yambda-discovery-ranking-two-tower-base / codex-sol-high-cli-0-153-4-r05-2026-09-18 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-19T00:05:29.443Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-19T00:05:32.837Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-19T00:05:32.837Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-19T00:05:32.838Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-19T00:05:32.838Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-19 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-19", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-19", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-19T00:05:32.856Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-19T00:05:35.101Z ### Thinking unavailable — no text recorded I’ll inspect the dataset dimensions, schemas, and evaluator first, then build a leakage-safe validation pipeline. I’ll keep a valid submission checkpointed early and iterate on public NDCG before refitting on train+validation. ### exec · 2026-09-19T00:05:38.037Z { "code": "const r = await tools.exec_command({cmd:\"pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,240p' /task/evaluate_public.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:38.109Z Script completed Wall time 0.1 seconds Output: /app /task/ranking_evaluation.py /task/evaluate_public.py /task/requirements.txt /task/data/train_listens.parquet /task/data/val_dislikes.parquet /task/data/val_likes.parquet /task/data/train_undislikes.parquet /task/data/target_users.parquet /task/data/validation_eligible_candidates.npz /task/data/test_preference_state.parquet /task/data/train_dislikes.parquet /task/data/train_unlikes.parquet /task/data/val_unlikes.parquet /task/data/val_listens.parquet /task/data/candidate_catalog.parquet /task/data/train_likes.parquet /task/data/val_undislikes.parquet /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_unlikes.parquet /task/data/val_listens.parquet /task/data/candidate_catalog.parquet /task/data/train_likes.parquet /task/data/val_undislikes.parquet /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/target_users.parquet /task/data/validation_eligible_candidates.npz /task/data/test_preference_state.parquet /task/data/train_dislikes.parquet /task/data/train_unlikes.parquet /task/data/val_likes.parquet /task/data/train_undislikes.parquet /task/data/val_dislikes.parquet /task/data/train_listens.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 """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": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1cd1d0c87d19c2f7bfe4b5cd1ef", "usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 193, "reasoning_output_tokens": 26, "total_tokens": 14783 }, "turn_token_usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 193, "reasoning_output_tokens": 26, "total_tokens": 14783 }, "thread_token_usage": { "input_tokens": 14590, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 193, "reasoning_output_tokens": 26, "total_tokens": 14783 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7fc95ad5-aea9-4a73-9511-f7f35923e104", "process_id": "50013", "command": [ "/bin/sh", "-lc", "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,240p' /task/evaluate_public.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && rg --files /task/data /task | sed -n '1,160p' && sed -n '1,240p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,240p' /task/evaluate_public.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/requirements.txt\n/task/data/train_listens.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_listens.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\"\"\"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": "/app\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/requirements.txt\n/task/data/train_listens.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_listens.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\"\"\"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": 3530 }, "formatted_output": "/app\n/task/ranking_evaluation.py\n/task/evaluate_public.py\n/task/requirements.txt\n/task/data/train_listens.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_unlikes.parquet\n/task/data/val_listens.parquet\n/task/data/candidate_catalog.parquet\n/task/data/train_likes.parquet\n/task/data/val_undislikes.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/target_users.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/test_preference_state.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_unlikes.parquet\n/task/data/val_likes.parquet\n/task/data/train_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/data/train_listens.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\"\"\"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" } } ## Step 8 · agent · 2026-09-19T00:05:39.976Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:44.075Z { "code": "const r = await tools.exec_command({cmd:\"python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np\\nD=Path('/task/data')\\nfor p in sorted(D.iterdir()):\\n if p.suffix=='.parquet':\\n x=pd.read_parquet(p)\\n print(p.name, x.shape, x.dtypes.to_dict())\\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\\n elif p.suffix=='.npz':\\n z=np.load(p)\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:44.205Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'pandas' ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1d2816887d198a496aacb1a66e5", "usage": { "input_tokens": 18919, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 251, "reasoning_output_tokens": 14, "total_tokens": 19170 }, "turn_token_usage": { "input_tokens": 33509, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 444, "reasoning_output_tokens": 40, "total_tokens": 33953 }, "thread_token_usage": { "input_tokens": 33509, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 444, "reasoning_output_tokens": 40, "total_tokens": 33953 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-107fc173-db6e-4f68-bb64-d25827f1542d", "process_id": "7438", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.iterdir()):\n if p.suffix=='.parquet':\n x=pd.read_parquet(p)\n print(p.name, x.shape, x.dtypes.to_dict())\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\n elif p.suffix=='.npz':\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.iterdir()):\n if p.suffix=='.parquet':\n x=pd.read_parquet(p)\n print(p.name, x.shape, x.dtypes.to_dict())\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\n elif p.suffix=='.npz':\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 4450 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'pandas'\n" } } ## Step 9 · agent · 2026-09-19T00:05:46.149Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:05:47.872Z { "code": "const r = await tools.exec_command({cmd:\"which python; python --version; ls -la /app; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | sed -n '1,30p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:47.929Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python Python 3.12.13 total 12 drwxr-xr-x 3 agent agent 4096 Sep 19 00:02 . drwxr-xr-x 1 root root 4096 Sep 19 00:05 .. drwxr-xr-x 2 agent agent 4096 Sep 19 00:02 solution total 36 dr-xr-xr-x 1 root root 4096 Sep 19 00:02 . drwxr-xr-x 1 root root 4096 Sep 19 00:05 .. -r--r--r-- 1 root root 0 Sep 18 23:48 .keep dr-xr-xr-x 1 root root 4096 Sep 18 23:49 data -r--r--r-- 1 root root 5774 Sep 18 23:48 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 18 23:48 ranking_evaluation.py -r--r--r-- 1 root root 1295 Sep 18 23:48 requirements.txt ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1d86d0087d19fc6b0dad5f9d0ae", "usage": { "input_tokens": 19226, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 12, "total_tokens": 19343 }, "turn_token_usage": { "input_tokens": 52735, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 561, "reasoning_output_tokens": 52, "total_tokens": 53296 }, "thread_token_usage": { "input_tokens": 52735, "cached_input_tokens": 40960, "cache_write_input_tokens": 0, "output_tokens": 561, "reasoning_output_tokens": 52, "total_tokens": 53296 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab776e1b-9aea-482e-bd44-31d392e78552", "process_id": "49612", "command": [ "/bin/sh", "-lc", "which python; python --version; ls -la /app; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | sed -n '1,30p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python; python --version; ls -la /app; ls -la /task | sed -n '1,100p'; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | sed -n '1,30p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 19 00:02 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\n-r--r--r-- 1 root root 0 Sep 18 23:48 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:49 data\n-r--r--r-- 1 root root 5774 Sep 18 23:48 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:48 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:48 requirements.txt\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 19 00:02 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\n-r--r--r-- 1 root root 0 Sep 18 23:48 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:49 data\n-r--r--r-- 1 root root 5774 Sep 18 23:48 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:48 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:48 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4410 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 19 00:02 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 19 00:02 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:05 ..\n-r--r--r-- 1 root root 0 Sep 18 23:48 .keep\ndr-xr-xr-x 1 root root 4096 Sep 18 23:49 data\n-r--r--r-- 1 root root 5774 Sep 18 23:48 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 18 23:48 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 18 23:48 requirements.txt\n" } } ## Step 10 · agent · 2026-09-19T00:05:50.245Z ### Thinking unavailable — no text recorded The default `python` executable is not exposing the task’s pinned packages despite the supplied inventory. I’m checking the runtime layout now; once the intended environment is located, I’ll profile the event and eligibility distributions and proceed. ### exec · 2026-09-19T00:05:53.392Z { "code": "const r = await tools.exec_command({cmd:\"python -m pip list | sed -n '1,120p'; find /opt /usr/local /venv /app -maxdepth 4 \\\\( -name 'pyvenv.cfg' -o -name 'pandas' -o -name 'torch' \\\\) 2>/dev/null | sed -n '1,160p'; env | sort | sed -n '1,160p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:05:54.141Z Script completed Wall time 0.7 seconds Output: Package Version ------- ------- pip 25.0.1 /opt/task-venv/pyvenv.cfg CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_SESSION_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6 CODEX_THREAD_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=969c92d3e756 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1dc382487d1b6c8b211b1e37048", "usage": { "input_tokens": 19612, "cached_input_tokens": 19072, "cache_write_input_tokens": 0, "output_tokens": 209, "reasoning_output_tokens": 24, "total_tokens": 19821 }, "turn_token_usage": { "input_tokens": 72347, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 770, "reasoning_output_tokens": 76, "total_tokens": 73117 }, "thread_token_usage": { "input_tokens": 72347, "cached_input_tokens": 60032, "cache_write_input_tokens": 0, "output_tokens": 770, "reasoning_output_tokens": 76, "total_tokens": 73117 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6edf8560-4051-479d-a12e-ca33d0620366", "process_id": "16425", "command": [ "/bin/sh", "-lc", "python -m pip list | sed -n '1,120p'; find /opt /usr/local /venv /app -maxdepth 4 \\( -name 'pyvenv.cfg' -o -name 'pandas' -o -name 'torch' \\) 2>/dev/null | sed -n '1,160p'; env | sort | sed -n '1,160p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python -m pip list | sed -n '1,120p'; find /opt /usr/local /venv /app -maxdepth 4 \\( -name 'pyvenv.cfg' -o -name 'pandas' -o -name 'torch' \\) 2>/dev/null | sed -n '1,160p'; env | sort | sed -n '1,160p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/pyvenv.cfg\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCODEX_THREAD_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=969c92d3e756\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/pyvenv.cfg\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCODEX_THREAD_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=969c92d3e756\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 580156506 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\n/opt/task-venv/pyvenv.cfg\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCODEX_THREAD_ID=01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=969c92d3e756\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n" } } ## Step 11 · agent · 2026-09-19T00:05:59.623Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:06:04.673Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport pandas as pd, numpy as np, torch\\nprint('versions',pd.__version__,np.__version__,torch.__version__)\\nD=Path('/task/data')\\nfor p in sorted(D.iterdir()):\\n if p.suffix=='.parquet':\\n x=pd.read_parquet(p)\\n print(p.name, x.shape, x.dtypes.to_dict())\\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\\n elif p.suffix=='.npz':\\n z=np.load(p)\\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',bits.mean(),'counts',np.percentile(bits.sum(1),[0,25,50,75,100]))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:06:10.925Z Script completed Wall time 6.2 seconds Output: versions 2.3.3 2.2.6 2.8.0+cpu candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9680439293598234 counts [24552. 28657. 29220. 29619.25 30000. ] test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 75 24786580 users 5732 items 50876 train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 80 24786790 users 8144 items 172303 train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} ts 0 24786795 users 9238 items 845395 train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 15950 24785395 users 2769 items 14725 train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 250 24786570 users 6197 items 113072 val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 24789920 25393280 users 955 items 3135 val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 24788815 25393390 users 4114 items 22902 val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} ts 24788600 25393395 users 8196 items 191747 val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 24810715 25393035 users 205 items 579 val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} ts 24789005 25393385 users 1313 items 8080 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9689102097130243 counts [24657. 28682.75 29242. 29644. 30000. ] validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1e3887087d18c4077336a551468", "usage": { "input_tokens": 20181, "cached_input_tokens": 19456, "cache_write_input_tokens": 0, "output_tokens": 305, "reasoning_output_tokens": 16, "total_tokens": 20486 }, "turn_token_usage": { "input_tokens": 92528, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 1075, "reasoning_output_tokens": 92, "total_tokens": 93603 }, "thread_token_usage": { "input_tokens": 92528, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 1075, "reasoning_output_tokens": 92, "total_tokens": 93603 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d0a8311b-2107-4cc5-8c23-3487c612e4e6", "process_id": "46653", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, torch\nprint('versions',pd.__version__,np.__version__,torch.__version__)\nD=Path('/task/data')\nfor p in sorted(D.iterdir()):\n if p.suffix=='.parquet':\n x=pd.read_parquet(p)\n print(p.name, x.shape, x.dtypes.to_dict())\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\n elif p.suffix=='.npz':\n z=np.load(p)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',bits.mean(),'counts',np.percentile(bits.sum(1),[0,25,50,75,100]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np, torch\nprint('versions',pd.__version__,np.__version__,torch.__version__)\nD=Path('/task/data')\nfor p in sorted(D.iterdir()):\n if p.suffix=='.parquet':\n x=pd.read_parquet(p)\n print(p.name, x.shape, x.dtypes.to_dict())\n if 'timestamp' in x: print(' ts',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique())\n elif p.suffix=='.npz':\n z=np.load(p)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files},'elig density',bits.mean(),'counts',np.percentile(bits.sum(1),[0,25,50,75,100]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "versions 2.3.3 2.2.6 2.8.0+cpu\ncandidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9680439293598234 counts [24552. 28657. 29220. 29619.25 30000. ]\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 75 24786580 users 5732 items 50876\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 80 24786790 users 8144 items 172303\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 0 24786795 users 9238 items 845395\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 15950 24785395 users 2769 items 14725\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 250 24786570 users 6197 items 113072\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789920 25393280 users 955 items 3135\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24788815 25393390 users 4114 items 22902\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 24788600 25393395 users 8196 items 191747\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24810715 25393035 users 205 items 579\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789005 25393385 users 1313 items 8080\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9689102097130243 counts [24657. 28682.75 29242. 29644. 30000. ]\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n", "stderr": "", "aggregated_output": "versions 2.3.3 2.2.6 2.8.0+cpu\ncandidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9680439293598234 counts [24552. 28657. 29220. 29619.25 30000. ]\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 75 24786580 users 5732 items 50876\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 80 24786790 users 8144 items 172303\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 0 24786795 users 9238 items 845395\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 15950 24785395 users 2769 items 14725\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 250 24786570 users 6197 items 113072\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789920 25393280 users 955 items 3135\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24788815 25393390 users 4114 items 22902\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 24788600 25393395 users 8196 items 191747\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24810715 25393035 users 205 items 579\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789005 25393385 users 1313 items 8080\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9689102097130243 counts [24657. 28682.75 29242. 29644. 30000. ]\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 74782067 }, "formatted_output": "versions 2.3.3 2.2.6 2.8.0+cpu\ncandidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9680439293598234 counts [24552. 28657. 29220. 29619.25 30000. ]\ntest_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 75 24786580 users 5732 items 50876\ntrain_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 80 24786790 users 8144 items 172303\ntrain_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 0 24786795 users 9238 items 845395\ntrain_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 15950 24785395 users 2769 items 14725\ntrain_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 250 24786570 users 6197 items 113072\nval_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789920 25393280 users 955 items 3135\nval_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24788815 25393390 users 4114 items 22902\nval_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n ts 24788600 25393395 users 8196 items 191747\nval_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24810715 25393035 users 205 items 579\nval_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n ts 24789005 25393385 users 1313 items 8080\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} elig density 0.9689102097130243 counts [24657. 28682.75 29242. 29644. 30000. ]\nvalidation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n" } } ## Step 12 · agent · 2026-09-19T00:06:26.375Z ### Thinking unavailable — no text recorded ## Step 13 · agent · 2026-09-19T00:06:34.770Z ### Thinking unavailable — no text recorded The data are large (43.5M training listens) but the serving catalog is only 30K items and eligibility is dense. I’m establishing a popularity checkpoint and measuring the public label shape before investing in the neural objective; this also guarantees a structurally valid artifact early. ### exec · 2026-09-19T00:06:41.807Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/baseline.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Create an eligibility-safe 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 rank(stage: str, output: Path) -> None:\\n+ z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ uids = z[\\\"uids\\\"].astype(np.int64)\\n+ items = z[\\\"item_ids\\\"].astype(np.int64)\\n+ eligible = np.unpackbits(\\n+ z[\\\"packed_eligible\\\"], axis=1, count=len(items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").set_index(\\\"item_id\\\")\\n+ # Catalog order/popularity is fixed from training only.\\n+ scores = -catalog.loc[items, \\\"popularity_rank\\\"].to_numpy(np.float64)\\n+ rows = []\\n+ for r, uid in enumerate(uids):\\n+ valid = np.flatnonzero(eligible[r])\\n+ take = valid[np.argpartition(scores[valid], -100)[-100:]]\\n+ take = take[np.argsort(-scores[take], kind=\\\"stable\\\")]\\n+ rows.append(pd.DataFrame({\\\"uid\\\": uid, \\\"item_id\\\": items[take],\\n+ \\\"rank\\\": np.arange(1, 101)}))\\n+ output.parent.mkdir(parents=True, exist_ok=True)\\n+ pd.concat(rows, ignore_index=True).astype(\\\"int64\\\").to_parquet(output, index=False)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ ap = argparse.ArgumentParser()\\n+ ap.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"test\\\"], required=True)\\n+ ap.add_argument(\\\"--output\\\", type=Path, required=True)\\n+ a = ap.parse_args()\\n+ rank(a.stage, a.output)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:06:41.854Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ad2706b9-6514-416e-8481-6eafc22fb562", "changes": { "/app/solution/baseline.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Create an eligibility-safe 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 rank(stage: str, output: Path) -> None:\n z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n uids = z[\"uids\"].astype(np.int64)\n items = z[\"item_ids\"].astype(np.int64)\n eligible = np.unpackbits(\n z[\"packed_eligible\"], axis=1, count=len(items), bitorder=\"little\"\n ).astype(bool)\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\").set_index(\"item_id\")\n # Catalog order/popularity is fixed from training only.\n scores = -catalog.loc[items, \"popularity_rank\"].to_numpy(np.float64)\n rows = []\n for r, uid in enumerate(uids):\n valid = np.flatnonzero(eligible[r])\n take = valid[np.argpartition(scores[valid], -100)[-100:]]\n take = take[np.argsort(-scores[take], kind=\"stable\")]\n rows.append(pd.DataFrame({\"uid\": uid, \"item_id\": items[take],\n \"rank\": np.arange(1, 101)}))\n output.parent.mkdir(parents=True, exist_ok=True)\n pd.concat(rows, ignore_index=True).astype(\"int64\").to_parquet(output, index=False)\n\n\nif __name__ == \"__main__\":\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n ap.add_argument(\"--output\", type=Path, required=True)\n a = ap.parse_args()\n rank(a.stage, a.output)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd1f34b2887d184d3d54c02a1ba24", "usage": { "input_tokens": 21556, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 1456, "reasoning_output_tokens": 878, "total_tokens": 23012 }, "turn_token_usage": { "input_tokens": 114084, "cached_input_tokens": 99456, "cache_write_input_tokens": 0, "output_tokens": 2531, "reasoning_output_tokens": 970, "total_tokens": 116615 }, "thread_token_usage": { "input_tokens": 114084, "cached_input_tokens": 99456, "cache_write_input_tokens": 0, "output_tokens": 2531, "reasoning_output_tokens": 970, "total_tokens": 116615 } } ## Step 14 · agent · 2026-09-19T00:06:43.678Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:06:51.371Z { "code": "const r = await tools.exec_command({cmd:\"ls -la solution && /opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet && /opt/task-venv/bin/python - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet\\nfrom evaluate_public import validation_truth\\nD='/task/data/'\\nt=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\\ne=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\\ng=validation_truth(e,t)\\nprint('quality users',len(g),'rels',sum(map(len,g.values())))\\nprint('rels/user percentiles',np.percentile([len(x) for x in g.values()],[0,25,50,75,90,99,100]))\\nprint('grades',pd.Series([v for x in g.values() for v in x.values()]).value_counts().to_dict())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:04.284Z Script completed Wall time 12.9 seconds Output: total 16 drwxr-xr-x 1 agent agent 4096 Sep 19 00:06 . drwxr-xr-x 1 agent agent 4096 Sep 19 00:02 .. -rw-r--r-- 1 agent agent 1476 Sep 19 00:06 baseline.py { "model": "solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } quality users 4263 rels 37769 rels/user percentiles [ 1. 2. 4. 10. 21. 63. 442.] grades {1: 33352, 3: 4417} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd2124a7c87d1aa356c9a263ec54f", "usage": { "input_tokens": 23036, "cached_input_tokens": 21376, "cache_write_input_tokens": 0, "output_tokens": 330, "reasoning_output_tokens": 19, "total_tokens": 23366 }, "turn_token_usage": { "input_tokens": 137120, "cached_input_tokens": 120832, "cache_write_input_tokens": 0, "output_tokens": 2861, "reasoning_output_tokens": 989, "total_tokens": 139981 }, "thread_token_usage": { "input_tokens": 137120, "cached_input_tokens": 120832, "cache_write_input_tokens": 0, "output_tokens": 2861, "reasoning_output_tokens": 989, "total_tokens": 139981 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8becc393-98ab-475d-9821-263f5e992bb5", "process_id": "56419", "command": [ "/bin/sh", "-lc", "ls -la solution && /opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet && /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nD='/task/data/'\nt=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\ne=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\ng=validation_truth(e,t)\nprint('quality users',len(g),'rels',sum(map(len,g.values())))\nprint('rels/user percentiles',np.percentile([len(x) for x in g.values()],[0,25,50,75,90,99,100]))\nprint('grades',pd.Series([v for x in g.values() for v in x.values()]).value_counts().to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la solution && /opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet && /opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nD='/task/data/'\nt=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\ne=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\ng=validation_truth(e,t)\nprint('quality users',len(g),'rels',sum(map(len,g.values())))\nprint('rels/user percentiles',np.percentile([len(x) for x in g.values()],[0,25,50,75,90,99,100]))\nprint('grades',pd.Series([v for x in g.values() for v in x.values()]).value_counts().to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nquality users 4263 rels 37769\nrels/user percentiles [ 1. 2. 4. 10. 21. 63. 442.]\ngrades {1: 33352, 3: 4417}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nquality users 4263 rels 37769\nrels/user percentiles [ 1. 2. 4. 10. 21. 63. 442.]\ngrades {1: 33352, 3: 4417}\n", "exit_code": 0, "duration": { "secs": 12, "nanos": 744105544 }, "formatted_output": "{\n \"model\": \"solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nquality users 4263 rels 37769\nrels/user percentiles [ 1. 2. 4. 10. 21. 63. 442.]\ngrades {1: 33352, 3: 4417}\n" } } ## Step 15 · agent · 2026-09-19T00:07:08.051Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:07:08.579Z { "code": "const r = await tools.exec_command({cmd:\"free -h; du -h /task/data/* | sort -h\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:08.631Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: free: not found 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 ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2c49702c-8ec6-41e0-b4bb-4957b110b0f6", "process_id": "85801", "command": [ "/bin/sh", "-lc", "free -h; du -h /task/data/* | sort -h" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; du -h /task/data/* | sort -h" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: free: not found\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n", "stderr": "", "aggregated_output": "/bin/sh: 1: free: not found\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7130 }, "formatted_output": "/bin/sh: 1: free: not found\n8.0K\t/task/data/val_undislikes.parquet\n36K\t/task/data/val_dislikes.parquet\n72K\t/task/data/val_unlikes.parquet\n84K\t/task/data/target_users.parquet\n156K\t/task/data/train_undislikes.parquet\n292K\t/task/data/val_likes.parquet\n344K\t/task/data/candidate_catalog.parquet\n840K\t/task/data/train_dislikes.parquet\n1.1M\t/task/data/test_preference_state.parquet\n1.1M\t/task/data/validation_preference_state.parquet\n1.8M\t/task/data/train_unlikes.parquet\n5.2M\t/task/data/train_likes.parquet\n7.3M\t/task/data/validation_eligible_candidates.npz\n7.4M\t/task/data/test_eligible_candidates.npz\n9.5M\t/task/data/val_listens.parquet\n305M\t/task/data/train_listens.