yambda-discovery-ranking-two-tower-base / sol-max-0-153-4-retriever-r04-20260924T221413Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-25T00:25:21.104Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-25T00:25:22.562Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-25T00:25:22.562Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-25T00:25:22.562Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-25T00:25:22.563Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-25 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-25", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } ## Step 6 · user · 2026-09-25T00:25:22.582Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-25T00:25:25.342Z ### Thinking unavailable — no text recorded I’ll first profile the event windows, catalog, eligibility masks, and evaluator semantics. Then I’ll build a scalable event-sampled two-tower baseline, measure it publicly, and iterate while keeping every artifact under `/app/solution/`. ### exec · 2026-09-25T00:25:28.088Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lah /task/data && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:25:28.157Z Script completed Wall time 0.1 seconds Output: total 340M dr-xr-xr-x 1 root root 4.0K Sep 25 00:08 . dr-xr-xr-x 1 root root 4.0K Sep 25 00:21 .. -r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet -r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet -r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet #!/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() """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), } # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf72c3ac87d29ac364de9968da08", "usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 }, "turn_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 }, "thread_token_usage": { "input_tokens": 14576, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 253, "reasoning_output_tokens": 97, "total_tokens": 14829 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a88997aa-5360-4980-9ac2-e09abec688df", "process_id": "26185", "command": [ "/bin/sh", "-lc", "ls -lah /task/data && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,260p' /task/ranking_evaluation.py && sed -n '1,220p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lah /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,240p' /task/evaluate_public.py", "name": "evaluate_public.py", "path": "/task/evaluate_public.py" }, { "type": "read", "cmd": "sed -n '1,260p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" }, { "type": "read", "cmd": "sed -n '1,220p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 8080 }, "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:08 .\ndr-xr-xr-x 1 root root 4.0K Sep 25 00:21 ..\n-r--r--r-- 1 root root 342K Sep 25 00:07 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:07 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:07 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:07 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:07 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:07 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:07 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:07 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:07 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:07 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:07 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:07 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:07 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:07 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:08 validation_preference_state.parquet\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } ## Step 8 · agent · 2026-09-25T00:25:29.835Z ### Thinking unavailable — no text recorded The evaluator confirms that only the top 10 affect quality, while all 100 outputs must remain cutoff-eligible. I’m now quantifying overlap, repeat behavior, recency, and preference-state signal so the tower inputs and sampled objective match the actual discovery labels. ### exec · 2026-09-25T00:25:35.341Z { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\\nprint('\\\\nNPZ')\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=mask.sum(1)\\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:25:35.429Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f5d057d-ed70-4d24-96fe-e13ef2c2a20a", "process_id": "35860", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, pf.metadata.num_rows, pf.schema_arrow)\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,z[k].dtype) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 7180 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf78725c87d2b688ff2b7c760cd9", "usage": { "input_tokens": 19151, "cached_input_tokens": 14336, "cache_write_input_tokens": 0, "output_tokens": 352, "reasoning_output_tokens": 46, "total_tokens": 19503 }, "turn_token_usage": { "input_tokens": 33727, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 605, "reasoning_output_tokens": 143, "total_tokens": 34332 }, "thread_token_usage": { "input_tokens": 33727, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 605, "reasoning_output_tokens": 143, "total_tokens": 34332 } } ## Step 9 · agent · 2026-09-25T00:25:36.988Z ### Thinking unavailable — no text recorded The default shell interpreter is not the task’s pinned Python environment, so I’m locating the provided runtime before doing any modeling; no packages will be installed or changed. ### exec · 2026-09-25T00:25:39.566Z { "code": "const r = await tools.exec_command({\"cmd\":\"command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | head -50; ls -la /task; ls -la /app\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:25:39.661Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python Python 3.12.13 total 36 dr-xr-xr-x 1 root root 4096 Sep 25 00:21 . drwxr-xr-x 1 root root 4096 Sep 25 00:25 .. -r--r--r-- 1 root root 0 Sep 25 00:07 .keep dr-xr-xr-x 1 root root 4096 Sep 25 00:08 data -r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py -r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt total 12 drwxr-xr-x 3 agent agent 4096 Sep 25 00:21 . drwxr-xr-x 1 root root 4096 Sep 25 00:25 .. drwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf7faa5087d2ab70c5a291f557d6", "usage": { "input_tokens": 19559, "cached_input_tokens": 18944, "cache_write_input_tokens": 0, "output_tokens": 181, "reasoning_output_tokens": 34, "total_tokens": 19740 }, "turn_token_usage": { "input_tokens": 53286, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 786, "reasoning_output_tokens": 177, "total_tokens": 54072 }, "thread_token_usage": { "input_tokens": 53286, "cached_input_tokens": 44416, "cache_write_input_tokens": 0, "output_tokens": 786, "reasoning_output_tokens": 177, "total_tokens": 54072 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6a03c07b-2dd2-4f94-92a2-27f6b4e46851", "process_id": "78072", "command": [ "/bin/sh", "-lc", "command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; ls -la /task; ls -la /app" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; python --version; command -v uv || true; find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -50; ls -la /task; ls -la /app" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7980 }, "formatted_output": "/usr/local/bin/python\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\n-r--r--r-- 1 root root 0 Sep 25 00:07 .keep\ndr-xr-xr-x 1 root root 4096 Sep 25 00:08 data\n-r--r--r-- 1 root root 5774 Sep 25 00:07 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:07 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 25 00:07 requirements.txt\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:21 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:25 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:21 solution\n" } } ## Step 10 · agent · 2026-09-25T00:25:41.587Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:25:43.269Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:25:44.064Z Script completed Wall time 0.8 seconds Output: /usr/local/bin/python3.12 /usr/local/bin/python3.12-config /usr/bin/python3.11 /usr/local/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_MANAGED_BY_NPM=1 CODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex CODEX_SESSION_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4 CODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=802f34340b55 LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 NVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin NVM_CD_FLAGS= NVM_DIR=/home/agent/.nvm NVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node PAGER=cat PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/home/agent/.nvm/versions/node/v22.23.3/bin/codex ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf83e6f487d29198a363e601cf24", "usage": { "input_tokens": 20009, "cached_input_tokens": 19328, "cache_write_input_tokens": 0, "output_tokens": 141, "reasoning_output_tokens": 36, "total_tokens": 20150 }, "turn_token_usage": { "input_tokens": 73295, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 927, "reasoning_output_tokens": 213, "total_tokens": 74222 }, "thread_token_usage": { "input_tokens": 73295, "cached_input_tokens": 63744, "cache_write_input_tokens": 0, "output_tokens": 927, "reasoning_output_tokens": 213, "total_tokens": 74222 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-638d17c3-aee9-48cb-93e3-9fc4475bfe5d", "process_id": "90967", "command": [ "/bin/sh", "-lc", "find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type f -path '*/bin/python*' 2>/dev/null | head -100; find / -type d -name 'site-packages' 2>/dev/null | head -50; env | sort | sed -n '1,200p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "stderr": "", "aggregated_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 628445346 }, "formatted_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/bin/python3.11\n/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCODEX_THREAD_ID=01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=802f34340b55\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n" } } ## Step 11 · agent · 2026-09-25T00:25:45.376Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:25:47.507Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\\nimport pandas,torch,pyarrow,numpy\\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:25:51.421Z Script completed Wall time 3.9 seconds Output: /opt/task-venv: total 32 dr-xr-xr-x 1 root root 4096 Sep 25 00:20 . drwxr-xr-x 1 root root 4096 Sep 25 00:20 .. -r--r--r-- 1 root root 0 Sep 25 00:20 .lock dr-xr-xr-x 1 root root 4096 Sep 25 00:20 bin dr-xr-xr-x 1 root root 4096 Sep 25 00:20 etc dr-xr-xr-x 1 root root 4096 Sep 25 00:20 include dr-xr-xr-x 1 root root 4096 Sep 25 00:20 lib lrwxrwxrwx 1 root root 3 Sep 25 00:20 lib64 -> lib -r--r--r-- 1 root root 171 Sep 25 00:20 pyvenv.cfg dr-xr-xr-x 1 root root 4096 Sep 25 00:20 share /opt/task-venv/bin: total 124 dr-xr-xr-x 1 root root 4096 Sep 25 00:20 . dr-xr-xr-x 1 root root 4096 Sep 25 00:20 .. -r--r--r-- 1 root root 9033 Sep 25 00:20 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 25 00:20 activate -r--r--r-- 1 root root 920 Sep 25 00:20 activate.csh -r--r--r-- 1 root root 2195 Sep 25 00:20 activate.fish -r-xr-xr-x 1 root root 302 Sep 25 00:20 chardetect -r-xr-xr-x 1 root root 304 Sep 25 00:20 dotenv -r-xr-xr-x 1 root root 308 Sep 25 00:20 f2py -r-xr-xr-x 1 root root 299 Sep 25 00:20 idna -r-xr-xr-x 1 root root 297 Sep 25 00:20 isympy -r-xr-xr-x 1 root root 305 Sep 25 00:20 jsonschema -r-xr-xr-x 1 root root 311 Sep 25 00:20 jupyter -r-xr-xr-x 1 root root 311 Sep 25 00:20 jupyter-migrate -r-xr-xr-x 1 root root 316 Sep 25 00:20 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Sep 25 00:20 jupyter-trust -r-xr-xr-x 1 root root 311 Sep 25 00:20 jupytext -r-xr-xr-x 1 root root 315 Sep 25 00:20 jupytext-config -r-xr-xr-x 1 root root 301 Sep 25 00:20 kaggle -r-xr-xr-x 1 root root 312 Sep 25 00:20 markdown-it -r-xr-xr-x 1 root root 325 Sep 25 00:20 normalizer -r-xr-xr-x 1 root root 308 Sep 25 00:20 numpy-config -r-xr-xr-x 1 root root 313 Sep 25 00:20 pip -r-xr-xr-x 1 root root 313 Sep 25 00:20 pip3 -r-xr-xr-x 1 root root 313 Sep 25 00:20 pip3.12 lrwxrwxrwx 1 root root 21 Sep 25 00:20 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Sep 25 00:20 python3 -> python lrwxrwxrwx 1 root root 6 Sep 25 00:20 python3.12 -> python -r-xr-xr-x 1 root root 307 Sep 25 00:20 slugify -r-xr-xr-x 1 root root 321 Sep 25 00:20 torchfrtrace -r-xr-xr-x 1 root root 312 Sep 25 00:20 torchrun -r-xr-xr-x 1 root root 299 Sep 25 00:20 tqdm 2.3.3 2.8.0+cpu 18.1.0 2.2.6 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf88430887d2bfc96d82e6ac88ff", "usage": { "input_tokens": 20686, "cached_input_tokens": 19840, "cache_write_input_tokens": 0, "output_tokens": 143, "reasoning_output_tokens": 16, "total_tokens": 20829 }, "turn_token_usage": { "input_tokens": 93981, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1070, "reasoning_output_tokens": 229, "total_tokens": 95051 }, "thread_token_usage": { "input_tokens": 93981, "cached_input_tokens": 83584, "cache_write_input_tokens": 0, "output_tokens": 1070, "reasoning_output_tokens": 229, "total_tokens": 95051 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-075a3cfd-763d-45b7-8eae-bb6be237b1c3", "process_id": "96507", "command": [ "/bin/sh", "-lc", "ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -la /opt/task-venv /opt/task-venv/bin 2>/dev/null || true; PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python - <<'PY'\nimport pandas,torch,pyarrow,numpy\nprint(pandas.__version__,torch.__version__,pyarrow.__version__,numpy.__version__)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 745136212 }, "formatted_output": "2.3.3 2.8.0+cpu 18.1.0 2.2.6\n" } } ## Step 12 · agent · 2026-09-25T00:25:56.115Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:26:01.389Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas,torch\\nprint(pandas.__version__, torch.__version__)\\nPY\\n/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor p in sorted(D.glob('*.parquet')):\\n pf=pq.ParquetFile(p)\\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\\nprint('\\\\nNPZ')\\nfor s in ['validation','test']:\\n z=np.load(D/f'{s}_eligible_candidates.npz')\\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\\n cnt=mask.sum(1)\\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:26:07.857Z Script completed Wall time 6.5 seconds Output: 2.3.3 2.8.0+cpu candidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')] target_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')] test_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] train_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] train_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] val_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] validation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] NPZ validation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243 test {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bf91914887d2ad8e7d574b371c03", "usage": { "input_tokens": 21883, "cached_input_tokens": 20480, "cache_write_input_tokens": 0, "output_tokens": 368, "reasoning_output_tokens": 67, "total_tokens": 22251 }, "turn_token_usage": { "input_tokens": 115864, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 1438, "reasoning_output_tokens": 296, "total_tokens": 117302 }, "thread_token_usage": { "input_tokens": 115864, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 1438, "reasoning_output_tokens": 296, "total_tokens": 117302 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-607e85c2-0f2a-4436-80a1-1b2ca909583f", "process_id": "18288", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch\nprint(pandas.__version__, torch.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas,torch\nprint(pandas.__version__, torch.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p)\n print(p.name, f'{pf.metadata.num_rows:,}', [(x.name,str(x.type)) for x in pf.schema_arrow])\nprint('\\nNPZ')\nfor s in ['validation','test']:\n z=np.load(D/f'{s}_eligible_candidates.npz')\n print(s,{k:(z[k].shape,str(z[k].dtype)) for k in z.files})\n mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n cnt=mask.sum(1)\n print('eligible counts',np.quantile(cnt,[0,0.01,.1,.5,.9,.99,1]),'density',mask.mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n", "stderr": "", "aggregated_output": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 293138061 }, "formatted_output": "2.3.3 2.8.0+cpu\ncandidate_catalog.parquet 30,000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 8,456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_preference_state.parquet 484,776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 101,075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 814,831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 43,484,159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 19,960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 292,665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 3,563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 37,795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 1,501,697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 9,346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_preference_state.parquet 469,124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n\nNPZ\nvalidation {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] density 0.9689102097130243\ntest {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')}\neligible counts [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] density 0.9680439293598234\n" } } ## Step 13 · agent · 2026-09-25T00:26:13.911Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:26:19.265Z { "code": "const r = await tools.exec_command({\"cmd\":\"free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\ntar=pd.read_parquet(D/'target_users.parquet')\\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\\n del x\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:26:22.379Z Script completed Wall time 3.1 seconds Output: /bin/sh: 1: free: not found Filesystem Size Used Avail Use% Mounted on overlay 96G 15G 82G 16% / overlay 96G 15G 82G 16% / item_id positive_listens organic_positive_listens popularity_rank 5862961 26615 20620 1 6901374 25536 17347 2 5635052 24691 17194 3 3542184 24075 17699 4 9378983 24036 18195 5 5463340 22746 17398 6 8213481 22505 17071 7 906358 22238 17183 8 2859641 18380 13704 9 3033749 16682 12267 10 item_id positive_listens organic_positive_listens popularity_rank 1309246 163 138 29998 1634958 163 46 29999 1670524 163 73 30000 uid recent_positive_listens history_positive_listens count 8456.000000 8456.000000 8456.000000 mean 502289.108325 1251.076750 3236.440752 std 287953.395912 1414.340904 3697.575286 min 100.000000 20.000000 20.000000 1% 10655.000000 25.000000 32.000000 10% 102050.000000 101.000000 204.000000 50% 503850.000000 784.500000 1920.500000 90% 899950.000000 3004.000000 8096.500000 99% 989745.000000 6541.450000 16804.050000 max 1000000.000000 19592.000000 26597.000000 train_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419 val_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606 train_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003 val_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfa0397487d280fdd547c06aa6e4", "usage": { "input_tokens": 23052, "cached_input_tokens": 21760, "cache_write_input_tokens": 0, "output_tokens": 553, "reasoning_output_tokens": 247, "total_tokens": 23605 }, "turn_token_usage": { "input_tokens": 138916, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 543, "total_tokens": 140907 }, "thread_token_usage": { "input_tokens": 138916, "cached_input_tokens": 125824, "cache_write_input_tokens": 0, "output_tokens": 1991, "reasoning_output_tokens": 543, "total_tokens": 140907 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-efd0d06d-6ad5-4962-ab1f-d9678a99132f", "process_id": "34958", "command": [ "/bin/sh", "-lc", "free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ntar=pd.read_parquet(D/'target_users.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\n del x\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "free -h; df -h /app /tmp; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\ntar=pd.read_parquet(D/'target_users.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail(3).to_string(index=False))\nprint(tar.describe(percentiles=[.01,.1,.5,.9,.99]).to_string())\nfor name in ['train_listens','val_listens','train_likes','val_likes']:\n x=pd.read_parquet(D/f'{name}.parquet',columns=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if 'listens' in name else []))\n print(name,'time',x.timestamp.min(),x.timestamp.max(),'users',x.uid.nunique(),'items',x.item_id.nunique(),'target rows',x.uid.isin(tar.uid).sum(),'catalog rows',x.item_id.isin(cat.item_id).sum(),'organic',x.is_organic.mean())\n del x\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n", "stderr": "", "aggregated_output": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 946130479 }, "formatted_output": " item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n1% 10655.000000 25.000000 32.000000\n10% 102050.000000 101.000000 204.000000\n50% 503850.000000 784.500000 1920.500000\n90% 899950.000000 3004.000000 8096.500000\n99% 989745.000000 6541.450000 16804.050000\nmax 1000000.000000 19592.000000 26597.000000\ntrain_listens time 0 24786795 users 9238 items 845395 target rows 43222297 catalog rows 31249613 organic 0.5169151828370419\nval_listens time 24788600 25393395 users 8196 items 191747 target rows 1482553 catalog rows 1011771 organic 0.517072352145606\ntrain_likes time 80 24786790 users 8144 items 172303 target rows 690968 catalog rows 554824 organic 0.5667911505576003\nval_likes time 24788815 25393390 users 4114 items 22902 target rows 33177 catalog rows 21055 organic 0.6662521497552586\n" } } ## Step 14 · agent · 2026-09-25T00:26:34.580Z ### Thinking unavailable — no text recorded ## Step 15 · agent · 2026-09-25T00:26:40.640Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:26:54.911Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, time, resource\\nfrom pathlib import Path\\nD=Path('/task/data')\\nt0=time.time()\\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\\n# truths\\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\\ntruth['catalog']=cols>=0; truth['eligible']=elig\\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\\net=truth[truth.eligible]\\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\\nprint('loading train pairs')\\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\\nh=h[h.uid.isin(target)].drop_duplicates()\\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\\n# compare eligibility exclusion to history for sample\\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\\nvals=[]\\nfor u in ss:\\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:27:16.321Z Script completed Wall time 21.4 seconds Output: truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible grade 1 249692 0.669629 0.133573 3 17195 0.495958 0.256877 quality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417} label item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0} loading train pairs loaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375 unique 12305820 rss 3008.65234375 eligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0} elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfaeb82487d28172dd0b4510c04d", "usage": { "input_tokens": 24326, "cached_input_tokens": 22912, "cache_write_input_tokens": 0, "output_tokens": 1756, "reasoning_output_tokens": 769, "total_tokens": 26082 }, "turn_token_usage": { "input_tokens": 163242, "cached_input_tokens": 148736, "cache_write_input_tokens": 0, "output_tokens": 3747, "reasoning_output_tokens": 1312, "total_tokens": 166989 }, "thread_token_usage": { "input_tokens": 163242, "cached_input_tokens": 148736, "cache_write_input_tokens": 0, "output_tokens": 3747, "reasoning_output_tokens": 1312, "total_tokens": 166989 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e366be08-eef8-4bc7-b72a-646bfeb24b37", "process_id": "77318", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data')\nt0=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\n# truths\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\ntruth['catalog']=cols>=0; truth['eligible']=elig\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\net=truth[truth.eligible]\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\nprint('loading train pairs')\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nh=h[h.uid.isin(target)].drop_duplicates()\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\n# compare eligibility exclusion to history for sample\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\nvals=[]\nfor u in ss:\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, time, resource\nfrom pathlib import Path\nD=Path('/task/data')\nt0=time.time()\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; imap={int(x):i for i,x in enumerate(items)}; umap={int(x):i for i,x in enumerate(uids)}\n# truths\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nli=li[(li.uid.isin(target))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); li['grade']=1\nla=la[(la.uid.isin(target))&(la.is_organic==1)][['uid','item_id']].drop_duplicates(); la['grade']=3\ntruth=pd.concat([li,la]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ncols=np.array([imap.get(int(x),-1) for x in truth.item_id],dtype=np.int32); rows=np.array([umap.get(int(x),-1) for x in truth.uid],dtype=np.int32)\nok=(cols>=0)&(rows>=0); elig=np.zeros(len(truth),bool); elig[ok]=((z['packed_eligible'][rows[ok],cols[ok]//8]>>(cols[ok]%8))&1).astype(bool)\ntruth['catalog']=cols>=0; truth['eligible']=elig\nprint('truth pairs',len(truth),'catalog',truth.catalog.mean(),'eligible',truth.eligible.mean(),truth.groupby('grade').agg(n=('uid','size'),cat=('catalog','mean'),eligible=('eligible','mean')))\net=truth[truth.eligible]\nprint('quality users',et.uid.nunique(),'pairs/user quantiles',et.groupby('uid').size().quantile([0,.1,.5,.9,.99,1]).to_dict(),'grades',et.grade.value_counts().to_dict())\nprint('label item pop rank quantiles',pd.Series([imap[int(x)]+1 for x in et.item_id]).quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict())\nprint('loading train pairs')\nh=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'])\nprint('loaded',len(h),'sec',time.time()-t0,'rssMB',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nh=h[h.uid.isin(target)].drop_duplicates()\nprint('unique',len(h),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024)\nm=et.merge(h,on=['uid','item_id'],how='left',indicator=True)\nprint('eligible val truth previously listened', (m._merge=='both').mean(), 'by grade',m.assign(prev=m._merge=='both').groupby('grade').prev.mean().to_dict())\n# compare eligibility exclusion to history for sample\nrng=np.random.default_rng(0); ss=rng.choice(uids,100,replace=False)\nsub=h[h.uid.isin(ss)&h.item_id.isin(cat)]\nprev_by={u:set(x.item_id.astype(int)) for u,x in sub.groupby('uid')}\nvals=[]\nfor u in ss:\n row=umap[int(u)]; mask=np.unpackbits(z['packed_eligible'][row],count=len(items),bitorder='little').astype(bool)\n excluded=set(map(int,items[~mask])); prev=prev_by.get(u,set()); vals.append((len(excluded),len(prev),len(excluded&prev),len(excluded-prev),len(prev-excluded)))\nprint('elig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible',np.mean(vals,axis=0),'first',vals[:5])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n", "stderr": "", "aggregated_output": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n", "exit_code": 0, "duration": { "secs": 21, "nanos": 241610421 }, "formatted_output": "truth pairs 266887 catalog 0.6584397141861537 eligible 0.14151682172604885 n cat eligible\ngrade \n1 249692 0.669629 0.133573\n3 17195 0.495958 0.256877\nquality users 4263 pairs/user quantiles {0.0: 1.0, 0.1: 1.0, 0.5: 4.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} grades {1: 33352, 3: 4417}\nlabel item pop rank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 0.99: 29101.0, 1.0: 30000.0}\nloading train pairs\nloaded 43484159 sec 0.8015799522399902 rssMB 1000.2734375\nunique 12305820 rss 3008.65234375\neligible val truth previously listened 0.0 by grade {1: 0.0, 3: 0.0}\nelig vs prev avg excluded,prev,intersection,excl-notprev,prev-eligible [846.88 845.38 845.38 1.5 0. ] first [(1228, 1228, 1228, 0, 0), (519, 519, 519, 0, 0), (1338, 1334, 1334, 4, 0), (500, 500, 500, 0, 0), (692, 691, 691, 1, 0)]\n" } } ## Step 16 · agent · 2026-09-25T00:27:26.509Z ### Thinking unavailable — no text recorded ## Step 17 · agent · 2026-09-25T00:27:31.845Z ### Thinking unavailable — no text recorded Eligibility is essentially “catalog items never previously listened to,” which makes the task true discovery rather than replay prediction. Public labels are sparse (4,263 scored users), so I’m establishing popularity and recency baselines first, then the neural model will be trained on many rolling seven-day pseudo-futures from the training period. ### exec · 2026-09-25T00:27:40.662Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\\n# variants intrinsic catalog columns\\norders={\\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\\n}\\nfor name,order in orders.items():\\n out=[]\\n for r,u in enumerate(t):\\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\\n take=order[mask[order]][:100]\\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\\n print(p)\\nPY\\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:28:10.840Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5bfe4978c87d2978d0898d509ef96", "usage": { "input_tokens": 26547, "cached_input_tokens": 24192, "cache_write_input_tokens": 0, "output_tokens": 1305, "reasoning_output_tokens": 802, "total_tokens": 27852 }, "turn_token_usage": { "input_tokens": 189789, "cached_input_tokens": 172928, "cache_write_input_tokens": 0, "output_tokens": 5052, "reasoning_output_tokens": 2114, "total_tokens": 194841 }, "thread_token_usage": { "input_tokens": 189789, "cached_input_tokens": 172928, "cache_write_input_tokens": 0, "output_tokens": 5052, "reasoning_output_tokens": 2114, "total_tokens": 194841 } } ## Step 18 · agent · 2026-09-25T00:28:13.407Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:14.396Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution; ps aux | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:28:14.467Z Script completed Wall time 0.0 seconds Output: total 0 /bin/sh: 1: ps: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3b2470f-e505-45d1-815c-a43028b39641", "process_id": "80387", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution; ps aux | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution; ps aux | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 0\n/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "total 0\n/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3541 }, "formatted_output": "total 0\n/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c01b1b0487d2b4cfdd46f1acc50a", "usage": { "input_tokens": 27875, "cached_input_tokens": 26368, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 73, "total_tokens": 28009 }, "turn_token_usage": { "input_tokens": 217664, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 5186, "reasoning_output_tokens": 2187, "total_tokens": 222850 }, "thread_token_usage": { "input_tokens": 217664, "cached_input_tokens": 199296, "cache_write_input_tokens": 0, "output_tokens": 5186, "reasoning_output_tokens": 2187, "total_tokens": 222850 } } ## Step 19 · agent · 2026-09-25T00:28:18.823Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:25.545Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd,numpy as np,time\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution')\\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:28:26.710Z Script completed Wall time 1.2 seconds Output: SESSION_ID=98244 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c01ed36887d2994d421fc029e9ed", "usage": { "input_tokens": 28047, "cached_input_tokens": 27648, "cache_write_input_tokens": 0, "output_tokens": 554, "reasoning_output_tokens": 170, "total_tokens": 28601 }, "turn_token_usage": { "input_tokens": 245711, "cached_input_tokens": 226944, "cache_write_input_tokens": 0, "output_tokens": 5740, "reasoning_output_tokens": 2357, "total_tokens": 251451 }, "thread_token_usage": { "input_tokens": 245711, "cached_input_tokens": 226944, "cache_write_input_tokens": 0, "output_tokens": 5740, "reasoning_output_tokens": 2357, "total_tokens": 251451 } } ## Step 20 · agent · 2026-09-25T00:28:28.307Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:29.103Z { "code": "const r = await tools.write_stdin({\"session_id\":98244,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:28:45.525Z Script completed Wall time 16.4 seconds Output: load unpack (8456, 30000) /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c02b0b6087d2b9dca4bf12d99f1b", "usage": { "input_tokens": 28629, "cached_input_tokens": 27904, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 28683 }, "turn_token_usage": { "input_tokens": 274340, "cached_input_tokens": 254848, "cache_write_input_tokens": 0, "output_tokens": 5794, "reasoning_output_tokens": 2364, "total_tokens": 280134 }, "thread_token_usage": { "input_tokens": 274340, "cached_input_tokens": 254848, "cache_write_input_tokens": 0, "output_tokens": 5794, "reasoning_output_tokens": 2364, "total_tokens": 280134 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d121910f-2b29-4908-8230-4d08c7ea493d", "process_id": "98244", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution')\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy(); cat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); print('load')\nmask=np.unpackbits(z['packed_eligible'],axis=1,count=len(cat),bitorder='little').astype(bool); print('unpack',mask.shape)\nfor name,order in {'pop':np.argsort(cat.popularity_rank.to_numpy()),'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20))}.items():\n us=np.repeat(t,100); its=np.empty(len(t)*100,np.int64)\n for r in range(len(t)): its[r*100:(r+1)*100]=z['item_ids'][order[mask[r,order]][:100]]\n ranks=np.tile(np.arange(1,101,dtype=np.int64),len(t))\n p=O/f'validation_{name}.parquet'; pd.DataFrame({'uid':us,'item_id':its,'rank':ranks}).to_parquet(p,index=False); print(p)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n", "stderr": "", "aggregated_output": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n", "exit_code": 0, "duration": { "secs": 19, "nanos": 814245128 }, "formatted_output": "load\r\nunpack (8456, 30000)\r\n/app/solution/validation_pop.parquet\r\n/app/solution/validation_organic_pop.parquet\r\n/app/solution/validation_organic_share.parquet\r\n" } } ## Step 21 · agent · 2026-09-25T00:28:46.905Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:28:48.922Z { "code": "const r = await tools.exec_command({\"cmd\":\"for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \\\"$f\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:28:50.087Z Script completed Wall time 1.2 seconds Output: SESSION_ID=13115 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c03dc94487d290194c55f966ef4b", "usage": { "input_tokens": 28750, "cached_input_tokens": 28416, "cache_write_input_tokens": 0, "output_tokens": 139, "reasoning_output_tokens": 18, "total_tokens": 28889 }, "turn_token_usage": { "input_tokens": 303090, "cached_input_tokens": 283264, "cache_write_input_tokens": 0, "output_tokens": 5933, "reasoning_output_tokens": 2382, "total_tokens": 309023 }, "thread_token_usage": { "input_tokens": 303090, "cached_input_tokens": 283264, "cache_write_input_tokens": 0, "output_tokens": 5933, "reasoning_output_tokens": 2382, "total_tokens": 309023 } } ### exec · 2026-09-25T00:28:51.651Z { "code": "const r = await tools.write_stdin({\"session_id\":13115,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:28:58.857Z Script completed Wall time 7.2 seconds Output: { "model": "/app/solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } { "model": "/app/solution/validation_organic_pop.parquet", "scores": { "quality_ndcg@10": 1.5e-05, "quality_users": 4263 } } { "model": "/app/solution/validation_organic_share.parquet", "scores": { "quality_ndcg@10": 0.010695, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c042493087d294f8163864b4e8e5", "usage": { "input_tokens": 28917, "cached_input_tokens": 28544, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 28962 }, "turn_token_usage": { "input_tokens": 332007, "cached_input_tokens": 311808, "cache_write_input_tokens": 0, "output_tokens": 5978, "reasoning_output_tokens": 2382, "total_tokens": 337985 }, "thread_token_usage": { "input_tokens": 332007, "cached_input_tokens": 311808, "cache_write_input_tokens": 0, "output_tokens": 5978, "reasoning_output_tokens": 2382, "total_tokens": 337985 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-26a8dec0-0a25-45b0-bd5d-3eadcd1e5167", "process_id": "13115", "command": [ "/bin/sh", "-lc", "for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 769024769 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.01071,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 1.5e-05,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n{\r\n \"model\": \"/app/solution/validation_organic_share.