-dimensional branches concatenated with fixed positive\nscales). The **user tower has no user-ID embedding**. It embeds at most 192\nrecent positive history events, weights them by completion, organic intent,\nrecency, and active likes, performs weighted pooling, and applies a residual\ntwo-layer ReLU MLP. The separate item tower uses a trainable catalog-item\nembedding, standardized log-popularity, and its own residual two-layer ReLU\nMLP. Each branch adds one popularity-bias coordinate.\n\nAll item vectors are computed once. Concatenating `sqrt(weight) * vector` for\nthe three branches means the final score is exactly\n`dot(user_vector, item_vector)`. Production serving therefore computes one\ncomposite user vector and performs MIPS against an offline item index. The\nbenchmark implementation scores items in small blocks only to apply the\nprovided per-user eligibility masks.\n\n## Training\n\n`train_ranker.py` streams event logs in user order and never creates or\nfactorizes a global user-item interaction matrix. It creates rolling\ncutoff/gap/seven-day examples, then optimizes sampled pairwise logistic (BPR)\nloss. Organic 80%-complete listens are positives, organic likes receive the\ngrade-3 gain weight, and negatives are a mixture of uniform and\npopularity-skewed catalog samples. The selected branches use 6-, 3-, and\n1-window horizons. Selection used only train history and validation outcomes;\nthe saved test checkpoints were refit with validation events added as observed\ntraining/history data.\n\nPublic validation result for `validation.parquet`:\n\n```text\ngraded NDCG@10 = 0.031779 (4,263 scored users)\n```\n\n## Reproduce the final file\n\nThe refitted checkpoints and cached cutoff-correct history tensors are saved in\nthis directory. Generate the submission with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/make_ensemble.py \\\n --stage test --output /app/solution/submission.parquet\n```\n\nTo refit from raw logs, run `train_ranker.py` three times with\n`(--snapshots, --epochs, --seed)` equal to `(6,6,20260925)`,\n`(3,8,20260926)`, and `(1,10,20260927)`, preserve the resulting checkpoints as\n`model_test_v1.pt` through `model_test_v3.pt`, then run the command above.\n\nThe final artifact has 845,600 rows for 8,456 users, ranks 1-100 exactly once\nper user, no duplicate items, and zero test-ineligible items.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c2969dc487d2b6d074ba6f2672d7", "usage": { "input_tokens": 52383, "cached_input_tokens": 50304, "cache_write_input_tokens": 0, "output_tokens": 1007, "reasoning_output_tokens": 299, "total_tokens": 53390 }, "turn_token_usage": { "input_tokens": 2565669, "cached_input_tokens": 2512640, "cache_write_input_tokens": 0, "output_tokens": 25744, "reasoning_output_tokens": 8190, "total_tokens": 2591413 }, "thread_token_usage": { "input_tokens": 2565669, "cached_input_tokens": 2512640, "cache_write_input_tokens": 0, "output_tokens": 25744, "reasoning_output_tokens": 8190, "total_tokens": 2591413 } } ## Step 65 · agent · 2026-09-25T00:39:12.012Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:39:14.929Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/make_ensemble.py solution/make_popularity.py\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, pyarrow.parquet as pq, hashlib\\np='/app/solution/submission.parquet'; f=pd.read_parquet(p)\\nprint(pq.read_schema(p))\\nprint(f.groupby('uid').size().value_counts().to_dict())\\nprint('sha256',hashlib.sha256(open(p,'rb').read()).hexdigest())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-25T00:39:16.719Z Script completed Wall time 1.8 seconds Output: uid: int64 item_id: int64 rank: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 394 {100: 8456} sha256 8c65dc7c082ed9a791a1c39cfca8d4e39f3571b02ab741fc0bc424fc9d1dbade ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c2aa95b487d286032a8d27049f35", "usage": { "input_tokens": 53414, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 432, "reasoning_output_tokens": 262, "total_tokens": 53846 }, "turn_token_usage": { "input_tokens": 2619083, "cached_input_tokens": 2564864, "cache_write_input_tokens": 0, "output_tokens": 26176, "reasoning_output_tokens": 8452, "total_tokens": 2645259 }, "thread_token_usage": { "input_tokens": 2619083, "cached_input_tokens": 2564864, "cache_write_input_tokens": 0, "output_tokens": 26176, "reasoning_output_tokens": 8452, "total_tokens": 2645259 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-96e36db6-f0c3-4a2e-a5da-8baa3aebb804", "process_id": "87108", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/make_ensemble.py solution/make_popularity.py\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, hashlib\np='/app/solution/submission.parquet'; f=pd.read_parquet(p)\nprint(pq.read_schema(p))\nprint(f.groupby('uid').size().value_counts().to_dict())\nprint('sha256',hashlib.sha256(open(p,'rb').read()).hexdigest())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/make_ensemble.py solution/make_popularity.py\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, hashlib\np='/app/solution/submission.parquet'; f=pd.read_parquet(p)\nprint(pq.read_schema(p))\nprint(f.groupby('uid').size().value_counts().to_dict())\nprint('sha256',hashlib.sha256(open(p,'rb').read()).hexdigest())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "uid: int64\nitem_id: int64\nrank: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 394\n{100: 8456}\nsha256 8c65dc7c082ed9a791a1c39cfca8d4e39f3571b02ab741fc0bc424fc9d1dbade\n", "stderr": "", "aggregated_output": "uid: int64\nitem_id: int64\nrank: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 394\n{100: 8456}\nsha256 8c65dc7c082ed9a791a1c39cfca8d4e39f3571b02ab741fc0bc424fc9d1dbade\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 615934997 }, "formatted_output": "uid: int64\nitem_id: int64\nrank: int64\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 394\n{100: 8456}\nsha256 8c65dc7c082ed9a791a1c39cfca8d4e39f3571b02ab741fc0bc424fc9d1dbade\n" } } ## Step 66 · agent · 2026-09-25T00:39:18.564Z ### Thinking unavailable — no text recorded Implemented and verified the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/application code: [train_ranker.py](/app/solution/train_ranker.py) - Ensemble application: [make_ensemble.py](/app/solution/make_ensemble.py) - Documentation: [README.md](/app/solution/README.md) - Public validation ranking: [validation.parquet](/app/solution/validation.parquet) Results: - Public NDCG@10: **0.031779** - 845,600 submission rows - 8,456 users - Exactly 100 unique items and ranks 1–100 per user - Zero test-ineligible items - Submission SHA-256: `8c65dc7c082ed9a791a1c39cfca8d4e39f3571b02ab741fc0bc424fc9d1dbade` The final composite model remains exactly MIPS-compatible: one 291-dimensional history-derived user vector dotted with independently precomputable item vectors. ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "session_id": "01a0d5e9-f2af-7842-9fd1-1050dbb24f2f", "root_turn_id": "01a0d5e9-f2c0-7251-8bc5-ecf019493195", "response_id": "resp_0cc268e14b351f77016ab5c2b4e6c087d285c500bbb4680da4", "usage": { "input_tokens": 53963, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 268, "reasoning_output_tokens": 30, "total_tokens": 54231 }, "turn_token_usage": { "input_tokens": 2673046, "cached_input_tokens": 2618112, "cache_write_input_tokens": 0, "output_tokens": 26444, "reasoning_output_tokens": 8482, "total_tokens": 2699490 }, "thread_token_usage": { "input_tokens": 2673046, "cached_input_tokens": 2618112, "cache_write_input_tokens": 0, "output_tokens": 26444, "reasoning_output_tokens": 8482, "total_tokens": 2699490 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6e8a11bc-8f88-4707-a72b-a1386128e5ba", "process_id": "31938", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq\nD='/task/data/'\ntarget=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\nz=np.load(D+'validation_eligible_candidates.npz'); ur={int(x):i for i,x in enumerate(z['uids'])}; ic={int(x):i for i,x in enumerate(z['item_ids'])}\ndef elig(u,i):\n r=ur.get(u); c=ic.get(i); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\na=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1)\nb=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3)\nrel=pd.concat([a,b]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[[elig(int(u),int(i)) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nkeys={(int(u)<<32)|int(i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)}\nlast={k:-1 for k in keys}; cnt={k:0 for k in keys}; org={k:0 for k in keys}\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor batch in pf.iter_batches(columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\n d=batch.to_pandas(); kk=(d.uid.astype('uint64').to_numpy()<=50)\n for k,t,o in zip(kk[mask],d.timestamp.to_numpy()[mask],d.is_organic.to_numpy()[mask]):\n k=int(k); last[k]=max(last[k],int(t));cnt[k]+=1;org[k]+=int(o)\nvals=np.array([last[k] for k in keys]); counts=np.array([cnt[k] for k in keys]); orgs=np.array([org[k] for k in keys])\nprint('labels',len(keys),'ever',np.mean(vals>=0),'count percentiles',np.percentile(counts,[0,25,50,75,90,99,100]))\nfor days in [1,7,14,30,60,90,180]: print('within',days,np.mean(vals>=24786800-days*86400))\nprint('organic ever',np.mean(orgs>0))\npref=pd.read_parquet(D+'validation_preference_state.parquet'); pref['k']=(pref.uid.astype('uint64')<>(c%8))&1)\nls=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\na=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(g=1)\nb=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(g=3)\nrel=pd.concat([a,b]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[[elig(int(u),int(i)) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nkeys={(int(u)<<32)|int(i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)}\nlast={k:-1 for k in keys}; cnt={k:0 for k in keys}; org={k:0 for k in keys}\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor batch in pf.iter_batches(columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\n d=batch.to_pandas(); kk=(d.uid.astype('uint64').to_numpy()<=50)\n for k,t,o in zip(kk[mask],d.timestamp.to_numpy()[mask],d.is_organic.to_numpy()[mask]):\n k=int(k); last[k]=max(last[k],int(t));cnt[k]+=1;org[k]+=int(o)\nvals=np.array([last[k] for k in keys]); counts=np.array([cnt[k] for k in keys]); orgs=np.array([org[k] for k in keys])\nprint('labels',len(keys),'ever',np.mean(vals>=0),'count percentiles',np.percentile(counts,[0,25,50,75,90,99,100]))\nfor days in [1,7,14,30,60,90,180]: print('within',days,np.mean(vals>=24786800-days*86400))\nprint('organic ever',np.mean(orgs>0))\npref=pd.read_parquet(D+'validation_preference_state.parquet'); pref['k']=(pref.uid.astype('uint64')<