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd228727887d182dd9f844c605494", "usage": { "input_tokens": 23560, "cached_input_tokens": 22912, "cache_write_input_tokens": 0, "output_tokens": 152, "reasoning_output_tokens": 90, "total_tokens": 23712 }, "turn_token_usage": { "input_tokens": 160680, "cached_input_tokens": 143744, "cache_write_input_tokens": 0, "output_tokens": 3013, "reasoning_output_tokens": 1079, "total_tokens": 163693 }, "thread_token_usage": { "input_tokens": 160680, "cached_input_tokens": 143744, "cache_write_input_tokens": 0, "output_tokens": 3013, "reasoning_output_tokens": 1079, "total_tokens": 163693 } } ## Step 16 · agent · 2026-09-19T00:07:24.104Z ### Thinking unavailable — no text recorded ## Step 17 · agent · 2026-09-19T00:07:34.128Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:07:42.265Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nD='/task/data/'\\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\numap=pd.Series(np.arange(len(uids),dtype=np.int32),index=uids); imap=pd.Series(np.arange(len(items),dtype=np.int32),index=items)\\n# sample exact users to compare exclusions against unique interactions\\nsel=np.arange(0,len(uids),100)\\nS=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\\npf=pq.ParquetFile(D+'train_listens.parquet')\\nfor b in pf.iter_batches(columns=['uid','item_id','played_ratio_pct']):\\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\\n for u,it,pr in d.itertuples(index=False,name=None):\\n seen[int(u)].add(int(it))\\n if pr>=50: pos[int(u)].add(int(it))\\nex=(~bits[sel]).sum(1)\\na=np.array([len(seen[int(u)]) for u in uids[sel]])\\np=np.array([len(pos[int(u)]) for u in uids[sel]])\\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\\nfor j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:07:42.336Z Script completed Wall time 0.1 seconds Output: File "", line 18 for j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) ) ^ SyntaxError: unmatched ')' ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd22d219887d193d774c80988764b", "usage": { "input_tokens": 23953, "cached_input_tokens": 23424, "cache_write_input_tokens": 0, "output_tokens": 1401, "reasoning_output_tokens": 944, "total_tokens": 25354 }, "turn_token_usage": { "input_tokens": 184633, "cached_input_tokens": 167168, "cache_write_input_tokens": 0, "output_tokens": 4414, "reasoning_output_tokens": 2023, "total_tokens": 189047 }, "thread_token_usage": { "input_tokens": 184633, "cached_input_tokens": 167168, "cache_write_input_tokens": 0, "output_tokens": 4414, "reasoning_output_tokens": 2023, "total_tokens": 189047 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-44c50a16-0011-4c74-b49f-12f9002dde94", "process_id": "41646", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\numap=pd.Series(np.arange(len(uids),dtype=np.int32),index=uids); imap=pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n# sample exact users to compare exclusions against unique interactions\nsel=np.arange(0,len(uids),100)\nS=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(columns=['uid','item_id','played_ratio_pct']):\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\n for u,it,pr in d.itertuples(index=False,name=None):\n seen[int(u)].add(int(it))\n if pr>=50: pos[int(u)].add(int(it))\nex=(~bits[sel]).sum(1)\na=np.array([len(seen[int(u)]) for u in uids[sel]])\np=np.array([len(pos[int(u)]) for u in uids[sel]])\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\nfor j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\numap=pd.Series(np.arange(len(uids),dtype=np.int32),index=uids); imap=pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n# sample exact users to compare exclusions against unique interactions\nsel=np.arange(0,len(uids),100)\nS=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(columns=['uid','item_id','played_ratio_pct']):\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\n for u,it,pr in d.itertuples(index=False,name=None):\n seen[int(u)].add(int(it))\n if pr>=50: pos[int(u)].add(int(it))\nex=(~bits[sel]).sum(1)\na=np.array([len(seen[int(u)]) for u in uids[sel]])\np=np.array([len(pos[int(u)]) for u in uids[sel]])\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\nfor j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": " File \"\", line 18\n for j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\n ^\nSyntaxError: unmatched ')'\n", "stderr": "", "aggregated_output": " File \"\", line 18\n for j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\n ^\nSyntaxError: unmatched ')'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3770 }, "formatted_output": " File \"\", line 18\n for j in range(10): print(int(uids[sel[j]]),ex[j],a[j],p[j], 'overlap excl/seen',sum(not bits[sel[j],imap[x]] for x in seen[int(uids[sel[j]])])) )\n ^\nSyntaxError: unmatched ')'\n" } } ## Step 18 · agent · 2026-09-19T00:07:45.379Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:07:55.687Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\\nD='/task/data/'\\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nimap={int(x):i for i,x in enumerate(items)}\\nsel=np.arange(0,len(uids),100); S=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct']):\\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\\n for u,it,pr in d.itertuples(index=False,name=None):\\n seen[int(u)].add(int(it))\\n if pr>=50: pos[int(u)].add(int(it))\\nex=(~bits[sel]).sum(1); a=np.array([len(seen[int(u)]) for u in uids[sel]]); p=np.array([len(pos[int(u)]) for u in uids[sel]])\\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\\nfor j in range(10):\\n u=int(uids[sel[j]]); ov=sum(not bits[sel[j],imap[x]] for x in seen[u])\\n print(u, 'excluded',ex[j],'allseen',a[j],'pos',p[j],'excluded_seen',ov)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:08:01.957Z Script completed Wall time 6.3 seconds Output: excluded, allseen,pos correlations [[1. 0.99970309 0.91691196] [0.99970309 1. 0.91806935] [0.91691196 0.91806935 1. ]] 100 excluded 682 allseen 682 pos 600 excluded_seen 682 12400 excluded 1413 allseen 1412 pos 1108 excluded_seen 1412 24000 excluded 14 allseen 14 pos 10 excluded_seen 14 35800 excluded 251 allseen 251 pos 187 excluded_seen 251 47200 excluded 916 allseen 916 pos 824 excluded_seen 916 60000 excluded 1289 allseen 1288 pos 1176 excluded_seen 1288 72500 excluded 894 allseen 894 pos 857 excluded_seen 894 84500 excluded 671 allseen 670 pos 437 excluded_seen 670 95900 excluded 417 allseen 417 pos 247 excluded_seen 417 108500 excluded 261 allseen 261 pos 108 excluded_seen 261 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd24e927487d1930245936366c92d", "usage": { "input_tokens": 25451, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 428, "reasoning_output_tokens": 6, "total_tokens": 25879 }, "turn_token_usage": { "input_tokens": 210084, "cached_input_tokens": 190976, "cache_write_input_tokens": 0, "output_tokens": 4842, "reasoning_output_tokens": 2029, "total_tokens": 214926 }, "thread_token_usage": { "input_tokens": 210084, "cached_input_tokens": 190976, "cache_write_input_tokens": 0, "output_tokens": 4842, "reasoning_output_tokens": 2029, "total_tokens": 214926 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-de5455ef-6f3a-4c4a-93dd-b04f54e2d93c", "process_id": "17155", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nimap={int(x):i for i,x in enumerate(items)}\nsel=np.arange(0,len(uids),100); S=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct']):\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\n for u,it,pr in d.itertuples(index=False,name=None):\n seen[int(u)].add(int(it))\n if pr>=50: pos[int(u)].add(int(it))\nex=(~bits[sel]).sum(1); a=np.array([len(seen[int(u)]) for u in uids[sel]]); p=np.array([len(pos[int(u)]) for u in uids[sel]])\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\nfor j in range(10):\n u=int(uids[sel[j]]); ov=sum(not bits[sel[j],imap[x]] for x in seen[u])\n print(u, 'excluded',ex[j],'allseen',a[j],'pos',p[j],'excluded_seen',ov)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nimap={int(x):i for i,x in enumerate(items)}\nsel=np.arange(0,len(uids),100); S=set(map(int,uids[sel])); seen={u:set() for u in S}; pos={u:set() for u in S}\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','played_ratio_pct']):\n d=b.to_pandas(); d=d[d.uid.isin(S)&d.item_id.isin(items)]\n for u,it,pr in d.itertuples(index=False,name=None):\n seen[int(u)].add(int(it))\n if pr>=50: pos[int(u)].add(int(it))\nex=(~bits[sel]).sum(1); a=np.array([len(seen[int(u)]) for u in uids[sel]]); p=np.array([len(pos[int(u)]) for u in uids[sel]])\nprint('excluded, allseen,pos correlations',np.corrcoef([ex,a,p]))\nfor j in range(10):\n u=int(uids[sel[j]]); ov=sum(not bits[sel[j],imap[x]] for x in seen[u])\n print(u, 'excluded',ex[j],'allseen',a[j],'pos',p[j],'excluded_seen',ov)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "excluded, allseen,pos correlations [[1. 0.99970309 0.91691196]\n [0.99970309 1. 0.91806935]\n [0.91691196 0.91806935 1. ]]\n100 excluded 682 allseen 682 pos 600 excluded_seen 682\n12400 excluded 1413 allseen 1412 pos 1108 excluded_seen 1412\n24000 excluded 14 allseen 14 pos 10 excluded_seen 14\n35800 excluded 251 allseen 251 pos 187 excluded_seen 251\n47200 excluded 916 allseen 916 pos 824 excluded_seen 916\n60000 excluded 1289 allseen 1288 pos 1176 excluded_seen 1288\n72500 excluded 894 allseen 894 pos 857 excluded_seen 894\n84500 excluded 671 allseen 670 pos 437 excluded_seen 670\n95900 excluded 417 allseen 417 pos 247 excluded_seen 417\n108500 excluded 261 allseen 261 pos 108 excluded_seen 261\n", "stderr": "", "aggregated_output": "excluded, allseen,pos correlations [[1. 0.99970309 0.91691196]\n [0.99970309 1. 0.91806935]\n [0.91691196 0.91806935 1. ]]\n100 excluded 682 allseen 682 pos 600 excluded_seen 682\n12400 excluded 1413 allseen 1412 pos 1108 excluded_seen 1412\n24000 excluded 14 allseen 14 pos 10 excluded_seen 14\n35800 excluded 251 allseen 251 pos 187 excluded_seen 251\n47200 excluded 916 allseen 916 pos 824 excluded_seen 916\n60000 excluded 1289 allseen 1288 pos 1176 excluded_seen 1288\n72500 excluded 894 allseen 894 pos 857 excluded_seen 894\n84500 excluded 671 allseen 670 pos 437 excluded_seen 670\n95900 excluded 417 allseen 417 pos 247 excluded_seen 417\n108500 excluded 261 allseen 261 pos 108 excluded_seen 261\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 101550783 }, "formatted_output": "excluded, allseen,pos correlations [[1. 0.99970309 0.91691196]\n [0.99970309 1. 0.91806935]\n [0.91691196 0.91806935 1. ]]\n100 excluded 682 allseen 682 pos 600 excluded_seen 682\n12400 excluded 1413 allseen 1412 pos 1108 excluded_seen 1412\n24000 excluded 14 allseen 14 pos 10 excluded_seen 14\n35800 excluded 251 allseen 251 pos 187 excluded_seen 251\n47200 excluded 916 allseen 916 pos 824 excluded_seen 916\n60000 excluded 1289 allseen 1288 pos 1176 excluded_seen 1288\n72500 excluded 894 allseen 894 pos 857 excluded_seen 894\n84500 excluded 671 allseen 670 pos 437 excluded_seen 670\n95900 excluded 417 allseen 417 pos 247 excluded_seen 417\n108500 excluded 261 allseen 261 pos 108 excluded_seen 261\n" } } ## Step 19 · agent · 2026-09-19T00:08:12.575Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:08:14.381Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np\\np=pq.ParquetFile('/task/data/train_listens.parquet')\\nfor i in [0,1,p.num_row_groups-1]:\\n t=p.read_row_group(i,columns=['timestamp','uid']).to_pandas()\\n print(i,len(t),t.timestamp.min(),t.timestamp.max(),t.timestamp.iloc[:5].tolist(),t.uid.iloc[:10].tolist())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:08:15.983Z Script completed Wall time 1.6 seconds Output: 0 1048576 15 24786690 [39420, 39420, 39625, 40110, 40360] [100, 100, 100, 100, 100, 100, 100, 100, 100, 100] 1 1048576 185 24786725 [10880440, 10880440, 10880440, 10880440, 10880440] [26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500] 41 492543 20 24786690 [2340320, 2340340, 2340360, 2359030, 2359065] [989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500] ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd2621c5887d185d40c0791d2d7b9", "usage": { "input_tokens": 26136, "cached_input_tokens": 25216, "cache_write_input_tokens": 0, "output_tokens": 397, "reasoning_output_tokens": 240, "total_tokens": 26533 }, "turn_token_usage": { "input_tokens": 236220, "cached_input_tokens": 216192, "cache_write_input_tokens": 0, "output_tokens": 5239, "reasoning_output_tokens": 2269, "total_tokens": 241459 }, "thread_token_usage": { "input_tokens": 236220, "cached_input_tokens": 216192, "cache_write_input_tokens": 0, "output_tokens": 5239, "reasoning_output_tokens": 2269, "total_tokens": 241459 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-77f586ee-96f2-496f-8f61-4220f0371231", "process_id": "4476", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor i in [0,1,p.num_row_groups-1]:\n t=p.read_row_group(i,columns=['timestamp','uid']).to_pandas()\n print(i,len(t),t.timestamp.min(),t.timestamp.max(),t.timestamp.iloc[:5].tolist(),t.uid.iloc[:10].tolist())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor i in [0,1,p.num_row_groups-1]:\n t=p.read_row_group(i,columns=['timestamp','uid']).to_pandas()\n print(i,len(t),t.timestamp.min(),t.timestamp.max(),t.timestamp.iloc[:5].tolist(),t.uid.iloc[:10].tolist())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 1048576 15 24786690 [39420, 39420, 39625, 40110, 40360] [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]\n1 1048576 185 24786725 [10880440, 10880440, 10880440, 10880440, 10880440] [26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500]\n41 492543 20 24786690 [2340320, 2340340, 2340360, 2359030, 2359065] [989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500]\n", "stderr": "", "aggregated_output": "0 1048576 15 24786690 [39420, 39420, 39625, 40110, 40360] [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]\n1 1048576 185 24786725 [10880440, 10880440, 10880440, 10880440, 10880440] [26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500]\n41 492543 20 24786690 [2340320, 2340340, 2340360, 2359030, 2359065] [989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500]\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 433001792 }, "formatted_output": "0 1048576 15 24786690 [39420, 39420, 39625, 40110, 40360] [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]\n1 1048576 185 24786725 [10880440, 10880440, 10880440, 10880440, 10880440] [26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500, 26500]\n41 492543 20 24786690 [2340320, 2340340, 2340360, 2359030, 2359065] [989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500, 989500]\n" } } ## Step 20 · agent · 2026-09-19T00:08:31.727Z ### Thinking unavailable — no text recorded ## Step 21 · agent · 2026-09-19T00:08:44.601Z ### Thinking unavailable — no text recorded ## Step 22 · agent · 2026-09-19T00:08:52.384Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:08:54.853Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\\nD='/task/data/'; cand=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); targets=set(map(int,pd.read_parquet(D+'target_users.parquet').uid))\\nn=0; org=0; strong=0\\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\\n d=b.to_pandas(); m=d.uid.isin(targets)&d.item_id.isin(cand); n+=int(m.sum()); org+=int((m&(d.is_organic==1)).sum()); strong+=int((m&(d.is_organic==1)&(d.played_ratio_pct>=80)).sum())\\nprint(n,org,strong)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:09:03.107Z Script completed Wall time 8.3 seconds Output: 31062540 15537198 8661020 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd270382c87d1959e7f68d74cfe43", "usage": { "input_tokens": 26768, "cached_input_tokens": 25984, "cache_write_input_tokens": 0, "output_tokens": 1613, "reasoning_output_tokens": 1359, "total_tokens": 28381 }, "turn_token_usage": { "input_tokens": 262988, "cached_input_tokens": 242176, "cache_write_input_tokens": 0, "output_tokens": 6852, "reasoning_output_tokens": 3628, "total_tokens": 269840 }, "thread_token_usage": { "input_tokens": 262988, "cached_input_tokens": 242176, "cache_write_input_tokens": 0, "output_tokens": 6852, "reasoning_output_tokens": 3628, "total_tokens": 269840 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-387a398b-705c-4d6a-9bd7-849b57f1815d", "process_id": "83221", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nD='/task/data/'; cand=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); targets=set(map(int,pd.read_parquet(D+'target_users.parquet').uid))\nn=0; org=0; strong=0\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\n d=b.to_pandas(); m=d.uid.isin(targets)&d.item_id.isin(cand); n+=int(m.sum()); org+=int((m&(d.is_organic==1)).sum()); strong+=int((m&(d.is_organic==1)&(d.played_ratio_pct>=80)).sum())\nprint(n,org,strong)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nD='/task/data/'; cand=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); targets=set(map(int,pd.read_parquet(D+'target_users.parquet').uid))\nn=0; org=0; strong=0\nfor b in pq.ParquetFile(D+'train_listens.parquet').iter_batches(columns=['uid','item_id','is_organic','played_ratio_pct'],batch_size=1000000):\n d=b.to_pandas(); m=d.uid.isin(targets)&d.item_id.isin(cand); n+=int(m.sum()); org+=int((m&(d.is_organic==1)).sum()); strong+=int((m&(d.is_organic==1)&(d.played_ratio_pct>=80)).sum())\nprint(n,org,strong)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "31062540 15537198 8661020\n", "stderr": "", "aggregated_output": "31062540 15537198 8661020\n", "exit_code": 0, "duration": { "secs": 8, "nanos": 86529697 }, "formatted_output": "31062540 15537198 8661020\n" } } ## Step 23 · agent · 2026-09-19T00:09:13.580Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:09:14.552Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,30p' /proc/meminfo; nproc\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:09:14.653Z Script completed Wall time 0.0 seconds Output: MemTotal: 15988972 kB MemFree: 2441724 kB MemAvailable: 15007944 kB Buffers: 226108 kB Cached: 12064192 kB SwapCached: 0 kB Active: 502720 kB Inactive: 12059988 kB Active(anon): 298860 kB Inactive(anon): 4 kB Active(file): 203860 kB Inactive(file): 12059984 kB Unevictable: 43852 kB Mlocked: 27220 kB SwapTotal: 0 kB SwapFree: 0 kB Zswap: 0 kB Zswapped: 0 kB Dirty: 6212 kB Writeback: 0 kB AnonPages: 316392 kB Mapped: 433264 kB Shmem: 3036 kB KReclaimable: 638612 kB Slab: 785940 kB SReclaimable: 638612 kB SUnreclaim: 147328 kB KernelStack: 5664 kB PageTables: 7072 kB SecPageTables: 0 kB 8 ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-91a1b213-2498-4994-9d43-d43235e40066", "process_id": "20109", "command": [ "/bin/sh", "-lc", "sed -n '1,30p' /proc/meminfo; nproc" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,30p' /proc/meminfo; nproc" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "MemTotal: 15988972 kB\nMemFree: 2441724 kB\nMemAvailable: 15007944 kB\nBuffers: 226108 kB\nCached: 12064192 kB\nSwapCached: 0 kB\nActive: 502720 kB\nInactive: 12059988 kB\nActive(anon): 298860 kB\nInactive(anon): 4 kB\nActive(file): 203860 kB\nInactive(file): 12059984 kB\nUnevictable: 43852 kB\nMlocked: 27220 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\nZswap: 0 kB\nZswapped: 0 kB\nDirty: 6212 kB\nWriteback: 0 kB\nAnonPages: 316392 kB\nMapped: 433264 kB\nShmem: 3036 kB\nKReclaimable: 638612 kB\nSlab: 785940 kB\nSReclaimable: 638612 kB\nSUnreclaim: 147328 kB\nKernelStack: 5664 kB\nPageTables: 7072 kB\nSecPageTables: 0 kB\n8\n", "stderr": "", "aggregated_output": "MemTotal: 15988972 kB\nMemFree: 2441724 kB\nMemAvailable: 15007944 kB\nBuffers: 226108 kB\nCached: 12064192 kB\nSwapCached: 0 kB\nActive: 502720 kB\nInactive: 12059988 kB\nActive(anon): 298860 kB\nInactive(anon): 4 kB\nActive(file): 203860 kB\nInactive(file): 12059984 kB\nUnevictable: 43852 kB\nMlocked: 27220 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\nZswap: 0 kB\nZswapped: 0 kB\nDirty: 6212 kB\nWriteback: 0 kB\nAnonPages: 316392 kB\nMapped: 433264 kB\nShmem: 3036 kB\nKReclaimable: 638612 kB\nSlab: 785940 kB\nSReclaimable: 638612 kB\nSUnreclaim: 147328 kB\nKernelStack: 5664 kB\nPageTables: 7072 kB\nSecPageTables: 0 kB\n8\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4450 }, "formatted_output": "MemTotal: 15988972 kB\nMemFree: 2441724 kB\nMemAvailable: 15007944 kB\nBuffers: 226108 kB\nCached: 12064192 kB\nSwapCached: 0 kB\nActive: 502720 kB\nInactive: 12059988 kB\nActive(anon): 298860 kB\nInactive(anon): 4 kB\nActive(file): 203860 kB\nInactive(file): 12059984 kB\nUnevictable: 43852 kB\nMlocked: 27220 kB\nSwapTotal: 0 kB\nSwapFree: 0 kB\nZswap: 0 kB\nZswapped: 0 kB\nDirty: 6212 kB\nWriteback: 0 kB\nAnonPages: 316392 kB\nMapped: 433264 kB\nShmem: 3036 kB\nKReclaimable: 638612 kB\nSlab: 785940 kB\nSReclaimable: 638612 kB\nSUnreclaim: 147328 kB\nKernelStack: 5664 kB\nPageTables: 7072 kB\nSecPageTables: 0 kB\n8\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd29f43dc87d18debebb8703bef7e", "usage": { "input_tokens": 28416, "cached_input_tokens": 26624, "cache_write_input_tokens": 0, "output_tokens": 437, "reasoning_output_tokens": 373, "total_tokens": 28853 }, "turn_token_usage": { "input_tokens": 291404, "cached_input_tokens": 268800, "cache_write_input_tokens": 0, "output_tokens": 7289, "reasoning_output_tokens": 4001, "total_tokens": 298693 }, "thread_token_usage": { "input_tokens": 291404, "cached_input_tokens": 268800, "cache_write_input_tokens": 0, "output_tokens": 7289, "reasoning_output_tokens": 4001, "total_tokens": 298693 } } ## Step 24 · agent · 2026-09-19T00:09:19.490Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:09:26.635Z { "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,time\\nD='/task/data/'; C=pd.read_parquet(D+'candidate_catalog.parquet'); ids=C.item_id.to_numpy(); idx=pd.Index(ids)\\ndf=pd.read_parquet(D+'train_listens.parquet',columns=['timestamp','item_id','uid','is_organic','played_ratio_pct'])\\nprint('loaded',df.shape)\\npos=idx.get_indexer(df.item_id); m=pos>=0; p=pos[m]; x=df[m]\\nprint('catalog events',len(x)); end=24786800\\nfeatures={}\\nfor days in [7,14,30,90,1000]:\\n mm=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=80)&(x.is_organic==1)\\n features[f'o{days}']=np.bincount(p[mm],minlength=len(ids))\\n mm2=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=50)\\n features[f'p{days}']=np.bincount(p[mm2],minlength=len(ids))\\n# organic likes\\nlk=pd.read_parquet(D+'train_likes.parquet'); q=idx.get_indexer(lk.item_id); ok=q>=0\\nfor days in [7,30,90,1000]: features[f'l{days}']=np.bincount(q[ok & (lk.timestamp>=end-days*86400)&(lk.is_organic==1)],minlength=len(ids))\\nnp.savez('/app/solution/global_features.npz',items=ids,**features)\\nprint({k:(v.sum(),np.count_nonzero(v)) for k,v in features.items()})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:09:30.918Z Script completed Wall time 4.3 seconds Output: loaded (43484159, 5) catalog events 31249613 {'o7': (np.int64(305188), 25543), 'p7': (np.int64(674579), 29120), 'o14': (np.int64(594362), 27805), 'p14': (np.int64(1321777), 29518), 'o30': (np.int64(1250671), 29007), 'p30': (np.int64(2788956), 29669), 'o90': (np.int64(3468243), 29676), 'p90': (np.int64(7755443), 29849), 'o1000': (np.int64(8699160), 29974), 'p1000': (np.int64(20056329), 30000), 'l7': (np.int64(11210), 6437), 'l30': (np.int64(44223), 14767), 'l90': (np.int64(123476), 22016), 'l1000': (np.int64(301527), 27464)} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd2aaf50c87d18aad6fab3925a50b", "usage": { "input_tokens": 29180, "cached_input_tokens": 28288, "cache_write_input_tokens": 0, "output_tokens": 540, "reasoning_output_tokens": 105, "total_tokens": 29720 }, "turn_token_usage": { "input_tokens": 320584, "cached_input_tokens": 297088, "cache_write_input_tokens": 0, "output_tokens": 7829, "reasoning_output_tokens": 4106, "total_tokens": 328413 }, "thread_token_usage": { "input_tokens": 320584, "cached_input_tokens": 297088, "cache_write_input_tokens": 0, "output_tokens": 7829, "reasoning_output_tokens": 4106, "total_tokens": 328413 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-09a62692-ef08-4558-8585-16214a94960d", "process_id": "14424", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,time\nD='/task/data/'; C=pd.read_parquet(D+'candidate_catalog.parquet'); ids=C.item_id.to_numpy(); idx=pd.Index(ids)\ndf=pd.read_parquet(D+'train_listens.parquet',columns=['timestamp','item_id','uid','is_organic','played_ratio_pct'])\nprint('loaded',df.shape)\npos=idx.get_indexer(df.item_id); m=pos>=0; p=pos[m]; x=df[m]\nprint('catalog events',len(x)); end=24786800\nfeatures={}\nfor days in [7,14,30,90,1000]:\n mm=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=80)&(x.is_organic==1)\n features[f'o{days}']=np.bincount(p[mm],minlength=len(ids))\n mm2=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=50)\n features[f'p{days}']=np.bincount(p[mm2],minlength=len(ids))\n# organic likes\nlk=pd.read_parquet(D+'train_likes.parquet'); q=idx.get_indexer(lk.item_id); ok=q>=0\nfor days in [7,30,90,1000]: features[f'l{days}']=np.bincount(q[ok & (lk.timestamp>=end-days*86400)&(lk.is_organic==1)],minlength=len(ids))\nnp.savez('/app/solution/global_features.npz',items=ids,**features)\nprint({k:(v.sum(),np.count_nonzero(v)) for k,v in features.items()})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,pyarrow.parquet as pq,time\nD='/task/data/'; C=pd.read_parquet(D+'candidate_catalog.parquet'); ids=C.item_id.to_numpy(); idx=pd.Index(ids)\ndf=pd.read_parquet(D+'train_listens.parquet',columns=['timestamp','item_id','uid','is_organic','played_ratio_pct'])\nprint('loaded',df.shape)\npos=idx.get_indexer(df.item_id); m=pos>=0; p=pos[m]; x=df[m]\nprint('catalog events',len(x)); end=24786800\nfeatures={}\nfor days in [7,14,30,90,1000]:\n mm=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=80)&(x.is_organic==1)\n features[f'o{days}']=np.bincount(p[mm],minlength=len(ids))\n mm2=(x.timestamp>=end-days*86400)&(x.played_ratio_pct>=50)\n features[f'p{days}']=np.bincount(p[mm2],minlength=len(ids))\n# organic likes\nlk=pd.read_parquet(D+'train_likes.parquet'); q=idx.get_indexer(lk.item_id); ok=q>=0\nfor days in [7,30,90,1000]: features[f'l{days}']=np.bincount(q[ok & (lk.timestamp>=end-days*86400)&(lk.is_organic==1)],minlength=len(ids))\nnp.savez('/app/solution/global_features.npz',items=ids,**features)\nprint({k:(v.sum(),np.count_nonzero(v)) for k,v in features.items()})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loaded (43484159, 5)\ncatalog events 31249613\n{'o7': (np.int64(305188), 25543), 'p7': (np.int64(674579), 29120), 'o14': (np.int64(594362), 27805), 'p14': (np.int64(1321777), 29518), 'o30': (np.int64(1250671), 29007), 'p30': (np.int64(2788956), 29669), 'o90': (np.int64(3468243), 29676), 'p90': (np.int64(7755443), 29849), 'o1000': (np.int64(8699160), 29974), 'p1000': (np.int64(20056329), 30000), 'l7': (np.int64(11210), 6437), 'l30': (np.int64(44223), 14767), 'l90': (np.int64(123476), 22016), 'l1000': (np.int64(301527), 27464)}\n", "stderr": "", "aggregated_output": "loaded (43484159, 5)\ncatalog events 31249613\n{'o7': (np.int64(305188), 25543), 'p7': (np.int64(674579), 29120), 'o14': (np.int64(594362), 27805), 'p14': (np.int64(1321777), 29518), 'o30': (np.int64(1250671), 29007), 'p30': (np.int64(2788956), 29669), 'o90': (np.int64(3468243), 29676), 'p90': (np.int64(7755443), 29849), 'o1000': (np.int64(8699160), 29974), 'p1000': (np.int64(20056329), 30000), 'l7': (np.int64(11210), 6437), 'l30': (np.int64(44223), 14767), 'l90': (np.int64(123476), 22016), 'l1000': (np.int64(301527), 27464)}\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 115321214 }, "formatted_output": "loaded (43484159, 5)\ncatalog events 31249613\n{'o7': (np.int64(305188), 25543), 'p7': (np.int64(674579), 29120), 'o14': (np.int64(594362), 27805), 'p14': (np.int64(1321777), 29518), 'o30': (np.int64(1250671), 29007), 'p30': (np.int64(2788956), 29669), 'o90': (np.int64(3468243), 29676), 'p90': (np.int64(7755443), 29849), 'o1000': (np.int64(8699160), 29974), 'p1000': (np.int64(20056329), 30000), 'l7': (np.int64(11210), 6437), 'l30': (np.int64(44223), 14767), 'l90': (np.int64(123476), 22016), 'l1000': (np.int64(301527), 27464)}\n" } } ## Step 25 · agent · 2026-09-19T00:09:37.006Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:09:47.269Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,pandas as pd,numpy as np\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nD=Path('/task/data'); e=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(e,targets)\\nz=np.load('/app/solution/global_features.npz'); items=z['items']; bits=np.unpackbits(e.packed,axis=1,count=len(items),bitorder='little').astype(bool); uidrow=e.uid_row\\ncat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\\nvalcnt=np.zeros(len(items)); im={int(x):i for i,x in enumerate(items)}\\nfor x in g.values():\\n for it,gr in x.items(): valcnt[im[it]] += (2**gr-1)\\n\\ndef score(s):\\n vals=[]\\n for u,gold in g.items():\\n ok=np.flatnonzero(bits[uidrow[u]])\\n ix=ok[np.argpartition(s[ok],-10)[-10:]]; ix=ix[np.argsort(-s[ix])]\\n vals.append(ndcg_at_k(items[ix].tolist(),gold,10))\\n return np.mean(vals)\\nscores={\\n'catalog_pop':-cat.popularity_rank.to_numpy(),\\n'p7':np.log1p(z['p7']), 'p30':np.log1p(z['p30']), 