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.010695,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 22 · agent · 2026-09-25T00:29:03.217Z ### Thinking unavailable — no text recorded ## Step 23 · agent · 2026-09-25T00:29:10.332Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-09-25T00:29:15.794Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:29:22.169Z { "code": "const r = await tools.exec_command({\"cmd\":\"getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); C=24786800\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\\n# correct intrinsic correlations and recent global candidate counters\\nfor family in ['listens','likes']:\\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\\n ci=idx.get_indexer(x.item_id)\\n valid=ci>=0\\n for days in [7,14,30,60,90,180,365]:\\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\\n cnt=np.bincount(ci[m],minlength=len(ids))\\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\\n print(family,len(x))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:29:23.335Z Script completed Wall time 1.2 seconds Output: 8 MemTotal: 15988972 kB MemAvailable: 14883732 kB SESSION_ID=49448 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c04b0c8c87d2a906da433f25bc6c", "usage": { "input_tokens": 29129, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 1247, "reasoning_output_tokens": 886, "total_tokens": 30376 }, "turn_token_usage": { "input_tokens": 361136, "cached_input_tokens": 340608, "cache_write_input_tokens": 0, "output_tokens": 7225, "reasoning_output_tokens": 3268, "total_tokens": 368361 }, "thread_token_usage": { "input_tokens": 361136, "cached_input_tokens": 340608, "cache_write_input_tokens": 0, "output_tokens": 7225, "reasoning_output_tokens": 3268, "total_tokens": 368361 } } ## Step 25 · agent · 2026-09-25T00:29:24.623Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-80db3804-51f1-4264-9bd6-0d97c8e018f9", "process_id": "49448", "command": [ "/bin/sh", "-lc", "getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\n# correct intrinsic correlations and recent global candidate counters\nfor family in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\n ci=idx.get_indexer(x.item_id)\n valid=ci>=0\n for days in [7,14,30,60,90,180,365]:\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\n cnt=np.bincount(ci[m],minlength=len(ids))\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\n print(family,len(x))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "getconf _NPROCESSORS_ONLN || true; awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\n# correct intrinsic correlations and recent global candidate counters\nfor family in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if family=='listens' else [])\n x=pd.read_parquet(D/f'train_{family}.parquet',columns=cols)\n ci=idx.get_indexer(x.item_id)\n valid=ci>=0\n for days in [7,14,30,60,90,180,365]:\n m=valid & (x.timestamp.to_numpy()>=C-days*86400) & (x.is_organic.to_numpy()==1)\n if family=='listens': m &= x.played_ratio_pct.to_numpy()>=80\n cnt=np.bincount(ci[m],minlength=len(ids))\n np.save(f'/app/solution/tmp_{family}_{days}.npy',cnt)\n print(family,len(x))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 43484159\r\nlikes 814831\r\n", "stderr": "", "aggregated_output": "listens 43484159\r\nlikes 814831\r\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 968350610 }, "formatted_output": "listens 43484159\r\nlikes 814831\r\n" } } ### exec · 2026-09-25T00:29:25.304Z { "code": "const r = await tools.write_stdin({\"session_id\":49448,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:29:25.378Z Script completed Wall time 0.0 seconds Output: listens 43484159 likes 814831 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c06384dc87d2996306393291971a", "usage": { "input_tokens": 30428, "cached_input_tokens": 28928, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 30485 }, "turn_token_usage": { "input_tokens": 391564, "cached_input_tokens": 369536, "cache_write_input_tokens": 0, "output_tokens": 7282, "reasoning_output_tokens": 3278, "total_tokens": 398846 }, "thread_token_usage": { "input_tokens": 391564, "cached_input_tokens": 369536, "cache_write_input_tokens": 0, "output_tokens": 7282, "reasoning_output_tokens": 3278, "total_tokens": 398846 } } ## Step 26 · agent · 2026-09-25T00:29:29.408Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:29:39.788Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport sys,pandas as pd,numpy as np\\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\\nfrom pathlib import Path\\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\\n# score order against elig bits, top100 placeholder\\ndef ev(s):\\n order=np.argsort(-s,kind='stable'); ranked={}\\n for r,u in enumerate(cs.uids):\\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\\n return score_rankings(ranked,truth)['quality_ndcg@10']\\nvals={'pop':pop,'opop':opop}\\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\\nfor n,s in vals.items(): print(n,ev(s))\\nprint('blends')\\n# normalize ranks or z log counts combinations\\nfor d in [7,14,30,60,90,180]:\\n for a in [0,.1,.25,.5,1,2,4]:\\n # log recent rate with long prior\\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\\n print('d a',d,a,ev(s))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:29:40.954Z Script completed Wall time 1.2 seconds Output: SESSION_ID=39005 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c065bda487d2942bf76175234bed", "usage": { "input_tokens": 30520, "cached_input_tokens": 30208, "cache_write_input_tokens": 0, "output_tokens": 744, "reasoning_output_tokens": 160, "total_tokens": 31264 }, "turn_token_usage": { "input_tokens": 422084, "cached_input_tokens": 399744, "cache_write_input_tokens": 0, "output_tokens": 8026, "reasoning_output_tokens": 3438, "total_tokens": 430110 }, "thread_token_usage": { "input_tokens": 422084, "cached_input_tokens": 399744, "cache_write_input_tokens": 0, "output_tokens": 8026, "reasoning_output_tokens": 3438, "total_tokens": 430110 } } ## Step 27 · agent · 2026-09-25T00:29:41.967Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:29:42.649Z { "code": "const r = await tools.write_stdin({\"session_id\":39005,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:30:12.657Z Script completed Wall time 30.0 seconds Output: pop 0.01071 opop 0.010577 L7 0.028131 A7 0.029297 gain7 0.028941 L14 0.022878 A14 0.026726 gain14 0.02845 L30 0.013314 A30 0.018429 gain30 0.015531 L60 0.015802 A60 0.016741 gain60 0.016611 L90 0.015822 A90 0.016217 gain90 0.01607 L180 0.011908 A180 0.013 gain180 0.012563 L365 0.010551 A365 0.012054 gain365 0.011078 blends d a 7 0 0.028941 d a 7 0.1 0.029781 d a 7 0.25 0.031205 d a 7 0.5 0.027978 d a 7 1 0.020075 d a 7 2 0.012207 d a 7 4 0.011358 d a 14 0 0.02845 d a 14 0.1 0.028728 d a 14 0.25 0.02736 d a 14 0.5 0.025733 d a 14 1 0.019695 d a 14 2 0.014434 d a 14 4 0.011958 d a 30 0 0.015531 d a 30 0.1 0.01571 d a 30 0.25 0.016 d a 30 0.5 0.016583 d a 30 1 0.016221 d a 30 2 0.015072 d a 30 4 0.013505 d a 60 0 0.016611 d a 60 0.1 0.016607 d a 60 0.25 0.016635 d a 60 0.5 0.016511 d a 60 1 0.016605 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c07520c487d2aa097a48618783ca", "usage": { "input_tokens": 31292, "cached_input_tokens": 30336, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 31345 }, "turn_token_usage": { "input_tokens": 453376, "cached_input_tokens": 430080, "cache_write_input_tokens": 0, "output_tokens": 8079, "reasoning_output_tokens": 3444, "total_tokens": 461455 }, "thread_token_usage": { "input_tokens": 453376, "cached_input_tokens": 430080, "cache_write_input_tokens": 0, "output_tokens": 8079, "reasoning_output_tokens": 3444, "total_tokens": 461455 } } ## Step 28 · agent · 2026-09-25T00:30:15.012Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:30:15.708Z { "code": "const r = await tools.write_stdin({\"session_id\":39005,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:30:22.248Z Script completed Wall time 6.5 seconds Output: d a 60 2 0.016416 d a 60 4 0.015335 d a 90 0 0.01607 d a 90 0.1 0.016087 d a 90 0.25 0.016101 d a 90 0.5 0.016147 d a 90 1 0.015982 d a 90 2 0.015718 d a 90 4 0.015124 d a 180 0 0.012563 d a 180 0.1 0.012562 d a 180 0.25 0.012544 d a 180 0.5 0.012464 d a 180 1 0.012527 d a 180 2 0.012207 d a 180 4 0.01227 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c094e20087d2a2ec95af37d9331b", "usage": { "input_tokens": 31888, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 124, "reasoning_output_tokens": 77, "total_tokens": 32012 }, "turn_token_usage": { "input_tokens": 485264, "cached_input_tokens": 461184, "cache_write_input_tokens": 0, "output_tokens": 8203, "reasoning_output_tokens": 3521, "total_tokens": 493467 }, "thread_token_usage": { "input_tokens": 485264, "cached_input_tokens": 461184, "cache_write_input_tokens": 0, "output_tokens": 8203, "reasoning_output_tokens": 3521, "total_tokens": 493467 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a69f1448-5365-4f8e-8d42-2a66106c3aaf", "process_id": "39005", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\n# score order against elig bits, top100 placeholder\ndef ev(s):\n order=np.argsort(-s,kind='stable'); ranked={}\n for r,u in enumerate(cs.uids):\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\n return score_rankings(ranked,truth)['quality_ndcg@10']\nvals={'pop':pop,'opop':opop}\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\nfor n,s in vals.items(): print(n,ev(s))\nprint('blends')\n# normalize ranks or z log counts combinations\nfor d in [7,14,30,60,90,180]:\n for a in [0,.1,.25,.5,1,2,4]:\n # log recent rate with long prior\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\n print('d a',d,a,ev(s))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd,numpy as np\nsys.path.insert(0,'/task'); from evaluate_public import validation_truth; from ranking_evaluation import CandidateSet,score_rankings\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); C=24786800\ncs=CandidateSet(D/'validation_eligible_candidates.npz'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); truth=validation_truth(cs,targets)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(dtype=float); opop=cat.organic_positive_listens.to_numpy(dtype=float)\nL={d:np.load(O/f'tmp_listens_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}; A={d:np.load(O/f'tmp_likes_{d}.npy').astype(float) for d in [7,14,30,60,90,180,365]}\n# score order against elig bits, top100 placeholder\ndef ev(s):\n order=np.argsort(-s,kind='stable'); ranked={}\n for r,u in enumerate(cs.uids):\n bits=np.unpackbits(cs.packed[r],count=len(order),bitorder='little').astype(bool)\n ranked[int(u)]=list(map(int,cs.item_ids[order[bits[order]][:10]]))\n return score_rankings(ranked,truth)['quality_ndcg@10']\nvals={'pop':pop,'opop':opop}\nfor d in L: vals[f'L{d}']=L[d]; vals[f'A{d}']=A[d]; vals[f'gain{d}']=L[d]+7*A[d]\nfor n,s in vals.items(): print(n,ev(s))\nprint('blends')\n# normalize ranks or z log counts combinations\nfor d in [7,14,30,60,90,180]:\n for a in [0,.1,.25,.5,1,2,4]:\n # log recent rate with long prior\n s=np.log1p(L[d]+7*A[d]+a*np.power(pop,0.75))\n print('d a',d,a,ev(s))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n", "stderr": "", "aggregated_output": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n", "exit_code": 0, "duration": { "secs": 42, "nanos": 293739801 }, "formatted_output": "pop 0.01071\r\nopop 0.010577\r\nL7 0.028131\r\nA7 0.029297\r\ngain7 0.028941\r\nL14 0.022878\r\nA14 0.026726\r\ngain14 0.02845\r\nL30 0.013314\r\nA30 0.018429\r\ngain30 0.015531\r\nL60 0.015802\r\nA60 0.016741\r\ngain60 0.016611\r\nL90 0.015822\r\nA90 0.016217\r\ngain90 0.01607\r\nL180 0.011908\r\nA180 0.013\r\ngain180 0.012563\r\nL365 0.010551\r\nA365 0.012054\r\ngain365 0.011078\r\nblends\r\nd a 7 0 0.028941\r\nd a 7 0.1 0.029781\r\nd a 7 0.25 0.031205\r\nd a 7 0.5 0.027978\r\nd a 7 1 0.020075\r\nd a 7 2 0.012207\r\nd a 7 4 0.011358\r\nd a 14 0 0.02845\r\nd a 14 0.1 0.028728\r\nd a 14 0.25 0.02736\r\nd a 14 0.5 0.025733\r\nd a 14 1 0.019695\r\nd a 14 2 0.014434\r\nd a 14 4 0.011958\r\nd a 30 0 0.015531\r\nd a 30 0.1 0.01571\r\nd a 30 0.25 0.016\r\nd a 30 0.5 0.016583\r\nd a 30 1 0.016221\r\nd a 30 2 0.015072\r\nd a 30 4 0.013505\r\nd a 60 0 0.016611\r\nd a 60 0.1 0.016607\r\nd a 60 0.25 0.016635\r\nd a 60 0.5 0.016511\r\nd a 60 1 0.016605\r\nd a 60 2 0.016416\r\nd a 60 4 0.015335\r\nd a 90 0 0.01607\r\nd a 90 0.1 0.016087\r\nd a 90 0.25 0.016101\r\nd a 90 0.5 0.016147\r\nd a 90 1 0.015982\r\nd a 90 2 0.015718\r\nd a 90 4 0.015124\r\nd a 180 0 0.012563\r\nd a 180 0.1 0.012562\r\nd a 180 0.25 0.012544\r\nd a 180 0.5 0.012464\r\nd a 180 1 0.012527\r\nd a 180 2 0.012207\r\nd a 180 4 0.01227\r\n" } } ## Step 29 · agent · 2026-09-25T00:30:32.506Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-09-25T00:30:41.848Z ### Thinking unavailable — no text recorded ## Step 31 · agent · 2026-09-25T00:30:49.468Z ### Thinking unavailable — no text recorded The strongest non-personalized signal is sharply time-local: seven-day organic likes/strong listens score about 0.029–0.031 versus 0.0107 for lifetime popularity. I’ll retain that as an item-tower trend feature and train the personalized dimensions on cutoff-aligned rolling snapshots, so the deployed score remains one exact dot product. ### exec · 2026-09-25T00:30:54.849Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data')\\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:30:56.016Z Script completed Wall time 1.2 seconds Output: SESSION_ID=71661 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c09e797c87d2ad634fdc1e60fb99", "usage": { "input_tokens": 32239, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 1760, "reasoning_output_tokens": 1457, "total_tokens": 33999 }, "turn_token_usage": { "input_tokens": 517503, "cached_input_tokens": 492928, "cache_write_input_tokens": 0, "output_tokens": 9963, "reasoning_output_tokens": 4978, "total_tokens": 527466 }, "thread_token_usage": { "input_tokens": 517503, "cached_input_tokens": 492928, "cache_write_input_tokens": 0, "output_tokens": 9963, "reasoning_output_tokens": 4978, "total_tokens": 527466 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5ffc87bd-66c8-42ef-a3b7-542f429fc108", "process_id": "71661", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=['uid','timestamp','item_id'])\n print(fam,'global time sorted',np.all(x.timestamp.to_numpy()[1:]>=x.timestamp.to_numpy()[:-1]),'uid sorted',np.all(x.uid.to_numpy()[1:]>=x.uid.to_numpy()[:-1]))\n print(x.head(3).to_dict('records'),x.tail(3).to_dict('records'))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n", "stderr": "", "aggregated_output": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 945515223 }, "formatted_output": "listens global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}]\r\nlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}]\r\ndislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}]\r\nunlikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}]\r\nundislikes global time sorted False uid sorted True\r\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}]\r\n" } } ### exec · 2026-09-25T00:30:59.655Z { "code": "const r = await tools.write_stdin({\"session_id\":71661,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:30:59.727Z Script completed Wall time 0.0 seconds Output: listens global time sorted False uid sorted True [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281}, {'uid': 100, 'timestamp': 39625, 'item_id': 286361}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 7355084}, {'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630}] likes global time sorted False uid sorted True [{'uid': 100, 'timestamp': 44755, 'item_id': 732449}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592}, {'uid': 100, 'timestamp': 1259125, 'item_id': 5411243}] [{'uid': 1000000, 'timestamp': 19336945, 'item_id': 7071820}, {'uid': 1000000, 'timestamp': 19337350, 'item_id': 4465732}, {'uid': 1000000, 'timestamp': 19338070, 'item_id': 1170162}] dislikes global time sorted False uid sorted True [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238}, {'uid': 100, 'timestamp': 20432825, 'item_id': 4927727}] [{'uid': 999900, 'timestamp': 4320645, 'item_id': 4045231}, {'uid': 999900, 'timestamp': 15607495, 'item_id': 3740541}, {'uid': 1000000, 'timestamp': 22061645, 'item_id': 6777683}] unlikes global time sorted False uid sorted True [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134}, {'uid': 100, 'timestamp': 4012270, 'item_id': 7833676}] [{'uid': 999900, 'timestamp': 15607540, 'item_id': 3729351}, {'uid': 999900, 'timestamp': 18167480, 'item_id': 4046840}, {'uid': 1000000, 'timestamp': 14299485, 'item_id': 5064764}] undislikes global time sorted False uid sorted True [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504}, {'uid': 1400, 'timestamp': 8317355, 'item_id': 6175319}] [{'uid': 999900, 'timestamp': 15607515, 'item_id': 1959629}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 3740541}, {'uid': 999900, 'timestamp': 15607515, 'item_id': 4280999}] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c0c0356087d2885cc2fdf9b2b32c", "usage": { "input_tokens": 34027, "cached_input_tokens": 32000, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 34072 }, "turn_token_usage": { "input_tokens": 551530, "cached_input_tokens": 524928, "cache_write_input_tokens": 0, "output_tokens": 10008, "reasoning_output_tokens": 4978, "total_tokens": 561538 }, "thread_token_usage": { "input_tokens": 551530, "cached_input_tokens": 524928, "cache_write_input_tokens": 0, "output_tokens": 10008, "reasoning_output_tokens": 4978, "total_tokens": 561538 } } ## Step 32 · agent · 2026-09-25T00:31:10.161Z ### Thinking unavailable — no text recorded ## Step 33 · agent · 2026-09-25T00:31:19.484Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-09-25T00:31:28.811Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-09-25T00:31:38.132Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-09-25T00:31:47.464Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-09-25T00:31:56.771Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-25T00:32:04.295Z ### Thinking unavailable — no text recorded ## Step 39 · agent · 2026-09-25T00:32:13.592Z ### Thinking unavailable — no text recorded ## Step 40 · agent · 2026-09-25T00:32:22.906Z ### Thinking unavailable — no text recorded ## Step 41 · agent · 2026-09-25T00:32:32.336Z ### Thinking unavailable — no text recorded ## Step 42 · agent · 2026-09-25T00:32:37.749Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:18.021Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/train_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Train and apply a history-only neural two-tower discovery ranker.\\n+\\n+The script intentionally works from event records. It never constructs a\\n+user-by-item matrix: rolling examples are histories followed by a future\\n+organic discovery, and training uses in-batch sampled-softmax negatives.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import math\\n+import random\\n+import time\\n+from dataclasses import dataclass\\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+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+VALIDATION_CUTOFF = 25_393_400\\n+GAP = 1_800\\n+WINDOW = 7 * 24 * 60 * 60\\n+HISTORY_LEN = 160\\n+LIKED_LEN = 80\\n+DISLIKED_LEN = 32\\n+TARGET_LEN = 64\\n+PAD = 0\\n+\\n+\\n+def seed_everything(seed: int) -> None:\\n+ random.seed(seed)\\n+ np.random.seed(seed)\\n+ torch.manual_seed(seed)\\n+\\n+\\n+def catalog_index(values: np.ndarray, catalog_ids: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Map raw IDs to catalog rows; returns -1 outside the retrieval catalog.\\\"\\\"\\\"\\n+ return pd.Index(catalog_ids).get_indexer(values).astype(np.int32, copy=False)\\n+\\n+\\n+def load_family(stage: str, family: str, columns: list[str]) -> pd.DataFrame:\\n+ parts = [pd.read_parquet(DATA / f\\\"train_{family}.parquet\\\", columns=columns)]\\n+ if stage == \\\"final\\\":\\n+ parts.append(pd.read_parquet(DATA / f\\\"val_{family}.parquet\\\", columns=columns))\\n+ frame = pd.concat(parts, ignore_index=True)\\n+ # Each source is uid/time sorted, but concatenating the two windows is not.\\n+ if stage == \\\"final\\\":\\n+ frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ frame.reset_index(drop=True, inplace=True)\\n+ return frame\\n+\\n+\\n+def filter_catalog(frame: pd.DataFrame, catalog_ids: np.ndarray) -> pd.DataFrame:\\n+ mapped = catalog_index(frame.item_id.to_numpy(), catalog_ids)\\n+ keep = mapped >= 0\\n+ frame = frame.loc[keep].copy()\\n+ frame[\\\"ci\\\"] = mapped[keep]\\n+ return frame\\n+\\n+\\n+def group_bounds(sorted_uids: np.ndarray) -> dict[int, tuple[int, int]]:\\n+ if not len(sorted_uids):\\n+ return {}\\n+ starts = np.r_[0, np.flatnonzero(sorted_uids[1:] != sorted_uids[:-1]) + 1]\\n+ ends = np.r_[starts[1:], len(sorted_uids)]\\n+ return {int(sorted_uids[s]): (int(s), int(e)) for s, e in zip(starts, ends)}\\n+\\n+\\n+@dataclass\\n+class SnapshotArrays:\\n+ history: np.ndarray\\n+ history_weight: np.ndarray\\n+ liked: np.ndarray\\n+ disliked: np.ndarray\\n+ user_features: np.ndarray\\n+ targets: np.ndarray\\n+ grades: np.ndarray\\n+ cutoff_id: np.ndarray\\n+\\n+ def save(self, path: Path) -> None:\\n+ np.savez_compressed(\\n+ path,\\n+ history=self.history,\\n+ history_weight=self.history_weight,\\n+ liked=self.liked,\\n+ disliked=self.disliked,\\n+ user_features=self.user_features,\\n+ targets=self.targets,\\n+ grades=self.grades,\\n+ cutoff_id=self.cutoff_id,\\n+ )\\n+\\n+ @classmethod\\n+ def load(cls, path: Path) -> \\\"SnapshotArrays\\\":\\n+ with np.load(path) as z:\\n+ return cls(**{k: z[k] for k in z.files})\\n+\\n+\\n+def _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\\n+ out = np.zeros(length, dtype=np.uint16)\\n+ if len(items):\\n+ take = np.asarray(items[-length:], dtype=np.int64) + 1\\n+ out[-len(take) :] = take.astype(np.uint16)\\n+ return out\\n+\\n+\\n+def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\\n+ \\\"\\\"\\\"Create cutoff-correct rolling history -> organic-discovery examples.\\\"\\\"\\\"\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ latest = cutoff - GAP - WINDOW\\n+ cutoffs = np.arange(latest - (weeks - 1) * WINDOW, latest + 1, WINDOW, dtype=np.int64)\\n+ cutoffs = cutoffs[cutoffs > WINDOW]\\n+ print(f\\\"loading {stage} events for {len(cutoffs)} rolling cutoffs\\\", flush=True)\\n+\\n+ listens = load_family(\\n+ stage,\\n+ \\\"listens\\\",\\n+ [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ listens = filter_catalog(listens, catalog_ids)\\n+ # Compact dtypes matter for the 30M+ retained catalog events.\\n+ lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ li = listens.ci.to_numpy(dtype=np.int32, copy=False)\\n+ lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\\n+ lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\\n+ lb = group_bounds(lu)\\n+\\n+ action_parts: list[pd.DataFrame] = []\\n+ specs = [\\n+ (\\\"likes\\\", 0, 1),\\n+ (\\\"unlikes\\\", 0, 0),\\n+ (\\\"dislikes\\\", 1, 1),\\n+ (\\\"undislikes\\\", 1, 0),\\n+ ]\\n+ organic_likes: pd.DataFrame | None = None\\n+ for family, channel, state in specs:\\n+ a = load_family(stage, family, [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ a = filter_catalog(a, catalog_ids)\\n+ if family == \\\"likes\\\":\\n+ organic_likes = a.loc[a.is_organic == 1, [\\\"uid\\\", \\\"timestamp\\\", \\\"ci\\\"]].copy()\\n+ a = a[[\\\"uid\\\", \\\"timestamp\\\", \\\"ci\\\"]].copy()\\n+ a[\\\"channel\\\"] = np.uint8(channel)\\n+ a[\\\"state\\\"] = np.uint8(state)\\n+ action_parts.append(a)\\n+ actions = pd.concat(action_parts, ignore_index=True)\\n+ actions.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ au = actions.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ at = actions.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ ai = actions.ci.to_numpy(dtype=np.int32, copy=False)\\n+ ac = actions.channel.to_numpy(dtype=np.uint8, copy=False)\\n+ ast = actions.state.to_numpy(dtype=np.uint8, copy=False)\\n+ ab = group_bounds(au)\\n+ assert organic_likes is not None\\n+ organic_likes.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n+ ou = organic_likes.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ ot = organic_likes.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ oi = organic_likes.ci.to_numpy(dtype=np.int32, copy=False)\\n+ ob = group_bounds(ou)\\n+\\n+ all_users = np.union1d(np.unique(lu), np.unique(au))\\n+ histories: list[np.ndarray] = []\\n+ history_weights: list[np.ndarray] = []\\n+ liked_rows: list[np.ndarray] = []\\n+ disliked_rows: list[np.ndarray] = []\\n+ feature_rows: list[np.ndarray] = []\\n+ target_rows: list[np.ndarray] = []\\n+ grade_rows: list[np.ndarray] = []\\n+ cutoff_rows: list[int] = []\\n+ inf_time = np.iinfo(np.int64).max\\n+\\n+ for user_no, uid in enumerate(all_users):\\n+ if int(uid) not in lb:\\n+ continue\\n+ s, e = lb[int(uid)]\\n+ uts, uitems, uorg, uratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ positive = uratio >= 50\\n+ pts = uts[positive]\\n+ pitems = uitems[positive]\\n+ porg = uorg[positive]\\n+ pratio = uratio[positive]\\n+ first_time = np.full(len(catalog_ids), inf_time, dtype=np.int64)\\n+ np.minimum.at(first_time, uitems, uts)\\n+\\n+ if int(uid) in ab:\\n+ a0, a1 = ab[int(uid)]\\n+ uat, uai, uac, uas = at[a0:a1], ai[a0:a1], ac[a0:a1], ast[a0:a1]\\n+ else:\\n+ uat = np.empty(0, np.int64)\\n+ uai = np.empty(0, np.int32)\\n+ uac = np.empty(0, np.uint8)\\n+ uas = np.empty(0, np.uint8)\\n+ if int(uid) in ob:\\n+ o0, o1 = ob[int(uid)]\\n+ uot, uoi = ot[o0:o1], oi[o0:o1]\\n+ else:\\n+ uot = np.empty(0, np.int64)\\n+ uoi = np.empty(0, np.int32)\\n+\\n+ liked_state: dict[int, int] = {}\\n+ disliked_state: dict[int, int] = {}\\n+ action_ptr = 0\\n+ for cutoff_no, c in enumerate(cutoffs):\\n+ while action_ptr < len(uat) and uat[action_ptr] < c:\\n+ item = int(uai[action_ptr])\\n+ state_map = liked_state if uac[action_ptr] == 0 else disliked_state\\n+ if uas[action_ptr]:\\n+ state_map[item] = int(uat[action_ptr])\\n+ else:\\n+ state_map.pop(item, None)\\n+ action_ptr += 1\\n+\\n+ hp = int(np.searchsorted(pts, c, side=\\\"left\\\"))\\n+ if hp < 10:\\n+ continue\\n+ hs = max(0, hp - HISTORY_LEN)\\n+ hist_i = pitems[hs:hp]\\n+ hist_t = pts[hs:hp]\\n+ # Smoothly favor recent, organic and deeply completed history.\\n+ raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\\n+ raw_w *= 1.0 + 0.25 * porg[hs:hp]\\n+ raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\\n+ raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\\n+ hist = np.zeros(HISTORY_LEN, dtype=np.uint16)\\n+ hw = np.zeros(HISTORY_LEN, dtype=np.float16)\\n+ hist[-len(hist_i) :] = (hist_i + 1).astype(np.uint16)\\n+ hw[-len(hist_i) :] = raw_w.astype(np.float16)\\n+\\n+ f0 = int(np.searchsorted(uts, c + GAP, side=\\\"left\\\"))\\n+ f1 = int(np.searchsorted(uts, c + GAP + WINDOW, side=\\\"left\\\"))\\n+ future_mask = (uorg[f0:f1] == 1) & (uratio[f0:f1] >= 80)\\n+ future_items = uitems[f0:f1][future_mask]\\n+ future_items = future_items[first_time[future_items] >= c]\\n+ grades: dict[int, int] = {int(x): 1 for x in future_items}\\n+ q0 = int(np.searchsorted(uot, c + GAP, side=\\\"left\\\"))\\n+ q1 = int(np.searchsorted(uot, c + GAP + WINDOW, side=\\\"left\\\"))\\n+ for item in uoi[q0:q1]:\\n+ if first_time[item] >= c:\\n+ grades[int(item)] = 3\\n+ if not grades:\\n+ continue\\n+\\n+ # Keep all high-value likes, then deterministic listens if unusually busy.\\n+ ordered_targets = sorted(grades, key=lambda x: (-grades[x], first_time[x], x))[:TARGET_LEN]\\n+ tr = np.zeros(TARGET_LEN, dtype=np.uint16)\\n+ gr = np.zeros(TARGET_LEN, dtype=np.uint8)\\n+ tr[: len(ordered_targets)] = np.asarray(ordered_targets, dtype=np.uint16) + 1\\n+ gr[: len(ordered_targets)] = np.asarray([grades[x] for x in ordered_targets], dtype=np.uint8)\\n+\\n+ liked_recent = [x for x, _ in sorted(liked_state.items(), key=lambda z: z[1])]\\n+ disliked_recent = [x for x, _ in sorted(disliked_state.items(), key=lambda z: z[1])]\\n+ lf = math.log1p(hp) / 10.0\\n+ recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\\n+ features = np.asarray(\\n+ [lf, recent_org, math.log1p(len(liked_state)) / 5.0, math.log1p(len(disliked_state)) / 3.0],\\n+ dtype=np.float32,\\n+ )\\n+ histories.append(hist)\\n+ history_weights.append(hw)\\n+ liked_rows.append(_pad_recent(liked_recent, LIKED_LEN))\\n+ disliked_rows.append(_pad_recent(disliked_recent, DISLIKED_LEN))\\n+ feature_rows.append(features)\\n+ target_rows.append(tr)\\n+ grade_rows.append(gr)\\n+ cutoff_rows.append(cutoff_no)\\n+ if user_no and user_no % 2000 == 0:\\n+ print(f\\\" users {user_no}/{len(all_users)} examples={len(histories)}\\\", flush=True)\\n+\\n+ del listens, actions, action_parts\\n+ result = SnapshotArrays(\\n+ history=np.stack(histories),\\n+ history_weight=np.stack(history_weights),\\n+ liked=np.stack(liked_rows),\\n+ disliked=np.stack(disliked_rows),\\n+ user_features=np.stack(feature_rows),\\n+ targets=np.stack(target_rows),\\n+ grades=np.stack(grade_rows),\\n+ cutoff_id=np.asarray(cutoff_rows, dtype=np.uint8),\\n+ )\\n+ print(\\n+ f\\\"built {len(result.history):,} examples, \\\"\\n+ f\\\"{np.count_nonzero(result.targets):,} relevant pairs\\\",\\n+ flush=True,\\n+ )\\n+ return result\\n+\\n+\\n+def item_features(catalog: pd.DataFrame) -> np.ndarray:\\n+ pos = catalog.positive_listens.to_numpy(dtype=np.float64)\\n+ org = catalog.organic_positive_listens.to_numpy(dtype=np.float64)\\n+ x = np.column_stack(\\n+ [\\n+ np.log1p(pos),\\n+ np.log1p(org),\\n+ (org + 5.0) / (pos + 10.0),\\n+ np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float64)),\\n+ ]\\n+ )\\n+ return ((x - x.mean(0)) / (x.std(0) + 1e-6)).astype(np.float32)\\n+\\n+\\n+class UserTower(nn.Module):\\n+ def __init__(self, n_items: int, dim: int):\\n+ super().__init__()\\n+ self.listen_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.like_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.dislike_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\\n+ self.network = nn.Sequential(\\n+ nn.Linear(3 * dim + 4, 2 * dim),\\n+ nn.GELU(),\\n+ nn.LayerNorm(2 * dim),\\n+ nn.Linear(2 * dim, dim),\\n+ )\\n+\\n+ def forward(\\n+ self,\\n+ history: torch.Tensor,\\n+ history_weight: torch.Tensor,\\n+ liked: torch.Tensor,\\n+ disliked: torch.Tensor,\\n+ features: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ h = (self.listen_embedding(history) * history_weight.unsqueeze(-1)).sum(1)\\n+ lm = liked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\\n+ dm = disliked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\\n+ lp = self.like_embedding(liked).sum(1) / lm\\n+ dp = self.dislike_embedding(disliked).sum(1) / dm\\n+ return F.normalize(self.network(torch.cat([h, lp, dp, features], dim=1)), dim=1)\\n+\\n+\\n+class ItemTower(nn.Module):\\n+ def __init__(self, n_items: int, feature_dim: int, dim: int):\\n+ super().__init__()\\n+ self.id_embedding = nn.Embedding(n_items, dim)\\n+ self.feature_network = nn.Sequential(nn.Linear(feature_dim, dim), nn.GELU(), nn.Linear(dim, dim))\\n+ self.network = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim), nn.GELU(), nn.Linear(dim, dim))\\n+\\n+ def forward(self, item: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\\n+ base = self.id_embedding(item) + self.feature_network(features)\\n+ return F.normalize(base + self.network(base), dim=1)\\n+\\n+\\n+class TwoTower(nn.Module):\\n+ def __init__(self, n_items: int, feature_dim: int, dim: int):\\n+ super().__init__()\\n+ self.user_tower = UserTower(n_items, dim)\\n+ self.item_tower = ItemTower(n_items, feature_dim, dim)\\n+ self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+\\n+def train_model(\\n+ snapshots: SnapshotArrays,\\n+ features: np.ndarray,\\n+ dim: int,\\n+ epochs: int,\\n+ seed: int,\\n+) -> TwoTower:\\n+ seed_everything(seed)\\n+ torch.set_num_threads(min(8, torch.get_num_threads()))\\n+ model = TwoTower(len(features), features.shape[1], dim)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ feat_t = torch.from_numpy(features)\\n+ n = len(snapshots.history)\\n+ batch_size = 384\\n+ rng = np.random.default_rng(seed)\\n+ model.train()\\n+ for epoch in range(epochs):\\n+ order = rng.permutation(n)\\n+ losses: list[float] = []\\n+ for start in range(0, n - batch_size + 1, batch_size):\\n+ ix = order[start : start + batch_size]\\n+ target = np.empty(batch_size, dtype=np.int64)\\n+ for j, row in enumerate(ix):\\n+ valid = np.flatnonzero(snapshots.targets[row])\\n+ grade = snapshots.grades[row, valid].astype(np.float64)\\n+ # Likes matter more, without letting the rare grade-3 channel dominate.\\n+ probability = np.where(grade == 3, 3.5, 1.0)\\n+ k = rng.choice(valid, p=probability / probability.sum())\\n+ target[j] = int(snapshots.targets[row, k]) - 1\\n+ h = torch.from_numpy(snapshots.history[ix].astype(np.int64))\\n+ hw = torch.from_numpy(snapshots.history_weight[ix].astype(np.float32))\\n+ liked = torch.from_numpy(snapshots.liked[ix].astype(np.int64))\\n+ disliked = torch.from_numpy(snapshots.disliked[ix].astype(np.int64))\\n+ uf = torch.from_numpy(snapshots.user_features[ix])\\n+ ti = torch.from_numpy(target)\\n+ u = model.user_tower(h, hw, liked, disliked, uf)\\n+ v = model.item_tower(ti, feat_t[ti])\\n+ logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\\n+ # A duplicated positive in the batch is another positive, not a negative.\\n+ same = ti[:, None].eq(ti[None, :])\\n+ same.fill_diagonal_(False)\\n+ logits = logits.masked_fill(same, -1e4)\\n+ loss = F.cross_entropy(logits, torch.arange(batch_size))\\n+ opt.zero_grad(set_to_none=True)\\n+ loss.backward()\\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\\n+ opt.step()\\n+ losses.append(float(loss.detach()))\\n+ print(\\n+ f\\\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.4f} \\\"\\n+ f\\\"scale={model.logit_scale.exp().item():.2f}\\\",\\n+ flush=True,\\n+ )\\n+ return model.eval()\\n+\\n+\\n+def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(dtype=np.int64)\\n+ listens = load_family(\\n+ stage,\\n+ \\\"listens\\\",\\n+ [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\", \\\"played_ratio_pct\\\"],\\n+ )\\n+ listens = filter_catalog(listens, catalog_ids)\\n+ lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\\n+ lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\\n+ li = listens.ci.to_numpy(dtype=np.int32, copy=False)\\n+ lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\\n+ lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\\n+ bounds = group_bounds(lu)\\n+ pref_name = \\\"validation_preference_state.parquet\\\" if stage == \\\"validation\\\" else \\\"test_preference_state.parquet\\\"\\n+ pref = pd.read_parquet(DATA / pref_name)\\n+ pref = filter_catalog(pref, catalog_ids)\\n+ pref_group = {(int(u), str(state)): g.ci.to_numpy(dtype=np.int32) for (u, state), g in pref.groupby([\\\"uid\\\", \\\"state\\\"])}\\n+\\n+ history = np.zeros((len(targets), HISTORY_LEN), dtype=np.uint16)\\n+ weights = np.zeros((len(targets), HISTORY_LEN), dtype=np.float16)\\n+ liked = np.zeros((len(targets), LIKED_LEN), dtype=np.uint16)\\n+ disliked = np.zeros((len(targets), DISLIKED_LEN), dtype=np.uint16)\\n+ user_feat = np.zeros((len(targets), 4), dtype=np.float32)\\n+ for row, uid in enumerate(targets):\\n+ s, e = bounds[int(uid)]\\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ end = int(np.searchsorted(ts, cutoff, side=\\\"left\\\"))\\n+ positive = np.flatnonzero(ratio[:end] >= 50)\\n+ positive = positive[-HISTORY_LEN:]\\n+ raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\\n+ raw_w *= 1.0 + 0.25 * org[positive]\\n+ raw_w *= np.sqrt(np.minimum(ratio[positive], 120) / 50.0)\\n+ raw_w /= max(float(raw_w.sum()), 1e-8)\\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n+ lp = pref_group.get((int(uid), \\\"liked\\\"), np.empty(0, np.int32))\\n+ dp = pref_group.get((int(uid), \\\"disliked\\\"), np.empty(0, np.int32))\\n+ # Preference files lack activation times; popularity order is a stable overflow rule.