'p90':np.log1p(z['p90']),\\n'o7':np.log1p(z['o7']),'o30':np.log1p(z['o30']),'o90':np.log1p(z['o90']),\\n'l30':np.log1p(z['l30']),'oracle_val':valcnt}\\nfor name,s in scores.items(): print(name,score(s))\\n# mix search standardized rank/log\\nbase=np.log1p(z['o30']); trend=np.log1p(z['o7']+1)-np.log1p(z['o30']+1); likes=np.log1p(z['l30']+0.1)\\nfor a in [-1,-.5,0,.25,.5,1,2]:\\n for b in [0,.1,.3,.6,1]:\\n s=base+a*trend+b*likes\\n print('mix',a,b,score(s))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:10:15.785Z Script completed Wall time 28.5 seconds Output: catalog_pop 0.010709951701903768 p7 0.030832861300826478 p30 0.01717508292206492 p90 0.01597249623822988 o7 0.02813072548782609 o30 0.013313102467609922 o90 0.015821738054804402 l30 0.018453126495723112 oracle_val 0.033811488168089016 mix -1 0 0.001654149423301626 mix -1 0.1 0.0034750928671362204 mix -1 0.3 0.009245628141489111 mix -1 0.6 0.014574221281334942 mix -1 1 0.0158288590029866 mix -0.5 0 0.011068419147521437 mix -0.5 0.1 0.01260377087256664 mix -0.5 0.3 0.015917036954254712 mix -0.5 0.6 0.01735252551436102 mix -0.5 1 0.017855324253430588 mix 0 0 0.013313102467609922 mix 0 0.1 0.016852484341666772 mix 0 0.3 0.019326233248638778 mix 0 0.6 0.020181184759534405 mix 0 1 0.01982270923694807 mix 0.25 0 0.016385331842222087 mix 0.25 0.1 0.020626767919303676 mix 0.25 0.3 0.022349171040778586 mix 0.25 0.6 0.022222024694802233 mix 0.25 1 0.021428729935885134 mix 0.5 0 0.02122972068537957 mix 0.5 0.1 0.02683080326547319 mix 0.5 0.3 0.027202758592584326 mix 0.5 0.6 0.026426706052136803 mix 0.5 1 0.025377958269833984 mix 1 0 0.02813072548782609 mix 1 0.1 0.0295675994167947 mix 1 0.3 0.030887871791557407 mix 1 0.6 0.030448481877290077 mix 1 1 0.03067028506591348 mix 2 0 0.02724365316357894 mix 2 0.1 0.02867458194845861 mix 2 0.3 0.029030912714527703 mix 2 0.6 0.030017287731415085 mix 2 1 0.03004937047427186 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd2bb0e6887d182272a43b8ff9243", "usage": { "input_tokens": 29993, "cached_input_tokens": 29056, "cache_write_input_tokens": 0, "output_tokens": 771, "reasoning_output_tokens": 155, "total_tokens": 30764 }, "turn_token_usage": { "input_tokens": 350577, "cached_input_tokens": 326144, "cache_write_input_tokens": 0, "output_tokens": 8600, "reasoning_output_tokens": 4261, "total_tokens": 359177 }, "thread_token_usage": { "input_tokens": 350577, "cached_input_tokens": 326144, "cache_write_input_tokens": 0, "output_tokens": 8600, "reasoning_output_tokens": 4261, "total_tokens": 359177 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c559aa7f-d1a4-49bf-9d35-3f6c25d613b4", "process_id": "32182", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nD=Path('/task/data'); e=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(e,targets)\nz=np.load('/app/solution/global_features.npz'); items=z['items']; bits=np.unpackbits(e.packed,axis=1,count=len(items),bitorder='little').astype(bool); uidrow=e.uid_row\ncat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\nvalcnt=np.zeros(len(items)); im={int(x):i for i,x in enumerate(items)}\nfor x in g.values():\n for it,gr in x.items(): valcnt[im[it]] += (2**gr-1)\n\ndef score(s):\n vals=[]\n for u,gold in g.items():\n ok=np.flatnonzero(bits[uidrow[u]])\n ix=ok[np.argpartition(s[ok],-10)[-10:]]; ix=ix[np.argsort(-s[ix])]\n vals.append(ndcg_at_k(items[ix].tolist(),gold,10))\n return np.mean(vals)\nscores={\n'catalog_pop':-cat.popularity_rank.to_numpy(),\n'p7':np.log1p(z['p7']), 'p30':np.log1p(z['p30']), 'p90':np.log1p(z['p90']),\n'o7':np.log1p(z['o7']),'o30':np.log1p(z['o30']),'o90':np.log1p(z['o90']),\n'l30':np.log1p(z['l30']),'oracle_val':valcnt}\nfor name,s in scores.items(): print(name,score(s))\n# mix search standardized rank/log\nbase=np.log1p(z['o30']); trend=np.log1p(z['o7']+1)-np.log1p(z['o30']+1); likes=np.log1p(z['l30']+0.1)\nfor a in [-1,-.5,0,.25,.5,1,2]:\n for b in [0,.1,.3,.6,1]:\n s=base+a*trend+b*likes\n print('mix',a,b,score(s))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nD=Path('/task/data'); e=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); g=validation_truth(e,targets)\nz=np.load('/app/solution/global_features.npz'); items=z['items']; bits=np.unpackbits(e.packed,axis=1,count=len(items),bitorder='little').astype(bool); uidrow=e.uid_row\ncat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\nvalcnt=np.zeros(len(items)); im={int(x):i for i,x in enumerate(items)}\nfor x in g.values():\n for it,gr in x.items(): valcnt[im[it]] += (2**gr-1)\n\ndef score(s):\n vals=[]\n for u,gold in g.items():\n ok=np.flatnonzero(bits[uidrow[u]])\n ix=ok[np.argpartition(s[ok],-10)[-10:]]; ix=ix[np.argsort(-s[ix])]\n vals.append(ndcg_at_k(items[ix].tolist(),gold,10))\n return np.mean(vals)\nscores={\n'catalog_pop':-cat.popularity_rank.to_numpy(),\n'p7':np.log1p(z['p7']), 'p30':np.log1p(z['p30']), 'p90':np.log1p(z['p90']),\n'o7':np.log1p(z['o7']),'o30':np.log1p(z['o30']),'o90':np.log1p(z['o90']),\n'l30':np.log1p(z['l30']),'oracle_val':valcnt}\nfor name,s in scores.items(): print(name,score(s))\n# mix search standardized rank/log\nbase=np.log1p(z['o30']); trend=np.log1p(z['o7']+1)-np.log1p(z['o30']+1); likes=np.log1p(z['l30']+0.1)\nfor a in [-1,-.5,0,.25,.5,1,2]:\n for b in [0,.1,.3,.6,1]:\n s=base+a*trend+b*likes\n print('mix',a,b,score(s))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "catalog_pop 0.010709951701903768\np7 0.030832861300826478\np30 0.01717508292206492\np90 0.01597249623822988\no7 0.02813072548782609\no30 0.013313102467609922\no90 0.015821738054804402\nl30 0.018453126495723112\noracle_val 0.033811488168089016\nmix -1 0 0.001654149423301626\nmix -1 0.1 0.0034750928671362204\nmix -1 0.3 0.009245628141489111\nmix -1 0.6 0.014574221281334942\nmix -1 1 0.0158288590029866\nmix -0.5 0 0.011068419147521437\nmix -0.5 0.1 0.01260377087256664\nmix -0.5 0.3 0.015917036954254712\nmix -0.5 0.6 0.01735252551436102\nmix -0.5 1 0.017855324253430588\nmix 0 0 0.013313102467609922\nmix 0 0.1 0.016852484341666772\nmix 0 0.3 0.019326233248638778\nmix 0 0.6 0.020181184759534405\nmix 0 1 0.01982270923694807\nmix 0.25 0 0.016385331842222087\nmix 0.25 0.1 0.020626767919303676\nmix 0.25 0.3 0.022349171040778586\nmix 0.25 0.6 0.022222024694802233\nmix 0.25 1 0.021428729935885134\nmix 0.5 0 0.02122972068537957\nmix 0.5 0.1 0.02683080326547319\nmix 0.5 0.3 0.027202758592584326\nmix 0.5 0.6 0.026426706052136803\nmix 0.5 1 0.025377958269833984\nmix 1 0 0.02813072548782609\nmix 1 0.1 0.0295675994167947\nmix 1 0.3 0.030887871791557407\nmix 1 0.6 0.030448481877290077\nmix 1 1 0.03067028506591348\nmix 2 0 0.02724365316357894\nmix 2 0.1 0.02867458194845861\nmix 2 0.3 0.029030912714527703\nmix 2 0.6 0.030017287731415085\nmix 2 1 0.03004937047427186\n", "stderr": "", "aggregated_output": "catalog_pop 0.010709951701903768\np7 0.030832861300826478\np30 0.01717508292206492\np90 0.01597249623822988\no7 0.02813072548782609\no30 0.013313102467609922\no90 0.015821738054804402\nl30 0.018453126495723112\noracle_val 0.033811488168089016\nmix -1 0 0.001654149423301626\nmix -1 0.1 0.0034750928671362204\nmix -1 0.3 0.009245628141489111\nmix -1 0.6 0.014574221281334942\nmix -1 1 0.0158288590029866\nmix -0.5 0 0.011068419147521437\nmix -0.5 0.1 0.01260377087256664\nmix -0.5 0.3 0.015917036954254712\nmix -0.5 0.6 0.01735252551436102\nmix -0.5 1 0.017855324253430588\nmix 0 0 0.013313102467609922\nmix 0 0.1 0.016852484341666772\nmix 0 0.3 0.019326233248638778\nmix 0 0.6 0.020181184759534405\nmix 0 1 0.01982270923694807\nmix 0.25 0 0.016385331842222087\nmix 0.25 0.1 0.020626767919303676\nmix 0.25 0.3 0.022349171040778586\nmix 0.25 0.6 0.022222024694802233\nmix 0.25 1 0.021428729935885134\nmix 0.5 0 0.02122972068537957\nmix 0.5 0.1 0.02683080326547319\nmix 0.5 0.3 0.027202758592584326\nmix 0.5 0.6 0.026426706052136803\nmix 0.5 1 0.025377958269833984\nmix 1 0 0.02813072548782609\nmix 1 0.1 0.0295675994167947\nmix 1 0.3 0.030887871791557407\nmix 1 0.6 0.030448481877290077\nmix 1 1 0.03067028506591348\nmix 2 0 0.02724365316357894\nmix 2 0.1 0.02867458194845861\nmix 2 0.3 0.029030912714527703\nmix 2 0.6 0.030017287731415085\nmix 2 1 0.03004937047427186\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 345422471 }, "formatted_output": "catalog_pop 0.010709951701903768\np7 0.030832861300826478\np30 0.01717508292206492\np90 0.01597249623822988\no7 0.02813072548782609\no30 0.013313102467609922\no90 0.015821738054804402\nl30 0.018453126495723112\noracle_val 0.033811488168089016\nmix -1 0 0.001654149423301626\nmix -1 0.1 0.0034750928671362204\nmix -1 0.3 0.009245628141489111\nmix -1 0.6 0.014574221281334942\nmix -1 1 0.0158288590029866\nmix -0.5 0 0.011068419147521437\nmix -0.5 0.1 0.01260377087256664\nmix -0.5 0.3 0.015917036954254712\nmix -0.5 0.6 0.01735252551436102\nmix -0.5 1 0.017855324253430588\nmix 0 0 0.013313102467609922\nmix 0 0.1 0.016852484341666772\nmix 0 0.3 0.019326233248638778\nmix 0 0.6 0.020181184759534405\nmix 0 1 0.01982270923694807\nmix 0.25 0 0.016385331842222087\nmix 0.25 0.1 0.020626767919303676\nmix 0.25 0.3 0.022349171040778586\nmix 0.25 0.6 0.022222024694802233\nmix 0.25 1 0.021428729935885134\nmix 0.5 0 0.02122972068537957\nmix 0.5 0.1 0.02683080326547319\nmix 0.5 0.3 0.027202758592584326\nmix 0.5 0.6 0.026426706052136803\nmix 0.5 1 0.025377958269833984\nmix 1 0 0.02813072548782609\nmix 1 0.1 0.0295675994167947\nmix 1 0.3 0.030887871791557407\nmix 1 0.6 0.030448481877290077\nmix 1 1 0.03067028506591348\nmix 2 0 0.02724365316357894\nmix 2 0.1 0.02867458194845861\nmix 2 0.3 0.029030912714527703\nmix 2 0.6 0.030017287731415085\nmix 2 1 0.03004937047427186\n" } } ## Step 26 · agent · 2026-09-19T00:10:30.870Z ### Thinking unavailable — no text recorded ## Step 27 · agent · 2026-09-19T00:10:48.622Z ### Thinking unavailable — no text recorded ## Step 28 · agent · 2026-09-19T00:10:55.858Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-09-19T00:11:08.126Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-09-19T00:11:20.047Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-19T00:11:23.357Z ### Thinking unavailable — no text recorded The strongest non-personalized signal is very recent catalog momentum: seven-day positive listens score about 0.031 NDCG@10 versus 0.011 for lifetime popularity. I’m now training the personalized component as a true retrieval model: a history-aggregation user tower and an independent item tower, supervised on several rolling seven-day novelty windows inside training. The final score remains exactly one user–item dot product; trend features will be folded into the tower vectors rather than added outside the model. ## Step 32 · agent · 2026-09-19T00:11:30.466Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:12:28.715Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a scalable history-to-item PyTorch two-tower ranker.\\n+\\n+No user-id parameters or global interaction matrix are used. Training examples\\n+are rolling temporal cuts; the user input is a bounded sequence of prior\\n+positive-listen item IDs and the target is a new organic discovery in the next\\n+week. At inference, each candidate is encoded once and ranking is MIPS.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import random\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+from torch import nn\\n+from torch.nn import functional as F\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_END = 24_786_800\\n+GAP = 1_800\\n+WEEK = 7 * 86_400\\n+MAX_HISTORY = 384\\n+MAX_LABELS = 192\\n+\\n+\\n+def seed_all(seed: int) -> None:\\n+ random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\\n+\\n+\\n+def positions(values: np.ndarray, index: pd.Index) -> np.ndarray:\\n+ return index.get_indexer(values).astype(np.int32)\\n+\\n+\\n+def load_events(include_validation: bool = False):\\n+ cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"]\\n+ listens = pd.read_parquet(DATA / \\\"train_listens.parquet\\\", columns=cols)\\n+ lcols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ likes = pd.read_parquet(DATA / \\\"train_likes.parquet\\\", columns=lcols)\\n+ if include_validation:\\n+ listens = pd.concat([listens, pd.read_parquet(DATA / \\\"val_listens.parquet\\\", columns=cols)], ignore_index=True)\\n+ likes = pd.concat([likes, pd.read_parquet(DATA / \\\"val_likes.parquet\\\", columns=lcols)], ignore_index=True)\\n+ return listens, likes\\n+\\n+\\n+def encode_raw(listens: pd.DataFrame, likes: pd.DataFrame):\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ target = pd.read_parquet(DATA / \\\"target_users.parquet\\\")\\n+ items = catalog.item_id.to_numpy(np.int64)\\n+ uids = target.uid.to_numpy(np.int64)\\n+ ii, ui = pd.Index(items), pd.Index(uids)\\n+ # Narrow arrays make all later rolling-cut operations cheap.\\n+ ev = dict(\\n+ u=positions(listens.uid.to_numpy(), ui),\\n+ i=positions(listens.item_id.to_numpy(), ii),\\n+ t=listens.timestamp.to_numpy(np.int64),\\n+ organic=listens.is_organic.to_numpy(np.uint8),\\n+ ratio=np.minimum(listens.played_ratio_pct.to_numpy(np.int32), 100),\\n+ )\\n+ lk = dict(\\n+ u=positions(likes.uid.to_numpy(), ui),\\n+ i=positions(likes.item_id.to_numpy(), ii),\\n+ t=likes.timestamp.to_numpy(np.int64),\\n+ organic=likes.is_organic.to_numpy(np.uint8),\\n+ )\\n+ return catalog, uids, items, ev, lk\\n+\\n+\\n+def make_context(ev, cutoff: int, n_users: int, n_items: int):\\n+ \\\"\\\"\\\"Return bounded recent unique positive histories and weights.\\\"\\\"\\\"\\n+ m = (ev[\\\"u\\\"] >= 0) & (ev[\\\"i\\\"] >= 0) & (ev[\\\"t\\\"] <= cutoff) & (ev[\\\"ratio\\\"] >= 50)\\n+ d = pd.DataFrame({\\\"u\\\": ev[\\\"u\\\"][m], \\\"i\\\": ev[\\\"i\\\"][m], \\\"t\\\": ev[\\\"t\\\"][m],\\n+ \\\"r\\\": ev[\\\"ratio\\\"][m], \\\"o\\\": ev[\\\"organic\\\"][m]})\\n+ # Input is capped, so memory/request cost does not grow with tenure.\\n+ d.drop_duplicates([\\\"u\\\", \\\"i\\\"], keep=\\\"last\\\", inplace=True)\\n+ d = d.groupby(\\\"u\\\", sort=False, observed=True).tail(MAX_HISTORY)\\n+ hist = np.full((n_users, MAX_HISTORY), n_items, dtype=np.int32)\\n+ weight = np.zeros((n_users, MAX_HISTORY), dtype=np.float32)\\n+ age = np.maximum(0, cutoff - d.t.to_numpy())\\n+ w = np.exp(-age / (90 * 86_400)) * (0.35 + d.r.to_numpy() / 100) * (1 + .25 * d.o.to_numpy())\\n+ for u, g in d.assign(w=w).groupby(\\\"u\\\", sort=False):\\n+ a = g.i.to_numpy(np.int32); b = g.w.to_numpy(np.float32)\\n+ hist[int(u), :len(a)] = a; weight[int(u), :len(a)] = b\\n+ weight /= np.maximum(weight.sum(1, keepdims=True), 1e-6)\\n+ return hist, weight\\n+\\n+\\n+def make_labels(ev, lk, cutoff: int, n_users: int, n_items: int):\\n+ \\\"\\\"\\\"Novel organic strong listens/likes in the post-gap week.\\\"\\\"\\\"\\n+ hm = (ev[\\\"u\\\"] >= 0) & (ev[\\\"i\\\"] >= 0) & (ev[\\\"t\\\"] <= cutoff)\\n+ seen = np.unique(ev[\\\"u\\\"][hm].astype(np.int64) * n_items + ev[\\\"i\\\"][hm])\\n+ lo, hi = cutoff + GAP, cutoff + GAP + WEEK\\n+ pm = ((ev[\\\"u\\\"] >= 0) & (ev[\\\"i\\\"] >= 0) & (ev[\\\"t\\\"] >= lo) & (ev[\\\"t\\\"] < hi)\\n+ & (ev[\\\"organic\\\"] == 1) & (ev[\\\"ratio\\\"] >= 80))\\n+ strong = np.unique(ev[\\\"u\\\"][pm].astype(np.int64) * n_items + ev[\\\"i\\\"][pm])\\n+ strong = strong[~np.isin(strong, seen, assume_unique=True)]\\n+ lm = ((lk[\\\"u\\\"] >= 0) & (lk[\\\"i\\\"] >= 0) & (lk[\\\"t\\\"] >= lo) & (lk[\\\"t\\\"] < hi)\\n+ & (lk[\\\"organic\\\"] == 1))\\n+ liked = np.unique(lk[\\\"u\\\"][lm].astype(np.int64) * n_items + lk[\\\"i\\\"][lm])\\n+ liked = liked[~np.isin(liked, seen, assume_unique=True)]\\n+ # Likes are oversampled to reflect their sevenfold DCG gain.\\n+ codes = np.concatenate([strong, liked, liked, liked, liked])\\n+ order = np.argsort(codes // n_items, kind=\\\"stable\\\"); codes = codes[order]\\n+ labels = np.full((n_users, MAX_LABELS), -1, dtype=np.int32)\\n+ counts = np.zeros(n_users, dtype=np.int32)\\n+ if len(codes):\\n+ us = codes // n_items\\n+ starts = np.r_[0, np.flatnonzero(np.diff(us)) + 1, len(codes)]\\n+ rng = np.random.default_rng(123 + cutoff)\\n+ for a, b in zip(starts[:-1], starts[1:]):\\n+ u = int(us[a]); vals = (codes[a:b] % n_items).astype(np.int32)\\n+ if len(vals) > MAX_LABELS: vals = rng.choice(vals, MAX_LABELS, replace=False)\\n+ labels[u, :len(vals)] = vals; counts[u] = len(vals)\\n+ return labels, counts\\n+\\n+\\n+def recent_features(ev, lk, cutoff: int, n_items: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Cutoff-correct item features available to the offline item tower.\\\"\\\"\\\"\\n+ feats = []\\n+ for days in (7, 30, 90):\\n+ m = ((ev[\\\"i\\\"] >= 0) & (ev[\\\"t\\\"] <= cutoff) & (ev[\\\"t\\\"] >= cutoff-days*86_400)\\n+ & (ev[\\\"ratio\\\"] >= 50))\\n+ feats.append(np.log1p(np.bincount(ev[\\\"i\\\"][m], minlength=n_items)))\\n+ m = ((lk[\\\"i\\\"] >= 0) & (lk[\\\"t\\\"] <= cutoff) & (lk[\\\"t\\\"] >= cutoff-30*86_400)\\n+ & (lk[\\\"organic\\\"] == 1))\\n+ feats.append(np.log1p(np.bincount(lk[\\\"i\\\"][m], minlength=n_items)))\\n+ x = np.stack(feats, 1).astype(np.float32)\\n+ x = (x - x.mean(0)) / np.maximum(x.std(0), 1e-4)\\n+ return x\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\\n+ self.net = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\\n+ nn.Linear(dim * 2, dim))\\n+ nn.init.normal_(self.embedding.weight, std=.03)\\n+\\n+ def forward(self, ids, weights):\\n+ pooled = (self.embedding(ids) * weights[..., None]).sum(1)\\n+ return F.normalize(pooled + self.net(pooled), dim=-1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int, static_features: np.ndarray):\\n+ super().__init__()\\n+ self.embedding = nn.Embedding(n_items, dim)\\n+ self.register_buffer(\\\"static_features\\\", torch.tensor(static_features))\\n+ self.feature_net = nn.Sequential(nn.Linear(static_features.shape[1], dim), nn.GELU(),\\n+ nn.Linear(dim, dim))\\n+ self.net = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\\n+ nn.Linear(dim * 2, dim))\\n+ nn.init.normal_(self.embedding.weight, std=.03)\\n+\\n+ def forward(self, ids):\\n+ x = self.embedding(ids) + self.feature_net(self.static_features[ids])\\n+ return F.normalize(x + self.net(x), dim=-1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items, dim, features):\\n+ super().__init__(); self.user_tower = UserTower(n_items, dim); self.item_tower = ItemTower(n_items, dim, features)\\n+ self.log_temperature = nn.Parameter(torch.tensor(np.log(.12), dtype=torch.float32))\\n+\\n+ def contrastive(self, h, w, target):\\n+ u = self.user_tower(h, w); v = self.item_tower(target)\\n+ logits = u @ v.T / self.log_temperature.exp().clamp(.04, .5)\\n+ # Duplicate targets are additional positives, not false negatives.\\n+ same = target[:, None].eq(target[None, :])\\n+ numerator = torch.logsumexp(logits.masked_fill(~same, -1e9), 1)\\n+ return (torch.logsumexp(logits, 1) - numerator).mean()\\n+\\n+\\n+def static_features(catalog: pd.DataFrame) -> np.ndarray:\\n+ x = np.stack([\\n+ np.log1p(catalog.positive_listens.to_numpy()),\\n+ np.log1p(catalog.organic_positive_listens.to_numpy()),\\n+ -np.log1p(catalog.popularity_rank.to_numpy()),\\n+ ], 1).astype(np.float32)\\n+ return (x-x.mean(0))/np.maximum(x.std(0),1e-5)\\n+\\n+\\n+def train_model(catalog, snapshots, epochs=14, dim=64, seed=42):\\n+ seed_all(seed); n_items=len(catalog)\\n+ model=TwoTower(n_items,dim,static_features(catalog)); model.train()\\n+ opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\\n+ rng=np.random.default_rng(seed); batch=192\\n+ for epoch in range(epochs):\\n+ losses=[]\\n+ for hist,weight,labels,counts in snapshots:\\n+ valid=np.flatnonzero(counts>0); rng.shuffle(valid)\\n+ for st in range(0,len(valid),batch):\\n+ ix=valid[st:st+batch]\\n+ if len(ix)<8: continue\\n+ choice=(rng.random(len(ix))*counts[ix]).astype(np.int32)\\n+ target=labels[ix,choice]\\n+ ht=torch.from_numpy(hist[ix].astype(np.int64)); wt=torch.from_numpy(weight[ix]); tt=torch.from_numpy(target.astype(np.int64))\\n+ opt.zero_grad(set_to_none=True); loss=model.contrastive(ht,wt,tt); loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\\n+ print(f\\\"epoch {epoch+1:02d} loss={np.mean(losses):.4f} temp={model.log_temperature.exp().item():.3f}\\\",flush=True)\\n+ return model\\n+\\n+\\n+@torch.inference_mode()\\n+def latent_scores(model, hist, weight, n_items):\\n+ model.eval(); users=[]\\n+ for s in range(0,len(hist),256):\\n+ users.append(model.user_tower(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\\n+ ids=torch.arange(n_items); item=[]\\n+ for s in range(0,n_items,2048): item.append(model.item_tower(ids[s:s+2048]).numpy())\\n+ return np.concatenate(users),np.concatenate(item)\\n+\\n+\\n+def write_rankings(uids,items,user_vec,item_vec,trend,elig_path,out_path,alpha):\\n+ z=np.load(elig_path); assert np.array_equal(z[\\\"uids\\\"].astype(np.int64),uids) and np.array_equal(z[\\\"item_ids\\\"].astype(np.int64),items)\\n+ packed=z[\\\"packed_eligible\\\"]; rows=[]\\n+ # alpha multiplies a standardized recent-demand feature. Appending it and a\\n+ # constant user coordinate makes the complete score one exact dot product.\\n+ for s in range(0,len(uids),128):\\n+ e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T + alpha*trend[None,:]\\n+ ok=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder=\\\"little\\\").astype(bool); score[~ok]=-np.inf\\n+ ix=np.argpartition(score,-100,axis=1)[:,-100:]\\n+ val=np.take_along_axis(score,ix,axis=1); order=np.argsort(-val,axis=1,kind=\\\"stable\\\"); ix=np.take_along_axis(ix,order,axis=1)\\n+ for r,u in enumerate(uids[s:e]):\\n+ rows.append(pd.DataFrame({\\\"uid\\\":u,\\\"item_id\\\":items[ix[r]],\\\"rank\\\":np.arange(1,101)}))\\n+ pd.concat(rows,ignore_index=True).astype(\\\"int64\\\").to_parquet(out_path,index=False)\\n+\\n+\\n+def main():\\n+ ap=argparse.ArgumentParser(); ap.add_argument(\\\"--stage\\\",choices=[\\\"validation\\\",\\\"final\\\"],required=True)\\n+ ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.22)\\n+ ap.add_argument(\\\"--output\\\",type=Path); ap.add_argument(\\\"--seed\\\",type=int,default=42); a=ap.parse_args()\\n+ OUT.mkdir(exist_ok=True); include=a.stage==\\\"final\\\"; listens,likes=load_events(include); catalog,uids,items,ev,lk=encode_raw(listens,likes)\\n+ # The last eight fully-observable rolling weeks provide leakage-free supervision.\\n+ cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in range(1,9)]\\n+ snaps=[]\\n+ for c in cutoffs:\\n+ h,w=make_context(ev,c,len(uids),len(items)); lab,cnt=make_labels(ev,lk,c,len(uids),len(items)); snaps.append((h,w,lab,cnt)); print('snapshot',c,'users',int((cnt>0).sum()),'weighted labels',int(cnt.sum()),flush=True)\\n+ model=train_model(catalog,snaps,a.epochs,64,a.seed)\\n+ cutoff=int(max(ev['t'])) if include else TRAIN_END\\n+ h,w=make_context(ev,cutoff,len(uids),len(items)); uv,iv=latent_scores(model,h,w,len(items)); feat=recent_features(ev,lk,cutoff,len(items)); trend=feat[:,0]\\n+ out=a.output or (OUT/(\\\"submission.parquet\\\" if include else \\\"validation.parquet\\\"))\\n+ elig=DATA/(\\\"test_eligible_candidates.npz\\\" if include else \\\"validation_eligible_candidates.npz\\\")\\n+ write_rankings(uids,items,uv,iv,trend,elig,out,a.alpha)\\n+ torch.save({\\\"model\\\":model.state_dict(),\\\"dim\\\":64,\\\"alpha\\\":a.alpha,\\\"stage\\\":a.stage},OUT/f\\\"model_{a.stage}.pt\\\")\\n+ print(json.dumps({\\\"output\\\":str(out),\\\"alpha\\\":a.alpha,\\\"cutoff\\\":cutoff}))\\n+\\n+\\n+if __name__==\\\"__main__\\\": main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:12:28.786Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4b216d57-0f37-41d2-8fb4-f243b3fb6db2", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-to-item PyTorch two-tower ranker.\n\nNo user-id parameters or global interaction matrix are used. Training examples\nare rolling temporal cuts; the user input is a bounded sequence of prior\npositive-listen item IDs and the target is a new organic discovery in the next\nweek. At inference, each candidate is encoded once and ranking is MIPS.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nGAP = 1_800\nWEEK = 7 * 86_400\nMAX_HISTORY = 384\nMAX_LABELS = 192\n\n\ndef seed_all(seed: int) -> None:\n random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\n\n\ndef positions(values: np.ndarray, index: pd.Index) -> np.ndarray:\n return index.get_indexer(values).astype(np.int32)\n\n\ndef load_events(include_validation: bool = False):\n cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n listens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=cols)\n lcols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n likes = pd.read_parquet(DATA / \"train_likes.parquet\", columns=lcols)\n if include_validation:\n listens = pd.concat([listens, pd.read_parquet(DATA / \"val_listens.parquet\", columns=cols)], ignore_index=True)\n likes = pd.concat([likes, pd.read_parquet(DATA / \"val_likes.parquet\", columns=lcols)], ignore_index=True)\n return listens, likes\n\n\ndef encode_raw(listens: pd.DataFrame, likes: pd.DataFrame):\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n target = pd.read_parquet(DATA / \"target_users.parquet\")\n items = catalog.item_id.to_numpy(np.int64)\n uids = target.uid.to_numpy(np.int64)\n ii, ui = pd.Index(items), pd.Index(uids)\n # Narrow arrays make all later rolling-cut operations cheap.\n ev = dict(\n u=positions(listens.uid.to_numpy(), ui),\n i=positions(listens.item_id.to_numpy(), ii),\n t=listens.timestamp.to_numpy(np.int64),\n organic=listens.is_organic.to_numpy(np.uint8),\n ratio=np.minimum(listens.played_ratio_pct.to_numpy(np.int32), 100),\n )\n lk = dict(\n u=positions(likes.uid.to_numpy(), ui),\n i=positions(likes.item_id.to_numpy(), ii),\n t=likes.timestamp.to_numpy(np.int64),\n organic=likes.is_organic.to_numpy(np.uint8),\n )\n return catalog, uids, items, ev, lk\n\n\ndef make_context(ev, cutoff: int, n_users: int, n_items: int):\n \"\"\"Return bounded recent unique positive histories and weights.\"\"\"\n m = (ev[\"u\"] >= 0) & (ev[\"i\"] >= 0) & (ev[\"t\"] <= cutoff) & (ev[\"ratio\"] >= 50)\n d = pd.DataFrame({\"u\": ev[\"u\"][m], \"i\": ev[\"i\"][m], \"t\": ev[\"t\"][m],\n \"r\": ev[\"ratio\"][m], \"o\": ev[\"organic\"][m]})\n # Input is capped, so memory/request cost does not grow with tenure.\n d.drop_duplicates([\"u\", \"i\"], keep=\"last\", inplace=True)\n d = d.groupby(\"u\", sort=False, observed=True).tail(MAX_HISTORY)\n hist = np.full((n_users, MAX_HISTORY), n_items, dtype=np.int32)\n weight = np.zeros((n_users, MAX_HISTORY), dtype=np.float32)\n age = np.maximum(0, cutoff - d.t.to_numpy())\n w = np.exp(-age / (90 * 86_400)) * (0.35 + d.r.to_numpy() / 100) * (1 + .25 * d.o.to_numpy())\n for u, g in d.assign(w=w).groupby(\"u\", sort=False):\n a = g.i.to_numpy(np.int32); b = g.w.to_numpy(np.float32)\n hist[int(u), :len(a)] = a; weight[int(u), :len(a)] = b\n weight /= np.maximum(weight.sum(1, keepdims=True), 1e-6)\n return hist, weight\n\n\ndef make_labels(ev, lk, cutoff: int, n_users: int, n_items: int):\n \"\"\"Novel organic strong listens/likes in the post-gap week.\"\"\"\n hm = (ev[\"u\"] >= 0) & (ev[\"i\"] >= 0) & (ev[\"t\"] <= cutoff)\n seen = np.unique(ev[\"u\"][hm].astype(np.int64) * n_items + ev[\"i\"][hm])\n lo, hi = cutoff + GAP, cutoff + GAP + WEEK\n pm = ((ev[\"u\"] >= 0) & (ev[\"i\"] >= 0) & (ev[\"t\"] >= lo) & (ev[\"t\"] < hi)\n & (ev[\"organic\"] == 1) & (ev[\"ratio\"] >= 80))\n strong = np.unique(ev[\"u\"][pm].astype(np.int64) * n_items + ev[\"i\"][pm])\n strong = strong[~np.isin(strong, seen, assume_unique=True)]\n lm = ((lk[\"u\"] >= 0) & (lk[\"i\"] >= 0) & (lk[\"t\"] >= lo) & (lk[\"t\"] < hi)\n & (lk[\"organic\"] == 1))\n liked = np.unique(lk[\"u\"][lm].astype(np.int64) * n_items + lk[\"i\"][lm])\n liked = liked[~np.isin(liked, seen, assume_unique=True)]\n # Likes are oversampled to reflect their sevenfold DCG gain.\n codes = np.concatenate([strong, liked, liked, liked, liked])\n order = np.argsort(codes // n_items, kind=\"stable\"); codes = codes[order]\n labels = np.full((n_users, MAX_LABELS), -1, dtype=np.int32)\n counts = np.zeros(n_users, dtype=np.int32)\n if len(codes):\n us = codes // n_items\n starts = np.r_[0, np.flatnonzero(np.diff(us)) + 1, len(codes)]\n rng = np.random.default_rng(123 + cutoff)\n for a, b in zip(starts[:-1], starts[1:]):\n u = int(us[a]); vals = (codes[a:b] % n_items).astype(np.int32)\n if len(vals) > MAX_LABELS: vals = rng.choice(vals, MAX_LABELS, replace=False)\n labels[u, :len(vals)] = vals; counts[u] = len(vals)\n return labels, counts\n\n\ndef recent_features(ev, lk, cutoff: int, n_items: int) -> np.ndarray:\n \"\"\"Cutoff-correct item features available to the offline item tower.\"\"\"\n feats = []\n for days in (7, 30, 90):\n m = ((ev[\"i\"] >= 0) & (ev[\"t\"] <= cutoff) & (ev[\"t\"] >= cutoff-days*86_400)\n & (ev[\"ratio\"] >= 50))\n feats.append(np.log1p(np.bincount(ev[\"i\"][m], minlength=n_items)))\n m = ((lk[\"i\"] >= 0) & (lk[\"t\"] <= cutoff) & (lk[\"t\"] >= cutoff-30*86_400)\n & (lk[\"organic\"] == 1))\n feats.append(np.log1p(np.bincount(lk[\"i\"][m], minlength=n_items)))\n x = np.stack(feats, 1).astype(np.float32)\n x = (x - x.mean(0)) / np.maximum(x.std(0), 1e-4)\n return x\n\n\nclass UserTower(nn.Module):\n def __init__(self, n_items: int, dim: int):\n super().__init__()\n self.embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n self.net = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\n nn.Linear(dim * 2, dim))\n nn.init.normal_(self.embedding.weight, std=.03)\n\n def forward(self, ids, weights):\n pooled = (self.embedding(ids) * weights[..., None]).sum(1)\n return F.normalize(pooled + self.net(pooled), dim=-1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, n_items: int, dim: int, static_features: np.ndarray):\n super().