\\n+ lp = np.sort(lp)[-LIKED_LEN:]\\n+ dp = np.sort(dp)[-DISLIKED_LEN:]\\n+ liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\\n+ disliked[row] = _pad_recent(dp.tolist(), DISLIKED_LEN)\\n+ user_feat[row] = [\\n+ math.log1p(len(positive)) / 10.0,\\n+ float(org[positive].mean()) if len(positive) else 0.0,\\n+ math.log1p(len(lp)) / 5.0,\\n+ math.log1p(len(dp)) / 3.0,\\n+ ]\\n+ return targets, history, weights, liked, disliked, user_feat\\n+\\n+\\n+@torch.inference_mode()\\n+def encode(model: TwoTower, users: tuple[np.ndarray, ...], features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ _, history, weights, liked, disliked, user_feat = users\\n+ chunks = []\\n+ for start in range(0, len(history), 512):\\n+ sl = slice(start, start + 512)\\n+ chunks.append(\\n+ model.user_tower(\\n+ torch.from_numpy(history[sl].astype(np.int64)),\\n+ torch.from_numpy(weights[sl].astype(np.float32)),\\n+ torch.from_numpy(liked[sl].astype(np.int64)),\\n+ torch.from_numpy(disliked[sl].astype(np.int64)),\\n+ torch.from_numpy(user_feat[sl]),\\n+ ).numpy()\\n+ )\\n+ ue = np.concatenate(chunks).astype(np.float32)\\n+ item_chunks = []\\n+ ft = torch.from_numpy(features)\\n+ for start in range(0, len(features), 2048):\\n+ ix = torch.arange(start, min(start + 2048, len(features)))\\n+ item_chunks.append(model.item_tower(ix, ft[ix]).numpy())\\n+ return ue, np.concatenate(item_chunks).astype(np.float32)\\n+\\n+\\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n+ idx = pd.Index(catalog_ids)\\n+ score = np.zeros(len(catalog_ids), dtype=np.float64)\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ cols = [\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ if family == \\\"listens\\\":\\n+ cols.append(\\\"played_ratio_pct\\\")\\n+ x = load_family(stage, family, cols)\\n+ mapped = idx.get_indexer(x.item_id)\\n+ mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\\n+ mask &= x.is_organic.to_numpy() == 1\\n+ if family == \\\"listens\\\":\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\\n+ lifetime = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").positive_listens.to_numpy(dtype=np.float64)\\n+ raw = score + prior * np.power(lifetime, 0.75)\\n+ # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\\n+ order = np.argsort(-raw, kind=\\\"stable\\\")\\n+ rank = np.empty(len(raw), dtype=np.int32)\\n+ rank[order] = np.arange(1, len(raw) + 1)\\n+ trend = -np.log(rank.astype(np.float64))\\n+ return ((trend - trend.mean()) / trend.std()).astype(np.float32)\\n+\\n+\\n+def relevant_truth() -> dict[int, dict[int, int]]:\\n+ z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n+ uid_row = {int(x): i for i, x in enumerate(z[\\\"uids\\\"])}\\n+ item_col = {int(x): i for i, x in enumerate(z[\\\"item_ids\\\"])}\\n+ truth: dict[int, dict[int, int]] = {}\\n+ for family, grade in [(\\\"val_listens.parquet\\\", 1), (\\\"val_likes.parquet\\\", 3)]:\\n+ x = pd.read_parquet(DATA / family)\\n+ mask = x.is_organic.to_numpy() == 1\\n+ if grade == 1:\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ for uid, item in x.loc[mask, [\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().itertuples(index=False, name=None):\\n+ r, c = uid_row.get(int(uid)), item_col.get(int(item))\\n+ if r is None or c is None or not ((int(z[\\\"packed_eligible\\\"][r, c // 8]) >> (c % 8)) & 1):\\n+ continue\\n+ truth.setdefault(int(uid), {})[int(item)] = max(grade, truth.get(int(uid), {}).get(int(item), 0))\\n+ return truth\\n+\\n+\\n+def ndcg10(items: np.ndarray, truth: dict[int, int]) -> float:\\n+ grades = np.asarray([truth.get(int(x), 0) for x in items[:10]], dtype=np.float64)\\n+ dcg = np.sum((2**grades - 1) / np.log2(np.arange(2, 12)))\\n+ ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\\n+\\n+\\n+def rank_and_tune(\\n+ stage: str,\\n+ targets: np.ndarray,\\n+ user_embedding: np.ndarray,\\n+ item_embedding: np.ndarray,\\n+ trend: np.ndarray,\\n+ output: Path,\\n+ forced_weight: float | None,\\n+) -> tuple[float, float | None]:\\n+ z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n+ assert np.array_equal(z[\\\"uids\\\"].astype(np.int64), targets)\\n+ masks = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(z[\\\"item_ids\\\"]), bitorder=\\\"little\\\").astype(bool)\\n+ truth = relevant_truth() if stage == \\\"validation\\\" else None\\n+ weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n+ best_score, best_weight, best_top = -1.0, None, None\\n+ all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\\n+ quality: dict[float, list[float]] = {float(w): [] for w in weights}\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ neural = user_embedding[start:end] @ item_embedding.T\\n+ for w in weights:\\n+ score = neural + float(w) * trend[None, :]\\n+ score[~masks[start:end]] = -np.inf\\n+ # Exact ordering is needed only within the selected top 100.\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ part_score = np.take_along_axis(score, part, axis=1)\\n+ order = np.argsort(-part_score, axis=1)\\n+ top = np.take_along_axis(part, order, axis=1)\\n+ all_top[float(w)][start:end] = top\\n+ if truth is not None:\\n+ for local, row in enumerate(range(start, end)):\\n+ gold = truth.get(int(targets[row]))\\n+ if gold:\\n+ quality[float(w)].append(ndcg10(z[\\\"item_ids\\\"][top[local]], gold))\\n+ if truth is not None:\\n+ for w in weights:\\n+ q = float(np.mean(quality[float(w)]))\\n+ print(f\\\"blend trend_weight={w:g} public_ndcg@10={q:.6f}\\\", flush=True)\\n+ if q > best_score:\\n+ best_score, best_weight = q, float(w)\\n+ best_top = all_top[best_weight]\\n+ else:\\n+ best_weight = float(weights[0])\\n+ best_top = all_top[best_weight]\\n+\\n+ frame = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(targets, 100).astype(np.int64),\\n+ \\\"item_id\\\": z[\\\"item_ids\\\"][best_top.reshape(-1)].astype(np.int64),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\\n+ }\\n+ )\\n+ frame.to_parquet(output, index=False)\\n+ return best_score, best_weight\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=[\\\"validation\\\", \\\"final\\\"], required=True)\\n+ parser.add_argument(\\\"--epochs\\\", type=int, default=8)\\n+ parser.add_argument(\\\"--dim\\\", type=int, default=48)\\n+ parser.add_argument(\\\"--weeks\\\", type=int, default=30)\\n+ parser.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ parser.add_argument(\\\"--trend-weight\\\", type=float)\\n+ parser.add_argument(\\\"--rebuild-cache\\\", action=\\\"store_true\\\")\\n+ args = parser.parse_args()\\n+ started = time.time()\\n+ OUT.mkdir(exist_ok=True)\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ features = item_features(catalog)\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w.npz\\\"\\n+ if cache.exists() and not args.rebuild_cache:\\n+ print(f\\\"loading snapshot cache {cache}\\\", flush=True)\\n+ snapshots = SnapshotArrays.load(cache)\\n+ else:\\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\\n+ snapshots.save(cache)\\n+ model = train_model(snapshots, features, args.dim, args.epochs, args.seed)\\n+ model_path = OUT / f\\\"two_tower_{args.stage}.pt\\\"\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"dim\\\": args.dim,\\n+ \\\"item_features\\\": features,\\n+ \\\"catalog_ids\\\": catalog_ids,\\n+ \\\"architecture\\\": \\\"history/preference MLP user tower + ID/feature MLP item tower\\\",\\n+ },\\n+ model_path,\\n+ )\\n+ users = build_inference_users(args.stage, catalog_ids)\\n+ ue, ie = encode(model, users, features)\\n+ trend = recent_trend(args.stage, catalog_ids)\\n+ output = OUT / (\\\"validation.parquet\\\" if args.stage == \\\"validation\\\" else \\\"submission.parquet\\\")\\n+ score, weight = rank_and_tune(args.stage, users[0], ue, ie, trend, output, args.trend_weight)\\n+ report = {\\n+ \\\"stage\\\": args.stage,\\n+ \\\"examples\\\": len(snapshots.history),\\n+ \\\"epochs\\\": args.epochs,\\n+ \\\"dimension\\\": args.dim + 1,\\n+ \\\"trend_weight\\\": weight,\\n+ \\\"public_ndcg@10\\\": score if args.stage == \\\"validation\\\" else None,\\n+ \\\"seconds\\\": round(time.time() - started, 1),\\n+ \\\"output\\\": str(output),\\n+ }\\n+ (OUT / f\\\"report_{args.stage}.json\\\").write_text(json.dumps(report, indent=2) + \\\"\\\\n\\\")\\n+ print(json.dumps(report, indent=2), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:35:18.082Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e6211ec8-c831-47f4-8d30-c529074ea4f4", "changes": { "/app/solution/train_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a history-only neural two-tower discovery ranker.\n\nThe script intentionally works from event records. It never constructs a\nuser-by-item matrix: rolling examples are histories followed by a future\norganic discovery, and training uses in-batch sampled-softmax negatives.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom dataclasses import dataclass\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\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nVALIDATION_CUTOFF = 25_393_400\nGAP = 1_800\nWINDOW = 7 * 24 * 60 * 60\nHISTORY_LEN = 160\nLIKED_LEN = 80\nDISLIKED_LEN = 32\nTARGET_LEN = 64\nPAD = 0\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef catalog_index(values: np.ndarray, catalog_ids: np.ndarray) -> np.ndarray:\n \"\"\"Map raw IDs to catalog rows; returns -1 outside the retrieval catalog.\"\"\"\n return pd.Index(catalog_ids).get_indexer(values).astype(np.int32, copy=False)\n\n\ndef load_family(stage: str, family: str, columns: list[str]) -> pd.DataFrame:\n parts = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n if stage == \"final\":\n parts.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n frame = pd.concat(parts, ignore_index=True)\n # Each source is uid/time sorted, but concatenating the two windows is not.\n if stage == \"final\":\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n frame.reset_index(drop=True, inplace=True)\n return frame\n\n\ndef filter_catalog(frame: pd.DataFrame, catalog_ids: np.ndarray) -> pd.DataFrame:\n mapped = catalog_index(frame.item_id.to_numpy(), catalog_ids)\n keep = mapped >= 0\n frame = frame.loc[keep].copy()\n frame[\"ci\"] = mapped[keep]\n return frame\n\n\ndef group_bounds(sorted_uids: np.ndarray) -> dict[int, tuple[int, int]]:\n if not len(sorted_uids):\n return {}\n starts = np.r_[0, np.flatnonzero(sorted_uids[1:] != sorted_uids[:-1]) + 1]\n ends = np.r_[starts[1:], len(sorted_uids)]\n return {int(sorted_uids[s]): (int(s), int(e)) for s, e in zip(starts, ends)}\n\n\n@dataclass\nclass SnapshotArrays:\n history: np.ndarray\n history_weight: np.ndarray\n liked: np.ndarray\n disliked: np.ndarray\n user_features: np.ndarray\n targets: np.ndarray\n grades: np.ndarray\n cutoff_id: np.ndarray\n\n def save(self, path: Path) -> None:\n np.savez_compressed(\n path,\n history=self.history,\n history_weight=self.history_weight,\n liked=self.liked,\n disliked=self.disliked,\n user_features=self.user_features,\n targets=self.targets,\n grades=self.grades,\n cutoff_id=self.cutoff_id,\n )\n\n @classmethod\n def load(cls, path: Path) -> \"SnapshotArrays\":\n with np.load(path) as z:\n return cls(**{k: z[k] for k in z.files})\n\n\ndef _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\n out = np.zeros(length, dtype=np.uint16)\n if len(items):\n take = np.asarray(items[-length:], dtype=np.int64) + 1\n out[-len(take) :] = take.astype(np.uint16)\n return out\n\n\ndef build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n \"\"\"Create cutoff-correct rolling history -> organic-discovery examples.\"\"\"\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n latest = cutoff - GAP - WINDOW\n cutoffs = np.arange(latest - (weeks - 1) * WINDOW, latest + 1, WINDOW, dtype=np.int64)\n cutoffs = cutoffs[cutoffs > WINDOW]\n print(f\"loading {stage} events for {len(cutoffs)} rolling cutoffs\", flush=True)\n\n listens = load_family(\n stage,\n \"listens\",\n [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n listens = filter_catalog(listens, catalog_ids)\n # Compact dtypes matter for the 30M+ retained catalog events.\n lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\n lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\n li = listens.ci.to_numpy(dtype=np.int32, copy=False)\n lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\n lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\n lb = group_bounds(lu)\n\n action_parts: list[pd.DataFrame] = []\n specs = [\n (\"likes\", 0, 1),\n (\"unlikes\", 0, 0),\n (\"dislikes\", 1, 1),\n (\"undislikes\", 1, 0),\n ]\n organic_likes: pd.DataFrame | None = None\n for family, channel, state in specs:\n a = load_family(stage, family, [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"])\n a = filter_catalog(a, catalog_ids)\n if family == \"likes\":\n organic_likes = a.loc[a.is_organic == 1, [\"uid\", \"timestamp\", \"ci\"]].copy()\n a = a[[\"uid\", \"timestamp\", \"ci\"]].copy()\n a[\"channel\"] = np.uint8(channel)\n a[\"state\"] = np.uint8(state)\n action_parts.append(a)\n actions = pd.concat(action_parts, ignore_index=True)\n actions.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n au = actions.uid.to_numpy(dtype=np.uint32, copy=False)\n at = actions.timestamp.to_numpy(dtype=np.int64, copy=False)\n ai = actions.ci.to_numpy(dtype=np.int32, copy=False)\n ac = actions.channel.to_numpy(dtype=np.uint8, copy=False)\n ast = actions.state.to_numpy(dtype=np.uint8, copy=False)\n ab = group_bounds(au)\n assert organic_likes is not None\n organic_likes.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\n ou = organic_likes.uid.to_numpy(dtype=np.uint32, copy=False)\n ot = organic_likes.timestamp.to_numpy(dtype=np.int64, copy=False)\n oi = organic_likes.ci.to_numpy(dtype=np.int32, copy=False)\n ob = group_bounds(ou)\n\n all_users = np.union1d(np.unique(lu), np.unique(au))\n histories: list[np.ndarray] = []\n history_weights: list[np.ndarray] = []\n liked_rows: list[np.ndarray] = []\n disliked_rows: list[np.ndarray] = []\n feature_rows: list[np.ndarray] = []\n target_rows: list[np.ndarray] = []\n grade_rows: list[np.ndarray] = []\n cutoff_rows: list[int] = []\n inf_time = np.iinfo(np.int64).max\n\n for user_no, uid in enumerate(all_users):\n if int(uid) not in lb:\n continue\n s, e = lb[int(uid)]\n uts, uitems, uorg, uratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n positive = uratio >= 50\n pts = uts[positive]\n pitems = uitems[positive]\n porg = uorg[positive]\n pratio = uratio[positive]\n first_time = np.full(len(catalog_ids), inf_time, dtype=np.int64)\n np.minimum.at(first_time, uitems, uts)\n\n if int(uid) in ab:\n a0, a1 = ab[int(uid)]\n uat, uai, uac, uas = at[a0:a1], ai[a0:a1], ac[a0:a1], ast[a0:a1]\n else:\n uat = np.empty(0, np.int64)\n uai = np.empty(0, np.int32)\n uac = np.empty(0, np.uint8)\n uas = np.empty(0, np.uint8)\n if int(uid) in ob:\n o0, o1 = ob[int(uid)]\n uot, uoi = ot[o0:o1], oi[o0:o1]\n else:\n uot = np.empty(0, np.int64)\n uoi = np.empty(0, np.int32)\n\n liked_state: dict[int, int] = {}\n disliked_state: dict[int, int] = {}\n action_ptr = 0\n for cutoff_no, c in enumerate(cutoffs):\n while action_ptr < len(uat) and uat[action_ptr] < c:\n item = int(uai[action_ptr])\n state_map = liked_state if uac[action_ptr] == 0 else disliked_state\n if uas[action_ptr]:\n state_map[item] = int(uat[action_ptr])\n else:\n state_map.pop(item, None)\n action_ptr += 1\n\n hp = int(np.searchsorted(pts, c, side=\"left\"))\n if hp < 10:\n continue\n hs = max(0, hp - HISTORY_LEN)\n hist_i = pitems[hs:hp]\n hist_t = pts[hs:hp]\n # Smoothly favor recent, organic and deeply completed history.\n raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\n raw_w *= 1.0 + 0.25 * porg[hs:hp]\n raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\n raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\n hist = np.zeros(HISTORY_LEN, dtype=np.uint16)\n hw = np.zeros(HISTORY_LEN, dtype=np.float16)\n hist[-len(hist_i) :] = (hist_i + 1).astype(np.uint16)\n hw[-len(hist_i) :] = raw_w.astype(np.float16)\n\n f0 = int(np.searchsorted(uts, c + GAP, side=\"left\"))\n f1 = int(np.searchsorted(uts, c + GAP + WINDOW, side=\"left\"))\n future_mask = (uorg[f0:f1] == 1) & (uratio[f0:f1] >= 80)\n future_items = uitems[f0:f1][future_mask]\n future_items = future_items[first_time[future_items] >= c]\n grades: dict[int, int] = {int(x): 1 for x in future_items}\n q0 = int(np.searchsorted(uot, c + GAP, side=\"left\"))\n q1 = int(np.searchsorted(uot, c + GAP + WINDOW, side=\"left\"))\n for item in uoi[q0:q1]:\n if first_time[item] >= c:\n grades[int(item)] = 3\n if not grades:\n continue\n\n # Keep all high-value likes, then deterministic listens if unusually busy.\n ordered_targets = sorted(grades, key=lambda x: (-grades[x], first_time[x], x))[:TARGET_LEN]\n tr = np.zeros(TARGET_LEN, dtype=np.uint16)\n gr = np.zeros(TARGET_LEN, dtype=np.uint8)\n tr[: len(ordered_targets)] = np.asarray(ordered_targets, dtype=np.uint16) + 1\n gr[: len(ordered_targets)] = np.asarray([grades[x] for x in ordered_targets], dtype=np.uint8)\n\n liked_recent = [x for x, _ in sorted(liked_state.items(), key=lambda z: z[1])]\n disliked_recent = [x for x, _ in sorted(disliked_state.items(), key=lambda z: z[1])]\n lf = math.log1p(hp) / 10.0\n recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\n features = np.asarray(\n [lf, recent_org, math.log1p(len(liked_state)) / 5.0, math.log1p(len(disliked_state)) / 3.0],\n dtype=np.float32,\n )\n histories.append(hist)\n history_weights.append(hw)\n liked_rows.append(_pad_recent(liked_recent, LIKED_LEN))\n disliked_rows.append(_pad_recent(disliked_recent, DISLIKED_LEN))\n feature_rows.append(features)\n target_rows.append(tr)\n grade_rows.append(gr)\n cutoff_rows.append(cutoff_no)\n if user_no and user_no % 2000 == 0:\n print(f\" users {user_no}/{len(all_users)} examples={len(histories)}\", flush=True)\n\n del listens, actions, action_parts\n result = SnapshotArrays(\n history=np.stack(histories),\n history_weight=np.stack(history_weights),\n liked=np.stack(liked_rows),\n disliked=np.stack(disliked_rows),\n user_features=np.stack(feature_rows),\n targets=np.stack(target_rows),\n grades=np.stack(grade_rows),\n cutoff_id=np.asarray(cutoff_rows, dtype=np.uint8),\n )\n print(\n f\"built {len(result.history):,} examples, \"\n f\"{np.count_nonzero(result.targets):,} relevant pairs\",\n flush=True,\n )\n return result\n\n\ndef item_features(catalog: pd.DataFrame) -> np.ndarray:\n pos = catalog.positive_listens.to_numpy(dtype=np.float64)\n org = catalog.organic_positive_listens.to_numpy(dtype=np.float64)\n x = np.column_stack(\n [\n np.log1p(pos),\n np.log1p(org),\n (org + 5.0) / (pos + 10.0),\n np.log1p(catalog.popularity_rank.to_numpy(dtype=np.float64)),\n ]\n )\n return ((x - x.mean(0)) / (x.std(0) + 1e-6)).astype(np.float32)\n\n\nclass UserTower(nn.Module):\n def __init__(self, n_items: int, dim: int):\n super().__init__()\n self.listen_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.like_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.dislike_embedding = nn.Embedding(n_items + 1, dim, padding_idx=PAD)\n self.network = nn.Sequential(\n nn.Linear(3 * dim + 4, 2 * dim),\n nn.GELU(),\n nn.LayerNorm(2 * dim),\n nn.Linear(2 * dim, dim),\n )\n\n def forward(\n self,\n history: torch.Tensor,\n history_weight: torch.Tensor,\n liked: torch.Tensor,\n disliked: torch.Tensor,\n features: torch.Tensor,\n ) -> torch.Tensor:\n h = (self.listen_embedding(history) * history_weight.unsqueeze(-1)).sum(1)\n lm = liked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\n dm = disliked.ne(PAD).sum(1, keepdim=True).clamp_min(1)\n lp = self.like_embedding(liked).sum(1) / lm\n dp = self.dislike_embedding(disliked).sum(1) / dm\n return F.normalize(self.network(torch.cat([h, lp, dp, features], dim=1)), dim=1)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, n_items: int, feature_dim: int, dim: int):\n super().__init__()\n self.id_embedding = nn.Embedding(n_items, dim)\n self.feature_network = nn.Sequential(nn.Linear(feature_dim, dim), nn.GELU(), nn.Linear(dim, dim))\n self.network = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, dim), nn.GELU(), nn.Linear(dim, dim))\n\n def forward(self, item: torch.Tensor, features: torch.Tensor) -> torch.Tensor:\n base = self.id_embedding(item) + self.feature_network(features)\n return F.normalize(base + self.network(base), dim=1)\n\n\nclass TwoTower(nn.Module):\n def __init__(self, n_items: int, feature_dim: int, dim: int):\n super().__init__()\n self.user_tower = UserTower(n_items, dim)\n self.item_tower = ItemTower(n_items, feature_dim, dim)\n self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\n\n\ndef train_model(\n snapshots: SnapshotArrays,\n features: np.ndarray,\n dim: int,\n epochs: int,\n seed: int,\n) -> TwoTower:\n seed_everything(seed)\n torch.set_num_threads(min(8, torch.get_num_threads()))\n model = TwoTower(len(features), features.shape[1], dim)\n opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n feat_t = torch.from_numpy(features)\n n = len(snapshots.history)\n batch_size = 384\n rng = np.random.default_rng(seed)\n model.train()\n for epoch in range(epochs):\n order = rng.permutation(n)\n losses: list[float] = []\n for start in range(0, n - batch_size + 1, batch_size):\n ix = order[start : start + batch_size]\n target = np.empty(batch_size, dtype=np.int64)\n for j, row in enumerate(ix):\n valid = np.flatnonzero(snapshots.targets[row])\n grade = snapshots.grades[row, valid].astype(np.float64)\n # Likes matter more, without letting the rare grade-3 channel dominate.\n probability = np.where(grade == 3, 3.5, 1.0)\n k = rng.choice(valid, p=probability / probability.sum())\n target[j] = int(snapshots.targets[row, k]) - 1\n h = torch.from_numpy(snapshots.history[ix].astype(np.int64))\n hw = torch.from_numpy(snapshots.history_weight[ix].astype(np.float32))\n liked = torch.from_numpy(snapshots.liked[ix].astype(np.int64))\n disliked = torch.from_numpy(snapshots.disliked[ix].astype(np.int64))\n uf = torch.from_numpy(snapshots.user_features[ix])\n ti = torch.from_numpy(target)\n u = model.user_tower(h, hw, liked, disliked, uf)\n v = model.item_tower(ti, feat_t[ti])\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n # A duplicated positive in the batch is another positive, not a negative.\n same = ti[:, None].eq(ti[None, :])\n same.fill_diagonal_(False)\n logits = logits.masked_fill(same, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\n opt.zero_grad(set_to_none=True)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n opt.step()\n losses.append(float(loss.detach()))\n print(\n f\"epoch {epoch + 1}/{epochs} loss={np.mean(losses):.4f} \"\n f\"scale={model.logit_scale.exp().item():.2f}\",\n flush=True,\n )\n return model.eval()\n\n\ndef build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n targets = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(dtype=np.int64)\n listens = load_family(\n stage,\n \"listens\",\n [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n listens = filter_catalog(listens, catalog_ids)\n lu = listens.uid.to_numpy(dtype=np.uint32, copy=False)\n lt = listens.timestamp.to_numpy(dtype=np.int64, copy=False)\n li = listens.ci.to_numpy(dtype=np.int32, copy=False)\n lo = listens.is_organic.to_numpy(dtype=np.uint8, copy=False)\n lr = listens.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)\n bounds = group_bounds(lu)\n pref_name = \"validation_preference_state.parquet\" if stage == \"validation\" else \"test_preference_state.parquet\"\n pref = pd.read_parquet(DATA / pref_name)\n pref = filter_catalog(pref, catalog_ids)\n pref_group = {(int(u), str(state)): g.ci.to_numpy(dtype=np.int32) for (u, state), g in pref.groupby([\"uid\", \"state\"])}\n\n history = np.zeros((len(targets), HISTORY_LEN), dtype=np.uint16)\n weights = np.zeros((len(targets), HISTORY_LEN), dtype=np.float16)\n liked = np.zeros((len(targets), LIKED_LEN), dtype=np.uint16)\n disliked = np.zeros((len(targets), DISLIKED_LEN), dtype=np.uint16)\n user_feat = np.zeros((len(targets), 4), dtype=np.float32)\n for row, uid in enumerate(targets):\n s, e = bounds[int(uid)]\n ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n positive = np.flatnonzero(ratio[:end] >= 50)\n positive = positive[-HISTORY_LEN:]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n raw_w *= 1.0 + 0.25 * org[positive]\n raw_w *= np.sqrt(np.minimum(ratio[positive], 120) / 50.0)\n raw_w /= max(float(raw_w.sum()), 1e-8)\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n weights[row, -len(positive) :] = raw_w.astype(np.float16)\n lp = pref_group.get((int(uid), \"liked\"), np.empty(0, np.int32))\n dp = pref_group.get((int(uid), \"disliked\"), np.empty(0, np.int32))\n # Preference files lack activation times; popularity order is a stable overflow rule.\n lp = np.sort(lp)[-LIKED_LEN:]\n dp = np.sort(dp)[-DISLIKED_LEN:]\n liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\n disliked[row] = _pad_recent(dp.tolist(), DISLIKED_LEN)\n user_feat[row] = [\n math.log1p(len(positive)) / 10.0,\n float(org[positive].mean()) if len(positive) else 0.0,\n math.log1p(len(lp)) / 5.0,\n math.log1p(len(dp)) / 3.0,\n ]\n return targets, history, weights, liked, disliked, user_feat\n\n\n@torch.inference_mode()\ndef encode(model: TwoTower, users: tuple[np.ndarray, ...], features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n _, history, weights, liked, disliked, user_feat = users\n chunks = []\n for start in range(0, len(history), 512):\n sl = slice(start, start + 512)\n chunks.append(\n model.user_tower(\n torch.from_numpy(history[sl].astype(np.int64)),\n torch.from_numpy(weights[sl].astype(np.float32)),\n torch.from_numpy(liked[sl].astype(np.int64)),\n torch.from_numpy(disliked[sl].astype(np.int64)),\n torch.from_numpy(user_feat[sl]),\n ).numpy()\n )\n ue = np.concatenate(chunks).astype(np.float32)\n item_chunks = []\n ft = torch.from_numpy(features)\n for start in range(0, len(features), 2048):\n ix = torch.arange(start, min(start + 2048, len(features)))\n item_chunks.append(model.item_tower(ix, ft[ix]).numpy())\n return ue, np.concatenate(item_chunks).astype(np.float32)\n\n\ndef recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n idx = pd.Index(catalog_ids)\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n if family == \"listens\":\n cols.append(\"played_ratio_pct\")\n x = load_family(stage, family, cols)\n mapped = idx.get_indexer(x.item_id)\n mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\n mask &= x.is_organic.to_numpy() == 1\n if family == \"listens\":\n mask &= x.played_ratio_pct.to_numpy() >= 80\n score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\n lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\n raw = score + prior * np.power(lifetime, 0.75)\n # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\n order = np.argsort(-raw, kind=\"stable\")\n rank = np.empty(len(raw), dtype=np.int32)\n rank[order] = np.arange(1, len(raw) + 1)\n trend = -np.log(rank.astype(np.float64))\n return ((trend - trend.mean()) / trend.std()).astype(np.float32)\n\n\ndef relevant_truth() -> dict[int, dict[int, int]]:\n z = np.load(DATA / \"validation_eligible_candidates.npz\")\n uid_row = {int(x): i for i, x in enumerate(z[\"uids\"])}\n item_col = {int(x): i for i, x in enumerate(z[\"item_ids\"])}\n truth: dict[int, dict[int, int]] = {}\n for family, grade in [(\"val_listens.parquet\", 1), (\"val_likes.parquet\", 3)]:\n x = pd.read_parquet(DATA / family)\n mask = x.is_organic.to_numpy() == 1\n if grade == 1:\n mask &= x.played_ratio_pct.to_numpy() >= 80\n for uid, item in x.loc[mask, [\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False, name=None):\n r, c = uid_row.get(int(uid)), item_col.get(int(item))\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\n continue\n truth.setdefault(int(uid), {})[int(item)] = max(grade, truth.get(int(uid), {}).get(int(item), 0))\n return truth\n\n\ndef ndcg10(items: np.ndarray, truth: dict[int, int]) -> float:\n grades = np.asarray([truth.get(int(x), 0) for x in items[:10]], dtype=np.float64)\n dcg = np.sum((2**grades - 1) / np.log2(np.arange(2, 12)))\n ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n\n\ndef rank_and_tune(\n stage: str,\n targets: np.ndarray,\n user_embedding: np.ndarray,\n item_embedding: np.ndarray,\n trend: np.ndarray,\n output: Path,\n forced_weight: float | None,\n) -> tuple[float, float | None]:\n z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n assert np.array_equal(z[\"uids\"].astype(np.int64), targets)\n masks = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(z[\"item_ids\"]), bitorder=\"little\").astype(bool)\n truth = relevant_truth() if stage == \"validation\" else None\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n best_score, best_weight, best_top = -1.0, None, None\n all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\n for start in range(0, len(targets), 128):\n end = min(start + 128, len(targets))\n neural = user_embedding[start:end] @ item_embedding.T\n for w in weights:\n score = neural + float(w) * trend[None, :]\n score[~masks[start:end]] = -np.inf\n # Exact ordering is needed only within the selected top 100.\n part = np.argpartition(score, -100, axis=1)[:, -100:]\n part_score = np.take_along_axis(score, part, axis=1)\n order = np.argsort(-part_score, axis=1)\n top = np.take_along_axis(part, order, axis=1)\n all_top[float(w)][start:end] = top\n if truth is not None:\n for local, row in enumerate(range(start, end)):\n gold = truth.get(int(targets[row]))\n if gold:\n quality[float(w)].append(ndcg10(z[\"item_ids\"][top[local]], gold))\n if truth is not None:\n for w in weights:\n q = float(np.mean(quality[float(w)]))\n print(f\"blend trend_weight={w:g} public_ndcg@10={q:.6f}\", flush=True)\n if q > best_score:\n best_score, best_weight = q, float(w)\n best_top = all_top[best_weight]\n else:\n best_weight = float(weights[0])\n best_top = all_top[best_weight]\n\n frame = pd.DataFrame(\n {\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n \"item_id\": z[\"item_ids\"][best_top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\n }\n )\n frame.to_parquet(output, index=False)\n return best_score, best_weight\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=[\"validation\", \"final\"], required=True)\n parser.add_argument(\"--epochs\", type=int, default=8)\n parser.add_argument(\"--dim\", type=int, default=48)\n parser.add_argument(\"--weeks\", type=int, default=30)\n parser.add_argument(\"--seed\", type=int, default=20260925)\n parser.add_argument(\"--trend-weight\", type=float)\n parser.add_argument(\"--rebuild-cache\", action=\"store_true\")\n args = parser.parse_args()\n started = time.time()\n OUT.mkdir(exist_ok=True)\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n features = item_features(catalog)\n cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w.npz\"\n if cache.exists() and not args.rebuild_cache:\n print(f\"loading snapshot cache {cache}\", flush=True)\n snapshots = SnapshotArrays.load(cache)\n else:\n snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\n snapshots.save(cache)\n model = train_model(snapshots, features, args.dim, args.epochs, args.seed)\n model_path = OUT / f\"two_tower_{args.stage}.pt\"\n torch.save(\n {\n \"state_dict\": model.state_dict(),\n \"dim\": args.dim,\n \"item_features\": features,\n \"catalog_ids\": catalog_ids,\n \"architecture\": \"history/preference MLP user tower + ID/feature MLP item tower\",\n },\n model_path,\n )\n users = build_inference_users(args.stage, catalog_ids)\n ue, ie = encode(model, users, features)\n trend = recent_trend(args.stage, catalog_ids)\n output = OUT / (\"validation.parquet\" if args.stage == \"validation\" else \"submission.parquet\")\n score, weight = rank_and_tune(args.stage, users[0], ue, ie, trend, output, args.trend_weight)\n report = {\n \"stage\": args.stage,\n \"examples\": len(snapshots.history),\n \"epochs\": args.epochs,\n \"dimension\": args.dim + 1,\n \"trend_weight\": weight,\n \"public_ndcg@10\": score if args.stage == \"validation\" else None,\n \"seconds\": round(time.time() - started, 1),\n \"output\": str(output),\n }\n (OUT / f\"report_{args.stage}.json\").write_text(json.dumps(report, indent=2) + \"\\n\")\n print(json.dumps(report, indent=2), flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c0c401c487d2b444116dbf6dfbc6", "usage": { "input_tokens": 34863, "cached_input_tokens": 33792, "cache_write_input_tokens": 0, "output_tokens": 14283, "reasoning_output_tokens": 5376, "total_tokens": 49146 }, "turn_token_usage": { "input_tokens": 586393, "cached_input_tokens": 558720, "cache_write_input_tokens": 0, "output_tokens": 24291, "reasoning_output_tokens": 10354, "total_tokens": 610684 }, "thread_token_usage": { "input_tokens": 586393, "cached_input_tokens": 558720, "cache_write_input_tokens": 0, "output_tokens": 24291, "reasoning_output_tokens": 10354, "total_tokens": 610684 } } ## Step 43 · agent · 2026-09-25T00:35:19.768Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:21.044Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:35:21.217Z Script completed Wall time 0.2 seconds Output: -rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py File "/app/solution/train_ranker.py", line 539 return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2))) ^ SyntaxError: '(' was never closed ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1c66de887d28d4dbb897b97dbcd", "usage": { "input_tokens": 49170, "cached_input_tokens": 34688, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 21, "total_tokens": 49270 }, "turn_token_usage": { "input_tokens": 635563, "cached_input_tokens": 593408, "cache_write_input_tokens": 0, "output_tokens": 24391, "reasoning_output_tokens": 10375, "total_tokens": 659954 }, "thread_token_usage": { "input_tokens": 635563, "cached_input_tokens": 593408, "cache_write_input_tokens": 0, "output_tokens": 24391, "reasoning_output_tokens": 10375, "total_tokens": 659954 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d2ad865-d3da-437b-803b-1792fb11349c", "process_id": "5642", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution/train_ranker.py && /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 2380 }, "formatted_output": "-rw-r--r-- 1 agent agent 28K Sep 25 00:35 /app/solution/train_ranker.py\n File \"/app/solution/train_ranker.py\", line 539\n return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n ^\nSyntaxError: '(' was never closed\n" } } ## Step 44 · agent · 2026-09-25T00:35:22.540Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:24.513Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2))))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:35:24.610Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d4cfb405-610a-4df9-b35d-88348c640867", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -538,3 +538,3 @@\n ideal = np.asarray(sorted(truth.values(), reverse=True)[:10], dtype=np.float64)\n- return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2)))\n+ return float(dcg / np.sum((2**ideal - 1) / np.log2(np.arange(2, len(ideal) + 2))))\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1c986d887d2884243a66b3fd59b", "usage": { "input_tokens": 49380, "cached_input_tokens": 49024, "cache_write_input_tokens": 0, "output_tokens": 129, "reasoning_output_tokens": 10, "total_tokens": 49509 }, "turn_token_usage": { "input_tokens": 684943, "cached_input_tokens": 642432, "cache_write_input_tokens": 0, "output_tokens": 24520, "reasoning_output_tokens": 10385, "total_tokens": 709463 }, "thread_token_usage": { "input_tokens": 684943, "cached_input_tokens": 642432, "cache_write_input_tokens": 0, "output_tokens": 24520, "reasoning_output_tokens": 10385, "total_tokens": 709463 } } ### exec · 2026-09-25T00:35:27.339Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:35:27.615Z Script completed Wall time 0.3 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1cce8c087d28fd8d38a34d428fe", "usage": { "input_tokens": 49533, "cached_input_tokens": 49152, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 0, "total_tokens": 49629 }, "turn_token_usage": { "input_tokens": 734476, "cached_input_tokens": 691584, "cache_write_input_tokens": 0, "output_tokens": 24616, "reasoning_output_tokens": 10385, "total_tokens": 759092 }, "thread_token_usage": { "input_tokens": 734476, "cached_input_tokens": 691584, "cache_write_input_tokens": 0, "output_tokens": 24616, "reasoning_output_tokens": 10385, "total_tokens": 759092 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fbd06e6d-3d8e-44bc-a0cd-41712511aac2", "process_id": "1793", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py && PYTHONPATH=/opt/task-venv/lib/python3.12/site-packages python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 0, "nanos": 107041312 }, "formatted_output": "" } } ## Step 45 · agent · 2026-09-25T00:35:30.358Z ### Thinking unavailable — no text recorded The first full model is now implemented: separate PyTorch history/preference and item towers, 48 learned personalized dimensions, plus one offline trend dimension whose user-side coefficient is tuned on validation. Training uses rolling cutoff-correct examples and in-batch sampled softmax, with no user-ID parameters or global interaction matrix. I’m running it now and will use the public score to decide the next refinement. ### exec · 2026-09-25T00:35:33.650Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:35:34.814Z Script completed Wall time 1.2 seconds Output: SESSION_ID=36951 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1cfda1487d2b4cc515e9829b7f8", "usage": { "input_tokens": 49652, "cached_input_tokens": 49408, "cache_write_input_tokens": 0, "output_tokens": 287, "reasoning_output_tokens": 101, "total_tokens": 49939 }, "turn_token_usage": { "input_tokens": 784128, "cached_input_tokens": 740992, "cache_write_input_tokens": 0, "output_tokens": 24903, "reasoning_output_tokens": 10486, "total_tokens": 809031 }, "thread_token_usage": { "input_tokens": 784128, "cached_input_tokens": 740992, "cache_write_input_tokens": 0, "output_tokens": 24903, "reasoning_output_tokens": 10486, "total_tokens": 809031 } } ## Step 46 · agent · 2026-09-25T00:35:36.081Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:35:36.761Z { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:36:06.770Z Script completed Wall time 30.0 seconds Output: loading validation events for 30 rolling cutoffs users 4000/9979 examples=39421 users 6000/9979 examples=58953 users 8000/9979 examples=78890 built 98,532 examples, 837,405 relevant pairs ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1d6fc8087d2be2dffa57b9fa76f", "usage": { "input_tokens": 49967, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 50025 }, "turn_token_usage": { "input_tokens": 834095, "cached_input_tokens": 790528, "cache_write_input_tokens": 0, "output_tokens": 24961, "reasoning_output_tokens": 10497, "total_tokens": 859056 }, "thread_token_usage": { "input_tokens": 834095, "cached_input_tokens": 790528, "cache_write_input_tokens": 0, "output_tokens": 24961, "reasoning_output_tokens": 10497, "total_tokens": 859056 } } ## Step 47 · agent · 2026-09-25T00:36:07.886Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:36:08.575Z { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:36:38.583Z Script completed Wall time 30.0 seconds Output: epoch 1/6 loss=6.0179 scale=11.10 epoch 2/6 loss=5.9123 scale=11.07 epoch 3/6 loss=5.8714 scale=11.06 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c1f6f68487d29eb5dd9d5c676444", "usage": { "input_tokens": 50111, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 60, "reasoning_output_tokens": 13, "total_tokens": 50171 }, "turn_token_usage": { "input_tokens": 884206, "cached_input_tokens": 840320, "cache_write_input_tokens": 0, "output_tokens": 25021, "reasoning_output_tokens": 10510, "total_tokens": 909227 }, "thread_token_usage": { "input_tokens": 884206, "cached_input_tokens": 840320, "cache_write_input_tokens": 0, "output_tokens": 25021, "reasoning_output_tokens": 10510, "total_tokens": 909227 } } ## Step 48 · agent · 2026-09-25T00:36:40.562Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:36:41.139Z { "code": "const r = await tools.write_stdin({\"session_id\":36951,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:37:01.441Z Script completed Wall time 20.3 seconds Output: epoch 4/6 loss=5.8221 scale=11.09 epoch 5/6 loss=5.7660 scale=11.13 epoch 6/6 loss=5.6887 scale=11.20 Traceback (most recent call last): File "/app/solution/train_ranker.py", line 652, in main() File "/app/solution/train_ranker.py", line 632, in main users = build_inference_users(args.stage, catalog_ids) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 450, in build_inference_users history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16) ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ ValueError: could not broadcast input array from shape (0,) into shape (160,) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c216c92887d2823f6162d218790b", "usage": { "input_tokens": 50245, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 43, "total_tokens": 50335 }, "turn_token_usage": { "input_tokens": 934451, "cached_input_tokens": 890240, "cache_write_input_tokens": 0, "output_tokens": 25111, "reasoning_output_tokens": 10553, "total_tokens": 959562 }, "thread_token_usage": { "input_tokens": 934451, "cached_input_tokens": 890240, "cache_write_input_tokens": 0, "output_tokens": 25111, "reasoning_output_tokens": 10553, "total_tokens": 959562 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eee8ee87-605e-463b-86b2-24dd794b3bed", "process_id": "36951", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/train_ranker.py --stage validation --epochs 6 --dim 48 --weeks 30" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n", "stderr": "", "aggregated_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n", "exit_code": 1, "duration": { "secs": 87, "nanos": 625947818 }, "formatted_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/6 loss=6.0179 scale=11.10\r\nepoch 2/6 loss=5.9123 scale=11.07\r\nepoch 3/6 loss=5.8714 scale=11.06\r\nepoch 4/6 loss=5.8221 scale=11.09\r\nepoch 5/6 loss=5.7660 scale=11.13\r\nepoch 6/6 loss=5.6887 scale=11.20\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_ranker.py\", line 652, in \r\n main()\r\n File \"/app/solution/train_ranker.py\", line 632, in main\r\n users = build_inference_users(args.stage, catalog_ids)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 450, in build_inference_users\r\n history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\r\n ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: could not broadcast input array from shape (0,) into shape (160,)\r\n" } } ## Step 49 · agent · 2026-09-25T00:37:06.779Z ### Thinking unavailable — no text recorded Training completed cleanly, but ranking hit an edge case: a few target users have no catalog-mapped positive history even though they meet the overall activity threshold. I’m handling those users with the tower’s zero/padding representation and reusing the completed checkpoint, so no training time is lost. ### exec · 2026-09-25T00:37:13.592Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- positive = np.flatnonzero(ratio[:end] >= 50)\\n- positive = positive[-HISTORY_LEN:]\\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\\n+ positive = all_positive[-HISTORY_LEN:]\\n@@\\n- history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n- weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n+ if len(positive):\\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\\n@@\\n- lp = np.sort(lp)[-LIKED_LEN:]\\n- dp = np.sort(dp)[-DISLIKED_LEN:]\\n+ lp = np.sort(lp)[:LIKED_LEN]\\n+ dp = np.sort(dp)[:DISLIKED_LEN]\\n@@\\n- math.log1p(len(positive)) / 10.0,\\n+ math.log1p(len(all_positive)) / 10.0,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:37:13.661Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-004a27c1-6169-4d19-8baa-cc9b65b311b9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -443,4 +443,4 @@\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\n- positive = np.flatnonzero(ratio[:end] >= 50)\n- positive = positive[-HISTORY_LEN:]\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\n+ positive = all_positive[-HISTORY_LEN:]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n@@ -449,4 +449,5 @@\n raw_w /= max(float(raw_w.sum()), 1e-8)\n- history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n- weights[row, -len(positive) :] = raw_w.astype(np.float16)\n+ if len(positive):\n+ history[row, -len(positive) :] = (items[positive] + 1).astype(np.uint16)\n+ weights[row, -len(positive) :] = raw_w.astype(np.float16)\n lp = pref_group.get((int(uid), \"liked\"), np.empty(0, np.int32))\n@@ -454,4 +455,4 @@\n # Preference files lack activation times; popularity order is a stable overflow rule.\n- lp = np.sort(lp)[-LIKED_LEN:]\n- dp = np.sort(dp)[-DISLIKED_LEN:]\n+ lp = np.sort(lp)[:LIKED_LEN]\n+ dp = np.sort(dp)[:DISLIKED_LEN]\n liked[row] = _pad_recent(lp.tolist(), LIKED_LEN)\n@@ -459,3 +460,3 @@\n user_feat[row] = [\n- math.log1p(len(positive)) / 10.0,\n+ math.log1p(len(all_positive)) / 10.0,\n float(org[positive].mean()) if len(positive) else 0.0,\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c22db5f087d2a3f402408c29d672", "usage": { "input_tokens": 50555, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 617, "reasoning_output_tokens": 236, "total_tokens": 51172 }, "turn_token_usage": { "input_tokens": 985006, "cached_input_tokens": 940288, "cache_write_input_tokens": 0, "output_tokens": 25728, "reasoning_output_tokens": 10789, "total_tokens": 1010734 }, "thread_token_usage": { "input_tokens": 985006, "cached_input_tokens": 940288, "cache_write_input_tokens": 0, "output_tokens": 25728, "reasoning_output_tokens": 10789, "total_tokens": 1010734 } } ## Step 50 · agent · 2026-09-25T00:37:14.994Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:37:19.866Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch,pandas as pd\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False)\\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\\nmodel.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\\nprint('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\\nprint('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:37:21.031Z Script completed Wall time 1.2 seconds Output: SESSION_ID=73380 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c239f85087d28182b276a39924b3", "usage": { "input_tokens": 51196, "cached_input_tokens": 50432, "cache_write_input_tokens": 0, "output_tokens": 290, "reasoning_output_tokens": 11, "total_tokens": 51486 }, "turn_token_usage": { "input_tokens": 1036202, "cached_input_tokens": 990720, "cache_write_input_tokens": 0, "output_tokens": 26018, "reasoning_output_tokens": 10800, "total_tokens": 1062220 }, "thread_token_usage": { "input_tokens": 1036202, "cached_input_tokens": 990720, "cache_write_input_tokens": 0, "output_tokens": 26018, "reasoning_output_tokens": 10800, "total_tokens": 1062220 } } ## Step 51 · agent · 2026-09-25T00:37:22.047Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:37:22.726Z { "code": "const r = await tools.write_stdin({\"session_id\":73380,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:37:26.234Z Script completed Wall time 3.5 seconds Output: Traceback (most recent call last): File "", line 6, in File "/app/solution/train_ranker.py", line 441, in build_inference_users s, e = bounds[int(uid)] ~~~~~~^^^^^^^^^^ KeyError: 213900 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c241359887d28984c177320d3a21", "usage": { "input_tokens": 51514, "cached_input_tokens": 51072, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 51567 }, "turn_token_usage": { "input_tokens": 1087716, "cached_input_tokens": 1041792, "cache_write_input_tokens": 0, "output_tokens": 26071, "reasoning_output_tokens": 10806, "total_tokens": 1113787 }, "thread_token_usage": { "input_tokens": 1087716, "cached_input_tokens": 1041792, "cache_write_input_tokens": 0, "output_tokens": 26071, "reasoning_output_tokens": 10806, "total_tokens": 1113787 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1e06dc19-aadf-4910-aa62-c70418cfd527", "process_id": "73380", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,pandas as pd\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False)\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\nmodel.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\nprint('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\nprint('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,pandas as pd\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False)\nmodel=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim'])\nmodel.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids'])\nprint('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None)\nprint('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n", "exit_code": 1, "duration": { "secs": 6, "nanos": 201716663 }, "formatted_output": "Traceback (most recent call last):\r\n File \"\", line 6, in \r\n File \"/app/solution/train_ranker.py\", line 441, in build_inference_users\r\n s, e = bounds[int(uid)]\r\n ~~~~~~^^^^^^^^^^\r\nKeyError: 213900\r\n" } } ## Step 52 · agent · 2026-09-25T00:37:27.710Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:37:31.735Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n user_feat = np.zeros((len(targets), 4), dtype=np.float32)\\n for row, uid in enumerate(targets):\\n- s, e = bounds[int(uid)]\\n- ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ if int(uid) in bounds:\\n+ s, e = bounds[int(uid)]\\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\\n+ else:\\n+ ts = np.empty(0, dtype=np.int64)\\n+ items = np.empty(0, dtype=np.int32)\\n+ org = np.empty(0, dtype=np.uint8)\\n+ ratio = np.empty(0, dtype=np.uint16)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:37:31.828Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-105fb2e6-4a50-412e-a830-b57e1a004575", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -440,4 +440,10 @@\n for row, uid in enumerate(targets):\n- s, e = bounds[int(uid)]\n- ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n+ if int(uid) in bounds:\n+ s, e = bounds[int(uid)]\n+ ts, items, org, ratio = lt[s:e], li[s:e], lo[s:e], lr[s:e]\n+ else:\n+ ts = np.empty(0, dtype=np.int64)\n+ items = np.empty(0, dtype=np.int32)\n+ org = np.empty(0, dtype=np.uint8)\n+ ratio = np.empty(0, dtype=np.uint16)\n end = int(np.searchsorted(ts, cutoff, side=\"left\"))\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2466aa887d2bcbd3d6e13a151ea", "usage": { "input_tokens": 51655, "cached_input_tokens": 51328, "cache_write_input_tokens": 0, "output_tokens": 260, "reasoning_output_tokens": 28, "total_tokens": 51915 }, "turn_token_usage": { "input_tokens": 1139371, "cached_input_tokens": 1093120, "cache_write_input_tokens": 0, "output_tokens": 26331, "reasoning_output_tokens": 10834, "total_tokens": 1165702 }, "thread_token_usage": { "input_tokens": 1139371, "cached_input_tokens": 1093120, "cache_write_input_tokens": 0, "output_tokens": 26331, "reasoning_output_tokens": 10834, "total_tokens": 1165702 } } ## Step 53 · agent · 2026-09-25T00:37:33.009Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:37:37.706Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:37:38.872Z Script completed Wall time 1.2 seconds Output: SESSION_ID=7754 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c24c18e487d2af9d8afe85b7756c", "usage": { "input_tokens": 51939, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 276, "reasoning_output_tokens": 7, "total_tokens": 52215 }, "turn_token_usage": { "input_tokens": 1191310, "cached_input_tokens": 1144576, "cache_write_input_tokens": 0, "output_tokens": 26607, "reasoning_output_tokens": 10841, "total_tokens": 1217917 }, "thread_token_usage": { "input_tokens": 1191310, "cached_input_tokens": 1144576, "cache_write_input_tokens": 0, "output_tokens": 26607, "reasoning_output_tokens": 10841, "total_tokens": 1217917 } } ### exec · 2026-09-25T00:37:41.973Z { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:38:11.982Z Script completed Wall time 30.0 seconds Output: users built encoded (8456, 48) (30000, 48) trend ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c25310a087d2813710a2b8c4f08f", "usage": { "input_tokens": 52243, "cached_input_tokens": 51712, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 52288 }, "turn_token_usage": { "input_tokens": 1243553, "cached_input_tokens": 1196288, "cache_write_input_tokens": 0, "output_tokens": 26652, "reasoning_output_tokens": 10841, "total_tokens": 1270205 }, "thread_token_usage": { "input_tokens": 1243553, "cached_input_tokens": 1196288, "cache_write_input_tokens": 0, "output_tokens": 26652, "reasoning_output_tokens": 10841, "total_tokens": 1270205 } } ## Step 54 · agent · 2026-09-25T00:38:13.419Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:38:14.102Z { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:38:44.111Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c274260c87d2ab4b0aa8f97c8946", "usage": { "input_tokens": 52331, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 71, "reasoning_output_tokens": 24, "total_tokens": 52402 }, "turn_token_usage": { "input_tokens": 1295884, "cached_input_tokens": 1248384, "cache_write_input_tokens": 0, "output_tokens": 26723, "reasoning_output_tokens": 10865, "total_tokens": 1322607 }, "thread_token_usage": { "input_tokens": 1295884, "cached_input_tokens": 1248384, "cache_write_input_tokens": 0, "output_tokens": 26723, "reasoning_output_tokens": 10865, "total_tokens": 1322607 } } ## Step 55 · agent · 2026-09-25T00:38:45.565Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:38:46.259Z { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:39:16.267Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2944d7887d281daec4794423284", "usage": { "input_tokens": 52425, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 77, "reasoning_output_tokens": 30, "total_tokens": 52502 }, "turn_token_usage": { "input_tokens": 1348309, "cached_input_tokens": 1300480, "cache_write_input_tokens": 0, "output_tokens": 26800, "reasoning_output_tokens": 10895, "total_tokens": 1375109 }, "thread_token_usage": { "input_tokens": 1348309, "cached_input_tokens": 1300480, "cache_write_input_tokens": 0, "output_tokens": 26800, "reasoning_output_tokens": 10895, "total_tokens": 1375109 } } ## Step 56 · agent · 2026-09-25T00:39:20.705Z ### Thinking unavailable — no text recorded The model embeddings are encoded successfully. Exact blend tuning is currently the slow part because it ranks all 30,000 eligible candidates for every user across ten coefficients; I’m tightening that implementation after this pass so later iterations only rescore a safe candidate union. ### exec · 2026-09-25T00:39:22.522Z { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:39:52.531Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2b4722887d28fcf7d437fbe8101", "usage": { "input_tokens": 52525, "cached_input_tokens": 52224, "cache_write_input_tokens": 0, "output_tokens": 134, "reasoning_output_tokens": 29, "total_tokens": 52659 }, "turn_token_usage": { "input_tokens": 1400834, "cached_input_tokens": 1352704, "cache_write_input_tokens": 0, "output_tokens": 26934, "reasoning_output_tokens": 10924, "total_tokens": 1427768 }, "thread_token_usage": { "input_tokens": 1400834, "cached_input_tokens": 1352704, "cache_write_input_tokens": 0, "output_tokens": 26934, "reasoning_output_tokens": 10924, "total_tokens": 1427768 } } ## Step 57 · agent · 2026-09-25T00:39:54.349Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:39:55.130Z { "code": "const r = await tools.write_stdin({\"session_id\":7754,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:39:55.454Z Script completed Wall time 0.3 seconds Output: ^CTraceback (most recent call last): File "", line 7, in File "/app/solution/train_ranker.py", line 561, in rank_and_tune truth = relevant_truth() if stage == "validation" else None ^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 536, in relevant_truth if r is None or c is None or not ((int(z["packed_eligible"][r, c // 8]) >> (c % 8)) & 1): ~^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1 data = self._decompressor.decompress(data, n) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2d8b54887d29ea61c82ddf05099", "usage": { "input_tokens": 52682, "cached_input_tokens": 52352, "cache_write_input_tokens": 0, "output_tokens": 90, "reasoning_output_tokens": 38, "total_tokens": 52772 }, "turn_token_usage": { "input_tokens": 1453516, "cached_input_tokens": 1405056, "cache_write_input_tokens": 0, "output_tokens": 27024, "reasoning_output_tokens": 10962, "total_tokens": 1480540 }, "thread_token_usage": { "input_tokens": 1453516, "cached_input_tokens": 1405056, "cache_write_input_tokens": 0, "output_tokens": 27024, "reasoning_output_tokens": 10962, "total_tokens": 1480540 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0863d59b-a0ba-454b-8069-c4eedd545774", "process_id": "7754", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); print('encoded',ue.shape,ie.shape,flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n", "stderr": "", "aggregated_output": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n", "exit_code": 1, "duration": { "secs": 137, "nanos": 581058123 }, "formatted_output": "users built\r\nencoded (8456, 48) (30000, 48)\r\ntrend\r\n^CTraceback (most recent call last):\r\n File \"\", line 7, in \r\n File \"/app/solution/train_ranker.py\", line 561, in rank_and_tune\r\n truth = relevant_truth() if stage == \"validation\" else None\r\n ^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 536, in relevant_truth\r\n if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\r\n ~^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\r\n return format.read_array(bytes,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\r\n data = _read_bytes(fp, read_size, \"array data\")\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\r\n r = fp.read(size - len(data))\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\r\n data = self._read1(n)\r\n ^^^^^^^^^^^^^^\r\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\r\n data = self._decompressor.decompress(data, n)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nKeyboardInterrupt\r\n" } } ## Step 58 · agent · 2026-09-25T00:40:05.693Z ### Thinking unavailable — no text recorded ## Step 59 · agent · 2026-09-25T00:40:07.162Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:40:36.206Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def relevant_truth() -> dict[int, dict[int, int]]:\\n- z = np.load(DATA / \\\"validation_eligible_candidates.npz\\\")\\n- uid_row = {int(x): i for i, x in enumerate(z[\\\"uids\\\"])}\\n- item_col = {int(x): i for i, x in enumerate(z[\\\"item_ids\\\"])}\\n+ with np.load(DATA / \\\"validation_eligible_candidates.npz\\\") as z:\\n+ uids = z[\\\"uids\\\"].copy()\\n+ item_ids = z[\\\"item_ids\\\"].copy()\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ uid_row = {int(x): i for i, x in enumerate(uids)}\\n+ item_col = {int(x): i for i, x in enumerate(item_ids)}\\n@@\\n- if r is None or c is None or not ((int(z[\\\"packed_eligible\\\"][r, c // 8]) >> (c % 8)) & 1):\\n+ if r is None or c is None or not ((int(packed[r, c // 8]) >> (c % 8)) & 1):\\n@@\\n ) -> tuple[float, float | None]:\\n- z = np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\")\\n- assert np.array_equal(z[\\\"uids\\\"].astype(np.int64), targets)\\n- masks = np.unpackbits(z[\\\"packed_eligible\\\"], axis=1, count=len(z[\\\"item_ids\\\"]), bitorder=\\\"little\\\").astype(bool)\\n+ with np.load(DATA / f\\\"{stage}_eligible_candidates.npz\\\") as z:\\n+ eligible_uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(eligible_uids, targets)\\n+ masks = np.unpackbits(packed, axis=1, count=len(eligible_items), bitorder=\\\"little\\\").astype(bool)\\n truth = relevant_truth() if stage == \\\"validation\\\" else None\\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n- best_score, best_weight, best_top = -1.0, None, None\\n- all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\\n+ best_score, best_weight = -1.0, None\\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\\n- for start in range(0, len(targets), 128):\\n- end = min(start + 128, len(targets))\\n- neural = user_embedding[start:end] @ item_embedding.T\\n- for w in weights:\\n- score = neural + float(w) * trend[None, :]\\n- score[~masks[start:end]] = -np.inf\\n- # Exact ordering is needed only within the selected top 100.\\n- part = np.argpartition(score, -100, axis=1)[:, -100:]\\n- part_score = np.take_along_axis(score, part, axis=1)\\n- order = np.argsort(-part_score, axis=1)\\n- top = np.take_along_axis(part, order, axis=1)\\n- all_top[float(w)][start:end] = top\\n- if truth is not None:\\n- for local, row in enumerate(range(start, end)):\\n- gold = truth.get(int(targets[row]))\\n- if gold:\\n- quality[float(w)].append(ndcg10(z[\\\"item_ids\\\"][top[local]], gold))\\n+ if truth is not None and len(weights) > 1:\\n+ # Tune on a safe union of each endpoint's top 700. This avoids ten full\\n+ # 8,456 x 30,000 partial sorts; the selected coefficient is ranked\\n+ # exactly over the full catalog below.\\n+ trend_order = np.argsort(-trend, kind=\\\"stable\\\")\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ neural = user_embedding[start:end] @ item_embedding.T\\n+ neural[~masks[start:end]] = -np.inf\\n+ neural_part = np.argpartition(neural, -700, axis=1)[:, -700:]\\n+ for local, row in enumerate(range(start, end)):\\n+ trend_part = trend_order[masks[row, trend_order]][:700]\\n+ candidates = np.union1d(neural_part[local], trend_part)\\n+ base = neural[local, candidates]\\n+ for w in weights:\\n+ s = base + float(w) * trend[candidates]\\n+ top = candidates[np.argsort(-s)[:10]]\\n+ gold = truth.get(int(targets[row]))\\n+ if gold:\\n+ quality[float(w)].append(ndcg10(eligible_items[top], gold))\\n if truth is not None:\\n for w in weights:\\n q = float(np.mean(quality[float(w)]))\\n@@\\n if q > best_score:\\n best_score, best_weight = q, float(w)\\n- best_top = all_top[best_weight]\\n else:\\n best_weight = float(weights[0])\\n- best_top = all_top[best_weight]\\n+\\n+ # One exact full-catalog ranking for the selected deployment coefficient.\\n+ best_top = np.empty((len(targets), 100), dtype=np.int32)\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ score = user_embedding[start:end] @ item_embedding.T\\n+ score += float(best_weight) * trend[None, :]\\n+ score[~masks[start:end]] = -np.inf\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ part_score = np.take_along_axis(score, part, axis=1)\\n+ order = np.argsort(-part_score, axis=1)\\n+ best_top[start:end] = np.take_along_axis(part, order, axis=1)\\n@@\\n- \\\"item_id\\\": z[\\\"item_ids\\\"][best_top.reshape(-1)].astype(np.int64),\\n+ \\\"item_id\\\": eligible_items[best_top.reshape(-1)].astype(np.int64),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:40:36.284Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-91af5fe8-beb2-4f7b-9e32-c6575b8bb9e3", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -524,5 +524,8 @@\n def relevant_truth() -> dict[int, dict[int, int]]:\n- z = np.load(DATA / \"validation_eligible_candidates.npz\")\n- uid_row = {int(x): i for i, x in enumerate(z[\"uids\"])}\n- item_col = {int(x): i for i, x in enumerate(z[\"item_ids\"])}\n+ with np.load(DATA / \"validation_eligible_candidates.npz\") as z:\n+ uids = z[\"uids\"].copy()\n+ item_ids = z[\"item_ids\"].copy()\n+ packed = z[\"packed_eligible\"].copy()\n+ uid_row = {int(x): i for i, x in enumerate(uids)}\n+ item_col = {int(x): i for i, x in enumerate(item_ids)}\n truth: dict[int, dict[int, int]] = {}\n@@ -535,3 +538,3 @@\n r, c = uid_row.get(int(uid)), item_col.get(int(item))\n- if r is None or c is None or not ((int(z[\"packed_eligible\"][r, c // 8]) >> (c % 8)) & 1):\n+ if r is None or c is None or not ((int(packed[r, c // 8]) >> (c % 8)) & 1):\n continue\n@@ -557,27 +560,32 @@\n ) -> tuple[float, float | None]:\n- z = np.load(DATA / f\"{stage}_eligible_candidates.npz\")\n- assert np.array_equal(z[\"uids\"].astype(np.int64), targets)\n- masks = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(z[\"item_ids\"]), bitorder=\"little\").astype(bool)\n+ with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+ eligible_uids = z[\"uids\"].astype(np.int64, copy=True)\n+ eligible_items = z[\"item_ids\"].astype(np.int64, copy=True)\n+ packed = z[\"packed_eligible\"].copy()\n+ assert np.array_equal(eligible_uids, targets)\n+ masks = np.unpackbits(packed, axis=1, count=len(eligible_items), bitorder=\"little\").astype(bool)\n truth = relevant_truth() if stage == \"validation\" else None\n weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n- best_score, best_weight, best_top = -1.0, None, None\n- all_top: dict[float, np.ndarray] = {float(w): np.empty((len(targets), 100), dtype=np.int32) for w in weights}\n+ best_score, best_weight = -1.0, None\n quality: dict[float, list[float]] = {float(w): [] for w in weights}\n- for start in range(0, len(targets), 128):\n- end = min(start + 128, len(targets))\n- neural = user_embedding[start:end] @ item_embedding.T\n- for w in weights:\n- score = neural + float(w) * trend[None, :]\n- score[~masks[start:end]] = -np.inf\n- # Exact ordering is needed only within the selected top 100.\n- part = np.argpartition(score, -100, axis=1)[:, -100:]\n- part_score = np.take_along_axis(score, part, axis=1)\n- order = np.argsort(-part_score, axis=1)\n- top = np.take_along_axis(part, order, axis=1)\n- all_top[float(w)][start:end] = top\n- if truth is not None:\n- for local, row in enumerate(range(start, end)):\n+ if truth is not None and len(weights) > 1:\n+ # Tune on a safe union of each endpoint's top 700. This avoids ten full\n+ # 8,456 x 30,000 partial sorts; the selected coefficient is ranked\n+ # exactly over the full catalog below.\n+ trend_order = np.argsort(-trend, kind=\"stable\")\n+ for start in range(0, len(targets), 128):\n+ end = min(start + 128, len(targets))\n+ neural = user_embedding[start:end] @ item_embedding.T\n+ neural[~masks[start:end]] = -np.inf\n+ neural_part = np.argpartition(neural, -700, axis=1)[:, -700:]\n+ for local, row in enumerate(range(start, end)):\n+ trend_part = trend_order[masks[row, trend_order]][:700]\n+ candidates = np.union1d(neural_part[local], trend_part)\n+ base = neural[local, candidates]\n+ for w in weights:\n+ s = base + float(w) * trend[candidates]\n+ top = candidates[np.argsort(-s)[:10]]\n gold = truth.get(int(targets[row]))\n if gold:\n- quality[float(w)].append(ndcg10(z[\"item_ids\"][top[local]], gold))\n+ quality[float(w)].append(ndcg10(eligible_items[top], gold))\n if truth is not None:\n@@ -588,7 +596,17 @@\n best_score, best_weight = q, float(w)\n- best_top = all_top[best_weight]\n else:\n best_weight = float(weights[0])\n- best_top = all_top[best_weight]\n \n+ # One exact full-catalog ranking for the selected deployment coefficient.\n+ best_top = np.empty((len(targets), 100), dtype=np.int32)\n+ for start in range(0, len(targets), 128):\n+ end = min(start + 128, len(targets))\n+ score = user_embedding[start:end] @ item_embedding.T\n+ score += float(best_weight) * trend[None, :]\n+ score[~masks[start:end]] = -np.inf\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\n+ part_score = np.take_along_axis(score, part, axis=1)\n+ order = np.argsort(-part_score, axis=1)\n+ best_top[start:end] = np.take_along_axis(part, order, axis=1)\n+\n frame = pd.DataFrame(\n@@ -596,3 +614,3 @@\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n- \"item_id\": z[\"item_ids\"][best_top.reshape(-1)].astype(np.int64),\n+ \"item_id\": eligible_items[best_top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c2dba3e887d2b63bcce2f9b2c34b", "usage": { "input_tokens": 53172, "cached_input_tokens": 52480, "cache_write_input_tokens": 0, "output_tokens": 2214, "reasoning_output_tokens": 592, "total_tokens": 55386 }, "turn_token_usage": { "input_tokens": 1506688, "cached_input_tokens": 1457536, "cache_write_input_tokens": 0, "output_tokens": 29238, "reasoning_output_tokens": 11554, "total_tokens": 1535926 }, "thread_token_usage": { "input_tokens": 1506688, "cached_input_tokens": 1457536, "cache_write_input_tokens": 0, "output_tokens": 29238, "reasoning_output_tokens": 11554, "total_tokens": 1535926 } } ## Step 60 · agent · 2026-09-25T00:40:38.729Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:40:43.816Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport torch,numpy as np\\nimport train_ranker as tr\\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:40:44.982Z Script completed Wall time 1.2 seconds Output: SESSION_ID=48313 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3049ac487d29e6b6cb9c776a811", "usage": { "input_tokens": 55410, "cached_input_tokens": 52992, "cache_write_input_tokens": 0, "output_tokens": 357, "reasoning_output_tokens": 66, "total_tokens": 55767 }, "turn_token_usage": { "input_tokens": 1562098, "cached_input_tokens": 1510528, "cache_write_input_tokens": 0, "output_tokens": 29595, "reasoning_output_tokens": 11620, "total_tokens": 1591693 }, "thread_token_usage": { "input_tokens": 1562098, "cached_input_tokens": 1510528, "cache_write_input_tokens": 0, "output_tokens": 29595, "reasoning_output_tokens": 11620, "total_tokens": 1591693 } } ### exec · 2026-09-25T00:40:46.825Z { "code": "const r = await tools.write_stdin({\"session_id\":48313,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:41:02.479Z Script completed Wall time 15.6 seconds Output: users built encoded trend blend trend_weight=0 public_ndcg@10=0.000891 blend trend_weight=0.08 public_ndcg@10=0.036140 blend trend_weight=0.15 public_ndcg@10=0.034566 blend trend_weight=0.25 public_ndcg@10=0.033490 blend trend_weight=0.4 public_ndcg@10=0.032638 blend trend_weight=0.65 public_ndcg@10=0.032172 blend trend_weight=1 public_ndcg@10=0.031737 blend trend_weight=1.5 public_ndcg@10=0.031563 blend trend_weight=2.5 public_ndcg@10=0.031373 blend trend_weight=4 public_ndcg@10=0.031230 RESULT 0.03613964138296928 0.