__init__()\n self.embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\"static_features\", torch.tensor(static_features))\n self.feature_net = nn.Sequential(nn.Linear(static_features.shape[1], dim), nn.GELU(),\n nn.Linear(dim, dim))\n self.net = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim * 2), nn.GELU(),\n nn.Linear(dim * 2, dim))\n nn.init.normal_(self.embedding.weight, std=.03)\n\n def forward(self, ids):\n x = self.embedding(ids) + self.feature_net(self.static_features[ids])\n return F.normalize(x + self.net(x), dim=-1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self, n_items, dim, features):\n super().__init__(); self.user_tower = UserTower(n_items, dim); self.item_tower = ItemTower(n_items, dim, features)\n self.log_temperature = nn.Parameter(torch.tensor(np.log(.12), dtype=torch.float32))\n\n def contrastive(self, h, w, target):\n u = self.user_tower(h, w); v = self.item_tower(target)\n logits = u @ v.T / self.log_temperature.exp().clamp(.04, .5)\n # Duplicate targets are additional positives, not false negatives.\n same = target[:, None].eq(target[None, :])\n numerator = torch.logsumexp(logits.masked_fill(~same, -1e9), 1)\n return (torch.logsumexp(logits, 1) - numerator).mean()\n\n\ndef static_features(catalog: pd.DataFrame) -> np.ndarray:\n x = np.stack([\n np.log1p(catalog.positive_listens.to_numpy()),\n np.log1p(catalog.organic_positive_listens.to_numpy()),\n -np.log1p(catalog.popularity_rank.to_numpy()),\n ], 1).astype(np.float32)\n return (x-x.mean(0))/np.maximum(x.std(0),1e-5)\n\n\ndef train_model(catalog, snapshots, epochs=14, dim=64, seed=42):\n seed_all(seed); n_items=len(catalog)\n model=TwoTower(n_items,dim,static_features(catalog)); model.train()\n opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n rng=np.random.default_rng(seed); batch=192\n for epoch in range(epochs):\n losses=[]\n for hist,weight,labels,counts in snapshots:\n valid=np.flatnonzero(counts>0); rng.shuffle(valid)\n for st in range(0,len(valid),batch):\n ix=valid[st:st+batch]\n if len(ix)<8: continue\n choice=(rng.random(len(ix))*counts[ix]).astype(np.int32)\n target=labels[ix,choice]\n ht=torch.from_numpy(hist[ix].astype(np.int64)); wt=torch.from_numpy(weight[ix]); tt=torch.from_numpy(target.astype(np.int64))\n opt.zero_grad(set_to_none=True); loss=model.contrastive(ht,wt,tt); loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\n print(f\"epoch {epoch+1:02d} loss={np.mean(losses):.4f} temp={model.log_temperature.exp().item():.3f}\",flush=True)\n return model\n\n\n@torch.inference_mode()\ndef latent_scores(model, hist, weight, n_items):\n model.eval(); users=[]\n for s in range(0,len(hist),256):\n users.append(model.user_tower(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\n ids=torch.arange(n_items); item=[]\n for s in range(0,n_items,2048): item.append(model.item_tower(ids[s:s+2048]).numpy())\n return np.concatenate(users),np.concatenate(item)\n\n\ndef write_rankings(uids,items,user_vec,item_vec,trend,elig_path,out_path,alpha):\n z=np.load(elig_path); assert np.array_equal(z[\"uids\"].astype(np.int64),uids) and np.array_equal(z[\"item_ids\"].astype(np.int64),items)\n packed=z[\"packed_eligible\"]; rows=[]\n # alpha multiplies a standardized recent-demand feature. Appending it and a\n # constant user coordinate makes the complete score one exact dot product.\n for s in range(0,len(uids),128):\n e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T + alpha*trend[None,:]\n ok=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder=\"little\").astype(bool); score[~ok]=-np.inf\n ix=np.argpartition(score,-100,axis=1)[:,-100:]\n val=np.take_along_axis(score,ix,axis=1); order=np.argsort(-val,axis=1,kind=\"stable\"); ix=np.take_along_axis(ix,order,axis=1)\n for r,u in enumerate(uids[s:e]):\n rows.append(pd.DataFrame({\"uid\":u,\"item_id\":items[ix[r]],\"rank\":np.arange(1,101)}))\n pd.concat(rows,ignore_index=True).astype(\"int64\").to_parquet(out_path,index=False)\n\n\ndef main():\n ap=argparse.ArgumentParser(); ap.add_argument(\"--stage\",choices=[\"validation\",\"final\"],required=True)\n ap.add_argument(\"--epochs\",type=int,default=14); ap.add_argument(\"--alpha\",type=float,default=.22)\n ap.add_argument(\"--output\",type=Path); ap.add_argument(\"--seed\",type=int,default=42); a=ap.parse_args()\n OUT.mkdir(exist_ok=True); include=a.stage==\"final\"; listens,likes=load_events(include); catalog,uids,items,ev,lk=encode_raw(listens,likes)\n # The last eight fully-observable rolling weeks provide leakage-free supervision.\n cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in range(1,9)]\n snaps=[]\n for c in cutoffs:\n h,w=make_context(ev,c,len(uids),len(items)); lab,cnt=make_labels(ev,lk,c,len(uids),len(items)); snaps.append((h,w,lab,cnt)); print('snapshot',c,'users',int((cnt>0).sum()),'weighted labels',int(cnt.sum()),flush=True)\n model=train_model(catalog,snaps,a.epochs,64,a.seed)\n cutoff=int(max(ev['t'])) if include else TRAIN_END\n h,w=make_context(ev,cutoff,len(uids),len(items)); uv,iv=latent_scores(model,h,w,len(items)); feat=recent_features(ev,lk,cutoff,len(items)); trend=feat[:,0]\n out=a.output or (OUT/(\"submission.parquet\" if include else \"validation.parquet\"))\n elig=DATA/(\"test_eligible_candidates.npz\" if include else \"validation_eligible_candidates.npz\")\n write_rankings(uids,items,uv,iv,trend,elig,out,a.alpha)\n torch.save({\"model\":model.state_dict(),\"dim\":64,\"alpha\":a.alpha,\"stage\":a.stage},OUT/f\"model_{a.stage}.pt\")\n print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"cutoff\":cutoff}))\n\n\nif __name__==\"__main__\": 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": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd2e7f0c887d190ada524fabebd75", "usage": { "input_tokens": 31493, "cached_input_tokens": 29824, "cache_write_input_tokens": 0, "output_tokens": 7282, "reasoning_output_tokens": 2934, "total_tokens": 38775 }, "turn_token_usage": { "input_tokens": 382070, "cached_input_tokens": 355968, "cache_write_input_tokens": 0, "output_tokens": 15882, "reasoning_output_tokens": 7195, "total_tokens": 397952 }, "thread_token_usage": { "input_tokens": 382070, "cached_input_tokens": 355968, "cache_write_input_tokens": 0, "output_tokens": 15882, "reasoning_output_tokens": 7195, "total_tokens": 397952 } } ## Step 33 · agent · 2026-09-19T00:12:30.607Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:12:32.556Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /usr/bin/time -v /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:12:32.723Z Script completed Wall time 0.2 seconds Output: /bin/sh: 1: /usr/bin/time: not found ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd36d0b8887d18d3b0dd3b92e5899", "usage": { "input_tokens": 38799, "cached_input_tokens": 31360, "cache_write_input_tokens": 0, "output_tokens": 141, "reasoning_output_tokens": 21, "total_tokens": 38940 }, "turn_token_usage": { "input_tokens": 420869, "cached_input_tokens": 387328, "cache_write_input_tokens": 0, "output_tokens": 16023, "reasoning_output_tokens": 7216, "total_tokens": 436892 }, "thread_token_usage": { "input_tokens": 420869, "cached_input_tokens": 387328, "cache_write_input_tokens": 0, "output_tokens": 16023, "reasoning_output_tokens": 7216, "total_tokens": 436892 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b3b83186-c6c6-4472-8a80-c59dbeadc104", "process_id": "52823", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /usr/bin/time -v /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /usr/bin/time -v /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: /usr/bin/time: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: /usr/bin/time: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 6900 }, "formatted_output": "/bin/sh: 1: /usr/bin/time: not found\n" } } ## Step 34 · agent · 2026-09-19T00:12:34.118Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:12:35.696Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:13:05.863Z Script completed Wall time 30.2 seconds Output: snapshot 24180200 users 4299 weighted labels 58700 snapshot 23573600 users 4256 weighted labels 56807 snapshot 22967000 users 4104 weighted labels 56648 snapshot 22360400 users 4060 weighted labels 53976 snapshot 21753800 users 4015 weighted labels 53747 snapshot 21147200 users 3860 weighted labels 49983 SESSION_ID=88359 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd370eb6887d195bfc3f69a70013c", "usage": { "input_tokens": 38977, "cached_input_tokens": 38656, "cache_write_input_tokens": 0, "output_tokens": 104, "reasoning_output_tokens": 8, "total_tokens": 39081 }, "turn_token_usage": { "input_tokens": 459846, "cached_input_tokens": 425984, "cache_write_input_tokens": 0, "output_tokens": 16127, "reasoning_output_tokens": 7224, "total_tokens": 475973 }, "thread_token_usage": { "input_tokens": 459846, "cached_input_tokens": 425984, "cache_write_input_tokens": 0, "output_tokens": 16127, "reasoning_output_tokens": 7224, "total_tokens": 475973 } } ## Step 35 · agent · 2026-09-19T00:13:07.429Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:13:08.135Z { "code": "const r = await tools.write_stdin({session_id:88359,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:13:38.145Z Script completed Wall time 30.0 seconds Output: snapshot 20540600 users 3816 weighted labels 50444 snapshot 19934000 users 3768 weighted labels 48530 /app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss)) epoch 01 loss=5.0723 temp=0.123 epoch 02 loss=4.8432 temp=0.122 epoch 03 loss=4.5788 temp=0.119 epoch 04 loss=4.3069 temp=0.109 epoch 05 loss=4.0827 temp=0.098 epoch 06 loss=3.8629 temp=0.086 epoch 07 loss=3.6515 temp=0.077 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd39203f487d18dd9f93cf6541a50", "usage": { "input_tokens": 39199, "cached_input_tokens": 38784, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 39263 }, "turn_token_usage": { "input_tokens": 499045, "cached_input_tokens": 464768, "cache_write_input_tokens": 0, "output_tokens": 16191, "reasoning_output_tokens": 7241, "total_tokens": 515236 }, "thread_token_usage": { "input_tokens": 499045, "cached_input_tokens": 464768, "cache_write_input_tokens": 0, "output_tokens": 16191, "reasoning_output_tokens": 7241, "total_tokens": 515236 } } ## Step 36 · agent · 2026-09-19T00:13:39.644Z ### Thinking unavailable — no text recorded Rolling-cut construction produced roughly 3.8K–4.3K supervised users and about 49K–59K gain-weighted discovery labels per weekly cut. Training is converging cleanly; the contrastive loss has fallen from 5.07 to 3.65 through epoch 7. I’ll score several exact-dot-product blends on validation next, then preserve the best one before final refitting. ### exec · 2026-09-19T00:13:42.041Z { "code": "const r = await tools.write_stdin({session_id:88359,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:13:53.312Z Script completed Wall time 11.3 seconds Output: epoch 08 loss=3.4601 temp=0.070 epoch 09 loss=3.2748 temp=0.065 epoch 10 loss=3.1348 temp=0.060 {"output": "solution/validation_neural.parquet", "alpha": 0.22, "cutoff": 24786800} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd3b2530887d1960cc0af76791780", "usage": { "input_tokens": 39500, "cached_input_tokens": 39040, "cache_write_input_tokens": 0, "output_tokens": 146, "reasoning_output_tokens": 8, "total_tokens": 39646 }, "turn_token_usage": { "input_tokens": 538545, "cached_input_tokens": 503808, "cache_write_input_tokens": 0, "output_tokens": 16337, "reasoning_output_tokens": 7249, "total_tokens": 554882 }, "thread_token_usage": { "input_tokens": 538545, "cached_input_tokens": 503808, "cache_write_input_tokens": 0, "output_tokens": 16337, "reasoning_output_tokens": 7249, "total_tokens": 554882 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5599927c-76a9-4c3b-9587-7455fecb726c", "process_id": "88359", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 10 --alpha 0.22 --output solution/validation_neural.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\n{\"output\": \"solution/validation_neural.parquet\", \"alpha\": 0.22, \"cutoff\": 24786800}\n", "stderr": "", "aggregated_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\n{\"output\": \"solution/validation_neural.parquet\", \"alpha\": 0.22, \"cutoff\": 24786800}\n", "exit_code": 0, "duration": { "secs": 77, "nanos": 449044667 }, "formatted_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\n{\"output\": \"solution/validation_neural.parquet\", \"alpha\": 0.22, \"cutoff\": 24786800}\n" } } ## Step 37 · agent · 2026-09-19T00:13:54.558Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:13:55.707Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_neural.parquet && ls -lh solution\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:13:59.132Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_neural.parquet", "scores": { "quality_ndcg@10": 0.036515, "quality_users": 4263 } } total 22M drwxr-xr-x 2 agent agent 4.0K Sep 19 00:12 __pycache__ -rw-r--r-- 1 agent agent 1.5K Sep 19 00:06 baseline.py -rw-r--r-- 1 agent agent 3.4M Sep 19 00:09 global_features.npz -rw-r--r-- 1 agent agent 16M Sep 19 00:13 model_validation.pt -rw-r--r-- 1 agent agent 719K Sep 19 00:06 submission.parquet -rw-r--r-- 1 agent agent 13K Sep 19 00:12 train_ranker.py -rw-r--r-- 1 agent agent 1.4M Sep 19 00:13 validation_neural.parquet -rw-r--r-- 1 agent agent 712K Sep 19 00:06 validation_pop.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd3c179a887d18a9158d57aebd228", "usage": { "input_tokens": 39743, "cached_input_tokens": 39296, "cache_write_input_tokens": 0, "output_tokens": 79, "reasoning_output_tokens": 6, "total_tokens": 39822 }, "turn_token_usage": { "input_tokens": 578288, "cached_input_tokens": 543104, "cache_write_input_tokens": 0, "output_tokens": 16416, "reasoning_output_tokens": 7255, "total_tokens": 594704 }, "thread_token_usage": { "input_tokens": 578288, "cached_input_tokens": 543104, "cache_write_input_tokens": 0, "output_tokens": 16416, "reasoning_output_tokens": 7255, "total_tokens": 594704 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a3603f58-f975-4741-9d85-98a47a255a12", "process_id": "40124", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_neural.parquet && ls -lh solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_neural.parquet && ls -lh solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_neural.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036515,\n \"quality_users\": 4263\n }\n}\ntotal 22M\ndrwxr-xr-x 2 agent agent 4.0K Sep 19 00:12 __pycache__\n-rw-r--r-- 1 agent agent 1.5K Sep 19 00:06 baseline.py\n-rw-r--r-- 1 agent agent 3.4M Sep 19 00:09 global_features.npz\n-rw-r--r-- 1 agent agent 16M Sep 19 00:13 model_validation.pt\n-rw-r--r-- 1 agent agent 719K Sep 19 00:06 submission.parquet\n-rw-r--r-- 1 agent agent 13K Sep 19 00:12 train_ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 19 00:13 validation_neural.parquet\n-rw-r--r-- 1 agent agent 712K Sep 19 00:06 validation_pop.parquet\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_neural.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036515,\n \"quality_users\": 4263\n }\n}\ntotal 22M\ndrwxr-xr-x 2 agent agent 4.0K Sep 19 00:12 __pycache__\n-rw-r--r-- 1 agent agent 1.5K Sep 19 00:06 baseline.py\n-rw-r--r-- 1 agent agent 3.4M Sep 19 00:09 global_features.npz\n-rw-r--r-- 1 agent agent 16M Sep 19 00:13 model_validation.pt\n-rw-r--r-- 1 agent agent 719K Sep 19 00:06 submission.parquet\n-rw-r--r-- 1 agent agent 13K Sep 19 00:12 train_ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 19 00:13 validation_neural.parquet\n-rw-r--r-- 1 agent agent 712K Sep 19 00:06 validation_pop.parquet\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 257540958 }, "formatted_output": "{\n \"model\": \"solution/validation_neural.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036515,\n \"quality_users\": 4263\n }\n}\ntotal 22M\ndrwxr-xr-x 2 agent agent 4.0K Sep 19 00:12 __pycache__\n-rw-r--r-- 1 agent agent 1.5K Sep 19 00:06 baseline.py\n-rw-r--r-- 1 agent agent 3.4M Sep 19 00:09 global_features.npz\n-rw-r--r-- 1 agent agent 16M Sep 19 00:13 model_validation.pt\n-rw-r--r-- 1 agent agent 719K Sep 19 00:06 submission.parquet\n-rw-r--r-- 1 agent agent 13K Sep 19 00:12 train_ranker.py\n-rw-r--r-- 1 agent agent 1.4M Sep 19 00:13 validation_neural.parquet\n-rw-r--r-- 1 agent agent 712K Sep 19 00:06 validation_pop.parquet\n" } } ## Step 38 · agent · 2026-09-19T00:14:07.813Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:14:15.893Z { "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\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nli,lkdf=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,lkdf); del li,lkdf\\nm=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/model_validation.pt',weights_only=True)['model']); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); uv,iv=tr.latent_scores(m,h,w,len(items)); feat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); trend=feat[:,0]\\nbase=uv@iv.T\\nE=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool)\\ntargets=set(map(int,uids)); gold=validation_truth(E,targets); im={int(x):i for i,x in enumerate(items)}\\nfor alpha in [0,.025,.05,.075,.1,.15,.22,.3,.4,.6,.9,1.2]:\\n vals=[]\\n for u,g in gold.items():\\n r=E.uid_row[u]; s=base[r]+alpha*trend; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]\\n vals.append(ndcg_at_k(items[ix].tolist(),g,10))\\n print(alpha,np.mean(vals),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:14:33.873Z Script completed Wall time 18.0 seconds Output: 0 0.0024440035833571505 0.025 0.007042281021289865 0.05 0.014649563459592542 0.075 0.021959228298268133 0.1 0.026129268846588023 0.15 0.03222319293680659 0.22 0.03651543477327172 0.3 0.036940939435460576 0.4 0.03633043484475655 0.6 0.035367005726579574 0.9 0.03462257458469235 1.2 0.034138394904330883 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd3c751c487d1a06d28762a8434e7", "usage": { "input_tokens": 40101, "cached_input_tokens": 39552, "cache_write_input_tokens": 0, "output_tokens": 745, "reasoning_output_tokens": 277, "total_tokens": 40846 }, "turn_token_usage": { "input_tokens": 618389, "cached_input_tokens": 582656, "cache_write_input_tokens": 0, "output_tokens": 17161, "reasoning_output_tokens": 7532, "total_tokens": 635550 }, "thread_token_usage": { "input_tokens": 618389, "cached_input_tokens": 582656, "cache_write_input_tokens": 0, "output_tokens": 17161, "reasoning_output_tokens": 7532, "total_tokens": 635550 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-06a1f297-1d0a-45c9-8399-e37d36ef19de", "process_id": "53689", "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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,lkdf=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,lkdf); del li,lkdf\nm=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/model_validation.pt',weights_only=True)['model']); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); uv,iv=tr.latent_scores(m,h,w,len(items)); feat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); trend=feat[:,0]\nbase=uv@iv.T\nE=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool)\ntargets=set(map(int,uids)); gold=validation_truth(E,targets); im={int(x):i for i,x in enumerate(items)}\nfor alpha in [0,.025,.05,.075,.1,.15,.22,.3,.4,.6,.9,1.2]:\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+alpha*trend; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]\n vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n print(alpha,np.mean(vals),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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,lkdf=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,lkdf); del li,lkdf\nm=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/model_validation.pt',weights_only=True)['model']); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); uv,iv=tr.latent_scores(m,h,w,len(items)); feat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); trend=feat[:,0]\nbase=uv@iv.T\nE=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool)\ntargets=set(map(int,uids)); gold=validation_truth(E,targets); im={int(x):i for i,x in enumerate(items)}\nfor alpha in [0,.025,.05,.075,.1,.15,.22,.3,.4,.6,.9,1.2]:\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+alpha*trend; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]\n vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n print(alpha,np.mean(vals),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 0.0024440035833571505\n0.025 0.007042281021289865\n0.05 0.014649563459592542\n0.075 0.021959228298268133\n0.1 0.026129268846588023\n0.15 0.03222319293680659\n0.22 0.03651543477327172\n0.3 0.036940939435460576\n0.4 0.03633043484475655\n0.6 0.035367005726579574\n0.9 0.03462257458469235\n1.2 0.034138394904330883\n", "stderr": "", "aggregated_output": "0 0.0024440035833571505\n0.025 0.007042281021289865\n0.05 0.014649563459592542\n0.075 0.021959228298268133\n0.1 0.026129268846588023\n0.15 0.03222319293680659\n0.22 0.03651543477327172\n0.3 0.036940939435460576\n0.4 0.03633043484475655\n0.6 0.035367005726579574\n0.9 0.03462257458469235\n1.2 0.034138394904330883\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 809392429 }, "formatted_output": "0 0.0024440035833571505\n0.025 0.007042281021289865\n0.05 0.014649563459592542\n0.075 0.021959228298268133\n0.1 0.026129268846588023\n0.15 0.03222319293680659\n0.22 0.03651543477327172\n0.3 0.036940939435460576\n0.4 0.03633043484475655\n0.6 0.035367005726579574\n0.9 0.03462257458469235\n1.2 0.034138394904330883\n" } } ## Step 39 · agent · 2026-09-19T00:14:45.992Z ### Thinking unavailable — no text recorded ## Step 40 · agent · 2026-09-19T00:14:49.150Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:14:50.139Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 77 --output solution/validation_seed77.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:15:20.306Z Script completed Wall time 30.2 seconds Output: snapshot 24180200 users 4299 weighted labels 58700 snapshot 23573600 users 4256 weighted labels 56807 snapshot 22967000 users 4104 weighted labels 56648 snapshot 22360400 users 4060 weighted labels 53976 snapshot 21753800 users 4015 weighted labels 53747 snapshot 21147200 users 3860 weighted labels 49983 SESSION_ID=7938 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd3ea03a887d193ddbe76a1058703", "usage": { "input_tokens": 41024, "cached_input_tokens": 39936, "cache_write_input_tokens": 0, "output_tokens": 753, "reasoning_output_tokens": 653, "total_tokens": 41777 }, "turn_token_usage": { "input_tokens": 659413, "cached_input_tokens": 622592, "cache_write_input_tokens": 0, "output_tokens": 17914, "reasoning_output_tokens": 8185, "total_tokens": 677327 }, "thread_token_usage": { "input_tokens": 659413, "cached_input_tokens": 622592, "cache_write_input_tokens": 0, "output_tokens": 17914, "reasoning_output_tokens": 8185, "total_tokens": 677327 } } ## Step 41 · agent · 2026-09-19T00:15:21.648Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:15:22.345Z { "code": "const r = await tools.write_stdin({session_id:7938,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:15:52.354Z Script completed Wall time 30.0 seconds Output: snapshot 20540600 users 3816 weighted labels 50444 snapshot 19934000 users 3768 weighted labels 48530 /app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss)) epoch 01 loss=5.0703 temp=0.123 epoch 02 loss=4.8443 temp=0.122 epoch 03 loss=4.5666 temp=0.117 epoch 04 loss=4.2968 temp=0.107 epoch 05 loss=4.0752 temp=0.096 epoch 06 loss=3.8351 temp=0.085 epoch 07 loss=3.6395 temp=0.077 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd418764487d194efbb7be81683d0", "usage": { "input_tokens": 41895, "cached_input_tokens": 40832, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 41948 }, "turn_token_usage": { "input_tokens": 701308, "cached_input_tokens": 663424, "cache_write_input_tokens": 0, "output_tokens": 17967, "reasoning_output_tokens": 8191, "total_tokens": 719275 }, "thread_token_usage": { "input_tokens": 701308, "cached_input_tokens": 663424, "cache_write_input_tokens": 0, "output_tokens": 17967, "reasoning_output_tokens": 8191, "total_tokens": 719275 } } ## Step 42 · agent · 2026-09-19T00:15:53.460Z ### Thinking unavailable — no text recorded The first model improves public NDCG@10 to 0.0365, and the best trend weight is near 0.30 (about 0.0369 in direct scoring). The learned component is behaving as a personalized likelihood-ratio correction to global momentum, which is what the contrastive negative distribution should produce. I’m checking a second initialization for ensemble stability; concatenating tower outputs preserves the exact single-dot-product serving contract. ### exec · 2026-09-19T00:15:55.985Z { "code": "const r = await tools.write_stdin({session_id:7938,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:16:20.872Z Script completed Wall time 24.9 seconds Output: epoch 08 loss=3.4501 temp=0.070 epoch 09 loss=3.2922 temp=0.065 epoch 10 loss=3.1674 temp=0.061 epoch 11 loss=3.0250 temp=0.057 epoch 12 loss=2.9149 temp=0.054 epoch 13 loss=2.8040 temp=0.052 epoch 14 loss=2.7042 temp=0.049 {"output": "solution/validation_seed77.parquet", "alpha": 0.3, "cutoff": 24786800} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd43883c887d1bbb2761bdb53dc14", "usage": { "input_tokens": 42185, "cached_input_tokens": 41728, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 6, "total_tokens": 42334 }, "turn_token_usage": { "input_tokens": 743493, "cached_input_tokens": 705152, "cache_write_input_tokens": 0, "output_tokens": 18116, "reasoning_output_tokens": 8197, "total_tokens": 761609 }, "thread_token_usage": { "input_tokens": 743493, "cached_input_tokens": 705152, "cache_write_input_tokens": 0, "output_tokens": 18116, "reasoning_output_tokens": 8197, "total_tokens": 761609 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-83ed0e4b-0ec4-490a-8dda-e34c087a5fb3", "process_id": "7938", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 77 --output solution/validation_seed77.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 77 --output solution/validation_seed77.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0703 temp=0.123\nepoch 02 loss=4.8443 temp=0.122\nepoch 03 loss=4.5666 temp=0.117\nepoch 04 loss=4.2968 temp=0.107\nepoch 05 loss=4.0752 temp=0.096\nepoch 06 loss=3.8351 temp=0.085\nepoch 07 loss=3.6395 temp=0.077\nepoch 08 loss=3.4501 temp=0.070\nepoch 09 loss=3.2922 temp=0.065\nepoch 10 loss=3.1674 temp=0.061\nepoch 11 loss=3.0250 temp=0.057\nepoch 12 loss=2.9149 temp=0.054\nepoch 13 loss=2.8040 temp=0.052\nepoch 14 loss=2.7042 temp=0.049\n{\"output\": \"solution/validation_seed77.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n", "stderr": "", "aggregated_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0703 temp=0.123\nepoch 02 loss=4.8443 temp=0.122\nepoch 03 loss=4.5666 temp=0.117\nepoch 04 loss=4.2968 temp=0.107\nepoch 05 loss=4.0752 temp=0.096\nepoch 06 loss=3.8351 temp=0.085\nepoch 07 loss=3.6395 temp=0.077\nepoch 08 loss=3.4501 temp=0.070\nepoch 09 loss=3.2922 temp=0.065\nepoch 10 loss=3.1674 temp=0.061\nepoch 11 loss=3.0250 temp=0.057\nepoch 12 loss=2.9149 temp=0.054\nepoch 13 loss=2.8040 temp=0.052\nepoch 14 loss=2.7042 temp=0.049\n{\"output\": \"solution/validation_seed77.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n", "exit_code": 0, "duration": { "secs": 90, "nanos": 559363459 }, "formatted_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\n/app/solution/train_ranker.py:204: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\nepoch 01 loss=5.0703 temp=0.123\nepoch 02 loss=4.8443 temp=0.122\nepoch 03 loss=4.5666 temp=0.117\nepoch 04 loss=4.2968 temp=0.107\nepoch 05 loss=4.0752 temp=0.096\nepoch 06 loss=3.8351 temp=0.085\nepoch 07 loss=3.6395 temp=0.077\nepoch 08 loss=3.4501 temp=0.070\nepoch 09 loss=3.2922 temp=0.065\nepoch 10 loss=3.1674 temp=0.061\nepoch 11 loss=3.0250 temp=0.057\nepoch 12 loss=2.9149 temp=0.054\nepoch 13 loss=2.8040 temp=0.052\nepoch 14 loss=2.7042 temp=0.049\n{\"output\": \"solution/validation_seed77.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n" } } ## Step 43 · agent · 2026-09-19T00:16:23.266Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:16:26.043Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed77.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:16:29.411Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_seed77.parquet", "scores": { "quality_ndcg@10": 0.036866, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd455052c87d18c06d412d16251f7", "usage": { "input_tokens": 42491, "cached_input_tokens": 41984, "cache_write_input_tokens": 0, "output_tokens": 116, "reasoning_output_tokens": 48, "total_tokens": 42607 }, "turn_token_usage": { "input_tokens": 785984, "cached_input_tokens": 747136, "cache_write_input_tokens": 0, "output_tokens": 18232, "reasoning_output_tokens": 8245, "total_tokens": 804216 }, "thread_token_usage": { "input_tokens": 785984, "cached_input_tokens": 747136, "cache_write_input_tokens": 0, "output_tokens": 18232, "reasoning_output_tokens": 8245, "total_tokens": 804216 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-da971f51-e0ac-44f7-bc8b-d4756d3c026c", "process_id": "5169", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed77.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed77.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_seed77.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036866,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_seed77.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036866,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 196707758 }, "formatted_output": "{\n \"model\": \"solution/validation_seed77.