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c30d2fe087d2a91bf684aa52e8c0", "usage": { "input_tokens": 55795, "cached_input_tokens": 55168, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 55840 }, "turn_token_usage": { "input_tokens": 1617893, "cached_input_tokens": 1565696, "cache_write_input_tokens": 0, "output_tokens": 29640, "reasoning_output_tokens": 11620, "total_tokens": 1647533 }, "thread_token_usage": { "input_tokens": 1617893, "cached_input_tokens": 1565696, "cache_write_input_tokens": 0, "output_tokens": 29640, "reasoning_output_tokens": 11620, "total_tokens": 1647533 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-86ba4517-872f-4e47-8a17-e9232818d899", "process_id": "48313", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,numpy as np\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport torch,numpy as np\nimport train_ranker as tr\nck=torch.load('two_tower_validation.pt',weights_only=False); model=tr.TwoTower(len(ck['catalog_ids']),ck['item_features'].shape[1],ck['dim']); model.load_state_dict(ck['state_dict']); model.eval()\nusers=tr.build_inference_users('validation',ck['catalog_ids']); print('users built',flush=True)\nue,ie=tr.encode(model,users,ck['item_features']); np.savez_compressed('validation_embeddings.npz',uids=users[0],user=ue,item=ie); print('encoded',flush=True)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend',flush=True)\nscore,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n", "stderr": "", "aggregated_output": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 496386594 }, "formatted_output": "users built\r\nencoded\r\ntrend\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.036140\r\nblend trend_weight=0.15 public_ndcg@10=0.034566\r\nblend trend_weight=0.25 public_ndcg@10=0.033490\r\nblend trend_weight=0.4 public_ndcg@10=0.032638\r\nblend trend_weight=0.65 public_ndcg@10=0.032172\r\nblend trend_weight=1 public_ndcg@10=0.031737\r\nblend trend_weight=1.5 public_ndcg@10=0.031563\r\nblend trend_weight=2.5 public_ndcg@10=0.031373\r\nblend trend_weight=4 public_ndcg@10=0.031230\r\nRESULT 0.03613964138296928 0.08\r\n" } } ## Step 61 · agent · 2026-09-25T00:41:03.884Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:41:04.996Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:41:08.348Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.03614, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c31eb11c87d28946f179a7516e34", "usage": { "input_tokens": 56059, "cached_input_tokens": 55680, "cache_write_input_tokens": 0, "output_tokens": 92, "reasoning_output_tokens": 23, "total_tokens": 56151 }, "turn_token_usage": { "input_tokens": 1673952, "cached_input_tokens": 1621376, "cache_write_input_tokens": 0, "output_tokens": 29732, "reasoning_output_tokens": 11643, "total_tokens": 1703684 }, "thread_token_usage": { "input_tokens": 1673952, "cached_input_tokens": 1621376, "cache_write_input_tokens": 0, "output_tokens": 29732, "reasoning_output_tokens": 11643, "total_tokens": 1703684 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-73ad83b3-8c0a-405b-a9ef-507e310fdb66", "process_id": "71308", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 184181411 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.03614,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 62 · agent · 2026-09-25T00:41:18.579Z ### Thinking unavailable — no text recorded ## Step 63 · agent · 2026-09-25T00:41:24.326Z ### Thinking unavailable — no text recorded The first exact public result is 0.036140. The learned component alone is weak—as expected for unseen-item discovery—but it adds a meaningful personalized reranking lift over the roughly 0.031 trend baseline. I’m now optimizing the offline item signal’s decay and like/listen weighting, then I’ll retrain the personalized tower longer against the best variant. ### exec · 2026-09-25T00:41:38.666Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,sys,itertools,time\\nfrom pathlib import Path\\nsys.path.insert(0,'.'); import train_ranker as tr\\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\\narr={}\\nfor fam in ['listens','likes']:\\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\\n base=(ci>=0)&(age>=0)&(age=80\\n a=np.zeros((nd,len(ids)),np.float32)\\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\\n# load truth/masks once\\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\\npop=cat.positive_listens.to_numpy(float)\\ndef ev(score):\\n order=np.argsort(-score,kind='stable'); probe=order[:300]\\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\\n out=[]\\n for r in rows:\\n top=probe[masks[r,probe]][:10]\\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\\n return np.mean(out)\\nresults=[]\\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\\n decay=2**(-np.arange(nd)/half)\\n L=decay@arr['listens']; A=decay@arr['likes']\\n for aw in [2,4,7,10,14,20,30]:\\n for prior in [0,.03,.1,.25,.5,1]:\\n s=L+aw*A+prior*pop**.75\\n results.append((ev(s),half,aw,prior))\\nresults.sort(reverse=True)\\nfor x in results[:30]: print(x,flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:41:39.832Z Script completed Wall time 1.2 seconds Output: SESSION_ID=85093 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c324862087d293f1d1f09d9ce42d", "usage": { "input_tokens": 56219, "cached_input_tokens": 55936, "cache_write_input_tokens": 0, "output_tokens": 1634, "reasoning_output_tokens": 834, "total_tokens": 57853 }, "turn_token_usage": { "input_tokens": 1730171, "cached_input_tokens": 1677312, "cache_write_input_tokens": 0, "output_tokens": 31366, "reasoning_output_tokens": 12477, "total_tokens": 1761537 }, "thread_token_usage": { "input_tokens": 1730171, "cached_input_tokens": 1677312, "cache_write_input_tokens": 0, "output_tokens": 31366, "reasoning_output_tokens": 12477, "total_tokens": 1761537 } } ### exec · 2026-09-25T00:41:41.468Z { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:42:11.476Z Script completed Wall time 30.0 seconds Output: listens 1170755.0 likes 41623.0 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c344047887d282b9eb6040013007", "usage": { "input_tokens": 57881, "cached_input_tokens": 56064, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 57926 }, "turn_token_usage": { "input_tokens": 1788052, "cached_input_tokens": 1733376, "cache_write_input_tokens": 0, "output_tokens": 31411, "reasoning_output_tokens": 12477, "total_tokens": 1819463 }, "thread_token_usage": { "input_tokens": 1788052, "cached_input_tokens": 1733376, "cache_write_input_tokens": 0, "output_tokens": 31411, "reasoning_output_tokens": 12477, "total_tokens": 1819463 } } ## Step 64 · agent · 2026-09-25T00:42:13.754Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:42:14.448Z { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:42:44.468Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c363ab2087d2aa51c66dde096acd", "usage": { "input_tokens": 57965, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 75, "reasoning_output_tokens": 28, "total_tokens": 58040 }, "turn_token_usage": { "input_tokens": 1846017, "cached_input_tokens": 1791104, "cache_write_input_tokens": 0, "output_tokens": 31486, "reasoning_output_tokens": 12505, "total_tokens": 1877503 }, "thread_token_usage": { "input_tokens": 1846017, "cached_input_tokens": 1791104, "cache_write_input_tokens": 0, "output_tokens": 31486, "reasoning_output_tokens": 12505, "total_tokens": 1877503 } } ## Step 65 · agent · 2026-09-25T00:42:45.686Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:42:46.404Z { "code": "const r = await tools.write_stdin({\"session_id\":85093,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:42:50.140Z Script completed Wall time 3.7 seconds Output: (np.float64(0.032643162062452914), 3, 30, 0.1) (np.float64(0.032497236990331364), 2, 30, 0.1) (np.float64(0.032473916787401975), 5, 30, 0.03) (np.float64(0.03245087915422957), 1.5, 30, 0.03) (np.float64(0.03241401022150992), 1.5, 30, 0) (np.float64(0.032336605613089694), 4, 30, 0.1) (np.float64(0.0323360249691029), 1.5, 30, 0.1) (np.float64(0.03232685633230199), 3, 30, 0.03) (np.float64(0.03231090493798152), 1, 30, 0.03) (np.float64(0.032296639059451675), 1, 30, 0.1) (np.float64(0.032293530636916326), 3, 30, 0.25) (np.float64(0.03215389246399263), 5, 30, 0) (np.float64(0.032141125503168756), 5, 20, 0) (np.float64(0.03211360162213496), 2, 30, 0.03) (np.float64(0.032061026306228875), 4, 30, 0.03) (np.float64(0.03190091759705678), 1, 30, 0) (np.float64(0.03188515841897388), 4, 30, 0) (np.float64(0.03185306621897185), 3, 30, 0) (np.float64(0.03182449275350494), 4, 20, 0.1) (np.float64(0.03179663867066262), 3, 20, 0.1) (np.float64(0.03170885001046369), 1.5, 20, 0.1) (np.float64(0.03167989852275455), 4, 30, 0.25) (np.float64(0.03166587754196166), 1.5, 14, 0.1) (np.float64(0.0316554473575147), 4, 20, 0.03) (np.float64(0.031646585719146525), 5, 30, 0.25) (np.float64(0.031629420198915834), 5, 30, 0.1) (np.float64(0.03159845642505134), 3, 14, 0.1) (np.float64(0.03158588943339609), 5, 20, 0.1) (np.float64(0.031573105472777356), 5, 14, 0.1) (np.float64(0.0315275327974698), 2, 30, 0) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c384a81087d2acd2e0179b324664", "usage": { "input_tokens": 58063, "cached_input_tokens": 57728, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 58120 }, "turn_token_usage": { "input_tokens": 1904080, "cached_input_tokens": 1848832, "cache_write_input_tokens": 0, "output_tokens": 31543, "reasoning_output_tokens": 12515, "total_tokens": 1935623 }, "thread_token_usage": { "input_tokens": 1904080, "cached_input_tokens": 1848832, "cache_write_input_tokens": 0, "output_tokens": 31543, "reasoning_output_tokens": 12515, "total_tokens": 1935623 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-942e2108-c9ac-4b96-bf04-af19c8a1bef6", "process_id": "85093", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,sys,itertools,time\nfrom pathlib import Path\nsys.path.insert(0,'.'); import train_ranker as tr\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\narr={}\nfor fam in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\n base=(ci>=0)&(age>=0)&(age=80\n a=np.zeros((nd,len(ids)),np.float32)\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\n# load truth/masks once\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\npop=cat.positive_listens.to_numpy(float)\ndef ev(score):\n order=np.argsort(-score,kind='stable'); probe=order[:300]\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\n out=[]\n for r in rows:\n top=probe[masks[r,probe]][:10]\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\n return np.mean(out)\nresults=[]\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\n decay=2**(-np.arange(nd)/half)\n L=decay@arr['listens']; A=decay@arr['likes']\n for aw in [2,4,7,10,14,20,30]:\n for prior in [0,.03,.1,.25,.5,1]:\n s=L+aw*A+prior*pop**.75\n results.append((ev(s),half,aw,prior))\nresults.sort(reverse=True)\nfor x in results[:30]: print(x,flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,sys,itertools,time\nfrom pathlib import Path\nsys.path.insert(0,'.'); import train_ranker as tr\nD=tr.DATA; C=tr.TRAIN_CUTOFF; cat=pd.read_parquet(D/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28\narr={}\nfor fam in ['listens','likes']:\n cols=['item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(D/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(int)\n base=(ci>=0)&(age>=0)&(age=80\n a=np.zeros((nd,len(ids)),np.float32)\n for d in range(nd): a[d]=np.bincount(ci[base&(age==d)],minlength=len(ids))\n arr[fam]=a; np.save(f'daily_{fam}.npy',a); print(fam,a.sum(),flush=True)\n# load truth/masks once\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[i for i,u in enumerate(uids) if int(u) in truth]\npop=cat.positive_listens.to_numpy(float)\ndef ev(score):\n order=np.argsort(-score,kind='stable'); probe=order[:300]\n # top ranks essentially never need beyond 300 (maximum exclusion among head is small)\n out=[]\n for r in rows:\n top=probe[masks[r,probe]][:10]\n out.append(tr.ndcg10(items[top],truth[int(uids[r])]))\n return np.mean(out)\nresults=[]\nfor half in [.5,1,1.5,2,3,4,5,7,10,14,21,100]:\n decay=2**(-np.arange(nd)/half)\n L=decay@arr['listens']; A=decay@arr['likes']\n for aw in [2,4,7,10,14,20,30]:\n for prior in [0,.03,.1,.25,.5,1]:\n s=L+aw*A+prior*pop**.75\n results.append((ev(s),half,aw,prior))\nresults.sort(reverse=True)\nfor x in results[:30]: print(x,flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n", "stderr": "", "aggregated_output": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n", "exit_code": 0, "duration": { "secs": 71, "nanos": 307377716 }, "formatted_output": "listens 1170755.0\r\nlikes 41623.0\r\n(np.float64(0.032643162062452914), 3, 30, 0.1)\r\n(np.float64(0.032497236990331364), 2, 30, 0.1)\r\n(np.float64(0.032473916787401975), 5, 30, 0.03)\r\n(np.float64(0.03245087915422957), 1.5, 30, 0.03)\r\n(np.float64(0.03241401022150992), 1.5, 30, 0)\r\n(np.float64(0.032336605613089694), 4, 30, 0.1)\r\n(np.float64(0.0323360249691029), 1.5, 30, 0.1)\r\n(np.float64(0.03232685633230199), 3, 30, 0.03)\r\n(np.float64(0.03231090493798152), 1, 30, 0.03)\r\n(np.float64(0.032296639059451675), 1, 30, 0.1)\r\n(np.float64(0.032293530636916326), 3, 30, 0.25)\r\n(np.float64(0.03215389246399263), 5, 30, 0)\r\n(np.float64(0.032141125503168756), 5, 20, 0)\r\n(np.float64(0.03211360162213496), 2, 30, 0.03)\r\n(np.float64(0.032061026306228875), 4, 30, 0.03)\r\n(np.float64(0.03190091759705678), 1, 30, 0)\r\n(np.float64(0.03188515841897388), 4, 30, 0)\r\n(np.float64(0.03185306621897185), 3, 30, 0)\r\n(np.float64(0.03182449275350494), 4, 20, 0.1)\r\n(np.float64(0.03179663867066262), 3, 20, 0.1)\r\n(np.float64(0.03170885001046369), 1.5, 20, 0.1)\r\n(np.float64(0.03167989852275455), 4, 30, 0.25)\r\n(np.float64(0.03166587754196166), 1.5, 14, 0.1)\r\n(np.float64(0.0316554473575147), 4, 20, 0.03)\r\n(np.float64(0.031646585719146525), 5, 30, 0.25)\r\n(np.float64(0.031629420198915834), 5, 30, 0.1)\r\n(np.float64(0.03159845642505134), 3, 14, 0.1)\r\n(np.float64(0.03158588943339609), 5, 20, 0.1)\r\n(np.float64(0.031573105472777356), 5, 14, 0.1)\r\n(np.float64(0.0315275327974698), 2, 30, 0)\r\n" } } ## Step 66 · agent · 2026-09-25T00:42:54.156Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:43:00.443Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\\n@@\\n- for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ # Public tuning favored a short three-day half-life and strong explicit\\n+ # actions. Counts use only information available strictly before cutoff.\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 30.0)]:\\n@@\\n- mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n@@\\n- score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\\n+ weighted = np.exp2(-age_day[mask] / 3.0)\\n+ score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:43:00.565Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-e03558f3-06f7-4b51-ae4d-63bfea081124", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -498,3 +498,3 @@\n \n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.25) -> np.ndarray:\n+def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -502,3 +502,5 @@\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n- for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n+ # Public tuning favored a short three-day half-life and strong explicit\n+ # actions. Counts use only information available strictly before cutoff.\n+ for family, gain in [(\"listens\", 1.0), (\"likes\", 30.0)]:\n cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n@@ -508,3 +510,4 @@\n mapped = idx.get_indexer(x.item_id)\n- mask = (mapped >= 0) & (x.timestamp.to_numpy() >= cutoff - WINDOW) & (x.timestamp.to_numpy() < cutoff)\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n mask &= x.is_organic.to_numpy() == 1\n@@ -512,3 +515,4 @@\n mask &= x.played_ratio_pct.to_numpy() >= 80\n- score += gain * np.bincount(mapped[mask], minlength=len(catalog_ids))\n+ weighted = np.exp2(-age_day[mask] / 3.0)\n+ score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\n lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c38a5b5887d286bb35d9d1933128", "usage": { "input_tokens": 58859, "cached_input_tokens": 57856, "cache_write_input_tokens": 0, "output_tokens": 517, "reasoning_output_tokens": 159, "total_tokens": 59376 }, "turn_token_usage": { "input_tokens": 1962939, "cached_input_tokens": 1906688, "cache_write_input_tokens": 0, "output_tokens": 32060, "reasoning_output_tokens": 12674, "total_tokens": 1994999 }, "thread_token_usage": { "input_tokens": 1962939, "cached_input_tokens": 1906688, "cache_write_input_tokens": 0, "output_tokens": 32060, "reasoning_output_tokens": 12674, "total_tokens": 1994999 } } ## Step 67 · agent · 2026-09-25T00:43:01.894Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:43:05.226Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:43:06.391Z Script completed Wall time 1.2 seconds Output: SESSION_ID=39451 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c394d19c87d297b9e78d3fce5bdb", "usage": { "input_tokens": 59400, "cached_input_tokens": 58624, "cache_write_input_tokens": 0, "output_tokens": 204, "reasoning_output_tokens": 12, "total_tokens": 59604 }, "turn_token_usage": { "input_tokens": 2022339, "cached_input_tokens": 1965312, "cache_write_input_tokens": 0, "output_tokens": 32264, "reasoning_output_tokens": 12686, "total_tokens": 2054603 }, "thread_token_usage": { "input_tokens": 2022339, "cached_input_tokens": 1965312, "cache_write_input_tokens": 0, "output_tokens": 32264, "reasoning_output_tokens": 12686, "total_tokens": 2054603 } } ### exec · 2026-09-25T00:43:09.827Z { "code": "const r = await tools.write_stdin({\"session_id\":39451,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:43:21.497Z Script completed Wall time 11.7 seconds Output: trend built blend trend_weight=0 public_ndcg@10=0.000891 blend trend_weight=0.08 public_ndcg@10=0.037047 blend trend_weight=0.15 public_ndcg@10=0.035843 blend trend_weight=0.25 public_ndcg@10=0.035078 blend trend_weight=0.4 public_ndcg@10=0.034051 blend trend_weight=0.65 public_ndcg@10=0.033535 blend trend_weight=1 public_ndcg@10=0.032942 blend trend_weight=1.5 public_ndcg@10=0.032694 blend trend_weight=2.5 public_ndcg@10=0.032609 blend trend_weight=4 public_ndcg@10=0.032649 RESULT 0.037047457753173044 0.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c39a919487d2bb9a84df9d48fb4c", "usage": { "input_tokens": 59632, "cached_input_tokens": 59264, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 59677 }, "turn_token_usage": { "input_tokens": 2081971, "cached_input_tokens": 2024576, "cache_write_input_tokens": 0, "output_tokens": 32309, "reasoning_output_tokens": 12686, "total_tokens": 2114280 }, "thread_token_usage": { "input_tokens": 2081971, "cached_input_tokens": 2024576, "cache_write_input_tokens": 0, "output_tokens": 32309, "reasoning_output_tokens": 12686, "total_tokens": 2114280 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae2aff46-0b96-4d04-af83-7a1b27a60ebe", "process_id": "39451", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings.npz'); ck=torch.load('two_tower_validation.pt',weights_only=False)\ntrend=tr.recent_trend('validation',ck['catalog_ids']); print('trend built',flush=True)\nscore,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n", "stderr": "", "aggregated_output": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 105807487 }, "formatted_output": "trend built\r\nblend trend_weight=0 public_ndcg@10=0.000891\r\nblend trend_weight=0.08 public_ndcg@10=0.037047\r\nblend trend_weight=0.15 public_ndcg@10=0.035843\r\nblend trend_weight=0.25 public_ndcg@10=0.035078\r\nblend trend_weight=0.4 public_ndcg@10=0.034051\r\nblend trend_weight=0.65 public_ndcg@10=0.033535\r\nblend trend_weight=1 public_ndcg@10=0.032942\r\nblend trend_weight=1.5 public_ndcg@10=0.032694\r\nblend trend_weight=2.5 public_ndcg@10=0.032609\r\nblend trend_weight=4 public_ndcg@10=0.032649\r\nRESULT 0.037047457753173044 0.08\r\n" } } ## Step 68 · agent · 2026-09-25T00:43:23.035Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:43:24.128Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:43:27.490Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.037047, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3a9cb7087d286893dbeb93f23c3", "usage": { "input_tokens": 59892, "cached_input_tokens": 59392, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 20, "total_tokens": 59981 }, "turn_token_usage": { "input_tokens": 2141863, "cached_input_tokens": 2083968, "cache_write_input_tokens": 0, "output_tokens": 32398, "reasoning_output_tokens": 12706, "total_tokens": 2174261 }, "thread_token_usage": { "input_tokens": 2141863, "cached_input_tokens": 2083968, "cache_write_input_tokens": 0, "output_tokens": 32398, "reasoning_output_tokens": 12706, "total_tokens": 2174261 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a08030a-088a-4634-8fa0-81261dde0041", "process_id": "49767", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 190531389 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.037047,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 69 · agent · 2026-09-25T00:43:31.511Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:43:37.577Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\\nmodel=tr.train_model(snap,feat,48,15,20260926)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:43:38.744Z Script completed Wall time 1.2 seconds Output: SESSION_ID=87853 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3afa8bc87d2a058c42655083deb", "usage": { "input_tokens": 60049, "cached_input_tokens": 59776, "cache_write_input_tokens": 0, "output_tokens": 513, "reasoning_output_tokens": 167, "total_tokens": 60562 }, "turn_token_usage": { "input_tokens": 2201912, "cached_input_tokens": 2143744, "cache_write_input_tokens": 0, "output_tokens": 32911, "reasoning_output_tokens": 12873, "total_tokens": 2234823 }, "thread_token_usage": { "input_tokens": 2201912, "cached_input_tokens": 2143744, "cache_write_input_tokens": 0, "output_tokens": 32911, "reasoning_output_tokens": 12873, "total_tokens": 2234823 } } ## Step 70 · agent · 2026-09-25T00:43:39.945Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:43:40.648Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:44:10.657Z Script completed Wall time 30.0 seconds Output: epoch 1/15 loss=6.0189 scale=11.10 epoch 2/15 loss=5.9172 scale=11.07 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3bafec887d2b05227bb5259f5bc", "usage": { "input_tokens": 60590, "cached_input_tokens": 59904, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 60645 }, "turn_token_usage": { "input_tokens": 2262502, "cached_input_tokens": 2203648, "cache_write_input_tokens": 0, "output_tokens": 32966, "reasoning_output_tokens": 12881, "total_tokens": 2295468 }, "thread_token_usage": { "input_tokens": 2262502, "cached_input_tokens": 2203648, "cache_write_input_tokens": 0, "output_tokens": 32966, "reasoning_output_tokens": 12881, "total_tokens": 2295468 } } ## Step 71 · agent · 2026-09-25T00:44:12.066Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:44:12.742Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:44:42.753Z Script completed Wall time 30.0 seconds Output: epoch 3/15 loss=5.8747 scale=11.07 epoch 4/15 loss=5.8240 scale=11.10 epoch 5/15 loss=5.7662 scale=11.16 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3dae2d887d2b2d3baa28232b883", "usage": { "input_tokens": 60702, "cached_input_tokens": 60416, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 60766 }, "turn_token_usage": { "input_tokens": 2323204, "cached_input_tokens": 2264064, "cache_write_input_tokens": 0, "output_tokens": 33030, "reasoning_output_tokens": 12898, "total_tokens": 2356234 }, "thread_token_usage": { "input_tokens": 2323204, "cached_input_tokens": 2264064, "cache_write_input_tokens": 0, "output_tokens": 33030, "reasoning_output_tokens": 12898, "total_tokens": 2356234 } } ## Step 72 · agent · 2026-09-25T00:44:46.511Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:44:46.512Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:45:16.520Z Script completed Wall time 30.0 seconds Output: epoch 6/15 loss=5.6829 scale=11.24 epoch 7/15 loss=5.5836 scale=11.40 epoch 8/15 loss=5.4721 scale=11.80 epoch 9/15 loss=5.3575 scale=12.58 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c3faf5c087d28edce423170e6d63", "usage": { "input_tokens": 60840, "cached_input_tokens": 60544, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 60893 }, "turn_token_usage": { "input_tokens": 2384044, "cached_input_tokens": 2324608, "cache_write_input_tokens": 0, "output_tokens": 33083, "reasoning_output_tokens": 12904, "total_tokens": 2417127 }, "thread_token_usage": { "input_tokens": 2384044, "cached_input_tokens": 2324608, "cache_write_input_tokens": 0, "output_tokens": 33083, "reasoning_output_tokens": 12904, "total_tokens": 2417127 } } ## Step 73 · agent · 2026-09-25T00:45:17.713Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:45:18.439Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:45:48.450Z Script completed Wall time 30.0 seconds Output: epoch 10/15 loss=5.2477 scale=13.63 epoch 11/15 loss=5.1378 scale=14.85 epoch 12/15 loss=5.0335 scale=16.30 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c41cb3a087d289aed72dd7bc2e4f", "usage": { "input_tokens": 60984, "cached_input_tokens": 60672, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 61038 }, "turn_token_usage": { "input_tokens": 2445028, "cached_input_tokens": 2385280, "cache_write_input_tokens": 0, "output_tokens": 33137, "reasoning_output_tokens": 12911, "total_tokens": 2478165 }, "thread_token_usage": { "input_tokens": 2445028, "cached_input_tokens": 2385280, "cache_write_input_tokens": 0, "output_tokens": 33137, "reasoning_output_tokens": 12911, "total_tokens": 2478165 } } ## Step 74 · agent · 2026-09-25T00:45:52.946Z ### Thinking unavailable — no text recorded The stronger trend decay raises the public score to 0.037047. Longer neural training is still improving contrastive loss substantially (from 6.02 to 5.03 by epoch 12), so I’m letting this run complete before comparing exact rankings; the existing best validation file remains intact. ### exec · 2026-09-25T00:45:52.951Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:46:22.968Z Script completed Wall time 30.0 seconds Output: epoch 13/15 loss=4.9336 scale=17.57 epoch 14/15 loss=4.8422 scale=18.81 epoch 15/15 loss=4.7480 scale=19.99 blend trend_weight=0 public_ndcg@10=0.002427 blend trend_weight=0.08 public_ndcg@10=0.039205 blend trend_weight=0.15 public_ndcg@10=0.037894 blend trend_weight=0.25 public_ndcg@10=0.035830 blend trend_weight=0.4 public_ndcg@10=0.034718 blend trend_weight=0.65 public_ndcg@10=0.034125 blend trend_weight=1 public_ndcg@10=0.033472 blend trend_weight=1.5 public_ndcg@10=0.032966 blend trend_weight=2.5 public_ndcg@10=0.032728 blend trend_weight=4 public_ndcg@10=0.032694 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c43ca8dc87d2a4cb50e872951bca", "usage": { "input_tokens": 61112, "cached_input_tokens": 60800, "cache_write_input_tokens": 0, "output_tokens": 121, "reasoning_output_tokens": 6, "total_tokens": 61233 }, "turn_token_usage": { "input_tokens": 2506140, "cached_input_tokens": 2446080, "cache_write_input_tokens": 0, "output_tokens": 33258, "reasoning_output_tokens": 12917, "total_tokens": 2539398 }, "thread_token_usage": { "input_tokens": 2506140, "cached_input_tokens": 2446080, "cache_write_input_tokens": 0, "output_tokens": 33258, "reasoning_output_tokens": 12917, "total_tokens": 2539398 } } ## Step 75 · agent · 2026-09-25T00:46:24.323Z ### Thinking unavailable — no text recorded ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a6ed944-6692-44af-aa9b-f6430e48e1a7", "process_id": "87853", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\nmodel=tr.train_model(snap,feat,48,15,20260926)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat)\nmodel=tr.train_model(snap,feat,48,15,20260926)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':cat.item_id.to_numpy(dtype=np.int64),'architecture':'history/preference MLP user tower + ID/feature MLP item tower'},'two_tower_validation_15e.pt')\nusers=tr.build_inference_users('validation',cat.item_id.to_numpy(dtype=np.int64)); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_15e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',cat.item_id.to_numpy(dtype=np.int64)); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_15e.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n", "stderr": "", "aggregated_output": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n", "exit_code": 0, "duration": { "secs": 166, "nanos": 854493071 }, "formatted_output": "epoch 1/15 loss=6.0189 scale=11.10\r\nepoch 2/15 loss=5.9172 scale=11.07\r\nepoch 3/15 loss=5.8747 scale=11.07\r\nepoch 4/15 loss=5.8240 scale=11.10\r\nepoch 5/15 loss=5.7662 scale=11.16\r\nepoch 6/15 loss=5.6829 scale=11.24\r\nepoch 7/15 loss=5.5836 scale=11.40\r\nepoch 8/15 loss=5.4721 scale=11.80\r\nepoch 9/15 loss=5.3575 scale=12.58\r\nepoch 10/15 loss=5.2477 scale=13.63\r\nepoch 11/15 loss=5.1378 scale=14.85\r\nepoch 12/15 loss=5.0335 scale=16.30\r\nepoch 13/15 loss=4.9336 scale=17.57\r\nepoch 14/15 loss=4.8422 scale=18.81\r\nepoch 15/15 loss=4.7480 scale=19.99\r\nblend trend_weight=0 public_ndcg@10=0.002427\r\nblend trend_weight=0.08 public_ndcg@10=0.039205\r\nblend trend_weight=0.15 public_ndcg@10=0.037894\r\nblend trend_weight=0.25 public_ndcg@10=0.035830\r\nblend trend_weight=0.4 public_ndcg@10=0.034718\r\nblend trend_weight=0.65 public_ndcg@10=0.034125\r\nblend trend_weight=1 public_ndcg@10=0.033472\r\nblend trend_weight=1.5 public_ndcg@10=0.032966\r\nblend trend_weight=2.5 public_ndcg@10=0.032728\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039204681489302264 0.08\r\n" } } ### exec · 2026-09-25T00:46:24.994Z { "code": "const r = await tools.write_stdin({\"session_id\":87853,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:46:25.097Z Script completed Wall time 0.0 seconds Output: RESULT 0.039204681489302264 0.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c45f3f2087d29b50fadaf8c3d817", "usage": { "input_tokens": 61481, "cached_input_tokens": 60928, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 61537 }, "turn_token_usage": { "input_tokens": 2567621, "cached_input_tokens": 2507008, "cache_write_input_tokens": 0, "output_tokens": 33314, "reasoning_output_tokens": 12926, "total_tokens": 2600935 }, "thread_token_usage": { "input_tokens": 2567621, "cached_input_tokens": 2507008, "cache_write_input_tokens": 0, "output_tokens": 33314, "reasoning_output_tokens": 12926, "total_tokens": 2600935 } } ## Step 76 · agent · 2026-09-25T00:46:26.396Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:46:27.520Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:46:30.880Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation_15e.parquet", "scores": { "quality_ndcg@10": 0.039205, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c461587c87d299cf6e60f71bd1a2", "usage": { "input_tokens": 61575, "cached_input_tokens": 61312, "cache_write_input_tokens": 0, "output_tokens": 81, "reasoning_output_tokens": 9, "total_tokens": 61656 }, "turn_token_usage": { "input_tokens": 2629196, "cached_input_tokens": 2568320, "cache_write_input_tokens": 0, "output_tokens": 33395, "reasoning_output_tokens": 12935, "total_tokens": 2662591 }, "thread_token_usage": { "input_tokens": 2629196, "cached_input_tokens": 2568320, "cache_write_input_tokens": 0, "output_tokens": 33395, "reasoning_output_tokens": 12935, "total_tokens": 2662591 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f2c000bf-2e93-4ff6-96b5-70495bface60", "process_id": "92536", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_15e.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 192039919 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_15e.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039205,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 77 · agent · 2026-09-25T00:46:35.076Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:46:38.850Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def train_model(\\n snapshots: SnapshotArrays,\\n features: np.ndarray,\\n dim: int,\\n epochs: int,\\n seed: int,\\n+ initial_model: TwoTower | None = None,\\n+ learning_rate: float = 2.5e-3,\\n ) -> TwoTower:\\n@@\\n- model = TwoTower(len(features), features.shape[1], dim)\\n- opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\\n+ model = initial_model if initial_model is not None else TwoTower(len(features), features.shape[1], dim)\\n+ opt = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:46:38.949Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-2aac2e6a-adae-4413-bac6-2950dbc4ea3c", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -364,2 +364,4 @@\n seed: int,\n+ initial_model: TwoTower | None = None,\n+ learning_rate: float = 2.5e-3,\n ) -> TwoTower:\n@@ -367,4 +369,4 @@\n torch.set_num_threads(min(8, torch.get_num_threads()))\n- model = TwoTower(len(features), features.shape[1], dim)\n- opt = torch.optim.AdamW(model.parameters(), lr=2.5e-3, weight_decay=2e-5)\n+ model = initial_model if initial_model is not None else TwoTower(len(features), features.shape[1], dim)\n+ opt = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=2e-5)\n feat_t = torch.from_numpy(features)\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c46727d887d29f836ff6c18b8e30", "usage": { "input_tokens": 61727, "cached_input_tokens": 61440, "cache_write_input_tokens": 0, "output_tokens": 390, "reasoning_output_tokens": 171, "total_tokens": 62117 }, "turn_token_usage": { "input_tokens": 2690923, "cached_input_tokens": 2629760, "cache_write_input_tokens": 0, "output_tokens": 33785, "reasoning_output_tokens": 13106, "total_tokens": 2724708 }, "thread_token_usage": { "input_tokens": 2690923, "cached_input_tokens": 2629760, "cache_write_input_tokens": 0, "output_tokens": 33785, "reasoning_output_tokens": 13106, "total_tokens": 2724708 } } ## Step 78 · agent · 2026-09-25T00:46:40.457Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:46:47.121Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:46:48.287Z Script completed Wall time 1.2 seconds Output: SESSION_ID=66792 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c46f4d7087d29e7ad2fb87e0c69b", "usage": { "input_tokens": 62141, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 395, "reasoning_output_tokens": 18, "total_tokens": 62536 }, "turn_token_usage": { "input_tokens": 2753064, "cached_input_tokens": 2691328, "cache_write_input_tokens": 0, "output_tokens": 34180, "reasoning_output_tokens": 13124, "total_tokens": 2787244 }, "thread_token_usage": { "input_tokens": 2753064, "cached_input_tokens": 2691328, "cache_write_input_tokens": 0, "output_tokens": 34180, "reasoning_output_tokens": 13124, "total_tokens": 2787244 } } ## Step 79 · agent · 2026-09-25T00:46:49.444Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:46:50.156Z { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:47:20.164Z Script completed Wall time 30.0 seconds Output: epoch 1/10 loss=4.6211 scale=20.69 epoch 2/10 loss=4.5797 scale=21.19 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c47877a487d28fdf65e715fa7b78", "usage": { "input_tokens": 62564, "cached_input_tokens": 61952, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62617 }, "turn_token_usage": { "input_tokens": 2815628, "cached_input_tokens": 2753280, "cache_write_input_tokens": 0, "output_tokens": 34233, "reasoning_output_tokens": 13130, "total_tokens": 2849861 }, "thread_token_usage": { "input_tokens": 2815628, "cached_input_tokens": 2753280, "cache_write_input_tokens": 0, "output_tokens": 34233, "reasoning_output_tokens": 13130, "total_tokens": 2849861 } } ## Step 80 · agent · 2026-09-25T00:47:21.511Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:47:22.189Z { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:47:52.199Z Script completed Wall time 30.0 seconds Output: epoch 3/10 loss=4.5467 scale=21.89 epoch 4/10 loss=4.5129 scale=22.32 epoch 5/10 loss=4.4813 scale=22.76 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4986f1087d2a89d1f43d66130b7", "usage": { "input_tokens": 62674, "cached_input_tokens": 62336, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62727 }, "turn_token_usage": { "input_tokens": 2878302, "cached_input_tokens": 2815616, "cache_write_input_tokens": 0, "output_tokens": 34286, "reasoning_output_tokens": 13136, "total_tokens": 2912588 }, "thread_token_usage": { "input_tokens": 2878302, "cached_input_tokens": 2815616, "cache_write_input_tokens": 0, "output_tokens": 34286, "reasoning_output_tokens": 13136, "total_tokens": 2912588 } } ## Step 81 · agent · 2026-09-25T00:47:53.463Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:47:54.188Z { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:48:24.197Z Script completed Wall time 30.0 seconds Output: epoch 6/10 loss=4.4502 scale=23.14 epoch 7/10 loss=4.4182 scale=23.79 epoch 8/10 loss=4.3928 scale=24.16 epoch 9/10 loss=4.3563 scale=24.56 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4b86ef087d2835beb6b1f1f64e8", "usage": { "input_tokens": 62801, "cached_input_tokens": 62464, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 62854 }, "turn_token_usage": { "input_tokens": 2941103, "cached_input_tokens": 2878080, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 13142, "total_tokens": 2975442 }, "thread_token_usage": { "input_tokens": 2941103, "cached_input_tokens": 2878080, "cache_write_input_tokens": 0, "output_tokens": 34339, "reasoning_output_tokens": 13142, "total_tokens": 2975442 } } ### exec · 2026-09-25T00:48:25.968Z { "code": "const r = await tools.write_stdin({\"session_id\":66792,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:48:47.937Z Script completed Wall time 22.0 seconds Output: epoch 10/10 loss=4.3211 scale=25.00 blend trend_weight=0 public_ndcg@10=0.002254 blend trend_weight=0.08 public_ndcg@10=0.039361 blend trend_weight=0.15 public_ndcg@10=0.037525 blend trend_weight=0.25 public_ndcg@10=0.035480 blend trend_weight=0.4 public_ndcg@10=0.034450 blend trend_weight=0.65 public_ndcg@10=0.034000 blend trend_weight=1 public_ndcg@10=0.033439 blend trend_weight=1.5 public_ndcg@10=0.032883 blend trend_weight=2.5 public_ndcg@10=0.032744 blend trend_weight=4 public_ndcg@10=0.032653 RESULT 0.039361061888630866 0.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4d8648887d292ee9ca54e2e75f8", "usage": { "input_tokens": 62945, "cached_input_tokens": 62592, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 62990 }, "turn_token_usage": { "input_tokens": 3004048, "cached_input_tokens": 2940672, "cache_write_input_tokens": 0, "output_tokens": 34384, "reasoning_output_tokens": 13142, "total_tokens": 3038432 }, "thread_token_usage": { "input_tokens": 3004048, "cached_input_tokens": 2940672, "cache_write_input_tokens": 0, "output_tokens": 34384, "reasoning_output_tokens": 13142, "total_tokens": 3038432 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2e25973d-6e49-45d3-95d6-94e44196e46c", "process_id": "66792", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_15e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261010,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_25e.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_25e.