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036866,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 44 · agent · 2026-09-19T00:16:33.294Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:16:36.090Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(loss.detach().item())\\n@@\\n- torch.save({\\\"model\\\":model.state_dict(),\\\"dim\\\":64,\\\"alpha\\\":a.alpha,\\\"stage\\\":a.stage},OUT/f\\\"model_{a.stage}.pt\\\")\\n+ torch.save({\\\"model\\\":model.state_dict(),\\\"dim\\\":64,\\\"alpha\\\":a.alpha,\\\"stage\\\":a.stage},OUT/f\\\"model_{a.stage}_seed{a.seed}.pt\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:16:36.143Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-8163df24-80af-4659-985d-b10ec0459f0c", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -203,3 +203,3 @@\n opt.zero_grad(set_to_none=True); loss=model.contrastive(ht,wt,tt); loss.backward()\n- torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(float(loss))\n+ torch.nn.utils.clip_grad_norm_(model.parameters(),5); opt.step(); losses.append(loss.detach().item())\n print(f\"epoch {epoch+1:02d} loss={np.mean(losses):.4f} temp={model.log_temperature.exp().item():.3f}\",flush=True)\n@@ -249,3 +249,3 @@\n write_rankings(uids,items,uv,iv,trend,elig,out,a.alpha)\n- torch.save({\"model\":model.state_dict(),\"dim\":64,\"alpha\":a.alpha,\"stage\":a.stage},OUT/f\"model_{a.stage}.pt\")\n+ torch.save({\"model\":model.state_dict(),\"dim\":64,\"alpha\":a.alpha,\"stage\":a.stage},OUT/f\"model_{a.stage}_seed{a.seed}.pt\")\n print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"cutoff\":cutoff}))\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd45d91f087d1b246621b7af3d52d", "usage": { "input_tokens": 42675, "cached_input_tokens": 42368, "cache_write_input_tokens": 0, "output_tokens": 320, "reasoning_output_tokens": 137, "total_tokens": 42995 }, "turn_token_usage": { "input_tokens": 828659, "cached_input_tokens": 789504, "cache_write_input_tokens": 0, "output_tokens": 18552, "reasoning_output_tokens": 8382, "total_tokens": 847211 }, "thread_token_usage": { "input_tokens": 828659, "cached_input_tokens": 789504, "cache_write_input_tokens": 0, "output_tokens": 18552, "reasoning_output_tokens": 8382, "total_tokens": 847211 } } ## Step 45 · agent · 2026-09-19T00:16:39.233Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:16:41.140Z { "code": "const r = await tools.exec_command({cmd:\"cp solution/model_validation.pt solution/model_validation_seed77.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 42 --output solution/validation_seed42_e14.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:17:11.307Z Script completed Wall time 30.2 seconds Output: snapshot 24180200 users 4299 weighted labels 58700 snapshot 23573600 users 4256 weighted labels 56807 snapshot 22967000 users 4104 weighted labels 56648 snapshot 22360400 users 4060 weighted labels 53976 snapshot 21753800 users 4015 weighted labels 53747 snapshot 21147200 users 3860 weighted labels 49983 SESSION_ID=57566 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd464654887d1a1e48c7ee0ce7f74", "usage": { "input_tokens": 43019, "cached_input_tokens": 42496, "cache_write_input_tokens": 0, "output_tokens": 145, "reasoning_output_tokens": 31, "total_tokens": 43164 }, "turn_token_usage": { "input_tokens": 871678, "cached_input_tokens": 832000, "cache_write_input_tokens": 0, "output_tokens": 18697, "reasoning_output_tokens": 8413, "total_tokens": 890375 }, "thread_token_usage": { "input_tokens": 871678, "cached_input_tokens": 832000, "cache_write_input_tokens": 0, "output_tokens": 18697, "reasoning_output_tokens": 8413, "total_tokens": 890375 } } ## Step 46 · agent · 2026-09-19T00:17:13.167Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:17:13.851Z { "code": "const r = await tools.write_stdin({session_id:57566,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:17:43.859Z Script completed Wall time 30.0 seconds Output: snapshot 20540600 users 3816 weighted labels 50444 snapshot 19934000 users 3768 weighted labels 48530 epoch 01 loss=5.0723 temp=0.123 epoch 02 loss=4.8432 temp=0.122 epoch 03 loss=4.5788 temp=0.119 epoch 04 loss=4.3069 temp=0.109 epoch 05 loss=4.0827 temp=0.098 epoch 06 loss=3.8629 temp=0.086 epoch 07 loss=3.6515 temp=0.077 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd4877df087d198bbfc57d547d2d9", "usage": { "input_tokens": 43282, "cached_input_tokens": 42880, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 43335 }, "turn_token_usage": { "input_tokens": 914960, "cached_input_tokens": 874880, "cache_write_input_tokens": 0, "output_tokens": 18750, "reasoning_output_tokens": 8419, "total_tokens": 933710 }, "thread_token_usage": { "input_tokens": 914960, "cached_input_tokens": 874880, "cache_write_input_tokens": 0, "output_tokens": 18750, "reasoning_output_tokens": 8419, "total_tokens": 933710 } } ## Step 47 · agent · 2026-09-19T00:17:45.851Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:17:46.538Z { "code": "const r = await tools.write_stdin({session_id:57566,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:18:11.225Z Script completed Wall time 24.7 seconds Output: epoch 08 loss=3.4601 temp=0.070 epoch 09 loss=3.2748 temp=0.065 epoch 10 loss=3.1348 temp=0.060 epoch 11 loss=2.9911 temp=0.057 epoch 12 loss=2.8738 temp=0.054 epoch 13 loss=2.7489 temp=0.051 epoch 14 loss=2.6559 temp=0.049 {"output": "solution/validation_seed42_e14.parquet", "alpha": 0.3, "cutoff": 24786800} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd4a8096087d1956be5ba939fc76c", "usage": { "input_tokens": 43493, "cached_input_tokens": 43136, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 43546 }, "turn_token_usage": { "input_tokens": 958453, "cached_input_tokens": 918016, "cache_write_input_tokens": 0, "output_tokens": 18803, "reasoning_output_tokens": 8425, "total_tokens": 977256 }, "thread_token_usage": { "input_tokens": 958453, "cached_input_tokens": 918016, "cache_write_input_tokens": 0, "output_tokens": 18803, "reasoning_output_tokens": 8425, "total_tokens": 977256 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-64f33399-3186-46d0-bd67-b5634ff0e4ff", "process_id": "57566", "command": [ "/bin/sh", "-lc", "cp solution/model_validation.pt solution/model_validation_seed77.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 42 --output solution/validation_seed42_e14.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp solution/model_validation.pt solution/model_validation_seed77.pt && /opt/task-venv/bin/python solution/train_ranker.py --stage validation --epochs 14 --alpha 0.30 --seed 42 --output solution/validation_seed42_e14.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\nepoch 11 loss=2.9911 temp=0.057\nepoch 12 loss=2.8738 temp=0.054\nepoch 13 loss=2.7489 temp=0.051\nepoch 14 loss=2.6559 temp=0.049\n{\"output\": \"solution/validation_seed42_e14.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n", "stderr": "", "aggregated_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\nepoch 11 loss=2.9911 temp=0.057\nepoch 12 loss=2.8738 temp=0.054\nepoch 13 loss=2.7489 temp=0.051\nepoch 14 loss=2.6559 temp=0.049\n{\"output\": \"solution/validation_seed42_e14.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n", "exit_code": 0, "duration": { "secs": 89, "nanos": 917376931 }, "formatted_output": "snapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\nsnapshot 19934000 users 3768 weighted labels 48530\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8432 temp=0.122\nepoch 03 loss=4.5788 temp=0.119\nepoch 04 loss=4.3069 temp=0.109\nepoch 05 loss=4.0827 temp=0.098\nepoch 06 loss=3.8629 temp=0.086\nepoch 07 loss=3.6515 temp=0.077\nepoch 08 loss=3.4601 temp=0.070\nepoch 09 loss=3.2748 temp=0.065\nepoch 10 loss=3.1348 temp=0.060\nepoch 11 loss=2.9911 temp=0.057\nepoch 12 loss=2.8738 temp=0.054\nepoch 13 loss=2.7489 temp=0.051\nepoch 14 loss=2.6559 temp=0.049\n{\"output\": \"solution/validation_seed42_e14.parquet\", \"alpha\": 0.3, \"cutoff\": 24786800}\n" } } ## Step 48 · agent · 2026-09-19T00:18:15.354Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:18:15.355Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed42_e14.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:18:18.800Z Script completed Wall time 3.4 seconds Output: { "model": "solution/validation_seed42_e14.parquet", "scores": { "quality_ndcg@10": 0.035972, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd4c366c087d1a4c14a7f71ab20ae", "usage": { "input_tokens": 43705, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 76, "reasoning_output_tokens": 6, "total_tokens": 43781 }, "turn_token_usage": { "input_tokens": 1002158, "cached_input_tokens": 961280, "cache_write_input_tokens": 0, "output_tokens": 18879, "reasoning_output_tokens": 8431, "total_tokens": 1021037 }, "thread_token_usage": { "input_tokens": 1002158, "cached_input_tokens": 961280, "cache_write_input_tokens": 0, "output_tokens": 18879, "reasoning_output_tokens": 8431, "total_tokens": 1021037 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-647afe89-fcc9-4c3e-99c6-d308c5fe8f14", "process_id": "18186", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed42_e14.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py solution/validation_seed42_e14.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation_seed42_e14.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035972,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation_seed42_e14.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035972,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 277285337 }, "formatted_output": "{\n \"model\": \"solution/validation_seed42_e14.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035972,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 49 · agent · 2026-09-19T00:18:22.423Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:18:32.645Z { "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\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); bases=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); uv,iv=tr.latent_scores(m,h,w,len(items)); bases.append(uv@iv.T); del m,uv,iv\\nfeat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\\ndef score(base,a,b=0,c=0):\\n vals=[]\\n for u,g in gold.items():\\n r=E.uid_row[u]; s=base[r]+a*feat[:,0]+b*feat[:,3]+c*feat[:,1]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\\n return np.mean(vals)\\nfor name,base in [('s42',bases[0]),('s77',bases[1]),('ens',(bases[0]+bases[1])/2)]:\\n for a in [.2,.25,.3,.35,.4]: print(name,a,score(base,a),flush=True)\\nbase=(bases[0]+bases[1])/2\\nfor a in [.25,.3,.35,.4]:\\n for b in [.01,.025,.05,.075,.1]: print('mix',a,b,score(base,a,b),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:19:02.813Z Script completed Wall time 30.2 seconds Output: s42 0.2 0.03477152537688297 s42 0.25 0.03617847665094481 s42 0.3 0.03597161743774999 s42 0.35 0.035937461035502175 s42 0.4 0.03568756088525914 s77 0.2 0.03553838916822313 s77 0.25 0.036331094570765124 s77 0.3 0.03686640554439258 s77 0.35 0.03687922805083699 s77 0.4 0.03695562142955238 ens 0.2 0.03739625019959236 ens 0.25 0.0381375270590552 ens 0.3 0.03761984093706692 ens 0.35 0.03721123626785236 ens 0.4 0.036688281940533564 mix 0.25 0.01 0.03920772868565724 mix 0.25 0.025 0.039751058740445384 mix 0.25 0.05 0.03985922511375615 mix 0.25 0.075 0.039411291066399914 mix 0.25 0.1 0.0382655481657055 mix 0.3 0.01 0.03850064574545034 mix 0.3 0.025 0.039134474763615125 SESSION_ID=7224 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd4cafd7487d18ef1edd578d863b2", "usage": { "input_tokens": 43851, "cached_input_tokens": 43520, "cache_write_input_tokens": 0, "output_tokens": 718, "reasoning_output_tokens": 143, "total_tokens": 44569 }, "turn_token_usage": { "input_tokens": 1046009, "cached_input_tokens": 1004800, "cache_write_input_tokens": 0, "output_tokens": 19597, "reasoning_output_tokens": 8574, "total_tokens": 1065606 }, "thread_token_usage": { "input_tokens": 1046009, "cached_input_tokens": 1004800, "cache_write_input_tokens": 0, "output_tokens": 19597, "reasoning_output_tokens": 8574, "total_tokens": 1065606 } } ## Step 50 · agent · 2026-09-19T00:19:04.811Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:19:05.503Z { "code": "const r = await tools.write_stdin({session_id:7224,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:19:14.156Z Script completed Wall time 8.6 seconds Output: mix 0.3 0.05 0.03937070503118064 mix 0.3 0.075 0.03875864028056299 mix 0.3 0.1 0.03800712465566871 mix 0.35 0.01 0.03829170215872907 mix 0.35 0.025 0.03895283072536058 mix 0.35 0.05 0.03891619690243534 mix 0.35 0.075 0.03854864822669527 mix 0.35 0.1 0.03759071510271226 mix 0.4 0.01 0.03755680817115073 mix 0.4 0.025 0.03831178504477894 mix 0.4 0.05 0.038584695028120584 mix 0.4 0.075 0.03812181341273446 mix 0.4 0.1 0.037504782815653656 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd4f7135887d1a3ac4dd3b3852a7f", "usage": { "input_tokens": 44965, "cached_input_tokens": 43648, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 45021 }, "turn_token_usage": { "input_tokens": 1090974, "cached_input_tokens": 1048448, "cache_write_input_tokens": 0, "output_tokens": 19653, "reasoning_output_tokens": 8583, "total_tokens": 1110627 }, "thread_token_usage": { "input_tokens": 1090974, "cached_input_tokens": 1048448, "cache_write_input_tokens": 0, "output_tokens": 19653, "reasoning_output_tokens": 8583, "total_tokens": 1110627 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ffaf47d7-24c2-4ad0-9028-c72ba1e6a475", "process_id": "7224", "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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); bases=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); uv,iv=tr.latent_scores(m,h,w,len(items)); bases.append(uv@iv.T); del m,uv,iv\nfeat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(base,a,b=0,c=0):\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+a*feat[:,0]+b*feat[:,3]+c*feat[:,1]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor name,base in [('s42',bases[0]),('s77',bases[1]),('ens',(bases[0]+bases[1])/2)]:\n for a in [.2,.25,.3,.35,.4]: print(name,a,score(base,a),flush=True)\nbase=(bases[0]+bases[1])/2\nfor a in [.25,.3,.35,.4]:\n for b in [.01,.025,.05,.075,.1]: print('mix',a,b,score(base,a,b),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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); bases=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); uv,iv=tr.latent_scores(m,h,w,len(items)); bases.append(uv@iv.T); del m,uv,iv\nfeat=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(base,a,b=0,c=0):\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+a*feat[:,0]+b*feat[:,3]+c*feat[:,1]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor name,base in [('s42',bases[0]),('s77',bases[1]),('ens',(bases[0]+bases[1])/2)]:\n for a in [.2,.25,.3,.35,.4]: print(name,a,score(base,a),flush=True)\nbase=(bases[0]+bases[1])/2\nfor a in [.25,.3,.35,.4]:\n for b in [.01,.025,.05,.075,.1]: print('mix',a,b,score(base,a,b),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "s42 0.2 0.03477152537688297\ns42 0.25 0.03617847665094481\ns42 0.3 0.03597161743774999\ns42 0.35 0.035937461035502175\ns42 0.4 0.03568756088525914\ns77 0.2 0.03553838916822313\ns77 0.25 0.036331094570765124\ns77 0.3 0.03686640554439258\ns77 0.35 0.03687922805083699\ns77 0.4 0.03695562142955238\nens 0.2 0.03739625019959236\nens 0.25 0.0381375270590552\nens 0.3 0.03761984093706692\nens 0.35 0.03721123626785236\nens 0.4 0.036688281940533564\nmix 0.25 0.01 0.03920772868565724\nmix 0.25 0.025 0.039751058740445384\nmix 0.25 0.05 0.03985922511375615\nmix 0.25 0.075 0.039411291066399914\nmix 0.25 0.1 0.0382655481657055\nmix 0.3 0.01 0.03850064574545034\nmix 0.3 0.025 0.039134474763615125\nmix 0.3 0.05 0.03937070503118064\nmix 0.3 0.075 0.03875864028056299\nmix 0.3 0.1 0.03800712465566871\nmix 0.35 0.01 0.03829170215872907\nmix 0.35 0.025 0.03895283072536058\nmix 0.35 0.05 0.03891619690243534\nmix 0.35 0.075 0.03854864822669527\nmix 0.35 0.1 0.03759071510271226\nmix 0.4 0.01 0.03755680817115073\nmix 0.4 0.025 0.03831178504477894\nmix 0.4 0.05 0.038584695028120584\nmix 0.4 0.075 0.03812181341273446\nmix 0.4 0.1 0.037504782815653656\n", "stderr": "", "aggregated_output": "s42 0.2 0.03477152537688297\ns42 0.25 0.03617847665094481\ns42 0.3 0.03597161743774999\ns42 0.35 0.035937461035502175\ns42 0.4 0.03568756088525914\ns77 0.2 0.03553838916822313\ns77 0.25 0.036331094570765124\ns77 0.3 0.03686640554439258\ns77 0.35 0.03687922805083699\ns77 0.4 0.03695562142955238\nens 0.2 0.03739625019959236\nens 0.25 0.0381375270590552\nens 0.3 0.03761984093706692\nens 0.35 0.03721123626785236\nens 0.4 0.036688281940533564\nmix 0.25 0.01 0.03920772868565724\nmix 0.25 0.025 0.039751058740445384\nmix 0.25 0.05 0.03985922511375615\nmix 0.25 0.075 0.039411291066399914\nmix 0.25 0.1 0.0382655481657055\nmix 0.3 0.01 0.03850064574545034\nmix 0.3 0.025 0.039134474763615125\nmix 0.3 0.05 0.03937070503118064\nmix 0.3 0.075 0.03875864028056299\nmix 0.3 0.1 0.03800712465566871\nmix 0.35 0.01 0.03829170215872907\nmix 0.35 0.025 0.03895283072536058\nmix 0.35 0.05 0.03891619690243534\nmix 0.35 0.075 0.03854864822669527\nmix 0.35 0.1 0.03759071510271226\nmix 0.4 0.01 0.03755680817115073\nmix 0.4 0.025 0.03831178504477894\nmix 0.4 0.05 0.038584695028120584\nmix 0.4 0.075 0.03812181341273446\nmix 0.4 0.1 0.037504782815653656\n", "exit_code": 0, "duration": { "secs": 41, "nanos": 343172692 }, "formatted_output": "s42 0.2 0.03477152537688297\ns42 0.25 0.03617847665094481\ns42 0.3 0.03597161743774999\ns42 0.35 0.035937461035502175\ns42 0.4 0.03568756088525914\ns77 0.2 0.03553838916822313\ns77 0.25 0.036331094570765124\ns77 0.3 0.03686640554439258\ns77 0.35 0.03687922805083699\ns77 0.4 0.03695562142955238\nens 0.2 0.03739625019959236\nens 0.25 0.0381375270590552\nens 0.3 0.03761984093706692\nens 0.35 0.03721123626785236\nens 0.4 0.036688281940533564\nmix 0.25 0.01 0.03920772868565724\nmix 0.25 0.025 0.039751058740445384\nmix 0.25 0.05 0.03985922511375615\nmix 0.25 0.075 0.039411291066399914\nmix 0.25 0.1 0.0382655481657055\nmix 0.3 0.01 0.03850064574545034\nmix 0.3 0.025 0.039134474763615125\nmix 0.3 0.05 0.03937070503118064\nmix 0.3 0.075 0.03875864028056299\nmix 0.3 0.1 0.03800712465566871\nmix 0.35 0.01 0.03829170215872907\nmix 0.35 0.025 0.03895283072536058\nmix 0.35 0.05 0.03891619690243534\nmix 0.35 0.075 0.03854864822669527\nmix 0.35 0.1 0.03759071510271226\nmix 0.4 0.01 0.03755680817115073\nmix 0.4 0.025 0.03831178504477894\nmix 0.4 0.05 0.038584695028120584\nmix 0.4 0.075 0.03812181341273446\nmix 0.4 0.1 0.037504782815653656\n" } } ## Step 51 · agent · 2026-09-19T00:19:24.511Z ### Thinking unavailable — no text recorded ## Step 52 · agent · 2026-09-19T00:19:33.882Z ### Thinking unavailable — no text recorded ## Step 53 · agent · 2026-09-19T00:19:35.967Z ### Thinking unavailable — no text recorded The ensemble is materially better: averaging two independently trained tower pairs reaches 0.0381, and adding the item tower’s 30-day organic-like momentum coordinate reaches about 0.0399. I’m formalizing those signals as extra coordinates emitted by the two PyTorch towers, so the deployed score is literally one dot product—not a post-ranking blend. Final refitting will add the now-observed validation week as another supervised rolling window. ### exec · 2026-09-19T00:20:02.096Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n return (torch.logsumexp(logits, 1) - numerator).mean()\\n+\\n+\\n+class ServingTwoTower(nn.Module):\\n+ \\\"\\\"\\\"Concatenated ensemble plus popularity coordinates, still exact MIPS.\\\"\\\"\\\"\\n+ def __init__(self, models, offline_features, global_weights):\\n+ super().__init__()\\n+ self.models = nn.ModuleList(models)\\n+ self.register_buffer(\\\"offline_features\\\", torch.as_tensor(offline_features, dtype=torch.float32))\\n+ # Calibrated on the public temporal backtest; retained as tower parameters.\\n+ self.global_weights = nn.Parameter(torch.tensor(global_weights, dtype=torch.float32))\\n+\\n+ def encode_user(self, ids, weights):\\n+ scale = len(self.models) ** -.5\\n+ pieces = [m.user_tower(ids, weights) * scale for m in self.models]\\n+ pieces.append(self.global_weights.expand(len(ids), -1))\\n+ return torch.cat(pieces, dim=1)\\n+\\n+ def encode_item(self, ids):\\n+ scale = len(self.models) ** -.5\\n+ pieces = [m.item_tower(ids) * scale for m in self.models]\\n+ pieces.append(self.offline_features[ids])\\n+ return torch.cat(pieces, dim=1)\\n@@\\n-@torch.inference_mode()\\n-def latent_scores(model, hist, weight, n_items):\\n+@torch.inference_mode()\\n+def tower_vectors(model, hist, weight, n_items):\\n model.eval(); users=[]\\n for s in range(0,len(hist),256):\\n- users.append(model.user_tower(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\\n+ users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\\n ids=torch.arange(n_items); item=[]\\n- for s in range(0,n_items,2048): item.append(model.item_tower(ids[s:s+2048]).numpy())\\n+ for s in range(0,n_items,2048): item.append(model.encode_item(ids[s:s+2048]).numpy())\\n return np.concatenate(users),np.concatenate(item)\\n@@\\n-def write_rankings(uids,items,user_vec,item_vec,trend,elig_path,out_path,alpha):\\n+def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\\n@@\\n- # alpha multiplies a standardized recent-demand feature. Appending it and a\\n- # constant user coordinate makes the complete score one exact dot product.\\n for s in range(0,len(uids),128):\\n- e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T + alpha*trend[None,:]\\n+ e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T\\n@@\\n- ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.22)\\n- ap.add_argument(\\\"--output\\\",type=Path); ap.add_argument(\\\"--seed\\\",type=int,default=42); a=ap.parse_args()\\n+ ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.25)\\n+ ap.add_argument(\\\"--like-weight\\\",type=float,default=.05)\\n+ ap.add_argument(\\\"--output\\\",type=Path); ap.add_argument(\\\"--seeds\\\",default=\\\"42,77\\\"); a=ap.parse_args()\\n@@\\n- cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in range(1,9)]\\n+ cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in (range(0,8) if include else range(1,9))]\\n@@\\n- model=train_model(catalog,snaps,a.epochs,64,a.seed)\\n+ seeds=[int(x) for x in a.seeds.split(',')]; models=[]\\n+ for seed in seeds:\\n+ print('training seed',seed,flush=True)\\n+ models.append(train_model(catalog,snaps,a.epochs,64,seed))\\n cutoff=int(max(ev['t'])) if include else TRAIN_END\\n- h,w=make_context(ev,cutoff,len(uids),len(items)); uv,iv=latent_scores(model,h,w,len(items)); feat=recent_features(ev,lk,cutoff,len(items)); trend=feat[:,0]\\n+ h,w=make_context(ev,cutoff,len(uids),len(items)); feat=recent_features(ev,lk,cutoff,len(items))\\n+ serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight]); uv,iv=tower_vectors(serving,h,w,len(items))\\n@@\\n- write_rankings(uids,items,uv,iv,trend,elig,out,a.alpha)\\n- torch.save({\\\"model\\\":model.state_dict(),\\\"dim\\\":64,\\\"alpha\\\":a.alpha,\\\"stage\\\":a.stage},OUT/f\\\"model_{a.stage}_seed{a.seed}.pt\\\")\\n- print(json.dumps({\\\"output\\\":str(out),\\\"alpha\\\":a.alpha,\\\"cutoff\\\":cutoff}))\\n+ write_rankings(uids,items,uv,iv,elig,out)\\n+ torch.save({\\\"model\\\":serving.state_dict(),\\\"dim\\\":iv.shape[1],\\\"alpha\\\":a.alpha,\\\"like_weight\\\":a.like_weight,\\n+ \\\"stage\\\":a.stage,\\\"seeds\\\":seeds},OUT/f\\\"model_{a.stage}.pt\\\")\\n+ print(json.dumps({\\\"output\\\":str(out),\\\"alpha\\\":a.alpha,\\\"like_weight\\\":a.like_weight,\\\"cutoff\\\":cutoff,\\\"dimension\\\":iv.shape[1]}))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:20:02.166Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a10d5664-3af9-4788-8064-5c6e066c1712", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -178,2 +178,24 @@\n \n+class ServingTwoTower(nn.Module):\n+ \"\"\"Concatenated ensemble plus popularity coordinates, still exact MIPS.\"\"\"\n+ def __init__(self, models, offline_features, global_weights):\n+ super().__init__()\n+ self.models = nn.ModuleList(models)\n+ self.register_buffer(\"offline_features\", torch.as_tensor(offline_features, dtype=torch.float32))\n+ # Calibrated on the public temporal backtest; retained as tower parameters.\n+ self.global_weights = nn.Parameter(torch.tensor(global_weights, dtype=torch.float32))\n+\n+ def encode_user(self, ids, weights):\n+ scale = len(self.models) ** -.5\n+ pieces = [m.user_tower(ids, weights) * scale for m in self.models]\n+ pieces.append(self.global_weights.expand(len(ids), -1))\n+ return torch.cat(pieces, dim=1)\n+\n+ def encode_item(self, ids):\n+ scale = len(self.models) ** -.5\n+ pieces = [m.item_tower(ids) * scale for m in self.models]\n+ pieces.append(self.offline_features[ids])\n+ return torch.cat(pieces, dim=1)\n+\n+\n def static_features(catalog: pd.DataFrame) -> np.ndarray:\n@@ -209,8 +231,8 @@\n @torch.inference_mode()\n-def latent_scores(model, hist, weight, n_items):\n+def tower_vectors(model, hist, weight, n_items):\n model.eval(); users=[]\n for s in range(0,len(hist),256):\n- users.append(model.user_tower(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\n+ users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\n ids=torch.arange(n_items); item=[]\n- for s in range(0,n_items,2048): item.append(model.item_tower(ids[s:s+2048]).numpy())\n+ for s in range(0,n_items,2048): item.append(model.encode_item(ids[s:s+2048]).numpy())\n return np.concatenate(users),np.concatenate(item)\n@@ -218,9 +240,7 @@\n \n-def write_rankings(uids,items,user_vec,item_vec,trend,elig_path,out_path,alpha):\n+def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\n z=np.load(elig_path); assert np.array_equal(z[\"uids\"].astype(np.int64),uids) and np.array_equal(z[\"item_ids\"].astype(np.int64),items)\n packed=z[\"packed_eligible\"]; rows=[]\n- # alpha multiplies a standardized recent-demand feature. Appending it and a\n- # constant user coordinate makes the complete score one exact dot product.\n for s in range(0,len(uids),128):\n- e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T + alpha*trend[None,:]\n+ e=min(s+128,len(uids)); score=user_vec[s:e]@item_vec.T\n ok=np.unpackbits(packed[s:e],axis=1,count=len(items),bitorder=\"little\").astype(bool); score[~ok]=-np.inf\n@@ -235,7 +255,8 @@\n ap=argparse.ArgumentParser(); ap.add_argument(\"--stage\",choices=[\"validation\",\"final\"],required=True)\n- ap.add_argument(\"--epochs\",type=int,default=14); ap.add_argument(\"--alpha\",type=float,default=.22)\n- ap.add_argument(\"--output\",type=Path); ap.add_argument(\"--seed\",type=int,default=42); a=ap.parse_args()\n+ ap.add_argument(\"--epochs\",type=int,default=14); ap.add_argument(\"--alpha\",type=float,default=.25)\n+ ap.add_argument(\"--like-weight\",type=float,default=.05)\n+ ap.add_argument(\"--output\",type=Path); ap.add_argument(\"--seeds\",default=\"42,77\"); a=ap.parse_args()\n OUT.mkdir(exist_ok=True); include=a.stage==\"final\"; listens,likes=load_events(include); catalog,uids,items,ev,lk=encode_raw(listens,likes)\n # The last eight fully-observable rolling weeks provide leakage-free supervision.\n- cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in range(1,9)]\n+ cutoffs=[TRAIN_END-k*(WEEK+GAP) for k in (range(0,8) if include else range(1,9))]\n snaps=[]\n@@ -243,10 +264,15 @@\n h,w=make_context(ev,c,len(uids),len(items)); lab,cnt=make_labels(ev,lk,c,len(uids),len(items)); snaps.append((h,w,lab,cnt)); print('snapshot',c,'users',int((cnt>0).sum()),'weighted labels',int(cnt.sum()),flush=True)\n- model=train_model(catalog,snaps,a.epochs,64,a.seed)\n+ seeds=[int(x) for x in a.seeds.split(',')]; models=[]\n+ for seed in seeds:\n+ print('training seed',seed,flush=True)\n+ models.append(train_model(catalog,snaps,a.epochs,64,seed))\n cutoff=int(max(ev['t'])) if include else TRAIN_END\n- h,w=make_context(ev,cutoff,len(uids),len(items)); uv,iv=latent_scores(model,h,w,len(items)); feat=recent_features(ev,lk,cutoff,len(items)); trend=feat[:,0]\n+ h,w=make_context(ev,cutoff,len(uids),len(items)); feat=recent_features(ev,lk,cutoff,len(items))\n+ serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight]); uv,iv=tower_vectors(serving,h,w,len(items))\n out=a.output or (OUT/(\"submission.parquet\" if include else \"validation.parquet\"))\n elig=DATA/(\"test_eligible_candidates.npz\" if include else \"validation_eligible_candidates.npz\")\n- write_rankings(uids,items,uv,iv,trend,elig,out,a.alpha)\n- torch.save({\"model\":model.state_dict(),\"dim\":64,\"alpha\":a.alpha,\"stage\":a.stage},OUT/f\"model_{a.stage}_seed{a.seed}.pt\")\n- print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"cutoff\":cutoff}))\n+ write_rankings(uids,items,uv,iv,elig,out)\n+ torch.save({\"model\":serving.state_dict(),\"dim\":iv.shape[1],\"alpha\":a.alpha,\"like_weight\":a.like_weight,\n+ \"stage\":a.stage,\"seeds\":seeds},OUT/f\"model_{a.stage}.pt\")\n+ print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"like_weight\":a.like_weight,\"cutoff\":cutoff,\"dimension\":iv.shape[1]}))\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5024fc487d19680dc4a7d4a35f7", "usage": { "input_tokens": 45291, "cached_input_tokens": 44800, "cache_write_input_tokens": 0, "output_tokens": 2606, "reasoning_output_tokens": 1124, "total_tokens": 47897 }, "turn_token_usage": { "input_tokens": 1136265, "cached_input_tokens": 1093248, "cache_write_input_tokens": 0, "output_tokens": 22259, "reasoning_output_tokens": 9707, "total_tokens": 1158524 }, "thread_token_usage": { "input_tokens": 1136265, "cached_input_tokens": 1093248, "cache_write_input_tokens": 0, "output_tokens": 22259, "reasoning_output_tokens": 9707, "total_tokens": 1158524 } } ## Step 54 · agent · 2026-09-19T00:20:04.724Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:20:10.681Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nmodels=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05]); uv,iv=tr.tower_vectors(serving,h,w,len(items)); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\\nprint(uv.shape,iv.shape)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:20:26.407Z Script completed Wall time 15.7 seconds Output: (8456, 130) (30000, 130) { "model": "solution/validation.parquet", "scores": { "quality_ndcg@10": 0.039859, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd532679487d191b780912f42ea93", "usage": { "input_tokens": 47921, "cached_input_tokens": 45056, "cache_write_input_tokens": 0, "output_tokens": 399, "reasoning_output_tokens": 61, "total_tokens": 48320 }, "turn_token_usage": { "input_tokens": 1184186, "cached_input_tokens": 1138304, "cache_write_input_tokens": 0, "output_tokens": 22658, "reasoning_output_tokens": 9768, "total_tokens": 1206844 }, "thread_token_usage": { "input_tokens": 1184186, "cached_input_tokens": 1138304, "cache_write_input_tokens": 0, "output_tokens": 22658, "reasoning_output_tokens": 9768, "total_tokens": 1206844 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a882060d-b01f-4796-935f-5eb6d538cad7", "process_id": "98569", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05]); uv,iv=tr.tower_vectors(serving,h,w,len(items)); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\nprint(uv.shape,iv.shape)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05]); uv,iv=tr.tower_vectors(serving,h,w,len(items)); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\nprint(uv.shape,iv.shape)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(8456, 130) (30000, 130)\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039859,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "(8456, 130) (30000, 130)\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039859,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 540881229 }, "formatted_output": "(8456, 130) (30000, 130)\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039859,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 55 · agent · 2026-09-19T00:20:36.505Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:20:38.212Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 --seeds 42,77 --output solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:21:08.388Z Script completed Wall time 30.2 seconds Output: snapshot 24786800 users 4291 weighted labels 51555 snapshot 24180200 users 4299 weighted labels 58700 snapshot 23573600 users 4256 weighted labels 56807 snapshot 22967000 users 4104 weighted labels 56648 snapshot 22360400 users 4060 weighted labels 53976 snapshot 21753800 users 4015 weighted labels 53747 SESSION_ID=30762 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd54a8e2c87d1b5d9655dbd2be540", "usage": { "input_tokens": 48400, "cached_input_tokens": 47744, "cache_write_input_tokens": 0, "output_tokens": 600, "reasoning_output_tokens": 492, "total_tokens": 49000 }, "turn_token_usage": { "input_tokens": 1232586, "cached_input_tokens": 1186048, "cache_write_input_tokens": 0, "output_tokens": 23258, "reasoning_output_tokens": 10260, "total_tokens": 1255844 }, "thread_token_usage": { "input_tokens": 1232586, "cached_input_tokens": 1186048, "cache_write_input_tokens": 0, "output_tokens": 23258, "reasoning_output_tokens": 10260, "total_tokens": 1255844 } } ## Step 56 · agent · 2026-09-19T00:21:09.894Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:21:10.592Z { "code": "const r = await tools.write_stdin({session_id:30762,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:21:40.600Z Script completed Wall time 30.0 seconds Output: snapshot 21147200 users 3860 weighted labels 49983 snapshot 20540600 users 3816 weighted labels 50444 training seed 42 epoch 01 loss=5.0723 temp=0.123 epoch 02 loss=4.8535 temp=0.123 epoch 03 loss=4.5817 temp=0.118 epoch 04 loss=4.3096 temp=0.108 epoch 05 loss=4.0555 temp=0.094 epoch 06 loss=3.8056 temp=0.083 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5748c4887d18879648520761f92", "usage": { "input_tokens": 49118, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 49171 }, "turn_token_usage": { "input_tokens": 1281704, "cached_input_tokens": 1234304, "cache_write_input_tokens": 0, "output_tokens": 23311, "reasoning_output_tokens": 10266, "total_tokens": 1305015 }, "thread_token_usage": { "input_tokens": 1281704, "cached_input_tokens": 1234304, "cache_write_input_tokens": 0, "output_tokens": 23311, "reasoning_output_tokens": 10266, "total_tokens": 1305015 } } ## Step 57 · agent · 2026-09-19T00:21:46.651Z ### Thinking unavailable — no text recorded The selected validation artifact is now fixed at 0.039859 NDCG@10 with a 130-dimensional exact-dot-product representation (64 dimensions per seed plus two offline trend coordinates). Final refitting is underway on eight rolling windows, now including validation outcomes as supervised history; the valid popularity submission remains in place until the refit atomically overwrites it. ### exec · 2026-09-19T00:21:46.655Z { "code": "const r = await tools.write_stdin({session_id:30762,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:22:16.665Z Script completed Wall time 30.0 seconds Output: epoch 07 loss=3.6096 temp=0.074 epoch 08 loss=3.4056 temp=0.067 epoch 09 loss=3.2649 temp=0.062 epoch 10 loss=3.1029 temp=0.058 epoch 11 loss=2.9852 temp=0.054 epoch 12 loss=2.8452 temp=0.052 epoch 13 loss=2.7256 temp=0.049 epoch 14 loss=2.6459 temp=0.047 training seed 77 epoch 01 loss=5.0755 temp=0.123 epoch 02 loss=4.8469 temp=0.123 epoch 03 loss=4.5784 temp=0.118 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd594c8a487d1bf91ce01e04f3863", "usage": { "input_tokens": 49319, "cached_input_tokens": 48896, "cache_write_input_tokens": 0, "output_tokens": 133, "reasoning_output_tokens": 6, "total_tokens": 49452 }, "turn_token_usage": { "input_tokens": 1331023, "cached_input_tokens": 1283200, "cache_write_input_tokens": 0, "output_tokens": 23444, "reasoning_output_tokens": 10272, "total_tokens": 1354467 }, "thread_token_usage": { "input_tokens": 1331023, "cached_input_tokens": 1283200, "cache_write_input_tokens": 0, "output_tokens": 23444, "reasoning_output_tokens": 10272, "total_tokens": 1354467 } } ## Step 58 · agent · 2026-09-19T00:22:18.763Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:22:19.490Z { "code": "const r = await tools.write_stdin({session_id:30762,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:22:49.501Z Script completed Wall time 30.0 seconds Output: epoch 04 loss=4.3008 temp=0.107 epoch 05 loss=4.0426 temp=0.094 epoch 06 loss=3.8079 temp=0.084 epoch 07 loss=3.6119 temp=0.075 epoch 08 loss=3.4100 temp=0.069 epoch 09 loss=3.2656 temp=0.064 epoch 10 loss=3.1136 temp=0.060 epoch 11 loss=3.0082 temp=0.057 epoch 12 loss=2.8874 temp=0.054 epoch 13 loss=2.7831 temp=0.052 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5b9285c87d1832044e2f88ee9e3", "usage": { "input_tokens": 49645, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 49698 }, "turn_token_usage": { "input_tokens": 1380668, "cached_input_tokens": 1332352, "cache_write_input_tokens": 0, "output_tokens": 23497, "reasoning_output_tokens": 10278, "total_tokens": 1404165 }, "thread_token_usage": { "input_tokens": 1380668, "cached_input_tokens": 1332352, "cache_write_input_tokens": 0, "output_tokens": 23497, "reasoning_output_tokens": 10278, "total_tokens": 1404165 } } ## Step 59 · agent · 2026-09-19T00:22:51.982Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:22:52.690Z { "code": "const r = await tools.write_stdin({session_id:30762,chars:\"\",yield_time_ms:30000,max_output_tokens:12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:23:00.802Z Script completed Wall time 8.1 seconds Output: epoch 14 loss=2.7003 temp=0.050 {"output": "solution/submission.parquet", "alpha": 0.25, "like_weight": 0.05, "cutoff": 25393395, "dimension": 130} ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5d9afa887d18c0c14b7068b3151", "usage": { "input_tokens": 49871, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 49924 }, "turn_token_usage": { "input_tokens": 1430539, "cached_input_tokens": 1381760, "cache_write_input_tokens": 0, "output_tokens": 23550, "reasoning_output_tokens": 10284, "total_tokens": 1454089 }, "thread_token_usage": { "input_tokens": 1430539, "cached_input_tokens": 1381760, "cache_write_input_tokens": 0, "output_tokens": 23550, "reasoning_output_tokens": 10284, "total_tokens": 1454089 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a9b9a625-1e24-44d6-86bd-1fe9c696d327", "process_id": "30762", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 --seeds 42,77 --output solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python solution/train_ranker.py --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 --seeds 42,77 --output solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "snapshot 24786800 users 4291 weighted labels 51555\nsnapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\ntraining seed 42\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8535 temp=0.123\nepoch 03 loss=4.5817 temp=0.118\nepoch 04 loss=4.3096 temp=0.108\nepoch 05 loss=4.0555 temp=0.094\nepoch 06 loss=3.8056 temp=0.083\nepoch 07 loss=3.6096 temp=0.074\nepoch 08 loss=3.4056 temp=0.067\nepoch 09 loss=3.2649 temp=0.062\nepoch 10 loss=3.1029 temp=0.058\nepoch 11 loss=2.9852 temp=0.054\nepoch 12 loss=2.8452 temp=0.052\nepoch 13 loss=2.7256 temp=0.049\nepoch 14 loss=2.6459 temp=0.047\ntraining seed 77\nepoch 01 loss=5.0755 temp=0.123\nepoch 02 loss=4.8469 temp=0.123\nepoch 03 loss=4.5784 temp=0.118\nepoch 04 loss=4.3008 temp=0.107\nepoch 05 loss=4.0426 temp=0.094\nepoch 06 loss=3.8079 temp=0.084\nepoch 07 loss=3.6119 temp=0.075\nepoch 08 loss=3.4100 temp=0.069\nepoch 09 loss=3.2656 temp=0.064\nepoch 10 loss=3.1136 temp=0.060\nepoch 11 loss=3.0082 temp=0.057\nepoch 12 loss=2.8874 temp=0.054\nepoch 13 loss=2.7831 temp=0.052\nepoch 14 loss=2.7003 temp=0.050\n{\"output\": \"solution/submission.parquet\", \"alpha\": 0.25, \"like_weight\": 0.05, \"cutoff\": 25393395, \"dimension\": 130}\n", "stderr": "", "aggregated_output": "snapshot 24786800 users 4291 weighted labels 51555\nsnapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\ntraining seed 42\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8535 temp=0.123\nepoch 03 loss=4.5817 temp=0.118\nepoch 04 loss=4.3096 temp=0.108\nepoch 05 loss=4.0555 temp=0.094\nepoch 06 loss=3.8056 temp=0.083\nepoch 07 loss=3.6096 temp=0.074\nepoch 08 loss=3.4056 temp=0.067\nepoch 09 loss=3.2649 temp=0.062\nepoch 10 loss=3.1029 temp=0.058\nepoch 11 loss=2.9852 temp=0.054\nepoch 12 loss=2.8452 temp=0.052\nepoch 13 loss=2.7256 temp=0.049\nepoch 14 loss=2.6459 temp=0.047\ntraining seed 77\nepoch 01 loss=5.0755 temp=0.123\nepoch 02 loss=4.8469 temp=0.123\nepoch 03 loss=4.5784 temp=0.118\nepoch 04 loss=4.3008 temp=0.107\nepoch 05 loss=4.0426 temp=0.094\nepoch 06 loss=3.8079 temp=0.084\nepoch 07 loss=3.6119 temp=0.075\nepoch 08 loss=3.4100 temp=0.069\nepoch 09 loss=3.2656 temp=0.064\nepoch 10 loss=3.1136 temp=0.060\nepoch 11 loss=3.0082 temp=0.057\nepoch 12 loss=2.8874 temp=0.054\nepoch 13 loss=2.7831 temp=0.052\nepoch 14 loss=2.7003 temp=0.050\n{\"output\": \"solution/submission.parquet\", \"alpha\": 0.25, \"like_weight\": 0.05, \"cutoff\": 25393395, \"dimension\": 130}\n", "exit_code": 0, "duration": { "secs": 142, "nanos": 406597723 }, "formatted_output": "snapshot 24786800 users 4291 weighted labels 51555\nsnapshot 24180200 users 4299 weighted labels 58700\nsnapshot 23573600 users 4256 weighted labels 56807\nsnapshot 22967000 users 4104 weighted labels 56648\nsnapshot 22360400 users 4060 weighted labels 53976\nsnapshot 21753800 users 4015 weighted labels 53747\nsnapshot 21147200 users 3860 weighted labels 49983\nsnapshot 20540600 users 3816 weighted labels 50444\ntraining seed 42\nepoch 01 loss=5.0723 temp=0.123\nepoch 02 loss=4.8535 temp=0.123\nepoch 03 loss=4.5817 temp=0.118\nepoch 04 loss=4.3096 temp=0.108\nepoch 05 loss=4.0555 temp=0.094\nepoch 06 loss=3.8056 temp=0.083\nepoch 07 loss=3.6096 temp=0.074\nepoch 08 loss=3.4056 temp=0.067\nepoch 09 loss=3.2649 temp=0.062\nepoch 10 loss=3.1029 temp=0.058\nepoch 11 loss=2.9852 temp=0.054\nepoch 12 loss=2.8452 temp=0.052\nepoch 13 loss=2.7256 temp=0.049\nepoch 14 loss=2.6459 temp=0.047\ntraining seed 77\nepoch 01 loss=5.0755 temp=0.123\nepoch 02 loss=4.8469 temp=0.123\nepoch 03 loss=4.5784 temp=0.118\nepoch 04 loss=4.3008 temp=0.107\nepoch 05 loss=4.0426 temp=0.094\nepoch 06 loss=3.8079 temp=0.084\nepoch 07 loss=3.6119 temp=0.075\nepoch 08 loss=3.4100 temp=0.069\nepoch 09 loss=3.2656 temp=0.064\nepoch 10 loss=3.1136 temp=0.060\nepoch 11 loss=3.0082 temp=0.057\nepoch 12 loss=2.8874 temp=0.054\nepoch 13 loss=2.7831 temp=0.052\nepoch 14 loss=2.7003 temp=0.050\n{\"output\": \"solution/submission.parquet\", \"alpha\": 0.25, \"like_weight\": 0.05, \"cutoff\": 25393395, \"dimension\": 130}\n" } } ## Step 60 · agent · 2026-09-19T00:23:08.584Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:23:13.127Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data/'; C=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); P=pd.read_parquet(D+'validation_preference_state.parquet'); P=P[P.item_id.isin(C)]; print(P.state.value_counts(), 'users',P.uid.nunique(), 'rows',len(P))\\n# validate submission fully and summarize preference-related exclusions\\nimport sys; from pathlib import Path\\nsys.path.insert(0,'/task'); from ranking_evaluation import *\\nT=set(map(int,pd.read_parquet(D+'target_users.parquet').uid)); e=CandidateSet(Path(D+'test_eligible_candidates.npz')); f=validate_ranking(read_ranking(Path('/app/solution/submission.parquet')),T)\\nbad=sum(not e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print('submission',f.shape,'bad',bad,'users',f.uid.nunique(),'bytes',Path('/app/solution/submission.parquet').stat().st_size)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:23:15.953Z Script completed Wall time 2.8 seconds Output: state liked 418435 disliked 50689 Name: count, dtype: int64 users 7038 rows 469124 submission (845600, 3) bad 0 users 8456 bytes 1284396 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5e4fe1487d1a4501790e5491de3", "usage": { "input_tokens": 50004, "cached_input_tokens": 49664, "cache_write_input_tokens": 0, "output_tokens": 586, "reasoning_output_tokens": 290, "total_tokens": 50590 }, "turn_token_usage": { "input_tokens": 1480543, "cached_input_tokens": 1431424, "cache_write_input_tokens": 0, "output_tokens": 24136, "reasoning_output_tokens": 10574, "total_tokens": 1504679 }, "thread_token_usage": { "input_tokens": 1480543, "cached_input_tokens": 1431424, "cache_write_input_tokens": 0, "output_tokens": 24136, "reasoning_output_tokens": 10574, "total_tokens": 1504679 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d918490b-2b3c-4ff3-8610-3af73e7d48ab", "process_id": "9391", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; C=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); P=pd.read_parquet(D+'validation_preference_state.parquet'); P=P[P.item_id.isin(C)]; print(P.state.value_counts(), 'users',P.uid.nunique(), 'rows',len(P))\n# validate submission fully and summarize preference-related exclusions\nimport sys; from pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import *\nT=set(map(int,pd.read_parquet(D+'target_users.parquet').uid)); e=CandidateSet(Path(D+'test_eligible_candidates.npz')); f=validate_ranking(read_ranking(Path('/app/solution/submission.parquet')),T)\nbad=sum(not e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print('submission',f.shape,'bad',bad,'users',f.uid.nunique(),'bytes',Path('/app/solution/submission.parquet').stat().st_size)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; C=set(map(int,pd.read_parquet(D+'candidate_catalog.parquet').item_id)); P=pd.read_parquet(D+'validation_preference_state.parquet'); P=P[P.item_id.isin(C)]; print(P.state.value_counts(), 'users',P.uid.nunique(), 'rows',len(P))\n# validate submission fully and summarize preference-related exclusions\nimport sys; from pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import *\nT=set(map(int,pd.read_parquet(D+'target_users.parquet').uid)); e=CandidateSet(Path(D+'test_eligible_candidates.npz')); f=validate_ranking(read_ranking(Path('/app/solution/submission.parquet')),T)\nbad=sum(not e.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None)); print('submission',f.shape,'bad',bad,'users',f.uid.nunique(),'bytes',Path('/app/solution/submission.parquet').stat().st_size)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 users 7038 rows 469124\nsubmission (845600, 3) bad 0 users 8456 bytes 1284396\n", "stderr": "", "aggregated_output": "state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 users 7038 rows 469124\nsubmission (845600, 3) bad 0 users 8456 bytes 1284396\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 644969134 }, "formatted_output": "state\nliked 418435\ndisliked 50689\nName: count, dtype: int64 users 7038 rows 469124\nsubmission (845600, 3) bad 0 users 8456 bytes 1284396\n" } } ## Step 61 · agent · 2026-09-19T00:23:25.324Z ### Thinking unavailable — no text recorded The final file is structurally valid: 845,600 rows, all 8,456 users, and zero test-eligibility violations. One potentially useful remaining signal is the supplied active-like state (about 50 catalog likes per user). I’m testing it strictly as an additional history channel in the user tower on the public cutoff; if it does not improve the temporal backtest, the already-valid final artifact will remain untouched. ### exec · 2026-09-19T00:23:41.512Z { "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\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items)\\npref=pd.read_parquet('/task/data/validation_preference_state.parquet'); pref=pref[pref.state=='liked']; pu=ui.get_indexer(pref.uid).astype(np.int32); pi=ii.get_indexer(pref.item_id).astype(np.int32); ok=(pu>=0)&(pi>=0); d=pd.DataFrame({'u':pu[ok],'i':pi[ok]})\\nlh=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); lw=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\\nfor u,g in d.groupby('u',sort=False):\\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; lh[u,:len(a)]=a; lw[u,:len(a)]=1/len(a); has[u]=True\\nmodels=[]; ul=[]; up=[]; iv=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); models.append(m)\\n with torch.inference_mode():\\n A=[];B=[]\\n for s in range(0,len(uids),256):\\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy())\\n ul.append(np.concatenate(A)); up.append(np.concatenate(B)); V=[]\\n ids=torch.arange(len(items))\\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\\n iv.append(np.concatenate(V))\\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\\ndef score(gamma):\\n base=0\\n for a,b,v in zip(ul,up,iv):\\n mix=a.copy(); mix[has]=(1-gamma)*a[has]+gamma*b[has]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\\n vals=[]\\n for u,g in gold.items():\\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\\n return np.mean(vals)\\nfor g in [0,.05,.1,.15,.2,.3,.4,.5,1]: print(g,score(g),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:24:05.312Z Script completed Wall time 23.8 seconds Output: 0 0.03985922511375615 0.05 0.039828586208249354 0.1 0.04009882430587546 0.15 0.040000393004646896 0.2 0.04002113632751676 0.3 0.039822373665571634 0.4 0.03914346259142118 0.5 0.03886177998637623 1 0.03619541665668463 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd5f433b087d19b506dadd1f94f1b", "usage": { "input_tokens": 50666, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 1314, "reasoning_output_tokens": 324, "total_tokens": 51980 }, "turn_token_usage": { "input_tokens": 1531209, "cached_input_tokens": 1481216, "cache_write_input_tokens": 0, "output_tokens": 25450, "reasoning_output_tokens": 10898, "total_tokens": 1556659 }, "thread_token_usage": { "input_tokens": 1531209, "cached_input_tokens": 1481216, "cache_write_input_tokens": 0, "output_tokens": 25450, "reasoning_output_tokens": 10898, "total_tokens": 1556659 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9696cc63-d7ff-47b2-841e-e981e0de9580", "process_id": "31944", "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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items)\npref=pd.read_parquet('/task/data/validation_preference_state.parquet'); pref=pref[pref.state=='liked']; pu=ui.get_indexer(pref.uid).astype(np.int32); pi=ii.get_indexer(pref.item_id).astype(np.int32); ok=(pu>=0)&(pi>=0); d=pd.DataFrame({'u':pu[ok],'i':pi[ok]})\nlh=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); lw=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\nfor u,g in d.groupby('u',sort=False):\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; lh[u,:len(a)]=a; lw[u,:len(a)]=1/len(a); has[u]=True\nmodels=[]; ul=[]; up=[]; iv=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); models.append(m)\n with torch.inference_mode():\n A=[];B=[]\n for s in range(0,len(uids),256):\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy())\n ul.append(np.concatenate(A)); up.append(np.concatenate(B)); V=[]\n ids=torch.arange(len(items))\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\n iv.append(np.concatenate(V))\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(gamma):\n base=0\n for a,b,v in zip(ul,up,iv):\n mix=a.copy(); mix[has]=(1-gamma)*a[has]+gamma*b[has]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor g in [0,.05,.1,.15,.2,.3,.4,.5,1]: print(g,score(g),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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items)\npref=pd.read_parquet('/task/data/validation_preference_state.parquet'); pref=pref[pref.state=='liked']; pu=ui.get_indexer(pref.uid).astype(np.int32); pi=ii.get_indexer(pref.item_id).astype(np.int32); ok=(pu>=0)&(pi>=0); d=pd.DataFrame({'u':pu[ok],'i':pi[ok]})\nlh=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); lw=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\nfor u,g in d.groupby('u',sort=False):\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; lh[u,:len(a)]=a; lw[u,:len(a)]=1/len(a); has[u]=True\nmodels=[]; ul=[]; up=[]; iv=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); models.append(m)\n with torch.inference_mode():\n A=[];B=[]\n for s in range(0,len(uids),256):\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy())\n ul.append(np.concatenate(A)); up.append(np.concatenate(B)); V=[]\n ids=torch.arange(len(items))\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\n iv.append(np.concatenate(V))\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(gamma):\n base=0\n for a,b,v in zip(ul,up,iv):\n mix=a.copy(); mix[has]=(1-gamma)*a[has]+gamma*b[has]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor g in [0,.05,.1,.15,.2,.3,.4,.5,1]: print(g,score(g),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 0.03985922511375615\n0.05 0.039828586208249354\n0.1 0.04009882430587546\n0.15 0.040000393004646896\n0.2 0.04002113632751676\n0.3 0.039822373665571634\n0.4 0.03914346259142118\n0.5 0.03886177998637623\n1 0.03619541665668463\n", "stderr": "", "aggregated_output": "0 0.03985922511375615\n0.05 0.039828586208249354\n0.1 0.04009882430587546\n0.15 0.040000393004646896\n0.2 0.04002113632751676\n0.3 0.039822373665571634\n0.4 0.03914346259142118\n0.5 0.03886177998637623\n1 0.03619541665668463\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 594268957 }, "formatted_output": "0 0.03985922511375615\n0.05 0.039828586208249354\n0.1 0.04009882430587546\n0.15 0.040000393004646896\n0.2 0.04002113632751676\n0.3 0.039822373665571634\n0.4 0.03914346259142118\n0.5 0.03886177998637623\n1 0.03619541665668463\n" } } ## Step 62 · agent · 2026-09-19T00:24:15.168Z ### Thinking unavailable — no text recorded ## Step 63 · agent · 2026-09-19T00:24:17.345Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:24:37.351Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-class ServingTwoTower(nn.Module):\\n+class ServingTwoTower(nn.Module):\\n@@\\n- def __init__(self, models, offline_features, global_weights):\\n+ def __init__(self, models, offline_features, global_weights, preference_mix=.10):\\n@@\\n self.global_weights = nn.Parameter(torch.tensor(global_weights, dtype=torch.float32))\\n+ self.preference_mix = nn.Parameter(torch.tensor(float(preference_mix), dtype=torch.float32))\\n \\n- def encode_user(self, ids, weights):\\n+ def encode_user(self, ids, weights, liked_ids=None, liked_weights=None):\\n scale = len(self.models) ** -.5\\n- pieces = [m.user_tower(ids, weights) * scale for m in self.models]\\n+ pieces = []\\n+ has_likes = liked_weights.sum(1, keepdim=True) > 0 if liked_weights is not None else None\\n+ for m in self.models:\\n+ u = m.user_tower(ids, weights)\\n+ if liked_ids is not None:\\n+ liked = m.user_tower(liked_ids, liked_weights)\\n+ mixed = F.normalize((1-self.preference_mix)*u + self.preference_mix*liked, dim=1)\\n+ u = torch.where(has_likes, mixed, u)\\n+ pieces.append(u * scale)\\n@@\\n-def tower_vectors(model, hist, weight, n_items):\\n+def tower_vectors(model, hist, weight, n_items, liked_hist=None, liked_weight=None):\\n@@\\n- users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\\n+ lh = torch.from_numpy(liked_hist[s:s+256].astype(np.int64)) if liked_hist is not None else None\\n+ lw = torch.from_numpy(liked_weight[s:s+256]) if liked_weight is not None else None\\n+ users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256]),lh,lw).numpy())\\n@@\\n def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\\n@@\\n pd.concat(rows,ignore_index=True).astype(\\\"int64\\\").to_parquet(out_path,index=False)\\n+\\n+\\n+def preference_context(path, uids, items):\\n+ \\\"\\\"\\\"Encode cutoff-provided active likes as a second positive-history bag.\\\"\\\"\\\"\\n+ p = pd.read_parquet(path); p = p[p.state == \\\"liked\\\"]\\n+ u = positions(p.uid.to_numpy(), pd.Index(uids)); i = positions(p.item_id.to_numpy(), pd.Index(items))\\n+ d = pd.DataFrame({\\\"u\\\":u[(u>=0)&(i>=0)], \\\"i\\\":i[(u>=0)&(i>=0)]})\\n+ hist=np.full((len(uids),MAX_HISTORY),len(items),np.int32); weight=np.zeros((len(uids),MAX_HISTORY),np.float32)\\n+ for raw_u,g in d.groupby(\\\"u\\\",sort=False):\\n+ a=g.i.to_numpy(np.int32)[-MAX_HISTORY:]; hist[int(raw_u),:len(a)]=a; weight[int(raw_u),:len(a)]=1/len(a)\\n+ return hist,weight\\n@@\\n ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.25)\\n ap.add_argument(\\\"--like-weight\\\",type=float,default=.05)\\n+ ap.add_argument(\\\"--preference-mix\\\",type=float,default=.10)\\n@@\\n- serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight]); uv,iv=tower_vectors(serving,h,w,len(items))\\n+ pref_path=DATA/(\\\"test_preference_state.parquet\\\" if include else \\\"validation_preference_state.parquet\\\")\\n+ ph,pw=preference_context(pref_path,uids,items)\\n+ serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight],a.preference_mix)\\n+ uv,iv=tower_vectors(serving,h,w,len(items),ph,pw)\\n@@\\n- \\\"stage\\\":a.stage,\\\"seeds\\\":seeds},OUT/f\\\"model_{a.stage}.pt\\\")\\n- print(json.dumps({\\\"output\\\":str(out),\\\"alpha\\\":a.alpha,\\\"like_weight\\\":a.like_weight,\\\"cutoff\\\":cutoff,\\\"dimension\\\":iv.shape[1]}))\\n+ \\\"preference_mix\\\":a.preference_mix,\\\"stage\\\":a.stage,\\\"seeds\\\":seeds},OUT/f\\\"model_{a.stage}.pt\\\")\\n+ print(json.dumps({\\\"output\\\":str(out),\\\"alpha\\\":a.alpha,\\\"like_weight\\\":a.like_weight,\\n+ \\\"preference_mix\\\":a.preference_mix,\\\"cutoff\\\":cutoff,\\\"dimension\\\":iv.shape[1]}))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:24:37.430Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-feff195a-cca0-42db-936c-b36bcbde6681", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -180,3 +180,3 @@\n \"\"\"Concatenated ensemble plus popularity coordinates, still exact MIPS.\"\"\"\n- def __init__(self, models, offline_features, global_weights):\n+ def __init__(self, models, offline_features, global_weights, preference_mix=.10):\n super().__init__()\n@@ -186,6 +186,15 @@\n self.global_weights = nn.Parameter(torch.tensor(global_weights, dtype=torch.float32))\n+ self.preference_mix = nn.Parameter(torch.tensor(float(preference_mix), dtype=torch.float32))\n \n- def encode_user(self, ids, weights):\n+ def encode_user(self, ids, weights, liked_ids=None, liked_weights=None):\n scale = len(self.models) ** -.5\n- pieces = [m.user_tower(ids, weights) * scale for m in self.models]\n+ pieces = []\n+ has_likes = liked_weights.sum(1, keepdim=True) > 0 if liked_weights is not None else None\n+ for m in self.models:\n+ u = m.user_tower(ids, weights)\n+ if liked_ids is not None:\n+ liked = m.user_tower(liked_ids, liked_weights)\n+ mixed = F.normalize((1-self.preference_mix)*u + self.preference_mix*liked, dim=1)\n+ u = torch.where(has_likes, mixed, u)\n+ pieces.append(u * scale)\n pieces.append(self.global_weights.expand(len(ids), -1))\n@@ -231,6 +240,8 @@\n @torch.inference_mode()\n-def tower_vectors(model, hist, weight, n_items):\n+def tower_vectors(model, hist, weight, n_items, liked_hist=None, liked_weight=None):\n model.eval(); users=[]\n for s in range(0,len(hist),256):\n- users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256])).numpy())\n+ lh = torch.from_numpy(liked_hist[s:s+256].astype(np.int64)) if liked_hist is not None else None\n+ lw = torch.from_numpy(liked_weight[s:s+256]) if liked_weight is not None else None\n+ users.append(model.encode_user(torch.from_numpy(hist[s:s+256].astype(np.int64)),torch.from_numpy(weight[s:s+256]),lh,lw).numpy())\n ids=torch.arange(n_items); item=[]\n@@ -253,2 +264,13 @@\n \n+def preference_context(path, uids, items):\n+ \"\"\"Encode cutoff-provided active likes as a second positive-history bag.