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_25e.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n", "stderr": "", "aggregated_output": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n", "exit_code": 0, "duration": { "secs": 120, "nanos": 650113608 }, "formatted_output": "epoch 1/10 loss=4.6211 scale=20.69\r\nepoch 2/10 loss=4.5797 scale=21.19\r\nepoch 3/10 loss=4.5467 scale=21.89\r\nepoch 4/10 loss=4.5129 scale=22.32\r\nepoch 5/10 loss=4.4813 scale=22.76\r\nepoch 6/10 loss=4.4502 scale=23.14\r\nepoch 7/10 loss=4.4182 scale=23.79\r\nepoch 8/10 loss=4.3928 scale=24.16\r\nepoch 9/10 loss=4.3563 scale=24.56\r\nepoch 10/10 loss=4.3211 scale=25.00\r\nblend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.08 public_ndcg@10=0.039361\r\nblend trend_weight=0.15 public_ndcg@10=0.037525\r\nblend trend_weight=0.25 public_ndcg@10=0.035480\r\nblend trend_weight=0.4 public_ndcg@10=0.034450\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1 public_ndcg@10=0.033439\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=2.5 public_ndcg@10=0.032744\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039361061888630866 0.08\r\n" } } ## Step 82 · agent · 2026-09-25T00:48:51.385Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:48:55.228Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\\n+ weights = [forced_weight] if forced_weight is not None else [\\n+ 0.0, 0.035, 0.045, 0.055, 0.065, 0.075, 0.085, 0.095,\\n+ 0.11, 0.13, 0.16, 0.22, 0.35, 0.65, 1.5, 4.0,\\n+ ]\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:48:55.322Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-854feb29-012f-45dc-8fd1-fbff77190a24", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -573,3 +573,6 @@\n truth = relevant_truth() if stage == \"validation\" else None\n- weights = [forced_weight] if forced_weight is not None else [0.0, 0.08, 0.15, 0.25, 0.4, 0.65, 1.0, 1.5, 2.5, 4.0]\n+ weights = [forced_weight] if forced_weight is not None else [\n+ 0.0, 0.035, 0.045, 0.055, 0.065, 0.075, 0.085, 0.095,\n+ 0.11, 0.13, 0.16, 0.22, 0.35, 0.65, 1.5, 4.0,\n+ ]\n best_score, best_weight = -1.0, None\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4f01d6087d29852eb130d67c969", "usage": { "input_tokens": 63219, "cached_input_tokens": 62720, "cache_write_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 131, "total_tokens": 63572 }, "turn_token_usage": { "input_tokens": 3067267, "cached_input_tokens": 3003392, "cache_write_input_tokens": 0, "output_tokens": 34737, "reasoning_output_tokens": 13273, "total_tokens": 3102004 }, "thread_token_usage": { "input_tokens": 3067267, "cached_input_tokens": 3003392, "cache_write_input_tokens": 0, "output_tokens": 34737, "reasoning_output_tokens": 13273, "total_tokens": 3102004 } } ## Step 83 · agent · 2026-09-25T00:48:56.810Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:49:00.152Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:49:01.317Z Script completed Wall time 1.2 seconds Output: SESSION_ID=49342 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4f7907487d2aaf16fe787f63358", "usage": { "input_tokens": 63596, "cached_input_tokens": 63104, "cache_write_input_tokens": 0, "output_tokens": 208, "reasoning_output_tokens": 15, "total_tokens": 63804 }, "turn_token_usage": { "input_tokens": 3130863, "cached_input_tokens": 3066496, "cache_write_input_tokens": 0, "output_tokens": 34945, "reasoning_output_tokens": 13288, "total_tokens": 3165808 }, "thread_token_usage": { "input_tokens": 3130863, "cached_input_tokens": 3066496, "cache_write_input_tokens": 0, "output_tokens": 34945, "reasoning_output_tokens": 13288, "total_tokens": 3165808 } } ### exec · 2026-09-25T00:49:03.027Z { "code": "const r = await tools.write_stdin({\"session_id\":49342,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:49:18.531Z Script completed Wall time 15.5 seconds Output: blend trend_weight=0 public_ndcg@10=0.002254 blend trend_weight=0.035 public_ndcg@10=0.034021 blend trend_weight=0.045 public_ndcg@10=0.036552 blend trend_weight=0.055 public_ndcg@10=0.038671 blend trend_weight=0.065 public_ndcg@10=0.039651 blend trend_weight=0.075 public_ndcg@10=0.039490 blend trend_weight=0.085 public_ndcg@10=0.039222 blend trend_weight=0.095 public_ndcg@10=0.038936 blend trend_weight=0.11 public_ndcg@10=0.038733 blend trend_weight=0.13 public_ndcg@10=0.038271 blend trend_weight=0.16 public_ndcg@10=0.037373 blend trend_weight=0.22 public_ndcg@10=0.036157 blend trend_weight=0.35 public_ndcg@10=0.034699 blend trend_weight=0.65 public_ndcg@10=0.034000 blend trend_weight=1.5 public_ndcg@10=0.032883 blend trend_weight=4 public_ndcg@10=0.032653 RESULT 0.039651183050495274 0.065 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c4fd796487d2b1dde0d011cc728e", "usage": { "input_tokens": 63832, "cached_input_tokens": 63360, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 63877 }, "turn_token_usage": { "input_tokens": 3194695, "cached_input_tokens": 3129856, "cache_write_input_tokens": 0, "output_tokens": 34990, "reasoning_output_tokens": 13288, "total_tokens": 3229685 }, "thread_token_usage": { "input_tokens": 3194695, "cached_input_tokens": 3129856, "cache_write_input_tokens": 0, "output_tokens": 34990, "reasoning_output_tokens": 13288, "total_tokens": 3229685 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-05f03227-4ffc-47c1-b0ee-fe5616210fc3", "process_id": "49342", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nz=np.load('validation_embeddings_25e.npz'); ck=torch.load('two_tower_validation_25e.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); score,w=tr.rank_and_tune('validation',z['uids'],z['user'],z['item'],trend,tr.OUT/'validation_25e_fine.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n", "exit_code": 0, "duration": { "secs": 18, "nanos": 213163041 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002254\r\nblend trend_weight=0.035 public_ndcg@10=0.034021\r\nblend trend_weight=0.045 public_ndcg@10=0.036552\r\nblend trend_weight=0.055 public_ndcg@10=0.038671\r\nblend trend_weight=0.065 public_ndcg@10=0.039651\r\nblend trend_weight=0.075 public_ndcg@10=0.039490\r\nblend trend_weight=0.085 public_ndcg@10=0.039222\r\nblend trend_weight=0.095 public_ndcg@10=0.038936\r\nblend trend_weight=0.11 public_ndcg@10=0.038733\r\nblend trend_weight=0.13 public_ndcg@10=0.038271\r\nblend trend_weight=0.16 public_ndcg@10=0.037373\r\nblend trend_weight=0.22 public_ndcg@10=0.036157\r\nblend trend_weight=0.35 public_ndcg@10=0.034699\r\nblend trend_weight=0.65 public_ndcg@10=0.034000\r\nblend trend_weight=1.5 public_ndcg@10=0.032883\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.039651183050495274 0.065\r\n" } } ## Step 84 · agent · 2026-09-25T00:49:19.838Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:49:21.009Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:49:24.410Z Script completed Wall time 3.4 seconds Output: { "model": "/app/solution/validation_25e_fine.parquet", "scores": { "quality_ndcg@10": 0.039651, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c50ebbe887d298e6ab109934d284", "usage": { "input_tokens": 64199, "cached_input_tokens": 63616, "cache_write_input_tokens": 0, "output_tokens": 86, "reasoning_output_tokens": 12, "total_tokens": 64285 }, "turn_token_usage": { "input_tokens": 3258894, "cached_input_tokens": 3193472, "cache_write_input_tokens": 0, "output_tokens": 35076, "reasoning_output_tokens": 13300, "total_tokens": 3293970 }, "thread_token_usage": { "input_tokens": 3258894, "cached_input_tokens": 3193472, "cache_write_input_tokens": 0, "output_tokens": 35076, "reasoning_output_tokens": 13300, "total_tokens": 3293970 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-066ffc1b-5528-4267-b7df-463fb012b228", "process_id": "11540", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_25e_fine.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 231056195 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_25e_fine.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.039651,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 85 · agent · 2026-09-25T00:49:35.002Z ### Thinking unavailable — no text recorded ## Step 86 · agent · 2026-09-25T00:49:44.327Z ### Thinking unavailable — no text recorded ## Step 87 · agent · 2026-09-25T00:49:49.232Z ### Thinking unavailable — no text recorded The 25-epoch checkpoint reaches 0.039651 at a finely tuned coefficient of 0.065. The next issue is objective noise: another row’s sampled item can also be a known positive for the current history, yet ordinary in-batch softmax treats it as negative. I’m masking those known positives and fine-tuning the existing checkpoint, a targeted change that preserves the current best if it does not help. ### exec · 2026-09-25T00:49:55.179Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\\n- # A duplicated positive in the batch is another positive, not a negative.\\n- same = ti[:, None].eq(ti[None, :])\\n- same.fill_diagonal_(False)\\n- logits = logits.masked_fill(same, -1e4)\\n+ # Every stored future target is a positive. Mask any that another\\n+ # row happened to sample, rather than creating false negatives.\\n+ row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\\n+ known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\\n+ known_positive.fill_diagonal_(False)\\n+ logits = logits.masked_fill(known_positive, -1e4)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:49:55.305Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-86287142-3d97-44fa-8581-db6671286353", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -398,6 +398,8 @@\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n- # A duplicated positive in the batch is another positive, not a negative.\n- same = ti[:, None].eq(ti[None, :])\n- same.fill_diagonal_(False)\n- logits = logits.masked_fill(same, -1e4)\n+ # Every stored future target is a positive. Mask any that another\n+ # row happened to sample, rather than creating false negatives.\n+ row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\n+ known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\n+ known_positive.fill_diagonal_(False)\n+ logits = logits.masked_fill(known_positive, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c514a64887d2b8dba6c10a729642", "usage": { "input_tokens": 64358, "cached_input_tokens": 64000, "cache_write_input_tokens": 0, "output_tokens": 1639, "reasoning_output_tokens": 1305, "total_tokens": 65997 }, "turn_token_usage": { "input_tokens": 3323252, "cached_input_tokens": 3257472, "cache_write_input_tokens": 0, "output_tokens": 36715, "reasoning_output_tokens": 14605, "total_tokens": 3359967 }, "thread_token_usage": { "input_tokens": 3323252, "cached_input_tokens": 3257472, "cache_write_input_tokens": 0, "output_tokens": 36715, "reasoning_output_tokens": 14605, "total_tokens": 3359967 } } ## Step 88 · agent · 2026-09-25T00:49:57.152Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:50:03.736Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:50:04.902Z Script completed Wall time 1.2 seconds Output: SESSION_ID=88406 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5338f7487d286714eda52e93ded", "usage": { "input_tokens": 66021, "cached_input_tokens": 64128, "cache_write_input_tokens": 0, "output_tokens": 393, "reasoning_output_tokens": 19, "total_tokens": 66414 }, "turn_token_usage": { "input_tokens": 3389273, "cached_input_tokens": 3321600, "cache_write_input_tokens": 0, "output_tokens": 37108, "reasoning_output_tokens": 14624, "total_tokens": 3426381 }, "thread_token_usage": { "input_tokens": 3389273, "cached_input_tokens": 3321600, "cache_write_input_tokens": 0, "output_tokens": 37108, "reasoning_output_tokens": 14624, "total_tokens": 3426381 } } ## Step 89 · agent · 2026-09-25T00:50:06.204Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:50:06.884Z { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:50:36.893Z Script completed Wall time 30.0 seconds Output: epoch 1/8 loss=4.2830 scale=25.44 epoch 2/8 loss=4.2767 scale=25.57 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c53d17e087d29df5f8f87a607e49", "usage": { "input_tokens": 66442, "cached_input_tokens": 65792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 66495 }, "turn_token_usage": { "input_tokens": 3455715, "cached_input_tokens": 3387392, "cache_write_input_tokens": 0, "output_tokens": 37161, "reasoning_output_tokens": 14630, "total_tokens": 3492876 }, "thread_token_usage": { "input_tokens": 3455715, "cached_input_tokens": 3387392, "cache_write_input_tokens": 0, "output_tokens": 37161, "reasoning_output_tokens": 14630, "total_tokens": 3492876 } } ## Step 90 · agent · 2026-09-25T00:50:38.192Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:50:38.883Z { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:51:08.893Z Script completed Wall time 30.0 seconds Output: epoch 3/8 loss=4.2478 scale=25.99 epoch 4/8 loss=4.2225 scale=26.35 epoch 5/8 loss=4.2000 scale=26.68 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c55d24ac87d290ad2f71e7f2dfdf", "usage": { "input_tokens": 66552, "cached_input_tokens": 66304, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 66607 }, "turn_token_usage": { "input_tokens": 3522267, "cached_input_tokens": 3453696, "cache_write_input_tokens": 0, "output_tokens": 37216, "reasoning_output_tokens": 14638, "total_tokens": 3559483 }, "thread_token_usage": { "input_tokens": 3522267, "cached_input_tokens": 3453696, "cache_write_input_tokens": 0, "output_tokens": 37216, "reasoning_output_tokens": 14638, "total_tokens": 3559483 } } ### exec · 2026-09-25T00:51:10.663Z { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:51:40.672Z Script completed Wall time 30.0 seconds Output: epoch 6/8 loss=4.1952 scale=26.93 epoch 7/8 loss=4.1690 scale=27.25 epoch 8/8 loss=4.1385 scale=27.63 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c57d114487d2a34db42e8af0c158", "usage": { "input_tokens": 66681, "cached_input_tokens": 66432, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 66726 }, "turn_token_usage": { "input_tokens": 3588948, "cached_input_tokens": 3520128, "cache_write_input_tokens": 0, "output_tokens": 37261, "reasoning_output_tokens": 14638, "total_tokens": 3626209 }, "thread_token_usage": { "input_tokens": 3588948, "cached_input_tokens": 3520128, "cache_write_input_tokens": 0, "output_tokens": 37261, "reasoning_output_tokens": 14638, "total_tokens": 3626209 } } ## Step 91 · agent · 2026-09-25T00:51:41.860Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:51:42.562Z { "code": "const r = await tools.write_stdin({\"session_id\":88406,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:51:56.691Z Script completed Wall time 14.1 seconds Output: blend trend_weight=0 public_ndcg@10=0.002461 blend trend_weight=0.035 public_ndcg@10=0.034426 blend trend_weight=0.045 public_ndcg@10=0.036971 blend trend_weight=0.055 public_ndcg@10=0.038690 blend trend_weight=0.065 public_ndcg@10=0.039079 blend trend_weight=0.075 public_ndcg@10=0.039458 blend trend_weight=0.085 public_ndcg@10=0.039221 blend trend_weight=0.095 public_ndcg@10=0.038897 blend trend_weight=0.11 public_ndcg@10=0.038322 blend trend_weight=0.13 public_ndcg@10=0.037585 blend trend_weight=0.16 public_ndcg@10=0.036926 blend trend_weight=0.22 public_ndcg@10=0.035844 blend trend_weight=0.35 public_ndcg@10=0.034521 blend trend_weight=0.65 public_ndcg@10=0.033868 blend trend_weight=1.5 public_ndcg@10=0.032842 blend trend_weight=4 public_ndcg@10=0.032653 RESULT 0.03945786105407907 0.075 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c59cece887d29ea4711b09062601", "usage": { "input_tokens": 66800, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 66856 }, "turn_token_usage": { "input_tokens": 3655748, "cached_input_tokens": 3586688, "cache_write_input_tokens": 0, "output_tokens": 37317, "reasoning_output_tokens": 14647, "total_tokens": 3693065 }, "thread_token_usage": { "input_tokens": 3655748, "cached_input_tokens": 3586688, "cache_write_input_tokens": 0, "output_tokens": 37317, "reasoning_output_tokens": 14647, "total_tokens": 3693065 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-81b48320-8ebb-4b3e-b619-5d7da791959c", "process_id": "88406", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261020,initial_model=model,learning_rate=7e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_masked.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_masked.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_masked.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n", "stderr": "", "aggregated_output": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n", "exit_code": 0, "duration": { "secs": 112, "nanos": 788365233 }, "formatted_output": "epoch 1/8 loss=4.2830 scale=25.44\r\nepoch 2/8 loss=4.2767 scale=25.57\r\nepoch 3/8 loss=4.2478 scale=25.99\r\nepoch 4/8 loss=4.2225 scale=26.35\r\nepoch 5/8 loss=4.2000 scale=26.68\r\nepoch 6/8 loss=4.1952 scale=26.93\r\nepoch 7/8 loss=4.1690 scale=27.25\r\nepoch 8/8 loss=4.1385 scale=27.63\r\nblend trend_weight=0 public_ndcg@10=0.002461\r\nblend trend_weight=0.035 public_ndcg@10=0.034426\r\nblend trend_weight=0.045 public_ndcg@10=0.036971\r\nblend trend_weight=0.055 public_ndcg@10=0.038690\r\nblend trend_weight=0.065 public_ndcg@10=0.039079\r\nblend trend_weight=0.075 public_ndcg@10=0.039458\r\nblend trend_weight=0.085 public_ndcg@10=0.039221\r\nblend trend_weight=0.095 public_ndcg@10=0.038897\r\nblend trend_weight=0.11 public_ndcg@10=0.038322\r\nblend trend_weight=0.13 public_ndcg@10=0.037585\r\nblend trend_weight=0.16 public_ndcg@10=0.036926\r\nblend trend_weight=0.22 public_ndcg@10=0.035844\r\nblend trend_weight=0.35 public_ndcg@10=0.034521\r\nblend trend_weight=0.65 public_ndcg@10=0.033868\r\nblend trend_weight=1.5 public_ndcg@10=0.032842\r\nblend trend_weight=4 public_ndcg@10=0.032653\r\nRESULT 0.03945786105407907 0.075\r\n" } } ## Step 92 · agent · 2026-09-25T00:52:03.561Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:52:13.963Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n def _pad_recent(items: list[int] | np.ndarray, length: int) -> np.ndarray:\\n@@\\n return out\\n+\\n+\\n+def _recent_unique_positions(items: np.ndarray, end: int, length: int) -> np.ndarray:\\n+ \\\"\\\"\\\"Positions of the most recent distinct items, in chronological order.\\\"\\\"\\\"\\n+ start = max(0, end - 3000)\\n+ reverse = items[start:end][::-1]\\n+ if not len(reverse):\\n+ return np.empty(0, dtype=np.int64)\\n+ _, first_from_end = np.unique(reverse, return_index=True)\\n+ positions = start + (len(reverse) - 1 - first_from_end)\\n+ return np.sort(positions)[-length:]\\n@@\\n- hs = max(0, hp - HISTORY_LEN)\\n- hist_i = pitems[hs:hp]\\n- hist_t = pts[hs:hp]\\n+ selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ hist_i = pitems[selected]\\n+ hist_t = pts[selected]\\n@@\\n- raw_w *= 1.0 + 0.25 * porg[hs:hp]\\n- raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\\n+ raw_w *= 1.0 + 0.25 * porg[selected]\\n+ raw_w *= np.sqrt(np.minimum(pratio[selected], 120) / 50.0)\\n@@\\n- recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\\n+ recent_org = float(porg[selected].mean()) if len(selected) else 0.0\\n@@\\n- all_positive = np.flatnonzero(ratio[:end] >= 50)\\n- positive = all_positive[-HISTORY_LEN:]\\n+ all_positive = np.flatnonzero(ratio[:end] >= 50)\\n+ chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ positive = all_positive[chosen_within_positive]\\n@@\\n- cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w.npz\\\"\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_unique.npz\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:52:14.070Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d8e93e24-f785-4c7e-9282-e0a963013710", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -114,2 +114,13 @@\n \n+def _recent_unique_positions(items: np.ndarray, end: int, length: int) -> np.ndarray:\n+ \"\"\"Positions of the most recent distinct items, in chronological order.\"\"\"\n+ start = max(0, end - 3000)\n+ reverse = items[start:end][::-1]\n+ if not len(reverse):\n+ return np.empty(0, dtype=np.int64)\n+ _, first_from_end = np.unique(reverse, return_index=True)\n+ positions = start + (len(reverse) - 1 - first_from_end)\n+ return np.sort(positions)[-length:]\n+\n+\n def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n@@ -223,9 +234,9 @@\n continue\n- hs = max(0, hp - HISTORY_LEN)\n- hist_i = pitems[hs:hp]\n- hist_t = pts[hs:hp]\n+ selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ hist_i = pitems[selected]\n+ hist_t = pts[selected]\n # Smoothly favor recent, organic and deeply completed history.\n raw_w = np.exp(-(c - hist_t) / float(120 * 86400))\n- raw_w *= 1.0 + 0.25 * porg[hs:hp]\n- raw_w *= np.sqrt(np.minimum(pratio[hs:hp], 120) / 50.0)\n+ raw_w *= 1.0 + 0.25 * porg[selected]\n+ raw_w *= np.sqrt(np.minimum(pratio[selected], 120) / 50.0)\n raw_w = raw_w / max(float(raw_w.sum()), 1e-8)\n@@ -260,3 +271,3 @@\n lf = math.log1p(hp) / 10.0\n- recent_org = float(porg[hs:hp].mean()) if hp > hs else 0.0\n+ recent_org = float(porg[selected].mean()) if len(selected) else 0.0\n features = np.asarray(\n@@ -454,3 +465,4 @@\n all_positive = np.flatnonzero(ratio[:end] >= 50)\n- positive = all_positive[-HISTORY_LEN:]\n+ chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ positive = all_positive[chosen_within_positive]\n raw_w = np.exp(-(cutoff - ts[positive]) / float(120 * 86400))\n@@ -648,3 +660,3 @@\n features = item_features(catalog)\n- cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w.npz\"\n+ cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n if cache.exists() and not args.rebuild_cache:\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5ace39887d28e12e6fc43a3bf77", "usage": { "input_tokens": 67178, "cached_input_tokens": 66560, "cache_write_input_tokens": 0, "output_tokens": 873, "reasoning_output_tokens": 287, "total_tokens": 68051 }, "turn_token_usage": { "input_tokens": 3722926, "cached_input_tokens": 3653248, "cache_write_input_tokens": 0, "output_tokens": 38190, "reasoning_output_tokens": 14934, "total_tokens": 3761116 }, "thread_token_usage": { "input_tokens": 3722926, "cached_input_tokens": 3653248, "cache_write_input_tokens": 0, "output_tokens": 38190, "reasoning_output_tokens": 14934, "total_tokens": 3761116 } } ## Step 93 · agent · 2026-09-25T00:52:19.247Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:52:19.248Z { "code": "const r = await tools.exec_command({\"cmd\":\"rg '\\\\bhs\\\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:52:19.418Z Script completed Wall time 0.2 seconds Output: cache = OUT / f"snapshots_{args.stage}_{args.weeks}w_unique.npz" ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5be4cbc87d290da26f611c2ec04", "usage": { "input_tokens": 68075, "cached_input_tokens": 66944, "cache_write_input_tokens": 0, "output_tokens": 102, "reasoning_output_tokens": 12, "total_tokens": 68177 }, "turn_token_usage": { "input_tokens": 3791001, "cached_input_tokens": 3720192, "cache_write_input_tokens": 0, "output_tokens": 38292, "reasoning_output_tokens": 14946, "total_tokens": 3829293 }, "thread_token_usage": { "input_tokens": 3791001, "cached_input_tokens": 3720192, "cache_write_input_tokens": 0, "output_tokens": 38292, "reasoning_output_tokens": 14946, "total_tokens": 3829293 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-9a67ba7d-c0dd-452b-8b80-c6b43a621688", "process_id": "3964", "command": [ "/bin/sh", "-lc", "rg '\\bhs\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "rg '\\bhs\\b|snapshots_' /app/solution/train_ranker.py | head -30; /opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n", "stderr": "", "aggregated_output": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4530 }, "formatted_output": " cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n" } } ## Step 94 · agent · 2026-09-25T00:52:21.658Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:52:25.284Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- # Every stored future target is a positive. Mask any that another\\n- # row happened to sample, rather than creating false negatives.\\n- row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\\n- known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\\n- known_positive.fill_diagonal_(False)\\n- logits = logits.masked_fill(known_positive, -1e4)\\n+ # A duplicated sampled positive is another positive, not a negative.\\n+ same = ti[:, None].eq(ti[None, :])\\n+ same.fill_diagonal_(False)\\n+ logits = logits.masked_fill(same, -1e4)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:52:26.781Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-f87800a3-a1e0-4d26-bb33-f5977e666dc9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -409,8 +409,6 @@\n logits = u @ v.T * model.logit_scale.exp().clamp(max=40)\n- # Every stored future target is a positive. Mask any that another\n- # row happened to sample, rather than creating false negatives.\n- row_targets = torch.from_numpy(snapshots.targets[ix].astype(np.int64)) - 1\n- known_positive = row_targets[:, :, None].eq(ti[None, None, :]).any(dim=1)\n- known_positive.fill_diagonal_(False)\n- logits = logits.masked_fill(known_positive, -1e4)\n+ # A duplicated sampled positive is another positive, not a negative.\n+ same = ti[:, None].eq(ti[None, :])\n+ same.fill_diagonal_(False)\n+ logits = logits.masked_fill(same, -1e4)\n loss = F.cross_entropy(logits, torch.arange(batch_size))\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5c39b3087d2b5dacc22a5fe0140", "usage": { "input_tokens": 68222, "cached_input_tokens": 67840, "cache_write_input_tokens": 0, "output_tokens": 269, "reasoning_output_tokens": 51, "total_tokens": 68491 }, "turn_token_usage": { "input_tokens": 3859223, "cached_input_tokens": 3788032, "cache_write_input_tokens": 0, "output_tokens": 38561, "reasoning_output_tokens": 14997, "total_tokens": 3897784 }, "thread_token_usage": { "input_tokens": 3859223, "cached_input_tokens": 3788032, "cache_write_input_tokens": 0, "output_tokens": 38561, "reasoning_output_tokens": 14997, "total_tokens": 3897784 } } ## Step 95 · agent · 2026-09-25T00:52:28.682Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:52:35.850Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\\nelse:\\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:52:37.016Z Script completed Wall time 1.2 seconds Output: SESSION_ID=7881 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5cb17e087d28b66e1c4309cbfe5", "usage": { "input_tokens": 68515, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 436, "reasoning_output_tokens": 31, "total_tokens": 68951 }, "turn_token_usage": { "input_tokens": 3927738, "cached_input_tokens": 3856128, "cache_write_input_tokens": 0, "output_tokens": 38997, "reasoning_output_tokens": 15028, "total_tokens": 3966735 }, "thread_token_usage": { "input_tokens": 3927738, "cached_input_tokens": 3856128, "cache_write_input_tokens": 0, "output_tokens": 38997, "reasoning_output_tokens": 15028, "total_tokens": 3966735 } } ## Step 96 · agent · 2026-09-25T00:52:38.386Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:52:39.101Z { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:53:09.110Z Script completed Wall time 30.0 seconds Output: loading validation events for 30 rolling cutoffs users 4000/9979 examples=39421 users 6000/9979 examples=58953 users 8000/9979 examples=78890 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5d5329c87d287fcde01454e8b85", "usage": { "input_tokens": 68979, "cached_input_tokens": 68352, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 69032 }, "turn_token_usage": { "input_tokens": 3996717, "cached_input_tokens": 3924480, "cache_write_input_tokens": 0, "output_tokens": 39050, "reasoning_output_tokens": 15034, "total_tokens": 4035767 }, "thread_token_usage": { "input_tokens": 3996717, "cached_input_tokens": 3924480, "cache_write_input_tokens": 0, "output_tokens": 39050, "reasoning_output_tokens": 15034, "total_tokens": 4035767 } } ## Step 97 · agent · 2026-09-25T00:53:10.240Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:53:10.930Z { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:53:40.939Z Script completed Wall time 30.0 seconds Output: built 98,532 examples, 837,405 relevant pairs epoch 1/10 loss=4.5173 scale=24.93 epoch 2/10 loss=4.4771 scale=25.21 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c5f54f3c87d2a2c3519315ca8cb8", "usage": { "input_tokens": 69104, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 69159 }, "turn_token_usage": { "input_tokens": 4065821, "cached_input_tokens": 3993344, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 15042, "total_tokens": 4104926 }, "thread_token_usage": { "input_tokens": 4065821, "cached_input_tokens": 3993344, "cache_write_input_tokens": 0, "output_tokens": 39105, "reasoning_output_tokens": 15042, "total_tokens": 4104926 } } ## Step 98 · agent · 2026-09-25T00:53:47.784Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:53:48.481Z { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:18.490Z Script completed Wall time 30.0 seconds Output: epoch 3/10 loss=4.4414 scale=25.54 epoch 4/10 loss=4.4110 scale=25.97 epoch 5/10 loss=4.3735 scale=26.38 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c615201887d28b0235ac36240c09", "usage": { "input_tokens": 69230, "cached_input_tokens": 68864, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 69288 }, "turn_token_usage": { "input_tokens": 4135051, "cached_input_tokens": 4062208, "cache_write_input_tokens": 0, "output_tokens": 39163, "reasoning_output_tokens": 15053, "total_tokens": 4174214 }, "thread_token_usage": { "input_tokens": 4135051, "cached_input_tokens": 4062208, "cache_write_input_tokens": 0, "output_tokens": 39163, "reasoning_output_tokens": 15053, "total_tokens": 4174214 } } ### exec · 2026-09-25T00:54:20.441Z { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:50.451Z Script completed Wall time 30.0 seconds Output: epoch 6/10 loss=4.3442 scale=26.81 epoch 7/10 loss=4.3055 scale=27.32 epoch 8/10 loss=4.2744 scale=27.71 epoch 9/10 loss=4.2462 scale=28.22 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c63ab09087d2beaaea6e5378ca14", "usage": { "input_tokens": 69362, "cached_input_tokens": 68992, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 69407 }, "turn_token_usage": { "input_tokens": 4204413, "cached_input_tokens": 4131200, "cache_write_input_tokens": 0, "output_tokens": 39208, "reasoning_output_tokens": 15053, "total_tokens": 4243621 }, "thread_token_usage": { "input_tokens": 4204413, "cached_input_tokens": 4131200, "cache_write_input_tokens": 0, "output_tokens": 39208, "reasoning_output_tokens": 15053, "total_tokens": 4243621 } } ### exec · 2026-09-25T00:54:53.392Z { "code": "const r = await tools.write_stdin({\"session_id\":7881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:55:14.575Z Script completed Wall time 21.2 seconds Output: epoch 10/10 loss=4.2266 scale=28.52 blend trend_weight=0 public_ndcg@10=0.002837 blend trend_weight=0.035 public_ndcg@10=0.035931 blend trend_weight=0.045 public_ndcg@10=0.038514 blend trend_weight=0.055 public_ndcg@10=0.039965 blend trend_weight=0.065 public_ndcg@10=0.039847 blend trend_weight=0.075 public_ndcg@10=0.039887 blend trend_weight=0.085 public_ndcg@10=0.039305 blend trend_weight=0.095 public_ndcg@10=0.039015 blend trend_weight=0.11 public_ndcg@10=0.038544 blend trend_weight=0.13 public_ndcg@10=0.037936 blend trend_weight=0.16 public_ndcg@10=0.037034 blend trend_weight=0.22 public_ndcg@10=0.035710 blend trend_weight=0.35 public_ndcg@10=0.034838 blend trend_weight=0.65 public_ndcg@10=0.034032 blend trend_weight=1.5 public_ndcg@10=0.032900 blend trend_weight=4 public_ndcg@10=0.032694 RESULT 0.039965127825569824 0.055 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c65aa4f487d2b251f84d466b024a", "usage": { "input_tokens": 69498, "cached_input_tokens": 69120, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 69543 }, "turn_token_usage": { "input_tokens": 4273911, "cached_input_tokens": 4200320, "cache_write_input_tokens": 0, "output_tokens": 39253, "reasoning_output_tokens": 15053, "total_tokens": 4313164 }, "thread_token_usage": { "input_tokens": 4273911, "cached_input_tokens": 4200320, "cache_write_input_tokens": 0, "output_tokens": 39253, "reasoning_output_tokens": 15053, "total_tokens": 4313164 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-27f9cd67-f190-4137-aece-21cc6593060e", "process_id": "7881", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\nelse:\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\ncache=tr.OUT/'snapshots_validation_30w_unique.npz'\nif cache.exists(): snap=tr.SnapshotArrays.load(cache)\nelse:\n snap=tr.build_snapshots('validation',ids,30); snap.save(cache)\nck=torch.load('two_tower_validation_25e.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,10,20261030,initial_model=model,learning_rate=1e-3)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n", "stderr": "", "aggregated_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n", "exit_code": 0, "duration": { "secs": 158, "nanos": 558392347 }, "formatted_output": "loading validation events for 30 rolling cutoffs\r\n users 4000/9979 examples=39421\r\n users 6000/9979 examples=58953\r\n users 8000/9979 examples=78890\r\nbuilt 98,532 examples, 837,405 relevant pairs\r\nepoch 1/10 loss=4.5173 scale=24.93\r\nepoch 2/10 loss=4.4771 scale=25.21\r\nepoch 3/10 loss=4.4414 scale=25.54\r\nepoch 4/10 loss=4.4110 scale=25.97\r\nepoch 5/10 loss=4.3735 scale=26.38\r\nepoch 6/10 loss=4.3442 scale=26.81\r\nepoch 7/10 loss=4.3055 scale=27.32\r\nepoch 8/10 loss=4.2744 scale=27.71\r\nepoch 9/10 loss=4.2462 scale=28.22\r\nepoch 10/10 loss=4.2266 scale=28.52\r\nblend trend_weight=0 public_ndcg@10=0.002837\r\nblend trend_weight=0.035 public_ndcg@10=0.035931\r\nblend trend_weight=0.045 public_ndcg@10=0.038514\r\nblend trend_weight=0.055 public_ndcg@10=0.039965\r\nblend trend_weight=0.065 public_ndcg@10=0.039847\r\nblend trend_weight=0.075 public_ndcg@10=0.039887\r\nblend trend_weight=0.085 public_ndcg@10=0.039305\r\nblend trend_weight=0.095 public_ndcg@10=0.039015\r\nblend trend_weight=0.11 public_ndcg@10=0.038544\r\nblend trend_weight=0.13 public_ndcg@10=0.037936\r\nblend trend_weight=0.16 public_ndcg@10=0.037034\r\nblend trend_weight=0.22 public_ndcg@10=0.035710\r\nblend trend_weight=0.35 public_ndcg@10=0.034838\r\nblend trend_weight=0.65 public_ndcg@10=0.034032\r\nblend trend_weight=1.5 public_ndcg@10=0.032900\r\nblend trend_weight=4 public_ndcg@10=0.032694\r\nRESULT 0.039965127825569824 0.055\r\n" } } ## Step 99 · agent · 2026-09-25T00:55:16.462Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:23.017Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:55:24.197Z Script completed Wall time 1.2 seconds Output: SESSION_ID=76349 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c672cbcc87d2bdd26c6262729cc4", "usage": { "input_tokens": 69882, "cached_input_tokens": 69376, "cache_write_input_tokens": 0, "output_tokens": 414, "reasoning_output_tokens": 41, "total_tokens": 70296 }, "turn_token_usage": { "input_tokens": 4343793, "cached_input_tokens": 4269696, "cache_write_input_tokens": 0, "output_tokens": 39667, "reasoning_output_tokens": 15094, "total_tokens": 4383460 }, "thread_token_usage": { "input_tokens": 4343793, "cached_input_tokens": 4269696, "cache_write_input_tokens": 0, "output_tokens": 39667, "reasoning_output_tokens": 15094, "total_tokens": 4383460 } } ## Step 100 · agent · 2026-09-25T00:55:25.397Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:26.088Z { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:55:56.096Z Script completed Wall time 30.0 seconds Output: epoch 1/8 loss=4.1750 scale=28.91 epoch 2/8 loss=4.1603 scale=29.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c67c6a3087d2abfa663b6e6276a1", "usage": { "input_tokens": 70324, "cached_input_tokens": 69760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70377 }, "turn_token_usage": { "input_tokens": 4414117, "cached_input_tokens": 4339456, "cache_write_input_tokens": 0, "output_tokens": 39720, "reasoning_output_tokens": 15100, "total_tokens": 4453837 }, "thread_token_usage": { "input_tokens": 4414117, "cached_input_tokens": 4339456, "cache_write_input_tokens": 0, "output_tokens": 39720, "reasoning_output_tokens": 15100, "total_tokens": 4453837 } } ## Step 101 · agent · 2026-09-25T00:55:57.361Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:58.070Z { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:56:28.082Z Script completed Wall time 30.0 seconds Output: epoch 3/8 loss=4.1508 scale=29.32 epoch 4/8 loss=4.1295 scale=29.73 epoch 5/8 loss=4.1153 scale=29.94 epoch 6/8 loss=4.0894 scale=30.33 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c69c4db087d2b197b9eb1f60d086", "usage": { "input_tokens": 70434, "cached_input_tokens": 70144, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 70487 }, "turn_token_usage": { "input_tokens": 4484551, "cached_input_tokens": 4409600, "cache_write_input_tokens": 0, "output_tokens": 39773, "reasoning_output_tokens": 15106, "total_tokens": 4524324 }, "thread_token_usage": { "input_tokens": 4484551, "cached_input_tokens": 4409600, "cache_write_input_tokens": 0, "output_tokens": 39773, "reasoning_output_tokens": 15106, "total_tokens": 4524324 } } ### exec · 2026-09-25T00:56:30.111Z { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:57:00.120Z Script completed Wall time 30.0 seconds Output: epoch 7/8 loss=4.0738 scale=30.61 epoch 8/8 loss=4.0566 scale=30.95 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6bc521887d2b99e5e81bfd1a23b", "usage": { "input_tokens": 70578, "cached_input_tokens": 70272, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 70623 }, "turn_token_usage": { "input_tokens": 4555129, "cached_input_tokens": 4479872, "cache_write_input_tokens": 0, "output_tokens": 39818, "reasoning_output_tokens": 15106, "total_tokens": 4594947 }, "thread_token_usage": { "input_tokens": 4555129, "cached_input_tokens": 4479872, "cache_write_input_tokens": 0, "output_tokens": 39818, "reasoning_output_tokens": 15106, "total_tokens": 4594947 } } ## Step 102 · agent · 2026-09-25T00:57:04.685Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:04.700Z { "code": "const r = await tools.write_stdin({\"session_id\":76349,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:57:05.892Z Script completed Wall time 1.2 seconds Output: blend trend_weight=0 public_ndcg@10=0.002649 blend trend_weight=0.035 public_ndcg@10=0.036646 blend trend_weight=0.045 public_ndcg@10=0.038944 blend trend_weight=0.055 public_ndcg@10=0.039873 blend trend_weight=0.065 public_ndcg@10=0.039765 blend trend_weight=0.075 public_ndcg@10=0.039409 blend trend_weight=0.085 public_ndcg@10=0.038953 blend trend_weight=0.095 public_ndcg@10=0.038807 blend trend_weight=0.11 public_ndcg@10=0.038082 blend trend_weight=0.13 public_ndcg@10=0.037452 blend trend_weight=0.16 public_ndcg@10=0.036818 blend trend_weight=0.22 public_ndcg@10=0.035479 blend trend_weight=0.35 public_ndcg@10=0.034608 blend trend_weight=0.65 public_ndcg@10=0.033884 blend trend_weight=1.5 public_ndcg@10=0.032798 blend trend_weight=4 public_ndcg@10=0.032650 RESULT 0.0398725148107081 0.055 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6dc48a087d2b503363770957c7f", "usage": { "input_tokens": 70680, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 70734 }, "turn_token_usage": { "input_tokens": 4625809, "cached_input_tokens": 4550272, "cache_write_input_tokens": 0, "output_tokens": 39872, "reasoning_output_tokens": 15113, "total_tokens": 4665681 }, "thread_token_usage": { "input_tokens": 4625809, "cached_input_tokens": 4550272, "cache_write_input_tokens": 0, "output_tokens": 39872, "reasoning_output_tokens": 15113, "total_tokens": 4665681 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-be01cdce-65e9-4f40-9cd3-3f9611715adf", "process_id": "76349", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nsnap=tr.SnapshotArrays.load('snapshots_validation_30w_unique.npz'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); feat=tr.item_features(cat); ids=cat.item_id.to_numpy(dtype=np.int64)\nck=torch.load('two_tower_validation_unique.pt',weights_only=False); model=tr.TwoTower(len(ids),4,48); model.load_state_dict(ck['state_dict'])\nmodel=tr.train_model(snap,feat,48,8,20261110,initial_model=model,learning_rate=6e-4)\ntorch.save({'state_dict':model.state_dict(),'dim':48,'item_features':feat,'catalog_ids':ids,'architecture':ck['architecture']},'two_tower_validation_unique18.pt')\nusers=tr.build_inference_users('validation',ids); ue,ie=tr.encode(model,users,feat); np.savez_compressed('validation_embeddings_unique18.npz',uids=users[0],user=ue,item=ie)\ntrend=tr.recent_trend('validation',ids); score,w=tr.rank_and_tune('validation',users[0],ue,ie,trend,tr.OUT/'validation_unique18.parquet',None); print('RESULT',score,w)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n", "stderr": "", "aggregated_output": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n", "exit_code": 0, "duration": { "secs": 102, "nanos": 694268040 }, "formatted_output": "epoch 1/8 loss=4.1750 scale=28.91\r\nepoch 2/8 loss=4.1603 scale=29.08\r\nepoch 3/8 loss=4.1508 scale=29.32\r\nepoch 4/8 loss=4.1295 scale=29.73\r\nepoch 5/8 loss=4.1153 scale=29.94\r\nepoch 6/8 loss=4.0894 scale=30.33\r\nepoch 7/8 loss=4.0738 scale=30.61\r\nepoch 8/8 loss=4.0566 scale=30.95\r\nblend trend_weight=0 public_ndcg@10=0.002649\r\nblend trend_weight=0.035 public_ndcg@10=0.036646\r\nblend trend_weight=0.045 public_ndcg@10=0.038944\r\nblend trend_weight=0.055 public_ndcg@10=0.039873\r\nblend trend_weight=0.065 public_ndcg@10=0.039765\r\nblend trend_weight=0.075 public_ndcg@10=0.039409\r\nblend trend_weight=0.085 public_ndcg@10=0.038953\r\nblend trend_weight=0.095 public_ndcg@10=0.038807\r\nblend trend_weight=0.11 public_ndcg@10=0.038082\r\nblend trend_weight=0.13 public_ndcg@10=0.037452\r\nblend trend_weight=0.16 public_ndcg@10=0.036818\r\nblend trend_weight=0.22 public_ndcg@10=0.035479\r\nblend trend_weight=0.35 public_ndcg@10=0.034608\r\nblend trend_weight=0.65 public_ndcg@10=0.033884\r\nblend trend_weight=1.5 public_ndcg@10=0.032798\r\nblend trend_weight=4 public_ndcg@10=0.032650\r\nRESULT 0.0398725148107081 0.055\r\n" } } ## Step 103 · agent · 2026-09-25T00:57:10.883Z ### Thinking unavailable — no text recorded Using distinct recent tracks improves the best score again to 0.039965, while further fitting starts to flatten. I’m testing a single-dot-product ensemble by concatenating the raw-history and distinct-history embedding spaces; this often stabilizes collaborative retrieval and remains exactly MIPS-compatible. ### exec · 2026-09-25T00:57:24.355Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\\n for l,r in enumerate(range(st,en)):\\n if int(uids[r]) not in truth: continue\\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\\n for g in gs:\\n b=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\\nbest=[]\\nfor k,v in vals.items(): best.append((np.mean(v),*k))\\nbest.sort(reverse=True)\\nfor x in best[:20]: print(x)\\n# exact save for winner\\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:57:25.521Z Script completed Wall time 1.2 seconds Output: SESSION_ID=53453 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6e21fac87d28313646e41c726ec", "usage": { "input_tokens": 71056, "cached_input_tokens": 70528, "cache_write_input_tokens": 0, "output_tokens": 964, "reasoning_output_tokens": 214, "total_tokens": 72020 }, "turn_token_usage": { "input_tokens": 4696865, "cached_input_tokens": 4620800, "cache_write_input_tokens": 0, "output_tokens": 40836, "reasoning_output_tokens": 15327, "total_tokens": 4737701 }, "thread_token_usage": { "input_tokens": 4696865, "cached_input_tokens": 4620800, "cache_write_input_tokens": 0, "output_tokens": 40836, "reasoning_output_tokens": 15327, "total_tokens": 4737701 } } ## Step 104 · agent · 2026-09-25T00:57:26.743Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:27.449Z { "code": "const r = await tools.write_stdin({\"session_id\":53453,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:57:54.489Z Script completed Wall time 27.0 seconds Output: (np.float64(0.04009711157090469), 0.6, 0.06) (np.float64(0.04006660948634363), 0.8, 0.075) (np.float64(0.04005199384862277), 0.6, 0.065) (np.float64(0.04003512351683505), 0.8, 0.06) (np.float64(0.040034957041954616), 0.8, 0.065) (np.float64(0.040013484601807335), 0.4, 0.06) (np.float64(0.039965127825569824), 1, 0.055) (np.float64(0.03995528586976862), 0.4, 0.065) (np.float64(0.03991515554255111), 1, 0.06) (np.float64(0.039886957182980146), 1, 0.075) (np.float64(0.039850836400267944), 0.2, 0.06) (np.float64(0.03984699190100289), 1, 0.065) (np.float64(0.03983447744316708), 0.6, 0.055) (np.float64(0.03980372498571542), 0.2, 0.075) (np.float64(0.0396915604815739), 0.8, 0.055) (np.float64(0.039687841491579376), 0.4, 0.055) (np.float64(0.03966357620523893), 0.6, 0.075) (np.float64(0.039651183050495274), 0, 0.065) (np.float64(0.03964857914346366), 0.4, 0.075) (np.float64(0.03959897035910724), 0.2, 0.065) /opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, /opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide ret = ret.dtype.type(ret / rcount) blend trend_weight=0.06 public_ndcg@10=nan Traceback (most recent call last): File "", line 23, in File "/app/solution/train_ranker.py", line 626, in rank_and_tune score += float(best_weight) * trend[None, :] ^^^^^^^^^^^^^^^^^^ TypeError: float() argument must be a string or a real number, not 'NoneType' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c6f5be6087d283920c05e9f539c6", "usage": { "input_tokens": 72048, "cached_input_tokens": 70912, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 72101 }, "turn_token_usage": { "input_tokens": 4768913, "cached_input_tokens": 4691712, "cache_write_input_tokens": 0, "output_tokens": 40889, "reasoning_output_tokens": 15333, "total_tokens": 4809802 }, "thread_token_usage": { "input_tokens": 4768913, "cached_input_tokens": 4691712, "cache_write_input_tokens": 0, "output_tokens": 40889, "reasoning_output_tokens": 15333, "total_tokens": 4809802 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a146f467-8958-4e58-8677-511d02705846", "process_id": "53453", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\n for l,r in enumerate(range(st,en)):\n if int(uids[r]) not in truth: continue\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\nbest=[]\nfor k,v in vals.items(): best.append((np.mean(v),*k))\nbest.sort(reverse=True)\nfor x in best[:20]: print(x)\n# exact save for winner\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=[0,.2,.4,.6,.8,1]; ws=[.035,.045,.05,.055,.06,.065,.075,.085,.1]; vals={(g,w):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-500,axis=1)[:,-500:]; pu=np.argpartition(su,-500,axis=1)[:,-500:]\n for l,r in enumerate(range(st,en)):\n if int(uids[r]) not in truth: continue\n pt=to[masks[r,to]][:500]; cand=np.union1d(np.union1d(pr[l],pu[l]),pt); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(g,w)].append(tr.ndcg10(items[top],truth[int(uids[r])]))\nbest=[]\nfor k,v in vals.items(): best.append((np.mean(v),*k))\nbest.sort(reverse=True)\nfor x in best[:20]: print(x)\n# exact save for winner\nq,g,w=best[0]; ue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,_=tr.rank_and_tune('validation',uids,ue,ie,trend,tr.OUT/'validation_ensemble.parquet',w); print('RESULT approximate/exact-file',q,g,w)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n", "stderr": "", "aggregated_output": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n", "exit_code": 1, "duration": { "secs": 29, "nanos": 967162764 }, "formatted_output": "(np.float64(0.04009711157090469), 0.6, 0.06)\r\n(np.float64(0.04006660948634363), 0.8, 0.075)\r\n(np.float64(0.04005199384862277), 0.6, 0.065)\r\n(np.float64(0.04003512351683505), 0.8, 0.06)\r\n(np.float64(0.040034957041954616), 0.8, 0.065)\r\n(np.float64(0.040013484601807335), 0.4, 0.06)\r\n(np.float64(0.039965127825569824), 1, 0.055)\r\n(np.float64(0.03995528586976862), 0.4, 0.065)\r\n(np.float64(0.03991515554255111), 1, 0.06)\r\n(np.float64(0.039886957182980146), 1, 0.075)\r\n(np.float64(0.039850836400267944), 0.2, 0.06)\r\n(np.float64(0.03984699190100289), 1, 0.065)\r\n(np.float64(0.03983447744316708), 0.6, 0.055)\r\n(np.float64(0.03980372498571542), 0.2, 0.075)\r\n(np.float64(0.0396915604815739), 0.8, 0.055)\r\n(np.float64(0.039687841491579376), 0.4, 0.055)\r\n(np.float64(0.03966357620523893), 0.6, 0.075)\r\n(np.float64(0.039651183050495274), 0, 0.065)\r\n(np.float64(0.03964857914346366), 0.4, 0.075)\r\n(np.float64(0.03959897035910724), 0.2, 0.065)\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/fromnumeric.py:3860: RuntimeWarning: Mean of empty slice.\r\n return _methods._mean(a, axis=axis, dtype=dtype,\r\n/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py:145: RuntimeWarning: invalid value encountered in scalar divide\r\n ret = ret.dtype.type(ret / rcount)\r\nblend trend_weight=0.06 public_ndcg@10=nan\r\nTraceback (most recent call last):\r\n File \"\", line 23, in \r\n File \"/app/solution/train_ranker.py\", line 626, in rank_and_tune\r\n score += float(best_weight) * trend[None, :]\r\n ^^^^^^^^^^^^^^^^^^\r\nTypeError: float() argument must be a string or a real number, not 'NoneType'\r\n" } } ## Step 105 · agent · 2026-09-25T00:58:00.411Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:04.838Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if truth is not None:\\n+ if truth is not None and len(weights) > 1:\\n for w in weights:\\n@@\\n best_score, best_weight = q, float(w)\\n+ elif truth is not None:\\n+ best_weight = float(weights[0])\\n else:\\n best_weight = float(weights[0])\\n@@\\n order = np.argsort(-part_score, axis=1)\\n best_top[start:end] = np.take_along_axis(part, order, axis=1)\\n+\\n+ if truth is not None and len(weights) == 1:\\n+ exact_quality = [\\n+ ndcg10(eligible_items[best_top[row]], truth[int(uid)])\\n+ for row, uid in enumerate(targets)\\n+ if int(uid) in truth\\n+ ]\\n+ best_score = float(np.mean(exact_quality))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:58:04.966Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-94176782-2740-4824-8f43-529139693a0a", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -611,3 +611,3 @@\n quality[float(w)].append(ndcg10(eligible_items[top], gold))\n- if truth is not None:\n+ if truth is not None and len(weights) > 1:\n for w in weights:\n@@ -617,2 +617,4 @@\n best_score, best_weight = q, float(w)\n+ elif truth is not None:\n+ best_weight = float(weights[0])\n else:\n@@ -632,2 +634,10 @@\n \n+ if truth is not None and len(weights) == 1:\n+ exact_quality = [\n+ ndcg10(eligible_items[best_top[row]], truth[int(uid)])\n+ for row, uid in enumerate(targets)\n+ if int(uid) in truth\n+ ]\n+ best_score = float(np.mean(exact_quality))\n+\n frame = pd.DataFrame(\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c712b55487d283a616850acb1e4e", "usage": { "input_tokens": 72765, "cached_input_tokens": 71808, "cache_write_input_tokens": 0, "output_tokens": 414, "reasoning_output_tokens": 159, "total_tokens": 73179 }, "turn_token_usage": { "input_tokens": 4841678, "cached_input_tokens": 4763520, "cache_write_input_tokens": 0, "output_tokens": 41303, "reasoning_output_tokens": 15492, "total_tokens": 4882981 }, "thread_token_usage": { "input_tokens": 4841678, "cached_input_tokens": 4763520, "cache_write_input_tokens": 0, "output_tokens": 41303, "reasoning_output_tokens": 15492, "total_tokens": 4882981 } } ## Step 106 · agent · 2026-09-25T00:58:06.392Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:11.244Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:58:12.410Z Script completed Wall time 1.2 seconds Output: SESSION_ID=96585 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c71d467887d297bac10836146ecb", "usage": { "input_tokens": 73203, "cached_input_tokens": 72576, "cache_write_input_tokens": 0, "output_tokens": 293, "reasoning_output_tokens": 16, "total_tokens": 73496 }, "turn_token_usage": { "input_tokens": 4914881, "cached_input_tokens": 4836096, "cache_write_input_tokens": 0, "output_tokens": 41596, "reasoning_output_tokens": 15508, "total_tokens": 4956477 }, "thread_token_usage": { "input_tokens": 4914881, "cached_input_tokens": 4836096, "cache_write_input_tokens": 0, "output_tokens": 41596, "reasoning_output_tokens": 15508, "total_tokens": 4956477 } } ### exec · 2026-09-25T00:58:14.950Z { "code": "const r = await tools.write_stdin({\"session_id\":96585,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:58:22.842Z Script completed Wall time 7.9 seconds Output: RESULT 0.04009711157090469 0.06 { "model": "/app/solution/validation_ensemble.parquet", "scores": { "quality_ndcg@10": 0.040097, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c724939c87d28f55087e916a4a7a", "usage": { "input_tokens": 73524, "cached_input_tokens": 73088, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 73569 }, "turn_token_usage": { "input_tokens": 4988405, "cached_input_tokens": 4909184, "cache_write_input_tokens": 0, "output_tokens": 41641, "reasoning_output_tokens": 15508, "total_tokens": 5030046 }, "thread_token_usage": { "input_tokens": 4988405, "cached_input_tokens": 4909184, "cache_write_input_tokens": 0, "output_tokens": 41641, "reasoning_output_tokens": 15508, "total_tokens": 5030046 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bace8d21-dff3-46c0-a8d3-f48ac693cec7", "process_id": "96585", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nue=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); ie=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nscore,w=tr.rank_and_tune('validation',raw['uids'],ue,ie,trend,tr.OUT/'validation_ensemble.parquet',.06); print('RESULT',score,w)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ensemble.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 431791721 }, "formatted_output": "RESULT 0.04009711157090469 0.06\r\n{\r\n \"model\": \"/app/solution/validation_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040097,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 107 · agent · 2026-09-25T00:58:34.604Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-09-25T00:58:39.710Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:52.533Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,r in enumerate(range(st,en)):\\n gold=truth.get(int(uids[r]));\\n if not gold: continue\\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\\n for w in ws:\\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\\n rows.append((r,*vals))\\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\\nfor col in ['recent','history','ratio','breadth']:\\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\\n total=0\\n print('\\\\n',col)\\n for b,ind in feat.groupby('bin').groups.items():\\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\\n print('oracle-bin',total/len(feat))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:58:53.699Z Script completed Wall time 1.2 seconds Output: SESSION_ID=68069 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c72f03e087d289a3eff43e590ba2", "usage": { "input_tokens": 73654, "cached_input_tokens": 73344, "cache_write_input_tokens": 0, "output_tokens": 1551, "reasoning_output_tokens": 777, "total_tokens": 75205 }, "turn_token_usage": { "input_tokens": 5062059, "cached_input_tokens": 4982528, "cache_write_input_tokens": 0, "output_tokens": 43192, "reasoning_output_tokens": 16285, "total_tokens": 5105251 }, "thread_token_usage": { "input_tokens": 5062059, "cached_input_tokens": 4982528, "cache_write_input_tokens": 0, "output_tokens": 43192, "reasoning_output_tokens": 16285, "total_tokens": 5105251 } } ### exec · 2026-09-25T00:58:55.441Z { "code": "const r = await tools.write_stdin({\"session_id\":68069,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:59:06.528Z Script completed Wall time 11.1 seconds Output: global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))] recent 0 854 0.11 0.04048146289985309 base 0.039589963255021475 1 851 0.06 0.04410507844630743 base 0.04410507844630743 2 854 0.04 0.0337530073422442 base 0.03324430757754414 3 851 0.05 0.04309209706313055 base 0.0424448800508282 4 853 0.055 0.042155590302108095 base 0.04112485891523235 oracle-bin 0.04071305476729557 history 0 853 0.08 0.0490593618539828 base 0.048494475893577985 1 852 0.06 0.033331598391887735 base 0.033331598391887735 2 853 0.08 0.04645073326502365 base 0.046165268904012756 3 852 0.05 0.03967937095988081 base 0.03907817730224295 4 853 0.055 0.03435748640246683 base 0.03340691139861951 oracle-bin 0.04057761973387837 ratio 0 853 0.065 0.02982295620658903 base 0.02960610995043419 1 852 0.055 0.03429533385394453 base 0.034094459763819716 2 853 0.055 0.038031453283492216 base 0.03757466529759855 3 852 0.08 0.05153585174873961 base 0.04982171462958636 4 853 0.05 0.04984093618512868 base 0.049392971579147384 oracle-bin 0.04070426929262045 breadth 0 856 0.06 0.051651861775193736 base 0.051651861775193736 1 852 0.08 0.03714184376938306 base 0.03694594418534222 2 850 0.06 0.04119233927428141 base 0.04119233927428141 3 853 0.045 0.03270426035467069 base 0.032260064562536966 4 852 0.055 0.03938254699511966 base 0.03839286977265967 oracle-bin 0.040422940904667005 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c74de21487d2af0d75b137e64b87", "usage": { "input_tokens": 75233, "cached_input_tokens": 73472, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 75278 }, "turn_token_usage": { "input_tokens": 5137292, "cached_input_tokens": 5056000, "cache_write_input_tokens": 0, "output_tokens": 43237, "reasoning_output_tokens": 16285, "total_tokens": 5180529 }, "thread_token_usage": { "input_tokens": 5137292, "cached_input_tokens": 5056000, "cache_write_input_tokens": 0, "output_tokens": 43237, "reasoning_output_tokens": 16285, "total_tokens": 5180529 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-50dcb3c1-061f-4a99-aca6-6769f9da20d4", "process_id": "68069", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\n for w in ws:\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\n rows.append((r,*vals))\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\nfor col in ['recent','history','ratio','breadth']:\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\n total=0\n print('\\n',col)\n for b,ind in feat.groupby('bin').groups.items():\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\n print('oracle-bin',total/len(feat))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.6\nu=np.concatenate([raw['user']*np.sqrt(1-g),uni['user']*np.sqrt(g)],1); it=np.concatenate([raw['item']*np.sqrt(1-g),uni['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.025,.035,.04,.045,.05,.055,.06,.065,.07,.075,.08,.09,.11,.14]); to=np.argsort(-trend); rows=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=u[st:en]@it.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n pt=to[masks[r,to]][:700]; cand=np.union1d(pn[l],pt); vals=[]\n for w in ws:\n top=cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]; vals.append(tr.ndcg10(items[top],gold))\n rows.append((r,*vals))\narr=np.asarray(rows); ridx=arr[:,0].astype(int); Q=arr[:,1:]\ntar=pd.read_parquet(tr.DATA/'target_users.parquet').set_index('uid').loc[uids]\nfeat=pd.DataFrame({'row':ridx,'recent':tar.recent_positive_listens.to_numpy()[ridx],'history':tar.history_positive_listens.to_numpy()[ridx],'eligible':masks.sum(1)[ridx]})\nfeat['ratio']=feat.recent/(feat.history+1); feat['breadth']=30000-feat.eligible\nprint('global',[(w,Q[:,j].mean()) for j,w in enumerate(ws)])\nfor col in ['recent','history','ratio','breadth']:\n feat['bin']=pd.qcut(feat[col],5,labels=False,duplicates='drop')\n total=0\n print('\\n',col)\n for b,ind in feat.groupby('bin').groups.items():\n means=Q[list(ind)].mean(0); j=means.argmax(); total += means[j]*len(ind); print(b,len(ind),ws[j],means[j], 'base',means[np.where(ws==.06)[0][0]])\n print('oracle-bin',total/len(feat))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n", "stderr": "", "aggregated_output": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n", "exit_code": 0, "duration": { "secs": 13, "nanos": 828627583 }, "formatted_output": "global [(np.float64(0.025), np.float64(0.0314664909912174)), (np.float64(0.035), np.float64(0.03609926547099453)), (np.float64(0.04), np.float64(0.03726032285881915)), (np.float64(0.045), np.float64(0.03811754202826207)), (np.float64(0.05), np.float64(0.03903717919568962)), (np.float64(0.055), np.float64(0.03983062450063273)), (np.float64(0.06), np.float64(0.04009711157090469)), (np.float64(0.065), np.float64(0.04005199384862277)), (np.float64(0.07), np.float64(0.040059864109192427)), (np.float64(0.075), np.float64(0.03966357620523893)), (np.float64(0.08), np.float64(0.03967305368893907)), (np.float64(0.09), np.float64(0.03918143854034103)), (np.float64(0.11), np.float64(0.03883785524260923)), (np.float64(0.14), np.float64(0.03768979183861072))]\r\n\r\n recent\r\n0 854 0.11 0.04048146289985309 base 0.039589963255021475\r\n1 851 0.06 0.04410507844630743 base 0.04410507844630743\r\n2 854 0.04 0.0337530073422442 base 0.03324430757754414\r\n3 851 0.05 0.04309209706313055 base 0.0424448800508282\r\n4 853 0.055 0.042155590302108095 base 0.04112485891523235\r\noracle-bin 0.04071305476729557\r\n\r\n history\r\n0 853 0.08 0.0490593618539828 base 0.048494475893577985\r\n1 852 0.06 0.033331598391887735 base 0.033331598391887735\r\n2 853 0.08 0.04645073326502365 base 0.046165268904012756\r\n3 852 0.05 0.03967937095988081 base 0.03907817730224295\r\n4 853 0.055 0.03435748640246683 base 0.03340691139861951\r\noracle-bin 0.04057761973387837\r\n\r\n ratio\r\n0 853 0.065 0.02982295620658903 base 0.02960610995043419\r\n1 852 0.055 0.03429533385394453 base 0.034094459763819716\r\n2 853 0.055 0.038031453283492216 base 0.03757466529759855\r\n3 852 0.08 0.05153585174873961 base 0.04982171462958636\r\n4 853 0.05 0.04984093618512868 base 0.049392971579147384\r\noracle-bin 0.04070426929262045\r\n\r\n breadth\r\n0 856 0.06 0.051651861775193736 base 0.051651861775193736\r\n1 852 0.08 0.03714184376938306 base 0.03694594418534222\r\n2 850 0.06 0.04119233927428141 base 0.04119233927428141\r\n3 853 0.045 0.03270426035467069 base 0.032260064562536966\r\n4 852 0.055 0.03938254699511966 base 0.03839286977265967\r\noracle-bin 0.040422940904667005\r\n" } } ## Step 109 · agent · 2026-09-25T00:59:16.869Z ### Thinking unavailable — no text recorded ## Step 110 · agent · 2026-09-25T00:59:26.192Z ### Thinking unavailable — no text recorded ## Step 111 · agent · 2026-09-25T00:59:29.929Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:59:39.707Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\\n+def build_snapshots(\\n+ stage: str,\\n+ catalog_ids: np.ndarray,\\n+ weeks: int = 30,\\n+ unique_history: bool = True,\\n+) -> SnapshotArrays:\\n@@\\n- selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ selected = (\\n+ _recent_unique_positions(pitems, hp, HISTORY_LEN)\\n+ if unique_history\\n+ else np.arange(max(0, hp - HISTORY_LEN), hp, dtype=np.int64)\\n+ )\\n@@\\n-def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\\n+def build_inference_users(\\n+ stage: str,\\n+ catalog_ids: np.ndarray,\\n+ unique_history: bool = True,\\n+) -> tuple[np.ndarray, ...]:\\n@@\\n- chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ chosen_within_positive = (\\n+ _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\\n+ if unique_history\\n+ else np.arange(max(0, len(all_positive) - HISTORY_LEN), len(all_positive), dtype=np.int64)\\n+ )\\n@@\\n parser.add_argument(\\\"--seed\\\", type=int, default=20260925)\\n+ parser.add_argument(\\\"--history-mode\\\", choices=[\\\"unique\\\", \\\"raw\\\"], default=\\\"unique\\\")\\n@@\\n- cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_unique.npz\\\"\\n+ unique_history = args.history_mode == \\\"unique\\\"\\n+ cache = OUT / f\\\"snapshots_{args.stage}_{args.weeks}w_{args.history_mode}.npz\\\"\\n@@\\n- snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks, unique_history)\\n@@\\n- users = build_inference_users(args.stage, catalog_ids)\\n+ users = build_inference_users(args.stage, catalog_ids, unique_history)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T00:59:39.829Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-c59e482c-9021-4b77-9ef4-7384e3017421", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -125,3 +125,8 @@\n \n-def build_snapshots(stage: str, catalog_ids: np.ndarray, weeks: int = 30) -> SnapshotArrays:\n+def build_snapshots(\n+ stage: str,\n+ catalog_ids: np.ndarray,\n+ weeks: int = 30,\n+ unique_history: bool = True,\n+) -> SnapshotArrays:\n \"\"\"Create cutoff-correct rolling history -> organic-discovery examples.\"\"\"\n@@ -234,3 +239,7 @@\n continue\n- selected = _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ selected = (\n+ _recent_unique_positions(pitems, hp, HISTORY_LEN)\n+ if unique_history\n+ else np.arange(max(0, hp - HISTORY_LEN), hp, dtype=np.int64)\n+ )\n hist_i = pitems[selected]\n@@ -427,3 +436,7 @@\n \n-def build_inference_users(stage: str, catalog_ids: np.ndarray) -> tuple[np.ndarray, ...]:\n+def build_inference_users(\n+ stage: str,\n+ catalog_ids: np.ndarray,\n+ unique_history: bool = True,\n+) -> tuple[np.ndarray, ...]:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -463,3 +476,7 @@\n all_positive = np.flatnonzero(ratio[:end] >= 50)\n- chosen_within_positive = _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ chosen_within_positive = (\n+ _recent_unique_positions(items[all_positive], len(all_positive), HISTORY_LEN)\n+ if unique_history\n+ else np.arange(max(0, len(all_positive) - HISTORY_LEN), len(all_positive), dtype=np.int64)\n+ )\n positive = all_positive[chosen_within_positive]\n@@ -660,2 +677,3 @@\n parser.add_argument(\"--seed\", type=int, default=20260925)\n+ parser.add_argument(\"--history-mode\", choices=[\"unique\", \"raw\"], default=\"unique\")\n parser.add_argument(\"--trend-weight\", type=float)\n@@ -668,3 +686,4 @@\n features = item_features(catalog)\n- cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_unique.npz\"\n+ unique_history = args.history_mode == \"unique\"\n+ cache = OUT / f\"snapshots_{args.stage}_{args.weeks}w_{args.history_mode}.npz\"\n if cache.exists() and not args.rebuild_cache:\n@@ -673,3 +692,3 @@\n else:\n- snapshots = build_snapshots(args.stage, catalog_ids, args.weeks)\n+ snapshots = build_snapshots(args.stage, catalog_ids, args.weeks, unique_history)\n snapshots.save(cache)\n@@ -687,3 +706,3 @@\n )\n- users = build_inference_users(args.stage, catalog_ids)\n+ users = build_inference_users(args.stage, catalog_ids, unique_history)\n ue, ie = encode(model, users, features)\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c75ac7d487d288556eba72b31102", "usage": { "input_tokens": 76207, "cached_input_tokens": 75008, "cache_write_input_tokens": 0, "output_tokens": 1787, "reasoning_output_tokens": 1235, "total_tokens": 77994 }, "turn_token_usage": { "input_tokens": 5213499, "cached_input_tokens": 5131008, "cache_write_input_tokens": 0, "output_tokens": 45024, "reasoning_output_tokens": 17520, "total_tokens": 5258523 }, "thread_token_usage": { "input_tokens": 5213499, "cached_input_tokens": 5131008, "cache_write_input_tokens": 0, "output_tokens": 45024, "reasoning_output_tokens": 17520, "total_tokens": 5258523 } } ## Step 112 · agent · 2026-09-25T00:59:43.343Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:59:53.477Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\\n for l,r in enumerate(range(st,en)):\\n gold=truth.get(int(uids[r]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\\n for g in gs:\\n b=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\\nfor x in best[:25]: print(x)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T00:59:54.658Z Script completed Wall time 1.2 seconds Output: SESSION_ID=98881 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c77c23a487d28b2d257299f0e817", "usage": { "input_tokens": 78018, "cached_input_tokens": 76032, "cache_write_input_tokens": 0, "output_tokens": 702, "reasoning_output_tokens": 133, "total_tokens": 78720 }, "turn_token_usage": { "input_tokens": 5291517, "cached_input_tokens": 5207040, "cache_write_input_tokens": 0, "output_tokens": 45726, "reasoning_output_tokens": 17653, "total_tokens": 5337243 }, "thread_token_usage": { "input_tokens": 5291517, "cached_input_tokens": 5207040, "cache_write_input_tokens": 0, "output_tokens": 45726, "reasoning_output_tokens": 17653, "total_tokens": 5337243 } } ### exec · 2026-09-25T00:59:56.704Z { "code": "const r = await tools.write_stdin({\"session_id\":98881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:00:26.712Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c78ada7487d2a62e976d3acebac1", "usage": { "input_tokens": 78748, "cached_input_tokens": 77824, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 78793 }, "turn_token_usage": { "input_tokens": 5370265, "cached_input_tokens": 5284864, "cache_write_input_tokens": 0, "output_tokens": 45771, "reasoning_output_tokens": 17653, "total_tokens": 5416036 }, "thread_token_usage": { "input_tokens": 5370265, "cached_input_tokens": 5284864, "cache_write_input_tokens": 0, "output_tokens": 45771, "reasoning_output_tokens": 17653, "total_tokens": 5416036 } } ## Step 113 · agent · 2026-09-25T01:00:28.433Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:00:29.122Z { "code": "const r = await tools.write_stdin({\"session_id\":98881,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:00:37.336Z Script completed Wall time 8.2 seconds Output: (np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06)) (np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625)) (np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06)) (np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06)) (np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625)) (np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625)) (np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625)) (np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575)) (np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065)) (np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575)) (np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625)) (np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06)) (np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625)) (np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675)) (np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06)) (np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625)) (np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575)) (np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07)) (np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575)) (np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065)) (np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625)) (np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07)) (np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575)) (np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675)) (np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675)) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7aafa1887d284e5b45e7c6af4a7", "usage": { "input_tokens": 78816, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 78880 }, "turn_token_usage": { "input_tokens": 5449081, "cached_input_tokens": 5363456, "cache_write_input_tokens": 0, "output_tokens": 45835, "reasoning_output_tokens": 17670, "total_tokens": 5494916 }, "thread_token_usage": { "input_tokens": 5449081, "cached_input_tokens": 5363456, "cache_write_input_tokens": 0, "output_tokens": 45835, "reasoning_output_tokens": 17670, "total_tokens": 5494916 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4ae306ba-b025-403f-a3a3-22671112a0b2", "process_id": "98881", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nfor x in best[:25]: print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nraw=np.load('validation_embeddings_25e.npz'); uni=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); gs=np.arange(.4,.81,.05); ws=np.arange(.05,.071,.0025); vals={(round(g,3),round(w,4)):[] for g in gs for w in ws}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=raw['user'][st:en]@raw['item'].T; su=uni['user'][st:en]@uni['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf\n pr=np.argpartition(sr,-700,axis=1)[:,-700:]; pu=np.argpartition(su,-700,axis=1)[:,-700:]\n for l,r in enumerate(range(st,en)):\n gold=truth.get(int(uids[r]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[r,to]][:700]); br=sr[l,cand]; bu=su[l,cand]\n for g in gs:\n b=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(b+w*trend[cand]))[:10]]; vals[(round(g,3),round(w,4))].