\"\"\"\n+ p = pd.read_parquet(path); p = p[p.state == \"liked\"]\n+ u = positions(p.uid.to_numpy(), pd.Index(uids)); i = positions(p.item_id.to_numpy(), pd.Index(items))\n+ d = pd.DataFrame({\"u\":u[(u>=0)&(i>=0)], \"i\":i[(u>=0)&(i>=0)]})\n+ hist=np.full((len(uids),MAX_HISTORY),len(items),np.int32); weight=np.zeros((len(uids),MAX_HISTORY),np.float32)\n+ for raw_u,g in d.groupby(\"u\",sort=False):\n+ a=g.i.to_numpy(np.int32)[-MAX_HISTORY:]; hist[int(raw_u),:len(a)]=a; weight[int(raw_u),:len(a)]=1/len(a)\n+ return hist,weight\n+\n+\n def main():\n@@ -257,2 +279,3 @@\n ap.add_argument(\"--like-weight\",type=float,default=.05)\n+ ap.add_argument(\"--preference-mix\",type=float,default=.10)\n ap.add_argument(\"--output\",type=Path); ap.add_argument(\"--seeds\",default=\"42,77\"); a=ap.parse_args()\n@@ -270,3 +293,6 @@\n h,w=make_context(ev,cutoff,len(uids),len(items)); feat=recent_features(ev,lk,cutoff,len(items))\n- serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight]); uv,iv=tower_vectors(serving,h,w,len(items))\n+ pref_path=DATA/(\"test_preference_state.parquet\" if include else \"validation_preference_state.parquet\")\n+ ph,pw=preference_context(pref_path,uids,items)\n+ serving=ServingTwoTower(models,feat[:,[0,3]],[a.alpha,a.like_weight],a.preference_mix)\n+ uv,iv=tower_vectors(serving,h,w,len(items),ph,pw)\n out=a.output or (OUT/(\"submission.parquet\" if include else \"validation.parquet\"))\n@@ -275,4 +301,5 @@\n torch.save({\"model\":serving.state_dict(),\"dim\":iv.shape[1],\"alpha\":a.alpha,\"like_weight\":a.like_weight,\n- \"stage\":a.stage,\"seeds\":seeds},OUT/f\"model_{a.stage}.pt\")\n- print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"like_weight\":a.like_weight,\"cutoff\":cutoff,\"dimension\":iv.shape[1]}))\n+ \"preference_mix\":a.preference_mix,\"stage\":a.stage,\"seeds\":seeds},OUT/f\"model_{a.stage}.pt\")\n+ print(json.dumps({\"output\":str(out),\"alpha\":a.alpha,\"like_weight\":a.like_weight,\n+ \"preference_mix\":a.preference_mix,\"cutoff\":cutoff,\"dimension\":iv.shape[1]}))\n \n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd625be4487d1be6a24b3a44e1d26", "usage": { "input_tokens": 52116, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 1679, "reasoning_output_tokens": 513, "total_tokens": 53795 }, "turn_token_usage": { "input_tokens": 1583325, "cached_input_tokens": 1531648, "cache_write_input_tokens": 0, "output_tokens": 27129, "reasoning_output_tokens": 11411, "total_tokens": 1610454 }, "thread_token_usage": { "input_tokens": 1583325, "cached_input_tokens": 1531648, "cache_write_input_tokens": 0, "output_tokens": 27129, "reasoning_output_tokens": 11411, "total_tokens": 1610454 } } ## Step 64 · agent · 2026-09-19T00:24:42.043Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:24:49.481Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; f=tr.recent_features(ev,lk,int(max(ev['t'])),len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10)\\nst=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; miss,extra=serving.load_state_dict(st,strict=False); print('load',miss,extra)\\nh,w=tr.make_context(ev,int(max(ev['t'])),len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet')\\n# Save complete serving state including preference parameter.\\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt')\\nprint('wrote',uv.shape,iv.shape)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:25:06.831Z Script completed Wall time 17.3 seconds Output: load ['preference_mix'] [] wrote (8456, 130) (30000, 130) ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd645e4d887d1b549f22360eec7f0", "usage": { "input_tokens": 53819, "cached_input_tokens": 51968, "cache_write_input_tokens": 0, "output_tokens": 545, "reasoning_output_tokens": 122, "total_tokens": 54364 }, "turn_token_usage": { "input_tokens": 1637144, "cached_input_tokens": 1583616, "cache_write_input_tokens": 0, "output_tokens": 27674, "reasoning_output_tokens": 11533, "total_tokens": 1664818 }, "thread_token_usage": { "input_tokens": 1637144, "cached_input_tokens": 1583616, "cache_write_input_tokens": 0, "output_tokens": 27674, "reasoning_output_tokens": 11533, "total_tokens": 1664818 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b4f38895-8802-40da-bda3-fb7cd30b9ed2", "process_id": "38542", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; f=tr.recent_features(ev,lk,int(max(ev['t'])),len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10)\nst=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; miss,extra=serving.load_state_dict(st,strict=False); print('load',miss,extra)\nh,w=tr.make_context(ev,int(max(ev['t'])),len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet')\n# Save complete serving state including preference parameter.\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt')\nprint('wrote',uv.shape,iv.shape)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; f=tr.recent_features(ev,lk,int(max(ev['t'])),len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10)\nst=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; miss,extra=serving.load_state_dict(st,strict=False); print('load',miss,extra)\nh,w=tr.make_context(ev,int(max(ev['t'])),len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet')\n# Save complete serving state including preference parameter.\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt')\nprint('wrote',uv.shape,iv.shape)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "load ['preference_mix'] []\nwrote (8456, 130) (30000, 130)\n", "stderr": "", "aggregated_output": "load ['preference_mix'] []\nwrote (8456, 130) (30000, 130)\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 143656108 }, "formatted_output": "load ['preference_mix'] []\nwrote (8456, 130) (30000, 130)\n" } } ## Step 65 · agent · 2026-09-19T00:25:09.543Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:25:16.527Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nmodels=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:25:33.148Z Script completed Wall time 16.6 seconds Output: { "model": "solution/validation.parquet", "scores": { "quality_ndcg@10": 0.040099, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd663038c87d194f2af5800c53202", "usage": { "input_tokens": 54410, "cached_input_tokens": 53632, "cache_write_input_tokens": 0, "output_tokens": 480, "reasoning_output_tokens": 85, "total_tokens": 54890 }, "turn_token_usage": { "input_tokens": 1691554, "cached_input_tokens": 1637248, "cache_write_input_tokens": 0, "output_tokens": 28154, "reasoning_output_tokens": 11618, "total_tokens": 1719708 }, "thread_token_usage": { "input_tokens": 1691554, "cached_input_tokens": 1637248, "cache_write_input_tokens": 0, "output_tokens": 28154, "reasoning_output_tokens": 11618, "total_tokens": 1719708 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27c0847c-a7ab-41b1-ad2a-ffe47da864d9", "process_id": "64871", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.25,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw)\ntr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet')\ntorch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.25,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040099,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040099,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 414059917 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040099,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 66 · agent · 2026-09-19T00:25:41.186Z ### Thinking unavailable — no text recorded Active-like history provides a small but repeatable lift, bringing the selected public score to 0.040099. I’ve applied the same cutoff-correct channel to the final refit and regenerated the submission; it still uses a 130-dimensional user vector and independently precomputable item vector. I’m doing the final schema/eligibility audit and documenting the reproducible command now. ### exec · 2026-09-19T00:25:51.368Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+`train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID\\n+table or a global user-item matrix. The user tower takes at most 384 recent\\n+unique positive-listen item IDs, recency/play-quality weights, and the supplied\\n+cutoff-correct active-like history. It performs learned embedding pooling and\\n+a residual MLP. The independent item tower combines a trainable item embedding,\\n+catalog features, and a residual MLP. Two 64-dimensional models are\\n+concatenated, with two offline item-momentum coordinates, producing matched\\n+130-dimensional vectors. The complete relevance score is exactly\\n+`user_vector @ item_vector`; item vectors can therefore be computed offline and\\n+served from a MIPS index.\\n+\\n+Training uses eight leakage-safe rolling cuts. Each cut predicts novel organic\\n+80%-complete listens and organic likes in the seven days after a 30-minute gap.\\n+The loss is multi-positive in-batch contrastive cross-entropy; likes are\\n+oversampled to reflect their larger graded-DCG gain. The selected ensemble\\n+scored **0.040099 NDCG@10** on 4,263 scored public-validation users.\\n+\\n+Final refitting adds the now-observed validation window as the newest supervised\\n+rolling cut, then builds histories and item momentum through the validation\\n+cutoff. Reproduce the final artifact with:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_ranker.py \\\\\\n+ --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\\\\n+ --preference-mix 0.10 --seeds 42,77 \\\\\\n+ --output /app/solution/submission.parquet\\n+```\\n+\\n+The checked-in `submission.parquet` has exactly 100 ranked, distinct,\\n+test-eligible items for each target user.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:25:51.449Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d82fa592-1f1c-4f61-a071-137ae778be51", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n`train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID\ntable or a global user-item matrix. The user tower takes at most 384 recent\nunique positive-listen item IDs, recency/play-quality weights, and the supplied\ncutoff-correct active-like history. It performs learned embedding pooling and\na residual MLP. The independent item tower combines a trainable item embedding,\ncatalog features, and a residual MLP. Two 64-dimensional models are\nconcatenated, with two offline item-momentum coordinates, producing matched\n130-dimensional vectors. The complete relevance score is exactly\n`user_vector @ item_vector`; item vectors can therefore be computed offline and\nserved from a MIPS index.\n\nTraining uses eight leakage-safe rolling cuts. Each cut predicts novel organic\n80%-complete listens and organic likes in the seven days after a 30-minute gap.\nThe loss is multi-positive in-batch contrastive cross-entropy; likes are\noversampled to reflect their larger graded-DCG gain. The selected ensemble\nscored **0.040099 NDCG@10** on 4,263 scored public-validation users.\n\nFinal refitting adds the now-observed validation window as the newest supervised\nrolling cut, then builds histories and item momentum through the validation\ncutoff. Reproduce the final artifact with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\n --preference-mix 0.10 --seeds 42,77 \\\n --output /app/solution/submission.parquet\n```\n\nThe checked-in `submission.parquet` has exactly 100 ranked, distinct,\ntest-eligible items for each target user.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd67d4f2487d1ae3fdd873a4b4d51", "usage": { "input_tokens": 54956, "cached_input_tokens": 54272, "cache_write_input_tokens": 0, "output_tokens": 905, "reasoning_output_tokens": 338, "total_tokens": 55861 }, "turn_token_usage": { "input_tokens": 1746510, "cached_input_tokens": 1691520, "cache_write_input_tokens": 0, "output_tokens": 29059, "reasoning_output_tokens": 11956, "total_tokens": 1775569 }, "thread_token_usage": { "input_tokens": 1746510, "cached_input_tokens": 1691520, "cache_write_input_tokens": 0, "output_tokens": 29059, "reasoning_output_tokens": 11956, "total_tokens": 1775569 } } ## Step 67 · agent · 2026-09-19T00:25:55.180Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:26:00.560Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys,numpy as np,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\\ntargets=set(map(int,pd.read_parquet(D/'target_users.parquet').uid)); f=validate_ranking(read_ranking(p),targets)\\ne=CandidateSet(D/'test_eligible_candidates.npz')\\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\\nprint({'rows':len(f),'users':f.uid.nunique(),'items_min':int(f.groupby('uid').item_id.nunique().min()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'ineligible':len(bad),'size_bytes':p.stat().st_size})\\nPY\\nfind solution -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort\\nsed -n '1,240p' solution/README.md\\nrg -n \\\"latent_scores|write_rankings\\\\(\\\" solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:26:03.289Z Script completed Wall time 2.7 seconds Output: {'rows': 845600, 'users': 8456, 'items_min': 100, 'rank_min': 1, 'rank_max': 100, 'ineligible': 0, 'size_bytes': 1284355} README.md 1653 bytes baseline.py 1476 bytes global_features.npz 3483602 bytes model_final.pt 31999078 bytes model_validation.pt 31999323 bytes model_validation_seed42.pt 15879559 bytes model_validation_seed77.pt 15879249 bytes submission.parquet 1284355 bytes train_ranker.py 15727 bytes validation.parquet 1284395 bytes validation_neural.parquet 1401755 bytes validation_pop.parquet 728599 bytes validation_seed42_e14.parquet 1286838 bytes validation_seed77.parquet 1287676 bytes # Made for You two-tower ranker `train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID table or a global user-item matrix. The user tower takes at most 384 recent unique positive-listen item IDs, recency/play-quality weights, and the supplied cutoff-correct active-like history. It performs learned embedding pooling and a residual MLP. The independent item tower combines a trainable item embedding, catalog features, and a residual MLP. Two 64-dimensional models are concatenated, with two offline item-momentum coordinates, producing matched 130-dimensional vectors. The complete relevance score is exactly `user_vector @ item_vector`; item vectors can therefore be computed offline and served from a MIPS index. Training uses eight leakage-safe rolling cuts. Each cut predicts novel organic 80%-complete listens and organic likes in the seven days after a 30-minute gap. The loss is multi-positive in-batch contrastive cross-entropy; likes are oversampled to reflect their larger graded-DCG gain. The selected ensemble scored **0.040099 NDCG@10** on 4,263 scored public-validation users. Final refitting adds the now-observed validation window as the newest supervised rolling cut, then builds histories and item momentum through the validation cutoff. Reproduce the final artifact with: ```sh /opt/task-venv/bin/python /app/solution/train_ranker.py \ --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \ --preference-mix 0.10 --seeds 42,77 \ --output /app/solution/submission.parquet ``` The checked-in `submission.parquet` has exactly 100 ranked, distinct, test-eligible items for each target user. 252:def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path): 300: write_rankings(uids,items,uv,iv,elig,out) ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd69012e887d198f4c455c98c4f2e", "usage": { "input_tokens": 55885, "cached_input_tokens": 54784, "cache_write_input_tokens": 0, "output_tokens": 371, "reasoning_output_tokens": 36, "total_tokens": 56256 }, "turn_token_usage": { "input_tokens": 1802395, "cached_input_tokens": 1746304, "cache_write_input_tokens": 0, "output_tokens": 29430, "reasoning_output_tokens": 11992, "total_tokens": 1831825 }, "thread_token_usage": { "input_tokens": 1802395, "cached_input_tokens": 1746304, "cache_write_input_tokens": 0, "output_tokens": 29430, "reasoning_output_tokens": 11992, "total_tokens": 1831825 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b5423074-d8c0-4adf-b6d0-e25f9e39ff90", "process_id": "18679", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(map(int,pd.read_parquet(D/'target_users.parquet').uid)); f=validate_ranking(read_ranking(p),targets)\ne=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\nprint({'rows':len(f),'users':f.uid.nunique(),'items_min':int(f.groupby('uid').item_id.nunique().min()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'ineligible':len(bad),'size_bytes':p.stat().st_size})\nPY\nfind solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort\nsed -n '1,240p' solution/README.md\nrg -n \"latent_scores|write_rankings\\(\" solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(map(int,pd.read_parquet(D/'target_users.parquet').uid)); f=validate_ranking(read_ranking(p),targets)\ne=CandidateSet(D/'test_eligible_candidates.npz')\nbad=[(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not e.is_eligible(u,i)]\nprint({'rows':len(f),'users':f.uid.nunique(),'items_min':int(f.groupby('uid').item_id.nunique().min()),'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max()),'ineligible':len(bad),'size_bytes':p.stat().st_size})\nPY\nfind solution -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort\nsed -n '1,240p' solution/README.md\nrg -n \"latent_scores|write_rankings\\(\" solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'items_min': 100, 'rank_min': 1, 'rank_max': 100, 'ineligible': 0, 'size_bytes': 1284355}\nREADME.md 1653 bytes\nbaseline.py 1476 bytes\nglobal_features.npz 3483602 bytes\nmodel_final.pt 31999078 bytes\nmodel_validation.pt 31999323 bytes\nmodel_validation_seed42.pt 15879559 bytes\nmodel_validation_seed77.pt 15879249 bytes\nsubmission.parquet 1284355 bytes\ntrain_ranker.py 15727 bytes\nvalidation.parquet 1284395 bytes\nvalidation_neural.parquet 1401755 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_seed42_e14.parquet 1286838 bytes\nvalidation_seed77.parquet 1287676 bytes\n# Made for You two-tower ranker\n\n`train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID\ntable or a global user-item matrix. The user tower takes at most 384 recent\nunique positive-listen item IDs, recency/play-quality weights, and the supplied\ncutoff-correct active-like history. It performs learned embedding pooling and\na residual MLP. The independent item tower combines a trainable item embedding,\ncatalog features, and a residual MLP. Two 64-dimensional models are\nconcatenated, with two offline item-momentum coordinates, producing matched\n130-dimensional vectors. The complete relevance score is exactly\n`user_vector @ item_vector`; item vectors can therefore be computed offline and\nserved from a MIPS index.\n\nTraining uses eight leakage-safe rolling cuts. Each cut predicts novel organic\n80%-complete listens and organic likes in the seven days after a 30-minute gap.\nThe loss is multi-positive in-batch contrastive cross-entropy; likes are\noversampled to reflect their larger graded-DCG gain. The selected ensemble\nscored **0.040099 NDCG@10** on 4,263 scored public-validation users.\n\nFinal refitting adds the now-observed validation window as the newest supervised\nrolling cut, then builds histories and item momentum through the validation\ncutoff. Reproduce the final artifact with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\n --preference-mix 0.10 --seeds 42,77 \\\n --output /app/solution/submission.parquet\n```\n\nThe checked-in `submission.parquet` has exactly 100 ranked, distinct,\ntest-eligible items for each target user.\n252:def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\n300: write_rankings(uids,items,uv,iv,elig,out)\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'items_min': 100, 'rank_min': 1, 'rank_max': 100, 'ineligible': 0, 'size_bytes': 1284355}\nREADME.md 1653 bytes\nbaseline.py 1476 bytes\nglobal_features.npz 3483602 bytes\nmodel_final.pt 31999078 bytes\nmodel_validation.pt 31999323 bytes\nmodel_validation_seed42.pt 15879559 bytes\nmodel_validation_seed77.pt 15879249 bytes\nsubmission.parquet 1284355 bytes\ntrain_ranker.py 15727 bytes\nvalidation.parquet 1284395 bytes\nvalidation_neural.parquet 1401755 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_seed42_e14.parquet 1286838 bytes\nvalidation_seed77.parquet 1287676 bytes\n# Made for You two-tower ranker\n\n`train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID\ntable or a global user-item matrix. The user tower takes at most 384 recent\nunique positive-listen item IDs, recency/play-quality weights, and the supplied\ncutoff-correct active-like history. It performs learned embedding pooling and\na residual MLP. The independent item tower combines a trainable item embedding,\ncatalog features, and a residual MLP. Two 64-dimensional models are\nconcatenated, with two offline item-momentum coordinates, producing matched\n130-dimensional vectors. The complete relevance score is exactly\n`user_vector @ item_vector`; item vectors can therefore be computed offline and\nserved from a MIPS index.\n\nTraining uses eight leakage-safe rolling cuts. Each cut predicts novel organic\n80%-complete listens and organic likes in the seven days after a 30-minute gap.\nThe loss is multi-positive in-batch contrastive cross-entropy; likes are\noversampled to reflect their larger graded-DCG gain. The selected ensemble\nscored **0.040099 NDCG@10** on 4,263 scored public-validation users.\n\nFinal refitting adds the now-observed validation window as the newest supervised\nrolling cut, then builds histories and item momentum through the validation\ncutoff. Reproduce the final artifact with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\n --preference-mix 0.10 --seeds 42,77 \\\n --output /app/solution/submission.parquet\n```\n\nThe checked-in `submission.parquet` has exactly 100 ranked, distinct,\ntest-eligible items for each target user.\n252:def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\n300: write_rankings(uids,items,uv,iv,elig,out)\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 551015338 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'items_min': 100, 'rank_min': 1, 'rank_max': 100, 'ineligible': 0, 'size_bytes': 1284355}\nREADME.md 1653 bytes\nbaseline.py 1476 bytes\nglobal_features.npz 3483602 bytes\nmodel_final.pt 31999078 bytes\nmodel_validation.pt 31999323 bytes\nmodel_validation_seed42.pt 15879559 bytes\nmodel_validation_seed77.pt 15879249 bytes\nsubmission.parquet 1284355 bytes\ntrain_ranker.py 15727 bytes\nvalidation.parquet 1284395 bytes\nvalidation_neural.parquet 1401755 bytes\nvalidation_pop.parquet 728599 bytes\nvalidation_seed42_e14.parquet 1286838 bytes\nvalidation_seed77.parquet 1287676 bytes\n# Made for You two-tower ranker\n\n`train_ranker.py` implements a CPU PyTorch retrieval model without a user-ID\ntable or a global user-item matrix. The user tower takes at most 384 recent\nunique positive-listen item IDs, recency/play-quality weights, and the supplied\ncutoff-correct active-like history. It performs learned embedding pooling and\na residual MLP. The independent item tower combines a trainable item embedding,\ncatalog features, and a residual MLP. Two 64-dimensional models are\nconcatenated, with two offline item-momentum coordinates, producing matched\n130-dimensional vectors. The complete relevance score is exactly\n`user_vector @ item_vector`; item vectors can therefore be computed offline and\nserved from a MIPS index.\n\nTraining uses eight leakage-safe rolling cuts. Each cut predicts novel organic\n80%-complete listens and organic likes in the seven days after a 30-minute gap.\nThe loss is multi-positive in-batch contrastive cross-entropy; likes are\noversampled to reflect their larger graded-DCG gain. The selected ensemble\nscored **0.040099 NDCG@10** on 4,263 scored public-validation users.\n\nFinal refitting adds the now-observed validation window as the newest supervised\nrolling cut, then builds histories and item momentum through the validation\ncutoff. Reproduce the final artifact with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_ranker.py \\\n --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\n --preference-mix 0.10 --seeds 42,77 \\\n --output /app/solution/submission.parquet\n```\n\nThe checked-in `submission.parquet` has exactly 100 ranked, distinct,\ntest-eligible items for each target user.\n252:def write_rankings(uids,items,user_vec,item_vec,elig_path,out_path):\n300: write_rankings(uids,items,uv,iv,elig,out)\n" } } ## Step 68 · agent · 2026-09-19T00:26:09.347Z ### Thinking unavailable — no text recorded ## Step 69 · agent · 2026-09-19T00:26:09.632Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:26:26.479Z { "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\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items); p=pd.read_parquet('/task/data/validation_preference_state.parquet')\\ndef ctx(state):\\n q=p[p.state==state]; u=ui.get_indexer(q.uid).astype(np.int32); i=ii.get_indexer(q.item_id).astype(np.int32); ok=(u>=0)&(i>=0); d=pd.DataFrame({'u':u[ok],'i':i[ok]}); H=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); W=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\\n for x,g in d.groupby('u',sort=False):\\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; H[x,:len(a)]=a; W[x,:len(a)]=1/len(a); has[x]=1\\n return H,W,has\\nlh,lw,lhas=ctx('liked'); dh,dw,dhas=ctx('disliked'); ul=[];up=[];ud=[];iv=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); A=[];B=[];C=[]\\n with torch.inference_mode():\\n for s in range(0,len(uids),256):\\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy()); C.append(m.user_tower(torch.from_numpy(dh[s:s+256].astype(np.int64)),torch.from_numpy(dw[s:s+256])).numpy())\\n ul.append(np.concatenate(A));up.append(np.concatenate(B));ud.append(np.concatenate(C)); V=[]; ids=torch.arange(len(items))\\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\\n iv.append(np.concatenate(V))\\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\\ndef score(delta):\\n base=0\\n for a,b,d,v in zip(ul,up,ud,iv):\\n mix=a.copy(); mix[lhas]=.9*a[lhas]+.1*b[lhas]; mix[dhas]-=delta*d[dhas]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\\n vals=[]\\n for u,g in gold.items():\\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\\n return np.mean(vals)\\nfor d in [0,.025,.05,.075,.1,.15,.2,.3]: print(d,score(d),flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:26:49.241Z Script completed Wall time 22.8 seconds Output: 0 0.04009882430587546 0.025 0.03996916033978143 0.05 0.04010213391471554 0.075 0.040114343878963815 0.1 0.040179189182868 0.15 0.040144344100384505 0.2 0.04007421638876688 0.3 0.03986589581664793 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd69b855487d19b9b667c25362f55", "usage": { "input_tokens": 56894, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 1224, "reasoning_output_tokens": 256, "total_tokens": 58118 }, "turn_token_usage": { "input_tokens": 1859289, "cached_input_tokens": 1801984, "cache_write_input_tokens": 0, "output_tokens": 30654, "reasoning_output_tokens": 12248, "total_tokens": 1889943 }, "thread_token_usage": { "input_tokens": 1859289, "cached_input_tokens": 1801984, "cache_write_input_tokens": 0, "output_tokens": 30654, "reasoning_output_tokens": 12248, "total_tokens": 1889943 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-784a1d74-5eda-47ad-9eb6-dbd91803855a", "process_id": "12886", "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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items); p=pd.read_parquet('/task/data/validation_preference_state.parquet')\ndef ctx(state):\n q=p[p.state==state]; u=ui.get_indexer(q.uid).astype(np.int32); i=ii.get_indexer(q.item_id).astype(np.int32); ok=(u>=0)&(i>=0); d=pd.DataFrame({'u':u[ok],'i':i[ok]}); H=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); W=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\n for x,g in d.groupby('u',sort=False):\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; H[x,:len(a)]=a; W[x,:len(a)]=1/len(a); has[x]=1\n return H,W,has\nlh,lw,lhas=ctx('liked'); dh,dw,dhas=ctx('disliked'); ul=[];up=[];ud=[];iv=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); A=[];B=[];C=[]\n with torch.inference_mode():\n for s in range(0,len(uids),256):\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy()); C.append(m.user_tower(torch.from_numpy(dh[s:s+256].astype(np.int64)),torch.from_numpy(dw[s:s+256])).numpy())\n ul.append(np.concatenate(A));up.append(np.concatenate(B));ud.append(np.concatenate(C)); V=[]; ids=torch.arange(len(items))\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\n iv.append(np.concatenate(V))\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(delta):\n base=0\n for a,b,d,v in zip(ul,up,ud,iv):\n mix=a.copy(); mix[lhas]=.9*a[lhas]+.1*b[lhas]; mix[dhas]-=delta*d[dhas]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor d in [0,.025,.05,.075,.1,.15,.2,.3]: print(d,score(d),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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ui=pd.Index(uids); ii=pd.Index(items); p=pd.read_parquet('/task/data/validation_preference_state.parquet')\ndef ctx(state):\n q=p[p.state==state]; u=ui.get_indexer(q.uid).astype(np.int32); i=ii.get_indexer(q.item_id).astype(np.int32); ok=(u>=0)&(i>=0); d=pd.DataFrame({'u':u[ok],'i':i[ok]}); H=np.full((len(uids),tr.MAX_HISTORY),len(items),np.int32); W=np.zeros((len(uids),tr.MAX_HISTORY),np.float32); has=np.zeros(len(uids),bool)\n for x,g in d.groupby('u',sort=False):\n a=g.i.to_numpy(np.int32)[-tr.MAX_HISTORY:]; H[x,:len(a)]=a; W[x,:len(a)]=1/len(a); has[x]=1\n return H,W,has\nlh,lw,lhas=ctx('liked'); dh,dw,dhas=ctx('disliked'); ul=[];up=[];ud=[];iv=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); m.eval(); A=[];B=[];C=[]\n with torch.inference_mode():\n for s in range(0,len(uids),256):\n A.append(m.user_tower(torch.from_numpy(h[s:s+256].astype(np.int64)),torch.from_numpy(w[s:s+256])).numpy()); B.append(m.user_tower(torch.from_numpy(lh[s:s+256].astype(np.int64)),torch.from_numpy(lw[s:s+256])).numpy()); C.append(m.user_tower(torch.from_numpy(dh[s:s+256].astype(np.int64)),torch.from_numpy(dw[s:s+256])).numpy())\n ul.append(np.concatenate(A));up.append(np.concatenate(B));ud.append(np.concatenate(C)); V=[]; ids=torch.arange(len(items))\n for s in range(0,len(items),2048):V.append(m.item_tower(ids[s:s+2048]).numpy())\n iv.append(np.concatenate(V))\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids)))\ndef score(delta):\n base=0\n for a,b,d,v in zip(ul,up,ud,iv):\n mix=a.copy(); mix[lhas]=.9*a[lhas]+.1*b[lhas]; mix[dhas]-=delta*d[dhas]; mix/=np.maximum(np.linalg.norm(mix,axis=1,keepdims=True),1e-8); base+=mix@v.T/2\n vals=[]\n for u,g in gold.items():\n r=E.uid_row[u]; s=base[r]+.25*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; vals.append(ndcg_at_k(items[ix].tolist(),g,10))\n return np.mean(vals)\nfor d in [0,.025,.05,.075,.1,.15,.2,.3]: print(d,score(d),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 0.04009882430587546\n0.025 0.03996916033978143\n0.05 0.04010213391471554\n0.075 0.040114343878963815\n0.1 0.040179189182868\n0.15 0.040144344100384505\n0.2 0.04007421638876688\n0.3 0.03986589581664793\n", "stderr": "", "aggregated_output": "0 0.04009882430587546\n0.025 0.03996916033978143\n0.05 0.04010213391471554\n0.075 0.040114343878963815\n0.1 0.040179189182868\n0.15 0.040144344100384505\n0.2 0.04007421638876688\n0.3 0.03986589581664793\n", "exit_code": 0, "duration": { "secs": 22, "nanos": 583703564 }, "formatted_output": "0 0.04009882430587546\n0.025 0.03996916033978143\n0.05 0.04010213391471554\n0.075 0.040114343878963815\n0.1 0.040179189182868\n0.15 0.040144344100384505\n0.2 0.04007421638876688\n0.3 0.03986589581664793\n" } } ## Step 70 · agent · 2026-09-19T00:26:57.889Z ### Thinking unavailable — no text recorded The negative-preference channel is effectively neutral (best change is only +0.00008), so I’m not adding that extra complexity. I’m doing one final robustness check on whether the global-momentum coordinate should vary with available history length; this remains a user-tower feature and can improve cold-versus-warm calibration without affecting serving latency. ### exec · 2026-09-19T00:27:11.119Z { "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\\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); bases=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); S=tr.ServingTwoTower([m],f[:,[0,3]],[0,0],.1); uv,iv=tr.tower_vectors(S,h,w,len(items),ph,pw); bases.append(uv[:,:64]@iv[:,:64].T)\\nbase=sum(bases)/2; E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids))); alphas=[.15,.2,.225,.25,.275,.3,.35,.4]; rec=[]\\nfor u,g in gold.items():\\n r=E.uid_row[u]; row=[]\\n for a in alphas:\\n s=base[r]+a*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; row.append(ndcg_at_k(items[ix].tolist(),g,10))\\n rec.append((r,int((h[r]!=len(items)).sum()),row))\\ncounts=np.array([x[1] for x in rec]); vals=np.array([x[2] for x in rec]); print('quantiles',np.percentile(counts,[0,20,40,60,80,100]))\\nfor lo,hi in zip([-1,100,200,300],[100,200,300,10000]):\\n m=(counts>lo)&(counts<=hi); means=vals[m].mean(0); print(lo,hi,m.sum(),list(zip(alphas,np.round(means,6))),'best',alphas[int(means.argmax())])\\nprint('constant',list(zip(alphas,np.round(vals.mean(0),6))))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-19T00:27:28.042Z Script completed Wall time 16.9 seconds Output: quantiles [ 1. 188. 384. 384. 384. 384.] -1 100 458 [(0.15, np.float64(0.046063)), (0.2, np.float64(0.048019)), (0.225, np.float64(0.047408)), (0.25, np.float64(0.046406)), (0.275, np.float64(0.046475)), (0.3, np.float64(0.046833)), (0.35, np.float64(0.045842)), (0.4, np.float64(0.045881))] best 0.2 100 200 437 [(0.15, np.float64(0.04132)), (0.2, np.float64(0.042699)), (0.225, np.float64(0.042424)), (0.25, np.float64(0.042281)), (0.275, np.float64(0.04162)), (0.3, np.float64(0.040528)), (0.35, np.float64(0.040481)), (0.4, np.float64(0.039911))] best 0.2 200 300 384 [(0.15, np.float64(0.048737)), (0.2, np.float64(0.052858)), (0.225, np.float64(0.053659)), (0.25, np.float64(0.053311)), (0.275, np.float64(0.051967)), (0.3, np.float64(0.052173)), (0.35, np.float64(0.051322)), (0.4, np.float64(0.051634))] best 0.225 300 10000 2984 [(0.15, np.float64(0.037367)), (0.2, np.float64(0.037247)), (0.225, np.float64(0.03711)), (0.25, np.float64(0.037111)), (0.275, np.float64(0.036922)), (0.3, np.float64(0.036446)), (0.35, np.float64(0.035821)), (0.4, np.float64(0.035325))] best 0.15 constant [(0.15, np.float64(0.039731)), (0.2, np.float64(0.040369)), (0.225, np.float64(0.040252)), (0.25, np.float64(0.040099)), (0.275, np.float64(0.039785)), (0.3, np.float64(0.039397)), (0.35, np.float64(0.038772)), (0.4, np.float64(0.038398))] ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd6c98a7c87d1adbc47d117e31f50", "usage": { "input_tokens": 58242, "cached_input_tokens": 56704, "cache_write_input_tokens": 0, "output_tokens": 1093, "reasoning_output_tokens": 332, "total_tokens": 59335 }, "turn_token_usage": { "input_tokens": 1917531, "cached_input_tokens": 1858688, "cache_write_input_tokens": 0, "output_tokens": 31747, "reasoning_output_tokens": 12580, "total_tokens": 1949278 }, "thread_token_usage": { "input_tokens": 1917531, "cached_input_tokens": 1858688, "cache_write_input_tokens": 0, "output_tokens": 31747, "reasoning_output_tokens": 12580, "total_tokens": 1949278 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4a51a99b-911b-4e03-bf21-c0ab7b5e98b1", "process_id": "70004", "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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); bases=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); S=tr.ServingTwoTower([m],f[:,[0,3]],[0,0],.1); uv,iv=tr.tower_vectors(S,h,w,len(items),ph,pw); bases.append(uv[:,:64]@iv[:,:64].T)\nbase=sum(bases)/2; E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids))); alphas=[.15,.2,.225,.25,.275,.3,.35,.4]; rec=[]\nfor u,g in gold.items():\n r=E.uid_row[u]; row=[]\n for a in alphas:\n s=base[r]+a*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; row.append(ndcg_at_k(items[ix].tolist(),g,10))\n rec.append((r,int((h[r]!=len(items)).sum()),row))\ncounts=np.array([x[1] for x in rec]); vals=np.array([x[2] for x in rec]); print('quantiles',np.percentile(counts,[0,20,40,60,80,100]))\nfor lo,hi in zip([-1,100,200,300],[100,200,300,10000]):\n m=(counts>lo)&(counts<=hi); means=vals[m].mean(0); print(lo,hi,m.sum(),list(zip(alphas,np.round(means,6))),'best',alphas[int(means.argmax())])\nprint('constant',list(zip(alphas,np.round(vals.mean(0),6))))\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\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet,ndcg_at_k; from evaluate_public import validation_truth\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nh,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); f=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); bases=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); S=tr.ServingTwoTower([m],f[:,[0,3]],[0,0],.1); uv,iv=tr.tower_vectors(S,h,w,len(items),ph,pw); bases.append(uv[:,:64]@iv[:,:64].T)\nbase=sum(bases)/2; E=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); bits=np.unpackbits(E.packed,axis=1,count=len(items),bitorder='little').astype(bool); gold=validation_truth(E,set(map(int,uids))); alphas=[.15,.2,.225,.25,.275,.3,.35,.4]; rec=[]\nfor u,g in gold.items():\n r=E.uid_row[u]; row=[]\n for a in alphas:\n s=base[r]+a*f[:,0]+.05*f[:,3]; s=np.where(bits[r],s,-np.inf); ix=np.argpartition(s,-10)[-10:]; ix=ix[np.argsort(-s[ix])]; row.append(ndcg_at_k(items[ix].tolist(),g,10))\n rec.append((r,int((h[r]!=len(items)).sum()),row))\ncounts=np.array([x[1] for x in rec]); vals=np.array([x[2] for x in rec]); print('quantiles',np.percentile(counts,[0,20,40,60,80,100]))\nfor lo,hi in zip([-1,100,200,300],[100,200,300,10000]):\n m=(counts>lo)&(counts<=hi); means=vals[m].mean(0); print(lo,hi,m.sum(),list(zip(alphas,np.round(means,6))),'best',alphas[int(means.argmax())])\nprint('constant',list(zip(alphas,np.round(vals.mean(0),6))))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "quantiles [ 1. 188. 384. 384. 384. 384.]\n-1 100 458 [(0.15, np.float64(0.046063)), (0.2, np.float64(0.048019)), (0.225, np.float64(0.047408)), (0.25, np.float64(0.046406)), (0.275, np.float64(0.046475)), (0.3, np.float64(0.046833)), (0.35, np.float64(0.045842)), (0.4, np.float64(0.045881))] best 0.2\n100 200 437 [(0.15, np.float64(0.04132)), (0.2, np.float64(0.042699)), (0.225, np.float64(0.042424)), (0.25, np.float64(0.042281)), (0.275, np.float64(0.04162)), (0.3, np.float64(0.040528)), (0.35, np.float64(0.040481)), (0.4, np.float64(0.039911))] best 0.2\n200 300 384 [(0.15, np.float64(0.048737)), (0.2, np.float64(0.052858)), (0.225, np.float64(0.053659)), (0.25, np.float64(0.053311)), (0.275, np.float64(0.051967)), (0.3, np.float64(0.052173)), (0.35, np.float64(0.051322)), (0.4, np.float64(0.051634))] best 0.225\n300 10000 2984 [(0.15, np.float64(0.037367)), (0.2, np.float64(0.037247)), (0.225, np.float64(0.03711)), (0.25, np.float64(0.037111)), (0.275, np.float64(0.036922)), (0.3, np.float64(0.036446)), (0.35, np.float64(0.035821)), (0.4, np.float64(0.035325))] best 0.15\nconstant [(0.15, np.float64(0.039731)), (0.2, np.float64(0.040369)), (0.225, np.float64(0.040252)), (0.25, np.float64(0.040099)), (0.275, np.float64(0.039785)), (0.3, np.float64(0.039397)), (0.35, np.float64(0.038772)), (0.4, np.float64(0.038398))]\n", "stderr": "", "aggregated_output": "quantiles [ 1. 188. 384. 384. 384. 384.]\n-1 100 458 [(0.15, np.float64(0.046063)), (0.2, np.float64(0.048019)), (0.225, np.float64(0.047408)), (0.25, np.float64(0.046406)), (0.275, np.float64(0.046475)), (0.3, np.float64(0.046833)), (0.35, np.float64(0.045842)), (0.4, np.float64(0.045881))] best 0.2\n100 200 437 [(0.15, np.float64(0.04132)), (0.2, np.float64(0.042699)), (0.225, np.float64(0.042424)), (0.25, np.float64(0.042281)), (0.275, np.float64(0.04162)), (0.3, np.float64(0.040528)), (0.35, np.float64(0.040481)), (0.4, np.float64(0.039911))] best 0.2\n200 300 384 [(0.15, np.float64(0.048737)), (0.2, np.float64(0.052858)), (0.225, np.float64(0.053659)), (0.25, np.float64(0.053311)), (0.275, np.float64(0.051967)), (0.3, np.float64(0.052173)), (0.35, np.float64(0.051322)), (0.4, np.float64(0.051634))] best 0.225\n300 10000 2984 [(0.15, np.float64(0.037367)), (0.2, np.float64(0.037247)), (0.225, np.float64(0.03711)), (0.25, np.float64(0.037111)), (0.275, np.float64(0.036922)), (0.3, np.float64(0.036446)), (0.35, np.float64(0.035821)), (0.4, np.float64(0.035325))] best 0.15\nconstant [(0.15, np.float64(0.039731)), (0.2, np.float64(0.040369)), (0.225, np.float64(0.040252)), (0.25, np.float64(0.040099)), (0.275, np.float64(0.039785)), (0.3, np.float64(0.039397)), (0.35, np.float64(0.038772)), (0.4, np.float64(0.038398))]\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 745079614 }, "formatted_output": "quantiles [ 1. 188. 384. 384. 384. 384.]\n-1 100 458 [(0.15, np.float64(0.046063)), (0.2, np.float64(0.048019)), (0.225, np.float64(0.047408)), (0.25, np.float64(0.046406)), (0.275, np.float64(0.046475)), (0.3, np.float64(0.046833)), (0.35, np.float64(0.045842)), (0.4, np.float64(0.045881))] best 0.2\n100 200 437 [(0.15, np.float64(0.04132)), (0.2, np.float64(0.042699)), (0.225, np.float64(0.042424)), (0.25, np.float64(0.042281)), (0.275, np.float64(0.04162)), (0.3, np.float64(0.040528)), (0.35, np.float64(0.040481)), (0.4, np.float64(0.039911))] best 0.2\n200 300 384 [(0.15, np.float64(0.048737)), (0.2, np.float64(0.052858)), (0.225, np.float64(0.053659)), (0.25, np.float64(0.053311)), (0.275, np.float64(0.051967)), (0.3, np.float64(0.052173)), (0.35, np.float64(0.051322)), (0.4, np.float64(0.051634))] best 0.225\n300 10000 2984 [(0.15, np.float64(0.037367)), (0.2, np.float64(0.037247)), (0.225, np.float64(0.03711)), (0.25, np.float64(0.037111)), (0.275, np.float64(0.036922)), (0.3, np.float64(0.036446)), (0.35, np.float64(0.035821)), (0.4, np.float64(0.035325))] best 0.15\nconstant [(0.15, np.float64(0.039731)), (0.2, np.float64(0.040369)), (0.225, np.float64(0.040252)), (0.25, np.float64(0.040099)), (0.275, np.float64(0.039785)), (0.3, np.float64(0.039397)), (0.35, np.float64(0.038772)), (0.4, np.float64(0.038398))]\n" } } ## Step 71 · agent · 2026-09-19T00:27:33.791Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:27:34.559Z { "code": "// @exec: {\"max_output_tokens\": 1000}\ntext((458*.048019+437*.042699+384*.053659+2984*.037367)/4263)\n" } ### Tool output · 2026-09-19T00:27:34.675Z Script completed Wall time 0.0 seconds Output: 0.040525533427163966 ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd6f039a087d18b840f45a0321586", "usage": { "input_tokens": 59992, "cached_input_tokens": 58112, "cache_write_input_tokens": 0, "output_tokens": 298, "reasoning_output_tokens": 247, "total_tokens": 60290 }, "turn_token_usage": { "input_tokens": 1977523, "cached_input_tokens": 1916800, "cache_write_input_tokens": 0, "output_tokens": 32045, "reasoning_output_tokens": 12827, "total_tokens": 2009568 }, "thread_token_usage": { "input_tokens": 1977523, "cached_input_tokens": 1916800, "cache_write_input_tokens": 0, "output_tokens": 32045, "reasoning_output_tokens": 12827, "total_tokens": 2009568 } } ## Step 72 · agent · 2026-09-19T00:27:39.392Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:27:43.297Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.25)\\n+ ap.add_argument(\\\"--epochs\\\",type=int,default=14); ap.add_argument(\\\"--alpha\\\",type=float,default=.20)\\n*** Update File: /app/solution/README.md\\n@@\\n-scored **0.040099 NDCG@10** on 4,263 scored public-validation users.\\n+scored **0.040369 NDCG@10** on 4,263 scored public-validation users.\\n@@\\n- --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\\\\n+ --stage final --epochs 14 --alpha 0.20 --like-weight 0.05 \\\\\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-19T00:27:43.394Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-bb22ded5-0026-4fa9-b5b4-17ae636d03e5", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -277,3 +277,3 @@\n ap=argparse.ArgumentParser(); ap.add_argument(\"--stage\",choices=[\"validation\",\"final\"],required=True)\n- ap.add_argument(\"--epochs\",type=int,default=14); ap.add_argument(\"--alpha\",type=float,default=.25)\n+ ap.add_argument(\"--epochs\",type=int,default=14); ap.add_argument(\"--alpha\",type=float,default=.20)\n ap.add_argument(\"--like-weight\",type=float,default=.05)\n", "move_path": null }, "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -17,3 +17,3 @@\n oversampled to reflect their larger graded-DCG gain. The selected ensemble\n-scored **0.040099 NDCG@10** on 4,263 scored public-validation users.\n+scored **0.040369 NDCG@10** on 4,263 scored public-validation users.\n \n@@ -25,3 +25,3 @@\n /opt/task-venv/bin/python /app/solution/train_ranker.py \\\n- --stage final --epochs 14 --alpha 0.25 --like-weight 0.05 \\\n+ --stage final --epochs 14 --alpha 0.20 --like-weight 0.05 \\\n --preference-mix 0.10 --seeds 42,77 \\\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\nM /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd6f7212087d1af564475ab3e3c8e", "usage": { "input_tokens": 60321, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 408, "reasoning_output_tokens": 182, "total_tokens": 60729 }, "turn_token_usage": { "input_tokens": 2037844, "cached_input_tokens": 1976576, "cache_write_input_tokens": 0, "output_tokens": 32453, "reasoning_output_tokens": 13009, "total_tokens": 2070297 }, "thread_token_usage": { "input_tokens": 2037844, "cached_input_tokens": 1976576, "cache_write_input_tokens": 0, "output_tokens": 32453, "reasoning_output_tokens": 13009, "total_tokens": 2070297 } } ## Step 73 · agent · 2026-09-19T00:27:48.840Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:27:55.486Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nmodels=[]\\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:28:11.764Z Script completed Wall time 16.3 seconds Output: { "model": "solution/validation.parquet", "scores": { "quality_ndcg@10": 0.040369, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd6ffa4e487d19876a701ab47e5f4", "usage": { "input_tokens": 60753, "cached_input_tokens": 60160, "cache_write_input_tokens": 0, "output_tokens": 415, "reasoning_output_tokens": 37, "total_tokens": 61168 }, "turn_token_usage": { "input_tokens": 2098597, "cached_input_tokens": 2036736, "cache_write_input_tokens": 0, "output_tokens": 32868, "reasoning_output_tokens": 13046, "total_tokens": 2131465 }, "thread_token_usage": { "input_tokens": 2098597, "cached_input_tokens": 2036736, "cache_write_input_tokens": 0, "output_tokens": 32868, "reasoning_output_tokens": 13046, "total_tokens": 2131465 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b6ae180-07bb-4b1f-8241-5a017e3fa584", "process_id": "37880", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(False); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[]\nfor fn in ['model_validation_seed42.pt','model_validation_seed77.pt']:\n m=tr.TwoTower(len(items),64,tr.static_features(cat)); m.load_state_dict(torch.load('/app/solution/'+fn,weights_only=True)['model']); models.append(m)\nf=tr.recent_features(ev,lk,tr.TRAIN_END,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); h,w=tr.make_context(ev,tr.TRAIN_END,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'validation_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'validation_eligible_candidates.npz',tr.OUT/'validation.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'validation','seeds':[42,77]},tr.OUT/'model_validation.pt')\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 105773999 }, "formatted_output": "{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 74 · agent · 2026-09-19T00:28:15.244Z ### Thinking unavailable — no text recorded ### exec · 2026-09-19T00:28:22.034Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,torch\\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; cutoff=int(max(ev['t'])); f=tr.recent_features(ev,lk,cutoff,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); state=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; serving.load_state_dict(state); serving.global_weights.data.copy_(torch.tensor([.20,.05])); serving.preference_mix.data.fill_(.10)\\nh,w=tr.make_context(ev,cutoff,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt'); print('wrote final',uv.shape,iv.shape)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:28:37.408Z Script completed Wall time 15.4 seconds Output: wrote final (8456, 130) (30000, 130) ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd71be77087d19e56e1004f9c4792", "usage": { "input_tokens": 61234, "cached_input_tokens": 60544, "cache_write_input_tokens": 0, "output_tokens": 431, "reasoning_output_tokens": 46, "total_tokens": 61665 }, "turn_token_usage": { "input_tokens": 2159831, "cached_input_tokens": 2097280, "cache_write_input_tokens": 0, "output_tokens": 33299, "reasoning_output_tokens": 13092, "total_tokens": 2193130 }, "thread_token_usage": { "input_tokens": 2159831, "cached_input_tokens": 2097280, "cache_write_input_tokens": 0, "output_tokens": 33299, "reasoning_output_tokens": 13092, "total_tokens": 2193130 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9f73b81b-99bf-4d24-8d56-ac1e84198b4e", "process_id": "81498", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; cutoff=int(max(ev['t'])); f=tr.recent_features(ev,lk,cutoff,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); state=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; serving.load_state_dict(state); serving.global_weights.data.copy_(torch.tensor([.20,.05])); serving.preference_mix.data.fill_(.10)\nh,w=tr.make_context(ev,cutoff,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt'); print('wrote final',uv.shape,iv.shape)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,torch\nsys.path.insert(0,'/app/solution'); import train_ranker as tr\nli,ld=tr.load_events(True); cat,uids,items,ev,lk=tr.encode_raw(li,ld); del li,ld\nmodels=[tr.TwoTower(len(items),64,tr.static_features(cat)) for _ in range(2)]; cutoff=int(max(ev['t'])); f=tr.recent_features(ev,lk,cutoff,len(items)); serving=tr.ServingTwoTower(models,f[:,[0,3]],[.20,.05],.10); state=torch.load('/app/solution/model_final.pt',weights_only=True)['model']; serving.load_state_dict(state); serving.global_weights.data.copy_(torch.tensor([.20,.05])); serving.preference_mix.data.fill_(.10)\nh,w=tr.make_context(ev,cutoff,len(uids),len(items)); ph,pw=tr.preference_context(tr.DATA/'test_preference_state.parquet',uids,items); uv,iv=tr.tower_vectors(serving,h,w,len(items),ph,pw); tr.write_rankings(uids,items,uv,iv,tr.DATA/'test_eligible_candidates.npz',tr.OUT/'submission.parquet'); torch.save({'model':serving.state_dict(),'dim':iv.shape[1],'alpha':.20,'like_weight':.05,'preference_mix':.10,'stage':'final','seeds':[42,77]},tr.OUT/'model_final.pt'); print('wrote final',uv.shape,iv.shape)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote final (8456, 130) (30000, 130)\n", "stderr": "", "aggregated_output": "wrote final (8456, 130) (30000, 130)\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 197629826 }, "formatted_output": "wrote final (8456, 130) (30000, 130)\n" } } ## Step 75 · agent · 2026-09-19T00:28:47.041Z ### Thinking unavailable — no text recorded The cold/warm calibration check favored a slightly more conservative momentum weight (0.20), improving the selected public score to 0.040369. I’ve regenerated both validation and final artifacts with that setting. The final step is a fresh contract audit after this last rewrite. ### exec · 2026-09-19T00:28:54.607Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport sys,pandas as pd\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(P),targets); c=CandidateSet(D/'test_eligible_candidates.npz'); bad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));\\nprint('FINAL_AUDIT',{'rows':len(f),'users':f.uid.nunique(),'rows_per_user':sorted(f.groupby('uid').size().unique().tolist()),'unique_items_min':int(f.groupby('uid').item_id.nunique().min()),'ranks':(int(f['rank'].min()),int(f['rank'].max())),'ineligible':bad,'schema':{x:str(y) for x,y in f.dtypes.items()}})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\\nls -lh solution/submission.parquet solution/validation.parquet solution/model_final.pt solution/train_ranker.py solution/README.md\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-19T00:29:00.894Z Script completed Wall time 6.3 seconds Output: FINAL_AUDIT {'rows': 845600, 'users': 8456, 'rows_per_user': [100], 'unique_items_min': 100, 'ranks': (1, 100), 'ineligible': 0, 'schema': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}} { "model": "solution/validation.parquet", "scores": { "quality_ndcg@10": 0.040369, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 1.7K Sep 19 00:27 solution/README.md -rw-r--r-- 1 agent agent 31M Sep 19 00:28 solution/model_final.pt -rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/submission.parquet -rw-r--r-- 1 agent agent 16K Sep 19 00:27 solution/train_ranker.py -rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/validation.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd73590f887d1a10297eb298dff32", "usage": { "input_tokens": 61705, "cached_input_tokens": 61056, "cache_write_input_tokens": 0, "output_tokens": 827, "reasoning_output_tokens": 403, "total_tokens": 62532 }, "turn_token_usage": { "input_tokens": 2221536, "cached_input_tokens": 2158336, "cache_write_input_tokens": 0, "output_tokens": 34126, "reasoning_output_tokens": 13495, "total_tokens": 2255662 }, "thread_token_usage": { "input_tokens": 2221536, "cached_input_tokens": 2158336, "cache_write_input_tokens": 0, "output_tokens": 34126, "reasoning_output_tokens": 13495, "total_tokens": 2255662 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-acae8b9f-3937-46a6-85c3-f00b1bba2cc6", "process_id": "27651", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(P),targets); c=CandidateSet(D/'test_eligible_candidates.npz'); bad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));\nprint('FINAL_AUDIT',{'rows':len(f),'users':f.uid.nunique(),'rows_per_user':sorted(f.groupby('uid').size().unique().tolist()),'unique_items_min':int(f.groupby('uid').item_id.nunique().min()),'ranks':(int(f['rank'].min()),int(f['rank'].max())),'ineligible':bad,'schema':{x:str(y) for x,y in f.dtypes.items()}})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/validation.parquet solution/model_final.pt solution/train_ranker.py solution/README.md" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py solution/baseline.py && /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,read_ranking,validate_ranking\nD=Path('/task/data'); P=Path('/app/solution/submission.parquet'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(read_ranking(P),targets); c=CandidateSet(D/'test_eligible_candidates.npz'); bad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None));\nprint('FINAL_AUDIT',{'rows':len(f),'users':f.uid.nunique(),'rows_per_user':sorted(f.groupby('uid').size().unique().tolist()),'unique_items_min':int(f.groupby('uid').item_id.nunique().min()),'ranks':(int(f['rank'].min()),int(f['rank'].max())),'ineligible':bad,'schema':{x:str(y) for x,y in f.dtypes.items()}})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/validation.parquet solution/model_final.pt solution/train_ranker.py solution/README.md" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "FINAL_AUDIT {'rows': 845600, 'users': 8456, 'rows_per_user': [100], 'unique_items_min': 100, 'ranks': (1, 100), 'ineligible': 0, 'schema': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.7K Sep 19 00:27 solution/README.md\n-rw-r--r-- 1 agent agent 31M Sep 19 00:28 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/submission.parquet\n-rw-r--r-- 1 agent agent 16K Sep 19 00:27 solution/train_ranker.py\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/validation.parquet\n", "stderr": "", "aggregated_output": "FINAL_AUDIT {'rows': 845600, 'users': 8456, 'rows_per_user': [100], 'unique_items_min': 100, 'ranks': (1, 100), 'ineligible': 0, 'schema': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.7K Sep 19 00:27 solution/README.md\n-rw-r--r-- 1 agent agent 31M Sep 19 00:28 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/submission.parquet\n-rw-r--r-- 1 agent agent 16K Sep 19 00:27 solution/train_ranker.py\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/validation.parquet\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 108465037 }, "formatted_output": "FINAL_AUDIT {'rows': 845600, 'users': 8456, 'rows_per_user': [100], 'unique_items_min': 100, 'ranks': (1, 100), 'ineligible': 0, 'schema': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n{\n \"model\": \"solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.040369,\n \"quality_users\": 4263\n }\n}\n-rw-r--r-- 1 agent agent 1.7K Sep 19 00:27 solution/README.md\n-rw-r--r-- 1 agent agent 31M Sep 19 00:28 solution/model_final.pt\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/submission.parquet\n-rw-r--r-- 1 agent agent 16K Sep 19 00:27 solution/train_ranker.py\n-rw-r--r-- 1 agent agent 1.3M Sep 19 00:28 solution/validation.parquet\n" } } ## Step 76 · agent · 2026-09-19T00:29:02.767Z ### Thinking unavailable — no text recorded Built and validated the CPU PyTorch two-tower ranker. - Final submission: [submission.parquet](/app/solution/submission.parquet) - Training/inference code: [train_ranker.py](/app/solution/train_ranker.py) - Documentation: [README.md](/app/solution/README.md) - Public validation ranking: [validation.parquet](/app/solution/validation.parquet) - Public NDCG@10: **0.040369** - Final audit: 8,456 users × 100 items, ranks 1–100, no duplicates, and zero ineligible items - Serving representation: exact dot product between independently computable 130-dimensional user and item vectors - Final model was refitted with validation interactions included as observed history and supervision. ### Harness context { "type": "token_usage_record", "thread_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "session_id": "01a0b6fb-7ac8-7ca2-a4ce-ab9627f5a4c6", "root_turn_id": "01a0b6fb-7ad8-7ba1-a6ec-87f646ac0af3", "response_id": "resp_0912a8362493a7ee016aadd74d100887d1bed0a00819a46951", "usage": { "input_tokens": 62810, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 209, "reasoning_output_tokens": 39, "total_tokens": 63019 }, "turn_token_usage": { "input_tokens": 2284346, "cached_input_tokens": 2219904, "cache_write_input_tokens": 0, "output_tokens": 34335, "reasoning_output_tokens": 13534, "total_tokens": 2318681 }, "thread_token_usage": { "input_tokens": 2284346, "cached_input_tokens": 2219904, "cache_write_input_tokens": 0, "output_tokens": 34335, "reasoning_output_tokens": 13534, "total_tokens": 2318681 } }