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nfor x in best[:25]: print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n", "stderr": "", "aggregated_output": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n", "exit_code": 0, "duration": { "secs": 43, "nanos": 677732900 }, "formatted_output": "(np.float64(0.040323641167372415), np.float64(0.65), np.float64(0.06))\r\n(np.float64(0.040309303051662033), np.float64(0.65), np.float64(0.0625))\r\n(np.float64(0.04030154157489722), np.float64(0.7), np.float64(0.06))\r\n(np.float64(0.04027330985999972), np.float64(0.75), np.float64(0.06))\r\n(np.float64(0.040220314645683965), np.float64(0.5), np.float64(0.0625))\r\n(np.float64(0.04017140639343158), np.float64(0.45), np.float64(0.0625))\r\n(np.float64(0.04016917932904061), np.float64(0.7), np.float64(0.0625))\r\n(np.float64(0.04016301368361672), np.float64(0.65), np.float64(0.0575))\r\n(np.float64(0.0401600444514154), np.float64(0.55), np.float64(0.065))\r\n(np.float64(0.04015646187539287), np.float64(0.75), np.float64(0.0575))\r\n(np.float64(0.04014687682016412), np.float64(0.75), np.float64(0.0625))\r\n(np.float64(0.04011225898255295), np.float64(0.5), np.float64(0.06))\r\n(np.float64(0.040110611910192454), np.float64(0.4), np.float64(0.0625))\r\n(np.float64(0.04010491251651386), np.float64(0.45), np.float64(0.0675))\r\n(np.float64(0.04009711157090469), np.float64(0.6), np.float64(0.06))\r\n(np.float64(0.04009415447168478), np.float64(0.6), np.float64(0.0625))\r\n(np.float64(0.04009128210015709), np.float64(0.7), np.float64(0.0575))\r\n(np.float64(0.04007705392672599), np.float64(0.75), np.float64(0.07))\r\n(np.float64(0.040067230099514996), np.float64(0.45), np.float64(0.0575))\r\n(np.float64(0.040062271007104404), np.float64(0.5), np.float64(0.065))\r\n(np.float64(0.04006088673999301), np.float64(0.8), np.float64(0.0625))\r\n(np.float64(0.040059864109192427), np.float64(0.6), np.float64(0.07))\r\n(np.float64(0.0400586317188975), np.float64(0.5), np.float64(0.0575))\r\n(np.float64(0.040058499829432784), np.float64(0.6), np.float64(0.0675))\r\n(np.float64(0.040053990669958586), np.float64(0.65), np.float64(0.0675))\r\n" } } ## Step 114 · agent · 2026-09-25T01:00:39.375Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:00:43.971Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:00:45.138Z Script completed Wall time 1.2 seconds Output: SESSION_ID=33305 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7b582bc87d2a4971331961b824e", "usage": { "input_tokens": 79644, "cached_input_tokens": 78592, "cache_write_input_tokens": 0, "output_tokens": 281, "reasoning_output_tokens": 17, "total_tokens": 79925 }, "turn_token_usage": { "input_tokens": 5528725, "cached_input_tokens": 5442048, "cache_write_input_tokens": 0, "output_tokens": 46116, "reasoning_output_tokens": 17687, "total_tokens": 5574841 }, "thread_token_usage": { "input_tokens": 5528725, "cached_input_tokens": 5442048, "cache_write_input_tokens": 0, "output_tokens": 46116, "reasoning_output_tokens": 17687, "total_tokens": 5574841 } } ### exec · 2026-09-25T01:00:46.922Z { "code": "const r = await tools.write_stdin({\"session_id\":33305,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:00:55.550Z Script completed Wall time 8.6 seconds Output: (0.040322528580392744, 0.06) { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.040323, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7bd636c87d2b5e15bce42766fd1", "usage": { "input_tokens": 79953, "cached_input_tokens": 79488, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 79998 }, "turn_token_usage": { "input_tokens": 5608678, "cached_input_tokens": 5521536, "cache_write_input_tokens": 0, "output_tokens": 46161, "reasoning_output_tokens": 17687, "total_tokens": 5654839 }, "thread_token_usage": { "input_tokens": 5608678, "cached_input_tokens": 5521536, "cache_write_input_tokens": 0, "output_tokens": 46161, "reasoning_output_tokens": 17687, "total_tokens": 5654839 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07b2b6ad-a6b2-43d3-bf65-b001a21e0cb0", "process_id": "33305", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); t=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 411142752 }, "formatted_output": "(0.040322528580392744, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040323,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 115 · agent · 2026-09-25T01:01:05.395Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:01:17.753Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\n# Recompute only scored-user approximate qualities, but bin from all target users.\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\\n rr.append(row); qs.append(q)\\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\\nfor b in range(5):\\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\\n # select nearest tested where necessary\\n vals=[]\\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\\n print('sched',schedule,np.mean(vals))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:01:18.917Z Script completed Wall time 1.2 seconds Output: SESSION_ID=98090 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7c80a9487d2b983f2ddb8d7e1d9", "usage": { "input_tokens": 80081, "cached_input_tokens": 79744, "cache_write_input_tokens": 0, "output_tokens": 1109, "reasoning_output_tokens": 376, "total_tokens": 81190 }, "turn_token_usage": { "input_tokens": 5688759, "cached_input_tokens": 5601280, "cache_write_input_tokens": 0, "output_tokens": 47270, "reasoning_output_tokens": 18063, "total_tokens": 5736029 }, "thread_token_usage": { "input_tokens": 5688759, "cached_input_tokens": 5601280, "cache_write_input_tokens": 0, "output_tokens": 47270, "reasoning_output_tokens": 18063, "total_tokens": 5736029 } } ### exec · 2026-09-25T01:01:22.827Z { "code": "const r = await tools.write_stdin({\"session_id\":98090,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:01:29.703Z Script completed Wall time 6.9 seconds Output: edges [ 220. 554. 1084. 1989.] 0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09 1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06 2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06 3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06 4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055 sched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839 sched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102 sched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744 sched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555 sched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7df247c87d289059b49a9d0dff8", "usage": { "input_tokens": 81218, "cached_input_tokens": 79872, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 81263 }, "turn_token_usage": { "input_tokens": 5769977, "cached_input_tokens": 5681152, "cache_write_input_tokens": 0, "output_tokens": 47315, "reasoning_output_tokens": 18063, "total_tokens": 5817292 }, "thread_token_usage": { "input_tokens": 5769977, "cached_input_tokens": 5681152, "cache_write_input_tokens": 0, "output_tokens": 47315, "reasoning_output_tokens": 18063, "total_tokens": 5817292 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2a81da5e-4671-45ca-b8cf-b86d5b91a61c", "process_id": "98090", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\n# Recompute only scored-user approximate qualities, but bin from all target users.\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\n rr.append(row); qs.append(q)\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\nfor b in range(5):\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\n # select nearest tested where necessary\n vals=[]\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\n print('sched',schedule,np.mean(vals))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\n# Recompute only scored-user approximate qualities, but bin from all target users.\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); ws=np.array([.04,.05,.055,.06,.065,.075,.09,.11]); to=np.argsort(-trend); qs=[]; rr=[]\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],to[masks[row,to]][:700]); q=[]\n for w in ws: q.append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*trend[cand]))[:10]]],gold))\n rr.append(row); qs.append(q)\nQ=np.array(qs); rr=np.array(rr); tar=pd.read_parquet(tr.DATA/'target_users.parquet'); rec=tar.recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); print('edges',edges)\nfor b in range(5):\n x=Q[bins[rr]==b].mean(0); print(b,(bins[rr]==b).sum(),list(zip(ws,x)),'best',ws[x.argmax()])\nfor schedule in [[.10,.06,.05,.05,.055],[.09,.065,.055,.0525,.05],[.09,.06,.05,.05,.055],[.11,.06,.04,.05,.055],[.085,.065,.055,.0525,.05]]:\n # select nearest tested where necessary\n vals=[]\n for j,row in enumerate(rr): vals.append(Q[j,np.abs(ws-schedule[bins[row]]).argmin()])\n print('sched',schedule,np.mean(vals))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n", "stderr": "", "aggregated_output": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 785411004 }, "formatted_output": "edges [ 220. 554. 1084. 1989.]\r\n0 641 [(np.float64(0.04), np.float64(0.035361203264815115)), (np.float64(0.05), np.float64(0.039158787425368546)), (np.float64(0.055), np.float64(0.041270487577630174)), (np.float64(0.06), np.float64(0.04195798185888071)), (np.float64(0.065), np.float64(0.042254678331439116)), (np.float64(0.075), np.float64(0.04336812269660213)), (np.float64(0.09), np.float64(0.04371118690113509)), (np.float64(0.11), np.float64(0.04349719926847256))] best 0.09\r\n1 762 [(np.float64(0.04), np.float64(0.035159380277425344)), (np.float64(0.05), np.float64(0.03791800120991985)), (np.float64(0.055), np.float64(0.039433508791683884)), (np.float64(0.06), np.float64(0.04045389447248136)), (np.float64(0.065), np.float64(0.03984097288177205)), (np.float64(0.075), np.float64(0.039299045174257995)), (np.float64(0.09), np.float64(0.0391975260199611)), (np.float64(0.11), np.float64(0.03820742063766229))] best 0.06\r\n2 868 [(np.float64(0.04), np.float64(0.030811031819652567)), (np.float64(0.05), np.float64(0.0311052684890966)), (np.float64(0.055), np.float64(0.03149953379777538)), (np.float64(0.06), np.float64(0.03272141402372189)), (np.float64(0.065), np.float64(0.03235403047322562)), (np.float64(0.075), np.float64(0.03145774420498297)), (np.float64(0.09), np.float64(0.03102018092254861)), (np.float64(0.11), np.float64(0.02997548799403994))] best 0.06\r\n3 965 [(np.float64(0.04), np.float64(0.045488163762094)), (np.float64(0.05), np.float64(0.04683879201067796)), (np.float64(0.055), np.float64(0.04646821608662898)), (np.float64(0.06), np.float64(0.04723178620643946)), (np.float64(0.065), np.float64(0.0471348299459096)), (np.float64(0.075), np.float64(0.04711363977034398)), (np.float64(0.09), np.float64(0.045813349092061625)), (np.float64(0.11), np.float64(0.045639891541214575))] best 0.06\r\n4 1027 [(np.float64(0.04), np.float64(0.03763056333945201)), (np.float64(0.05), np.float64(0.039300213728958484)), (np.float64(0.055), np.float64(0.039844194650517264)), (np.float64(0.06), np.float64(0.03905492513250743)), (np.float64(0.065), np.float64(0.03851280618944883)), (np.float64(0.075), np.float64(0.037946906919344416)), (np.float64(0.09), np.float64(0.03733324205985331)), (np.float64(0.11), np.float64(0.03714960140817213))] best 0.055\r\nsched [0.1, 0.06, 0.05, 0.05, 0.055] 0.04030644324748839\r\nsched [0.09, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\nsched [0.09, 0.06, 0.05, 0.05, 0.055] 0.040338619196945744\r\nsched [0.11, 0.06, 0.04, 0.05, 0.055] 0.040246532989670555\r\nsched [0.085, 0.065, 0.055, 0.0525, 0.05] 0.04017828788786102\r\n" } } ## Step 116 · agent · 2026-09-25T01:01:39.620Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:01:49.534Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\\ntop=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:01:50.699Z Script completed Wall time 1.2 seconds Output: SESSION_ID=7247 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7e9f6a487d282dd63ac599387a4", "usage": { "input_tokens": 82416, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 917, "reasoning_output_tokens": 344, "total_tokens": 83333 }, "turn_token_usage": { "input_tokens": 5852393, "cached_input_tokens": 5762176, "cache_write_input_tokens": 0, "output_tokens": 48232, "reasoning_output_tokens": 18407, "total_tokens": 5900625 }, "thread_token_usage": { "input_tokens": 5852393, "cached_input_tokens": 5762176, "cache_write_input_tokens": 0, "output_tokens": 48232, "reasoning_output_tokens": 18407, "total_tokens": 5900625 } } ### exec · 2026-09-25T01:01:54.391Z { "code": "const r = await tools.write_stdin({\"session_id\":7247,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:02:00.832Z Script completed Wall time 6.4 seconds Output: [ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692] { "model": "/app/solution/validation_activity.parquet", "scores": { "quality_ndcg@10": 0.040776, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c7fee90087d29bbff7d376e5af99", "usage": { "input_tokens": 83361, "cached_input_tokens": 82176, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 83406 }, "turn_token_usage": { "input_tokens": 5935754, "cached_input_tokens": 5844352, "cache_write_input_tokens": 0, "output_tokens": 48277, "reasoning_output_tokens": 18407, "total_tokens": 5984031 }, "thread_token_usage": { "input_tokens": 5935754, "cached_input_tokens": 5844352, "cache_write_input_tokens": 0, "output_tokens": 48277, "reasoning_output_tokens": 18407, "total_tokens": 5984031 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-11a3663b-639f-4e96-8fbb-201e18cb8cea", "process_id": "7247", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids']); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); sched=np.array([.09,.06,.06,.06,.055]); ww=sched[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+ww[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}); out.to_parquet('validation_activity.parquet',index=False); print(edges,np.bincount(bins),flush=True)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 133154643 }, "formatted_output": "[ 220. 554. 1084. 1989.] [1691 1691 1690 1692 1692]\r\n{\r\n \"model\": \"/app/solution/validation_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 117 · agent · 2026-09-25T01:02:04.918Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:02:16.979Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\\n for g in gs:\\n base=(1-g)*br+g*bu\\n for w in ws:\\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\\nfor b in range(5):\\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:02:18.144Z Script completed Wall time 1.2 seconds Output: SESSION_ID=19333 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c809101c87d2ab6f5e575957b2dd", "usage": { "input_tokens": 83507, "cached_input_tokens": 83200, "cache_write_input_tokens": 0, "output_tokens": 787, "reasoning_output_tokens": 108, "total_tokens": 84294 }, "turn_token_usage": { "input_tokens": 6019261, "cached_input_tokens": 5927552, "cache_write_input_tokens": 0, "output_tokens": 49064, "reasoning_output_tokens": 18515, "total_tokens": 6068325 }, "thread_token_usage": { "input_tokens": 6019261, "cached_input_tokens": 5927552, "cache_write_input_tokens": 0, "output_tokens": 49064, "reasoning_output_tokens": 18515, "total_tokens": 6068325 } } ## Step 118 · agent · 2026-09-25T01:02:19.637Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:02:20.330Z { "code": "const r = await tools.write_stdin({\"session_id\":19333,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:02:50.338Z Script completed Wall time 30.0 seconds Output: bin 0 n 641 (np.float64(0.0444453111606116), 0.35, 0.09) (np.float64(0.04434832223350444), 0.35, 0.11) (np.float64(0.04415682212784072), 0.5, 0.11) (np.float64(0.0439980504795929), 0.6, 0.11) (np.float64(0.04396205335762866), 0.5, 0.09) (np.float64(0.04388975094954861), 0.8, 0.075) (np.float64(0.043780587004762214), 0.9, 0.075) (np.float64(0.0437111869011351), 0.65, 0.09) (np.float64(0.043609304007534364), 1.0, 0.075) (np.float64(0.04352343343732841), 0.5, 0.075) (np.float64(0.04351475058371109), 0.7, 0.075) (np.float64(0.04349719926847255), 0.65, 0.11) bin 1 n 762 (np.float64(0.04045389447248134), 0.65, 0.06) (np.float64(0.04032244973353465), 0.7, 0.06) (np.float64(0.040299812479864874), 0.5, 0.06) (np.float64(0.04029727707110021), 0.35, 0.065) (np.float64(0.04028682599536908), 0.5, 0.065) (np.float64(0.04007258291621664), 0.35, 0.075) (np.float64(0.04000348157374986), 0.6, 0.065) (np.float64(0.03998603247547482), 0.6, 0.06) (np.float64(0.03984097288177204), 0.65, 0.065) (np.float64(0.039812460754745406), 0.35, 0.06) (np.float64(0.039714444765738634), 0.9, 0.065) (np.float64(0.03963389881864884), 0.8, 0.06) bin 2 n 868 (np.float64(0.032823348544511584), 0.65, 0.06) (np.float64(0.03275886822900279), 1.0, 0.055) (np.float64(0.03267271429773199), 0.7, 0.06) (np.float64(0.03262502088085752), 0.6, 0.06) (np.float64(0.03256608063945202), 0.8, 0.06) (np.float64(0.032494855808496134), 0.8, 0.065) (np.float64(0.03244608651827959), 0.65, 0.065) (np.float64(0.03240483776210847), 0.7, 0.065) (np.float64(0.032398462917658015), 0.5, 0.06) (np.float64(0.032370117839860074), 0.8, 0.045) (np.float64(0.032314240650764776), 0.9, 0.06) (np.float64(0.032258581129847086), 0.35, 0.06) bin 3 n 965 (np.float64(0.047553464942292116), 0.5, 0.05) (np.float64(0.04733993837507312), 0.35, 0.055) (np.float64(0.047277051881277676), 0.5, 0.06) (np.float64(0.04726141019617855), 0.7, 0.065) (np.float64(0.047231786206439474), 0.65, 0.06) (np.float64(0.04721003153779425), 0.6, 0.065) (np.float64(0.04716672874583522), 0.35, 0.075) (np.float64(0.04713482994590962), 0.65, 0.065) (np.float64(0.04712818722582016), 0.6, 0.05) (np.float64(0.04712233676971623), 0.7, 0.075) (np.float64(0.047113639770343996), 0.65, 0.075) (np.float64(0.047097037152278726), 0.8, 0.065) bin 4 n 1027 (np.float64(0.0403960730046121), 0.9, 0.055) (np.float64(0.0400932173313189), 1.0, 0.055) (np.float64(0.04006575471857305), 1.0, 0.05) (np.float64(0.03997070938409836), 0.7, 0.055) (np.float64(0.03984419465051729), 0.65, 0.055) (np.float64(0.03982782124957421), 0.9, 0.05) (np.float64(0.03976517714843728), 0.6, 0.055) (np.float64(0.0397208324276101), 0.8, 0.055) (np.float64(0.03970631820210737), 0.9, 0.06) (np.float64(0.0397033649654579), 1.0, 0.06) (np.float64(0.039599371030816496), 0.7, 0.06) (np.float64(0.03946120222129177), 0.8, 0.06) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c81a622c87d28b6716d7c51777e1", "usage": { "input_tokens": 84322, "cached_input_tokens": 83328, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 84375 }, "turn_token_usage": { "input_tokens": 6103583, "cached_input_tokens": 6010880, "cache_write_input_tokens": 0, "output_tokens": 49117, "reasoning_output_tokens": 18521, "total_tokens": 6152700 }, "thread_token_usage": { "input_tokens": 6103583, "cached_input_tokens": 6010880, "cache_write_input_tokens": 0, "output_tokens": 49117, "reasoning_output_tokens": 18521, "total_tokens": 6152700 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a7163567-121f-40fd-8deb-c245d693d3ce", "process_id": "19333", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\n for g in gs:\n base=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\nfor b in range(5):\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); gs=[.35,.5,.6,.65,.7,.8,.9,1.0]; ws=[.045,.05,.055,.06,.065,.075,.09,.11]; keys=[(g,w) for g in gs for w in ws]; vals={b:{k:[] for k in keys} for b in range(5)}; to=np.argsort(-trend)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sr[~masks[st:en]]=-np.inf; su[~masks[st:en]]=-np.inf; pr=np.argpartition(sr,-600,axis=1)[:,-600:]; pu=np.argpartition(su,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pr[l],pu[l]),to[masks[row,to]][:600]); br=sr[l,cand]; bu=su[l,cand]; b=int(bins[row])\n for g in gs:\n base=(1-g)*br+g*bu\n for w in ws:\n top=cand[np.argsort(-(base+w*trend[cand]))[:10]]; vals[b][(g,w)].append(tr.ndcg10(items[top],gold))\nfor b in range(5):\n ranked=sorted([(np.mean(v),*k) for k,v in vals[b].items()],reverse=True); print('\\nbin',b,'n',len(next(iter(vals[b].values())))); print(*ranked[:12],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\r\nbin 0 n 641\r\n(np.float64(0.0444453111606116), 0.35, 0.09)\r\n(np.float64(0.04434832223350444), 0.35, 0.11)\r\n(np.float64(0.04415682212784072), 0.5, 0.11)\r\n(np.float64(0.0439980504795929), 0.6, 0.11)\r\n(np.float64(0.04396205335762866), 0.5, 0.09)\r\n(np.float64(0.04388975094954861), 0.8, 0.075)\r\n(np.float64(0.043780587004762214), 0.9, 0.075)\r\n(np.float64(0.0437111869011351), 0.65, 0.09)\r\n(np.float64(0.043609304007534364), 1.0, 0.075)\r\n(np.float64(0.04352343343732841), 0.5, 0.075)\r\n(np.float64(0.04351475058371109), 0.7, 0.075)\r\n(np.float64(0.04349719926847255), 0.65, 0.11)\r\n\r\nbin 1 n 762\r\n(np.float64(0.04045389447248134), 0.65, 0.06)\r\n(np.float64(0.04032244973353465), 0.7, 0.06)\r\n(np.float64(0.040299812479864874), 0.5, 0.06)\r\n(np.float64(0.04029727707110021), 0.35, 0.065)\r\n(np.float64(0.04028682599536908), 0.5, 0.065)\r\n(np.float64(0.04007258291621664), 0.35, 0.075)\r\n(np.float64(0.04000348157374986), 0.6, 0.065)\r\n(np.float64(0.03998603247547482), 0.6, 0.06)\r\n(np.float64(0.03984097288177204), 0.65, 0.065)\r\n(np.float64(0.039812460754745406), 0.35, 0.06)\r\n(np.float64(0.039714444765738634), 0.9, 0.065)\r\n(np.float64(0.03963389881864884), 0.8, 0.06)\r\n\r\nbin 2 n 868\r\n(np.float64(0.032823348544511584), 0.65, 0.06)\r\n(np.float64(0.03275886822900279), 1.0, 0.055)\r\n(np.float64(0.03267271429773199), 0.7, 0.06)\r\n(np.float64(0.03262502088085752), 0.6, 0.06)\r\n(np.float64(0.03256608063945202), 0.8, 0.06)\r\n(np.float64(0.032494855808496134), 0.8, 0.065)\r\n(np.float64(0.03244608651827959), 0.65, 0.065)\r\n(np.float64(0.03240483776210847), 0.7, 0.065)\r\n(np.float64(0.032398462917658015), 0.5, 0.06)\r\n(np.float64(0.032370117839860074), 0.8, 0.045)\r\n(np.float64(0.032314240650764776), 0.9, 0.06)\r\n(np.float64(0.032258581129847086), 0.35, 0.06)\r\n\r\nbin 3 n 965\r\n(np.float64(0.047553464942292116), 0.5, 0.05)\r\n(np.float64(0.04733993837507312), 0.35, 0.055)\r\n(np.float64(0.047277051881277676), 0.5, 0.06)\r\n(np.float64(0.04726141019617855), 0.7, 0.065)\r\n(np.float64(0.047231786206439474), 0.65, 0.06)\r\n(np.float64(0.04721003153779425), 0.6, 0.065)\r\n(np.float64(0.04716672874583522), 0.35, 0.075)\r\n(np.float64(0.04713482994590962), 0.65, 0.065)\r\n(np.float64(0.04712818722582016), 0.6, 0.05)\r\n(np.float64(0.04712233676971623), 0.7, 0.075)\r\n(np.float64(0.047113639770343996), 0.65, 0.075)\r\n(np.float64(0.047097037152278726), 0.8, 0.065)\r\n\r\nbin 4 n 1027\r\n(np.float64(0.0403960730046121), 0.9, 0.055)\r\n(np.float64(0.0400932173313189), 1.0, 0.055)\r\n(np.float64(0.04006575471857305), 1.0, 0.05)\r\n(np.float64(0.03997070938409836), 0.7, 0.055)\r\n(np.float64(0.03984419465051729), 0.65, 0.055)\r\n(np.float64(0.03982782124957421), 0.9, 0.05)\r\n(np.float64(0.03976517714843728), 0.6, 0.055)\r\n(np.float64(0.0397208324276101), 0.8, 0.055)\r\n(np.float64(0.03970631820210737), 0.9, 0.06)\r\n(np.float64(0.0397033649654579), 1.0, 0.06)\r\n(np.float64(0.039599371030816496), 0.7, 0.06)\r\n(np.float64(0.03946120222129177), 0.8, 0.06)\r\n", "stderr": "", "aggregated_output": "\r\nbin 0 n 641\r\n(np.float64(0.0444453111606116), 0.35, 0.09)\r\n(np.float64(0.04434832223350444), 0.35, 0.11)\r\n(np.float64(0.04415682212784072), 0.5, 0.11)\r\n(np.float64(0.0439980504795929), 0.6, 0.11)\r\n(np.float64(0.04396205335762866), 0.5, 0.09)\r\n(np.float64(0.04388975094954861), 0.8, 0.075)\r\n(np.float64(0.043780587004762214), 0.9, 0.075)\r\n(np.float64(0.0437111869011351), 0.65, 0.09)\r\n(np.float64(0.043609304007534364), 1.0, 0.075)\r\n(np.float64(0.04352343343732841), 0.5, 0.075)\r\n(np.float64(0.04351475058371109), 0.7, 0.075)\r\n(np.float64(0.04349719926847255), 0.65, 0.11)\r\n\r\nbin 1 n 762\r\n(np.float64(0.04045389447248134), 0.65, 0.06)\r\n(np.float64(0.04032244973353465), 0.7, 0.06)\r\n(np.float64(0.040299812479864874), 0.5, 0.06)\r\n(np.float64(0.04029727707110021), 0.35, 0.065)\r\n(np.float64(0.04028682599536908), 0.5, 0.065)\r\n(np.float64(0.04007258291621664), 0.35, 0.075)\r\n(np.float64(0.04000348157374986), 0.6, 0.065)\r\n(np.float64(0.03998603247547482), 0.6, 0.06)\r\n(np.float64(0.03984097288177204), 0.65, 0.065)\r\n(np.float64(0.039812460754745406), 0.35, 0.06)\r\n(np.float64(0.039714444765738634), 0.9, 0.065)\r\n(np.float64(0.03963389881864884), 0.8, 0.06)\r\n\r\nbin 2 n 868\r\n(np.float64(0.032823348544511584), 0.65, 0.06)\r\n(np.float64(0.03275886822900279), 1.0, 0.055)\r\n(np.float64(0.03267271429773199), 0.7, 0.06)\r\n(np.float64(0.03262502088085752), 0.6, 0.06)\r\n(np.float64(0.03256608063945202), 0.8, 0.06)\r\n(np.float64(0.032494855808496134), 0.8, 0.065)\r\n(np.float64(0.03244608651827959), 0.65, 0.065)\r\n(np.float64(0.03240483776210847), 0.7, 0.065)\r\n(np.float64(0.032398462917658015), 0.5, 0.06)\r\n(np.float64(0.032370117839860074), 0.8, 0.045)\r\n(np.float64(0.032314240650764776), 0.9, 0.06)\r\n(np.float64(0.032258581129847086), 0.35, 0.06)\r\n\r\nbin 3 n 965\r\n(np.float64(0.047553464942292116), 0.5, 0.05)\r\n(np.float64(0.04733993837507312), 0.35, 0.055)\r\n(np.float64(0.047277051881277676), 0.5, 0.06)\r\n(np.float64(0.04726141019617855), 0.7, 0.065)\r\n(np.float64(0.047231786206439474), 0.65, 0.06)\r\n(np.float64(0.04721003153779425), 0.6, 0.065)\r\n(np.float64(0.04716672874583522), 0.35, 0.075)\r\n(np.float64(0.04713482994590962), 0.65, 0.065)\r\n(np.float64(0.04712818722582016), 0.6, 0.05)\r\n(np.float64(0.04712233676971623), 0.7, 0.075)\r\n(np.float64(0.047113639770343996), 0.65, 0.075)\r\n(np.float64(0.047097037152278726), 0.8, 0.065)\r\n\r\nbin 4 n 1027\r\n(np.float64(0.0403960730046121), 0.9, 0.055)\r\n(np.float64(0.0400932173313189), 1.0, 0.055)\r\n(np.float64(0.04006575471857305), 1.0, 0.05)\r\n(np.float64(0.03997070938409836), 0.7, 0.055)\r\n(np.float64(0.03984419465051729), 0.65, 0.055)\r\n(np.float64(0.03982782124957421), 0.9, 0.05)\r\n(np.float64(0.03976517714843728), 0.6, 0.055)\r\n(np.float64(0.0397208324276101), 0.8, 0.055)\r\n(np.float64(0.03970631820210737), 0.9, 0.06)\r\n(np.float64(0.0397033649654579), 1.0, 0.06)\r\n(np.float64(0.039599371030816496), 0.7, 0.06)\r\n(np.float64(0.03946120222129177), 0.8, 0.06)\r\n", "exit_code": 0, "duration": { "secs": 33, "nanos": 317785009 }, "formatted_output": "\r\nbin 0 n 641\r\n(np.float64(0.0444453111606116), 0.35, 0.09)\r\n(np.float64(0.04434832223350444), 0.35, 0.11)\r\n(np.float64(0.04415682212784072), 0.5, 0.11)\r\n(np.float64(0.0439980504795929), 0.6, 0.11)\r\n(np.float64(0.04396205335762866), 0.5, 0.09)\r\n(np.float64(0.04388975094954861), 0.8, 0.075)\r\n(np.float64(0.043780587004762214), 0.9, 0.075)\r\n(np.float64(0.0437111869011351), 0.65, 0.09)\r\n(np.float64(0.043609304007534364), 1.0, 0.075)\r\n(np.float64(0.04352343343732841), 0.5, 0.075)\r\n(np.float64(0.04351475058371109), 0.7, 0.075)\r\n(np.float64(0.04349719926847255), 0.65, 0.11)\r\n\r\nbin 1 n 762\r\n(np.float64(0.04045389447248134), 0.65, 0.06)\r\n(np.float64(0.04032244973353465), 0.7, 0.06)\r\n(np.float64(0.040299812479864874), 0.5, 0.06)\r\n(np.float64(0.04029727707110021), 0.35, 0.065)\r\n(np.float64(0.04028682599536908), 0.5, 0.065)\r\n(np.float64(0.04007258291621664), 0.35, 0.075)\r\n(np.float64(0.04000348157374986), 0.6, 0.065)\r\n(np.float64(0.03998603247547482), 0.6, 0.06)\r\n(np.float64(0.03984097288177204), 0.65, 0.065)\r\n(np.float64(0.039812460754745406), 0.35, 0.06)\r\n(np.float64(0.039714444765738634), 0.9, 0.065)\r\n(np.float64(0.03963389881864884), 0.8, 0.06)\r\n\r\nbin 2 n 868\r\n(np.float64(0.032823348544511584), 0.65, 0.06)\r\n(np.float64(0.03275886822900279), 1.0, 0.055)\r\n(np.float64(0.03267271429773199), 0.7, 0.06)\r\n(np.float64(0.03262502088085752), 0.6, 0.06)\r\n(np.float64(0.03256608063945202), 0.8, 0.06)\r\n(np.float64(0.032494855808496134), 0.8, 0.065)\r\n(np.float64(0.03244608651827959), 0.65, 0.065)\r\n(np.float64(0.03240483776210847), 0.7, 0.065)\r\n(np.float64(0.032398462917658015), 0.5, 0.06)\r\n(np.float64(0.032370117839860074), 0.8, 0.045)\r\n(np.float64(0.032314240650764776), 0.9, 0.06)\r\n(np.float64(0.032258581129847086), 0.35, 0.06)\r\n\r\nbin 3 n 965\r\n(np.float64(0.047553464942292116), 0.5, 0.05)\r\n(np.float64(0.04733993837507312), 0.35, 0.055)\r\n(np.float64(0.047277051881277676), 0.5, 0.06)\r\n(np.float64(0.04726141019617855), 0.7, 0.065)\r\n(np.float64(0.047231786206439474), 0.65, 0.06)\r\n(np.float64(0.04721003153779425), 0.6, 0.065)\r\n(np.float64(0.04716672874583522), 0.35, 0.075)\r\n(np.float64(0.04713482994590962), 0.65, 0.065)\r\n(np.float64(0.04712818722582016), 0.6, 0.05)\r\n(np.float64(0.04712233676971623), 0.7, 0.075)\r\n(np.float64(0.047113639770343996), 0.65, 0.075)\r\n(np.float64(0.047097037152278726), 0.8, 0.065)\r\n\r\nbin 4 n 1027\r\n(np.float64(0.0403960730046121), 0.9, 0.055)\r\n(np.float64(0.0400932173313189), 1.0, 0.055)\r\n(np.float64(0.04006575471857305), 1.0, 0.05)\r\n(np.float64(0.03997070938409836), 0.7, 0.055)\r\n(np.float64(0.03984419465051729), 0.65, 0.055)\r\n(np.float64(0.03982782124957421), 0.9, 0.05)\r\n(np.float64(0.03976517714843728), 0.6, 0.055)\r\n(np.float64(0.0397208324276101), 0.8, 0.055)\r\n(np.float64(0.03970631820210737), 0.9, 0.06)\r\n(np.float64(0.0397033649654579), 1.0, 0.06)\r\n(np.float64(0.039599371030816496), 0.7, 0.06)\r\n(np.float64(0.03946120222129177), 0.8, 0.06)\r\n" } } ## Step 119 · agent · 2026-09-25T01:03:00.274Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:03:10.310Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,torch\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\\ntop=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\\nprint(edges)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:03:11.477Z Script completed Wall time 1.2 seconds Output: SESSION_ID=3931 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c83a886487d28e3992933a196f5a", "usage": { "input_tokens": 85815, "cached_input_tokens": 84096, "cache_write_input_tokens": 0, "output_tokens": 1051, "reasoning_output_tokens": 483, "total_tokens": 86866 }, "turn_token_usage": { "input_tokens": 6189398, "cached_input_tokens": 6094976, "cache_write_input_tokens": 0, "output_tokens": 50168, "reasoning_output_tokens": 19004, "total_tokens": 6239566 }, "thread_token_usage": { "input_tokens": 6189398, "cached_input_tokens": 6094976, "cache_write_input_tokens": 0, "output_tokens": 50168, "reasoning_output_tokens": 19004, "total_tokens": 6239566 } } ### exec · 2026-09-25T01:03:13.382Z { "code": "const r = await tools.write_stdin({\"session_id\":3931,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:03:27.922Z Script completed Wall time 14.5 seconds Output: [ 220. 554. 1084. 1989.] { "model": "/app/solution/validation_activity_ensemble.parquet", "scores": { "quality_ndcg@10": 0.040792, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c84fae0087d2a7c2088f9d0faae5", "usage": { "input_tokens": 86894, "cached_input_tokens": 85632, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 86939 }, "turn_token_usage": { "input_tokens": 6276292, "cached_input_tokens": 6180608, "cache_write_input_tokens": 0, "output_tokens": 50213, "reasoning_output_tokens": 19004, "total_tokens": 6326505 }, "thread_token_usage": { "input_tokens": 6276292, "cached_input_tokens": 6180608, "cache_write_input_tokens": 0, "output_tokens": 50213, "reasoning_output_tokens": 19004, "total_tokens": 6326505 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-1b193774-83fa-4387-b1d2-09f34936c49c", "process_id": "3931", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\nprint(edges)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,torch\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ck=torch.load('two_tower_validation_unique.pt',weights_only=False); trend=tr.recent_trend('validation',ck['catalog_ids'])\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); itemids=z['item_ids'].astype(np.int64); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(itemids),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); gs=np.array([.35,.5,.65,.75,.9])[bins]; ws=np.array([.09,.065,.06,.055,.055])[bins]\ntop=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sr=r['user'][st:en]@r['item'].T; su=u['user'][st:en]@u['item'].T; sc=(1-gs[st:en,None])*sr+gs[st:en,None]*su+ws[st:en,None]*trend[None,:]; sc[~masks[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':itemids[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_activity_ensemble.parquet',index=False)\nprint(edges)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_activity_ensemble.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 17, "nanos": 444769644 }, "formatted_output": "[ 220. 554. 1084. 1989.]\r\n{\r\n \"model\": \"/app/solution/validation_activity_ensemble.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040792,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 120 · agent · 2026-09-25T01:03:35.651Z ### Thinking unavailable — no text recorded The stable activity calibration lifts the exact score to 0.040776: low-activity histories lean more on current catalog trends, while high-activity histories lean slightly more on personalized embeddings. I’m proceeding to the required final refit on train plus validation, reproducing both history views and using the observed validation week only as history/training data. ### exec · 2026-09-25T01:03:42.145Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nfrom pathlib import Path\\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\\nfor factor in [1]:\\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\\n print((c/t.recent_positive_listens).describe())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:03:43.310Z Script completed Wall time 1.2 seconds Output: SESSION_ID=67128 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8601cc087d28039b9260b2ca17e", "usage": { "input_tokens": 87027, "cached_input_tokens": 86656, "cache_write_input_tokens": 0, "output_tokens": 728, "reasoning_output_tokens": 366, "total_tokens": 87755 }, "turn_token_usage": { "input_tokens": 6363319, "cached_input_tokens": 6267264, "cache_write_input_tokens": 0, "output_tokens": 50941, "reasoning_output_tokens": 19370, "total_tokens": 6414260 }, "thread_token_usage": { "input_tokens": 6363319, "cached_input_tokens": 6267264, "cache_write_input_tokens": 0, "output_tokens": 50941, "reasoning_output_tokens": 19370, "total_tokens": 6414260 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ae3639dd-aa5b-4538-99d5-f8463b4f2057", "process_id": "67128", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\nfor factor in [1]:\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\n print((c/t.recent_positive_listens).describe())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); C=24786800; t=pd.read_parquet(D/'target_users.parquet').set_index('uid')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','played_ratio_pct'])\nx=x[(x.timestamp>=C-90*86400)&(x.played_ratio_pct>=50)&x.uid.isin(t.index)]\nc=x.groupby('uid').size().reindex(t.index,fill_value=0)\nfor factor in [1]:\n print('equal',np.mean(c.to_numpy()==t.recent_positive_listens.to_numpy()),'corr',np.corrcoef(c,t.recent_positive_listens)[0,1])\n print(pd.DataFrame({'calc':c,'given':t.recent_positive_listens}).head(20))\n print((c/t.recent_positive_listens).describe())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n", "stderr": "", "aggregated_output": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 309544433 }, "formatted_output": "equal 1.0 corr 1.0\r\n calc given\r\nuid \r\n100 844 844.0\r\n200 50 50.0\r\n300 168 168.0\r\n600 1985 1985.0\r\n700 1776 1776.0\r\n800 1703 1703.0\r\n900 42 42.0\r\n1000 525 525.0\r\n1100 1976 1976.0\r\n1400 782 782.0\r\n1500 570 570.0\r\n1600 2596 2596.0\r\n1700 1168 1168.0\r\n1900 2464 2464.0\r\n2000 460 460.0\r\n2100 866 866.0\r\n2200 961 961.0\r\n2300 1752 1752.0\r\n2400 313 313.0\r\n2500 935 935.0\r\ncount 8456.0\r\nmean 1.0\r\nstd 0.0\r\nmin 1.0\r\n25% 1.0\r\n50% 1.0\r\n75% 1.0\r\nmax 1.0\r\ndtype: float64\r\n" } } ### exec · 2026-09-25T01:03:45.836Z { "code": "const r = await tools.write_stdin({\"session_id\":67128,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:03:45.941Z Script completed Wall time 0.0 seconds Output: equal 1.0 corr 1.0 calc given uid 100 844 844.0 200 50 50.0 300 168 168.0 600 1985 1985.0 700 1776 1776.0 800 1703 1703.0 900 42 42.0 1000 525 525.0 1100 1976 1976.0 1400 782 782.0 1500 570 570.0 1600 2596 2596.0 1700 1168 1168.0 1900 2464 2464.0 2000 460 460.0 2100 866 866.0 2200 961 961.0 2300 1752 1752.0 2400 313 313.0 2500 935 935.0 count 8456.0 mean 1.0 std 0.0 min 1.0 25% 1.0 50% 1.0 75% 1.0 max 1.0 dtype: float64 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c86f7f6487d29c76463ae9cdb3b5", "usage": { "input_tokens": 87783, "cached_input_tokens": 86912, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 87828 }, "turn_token_usage": { "input_tokens": 6451102, "cached_input_tokens": 6354176, "cache_write_input_tokens": 0, "output_tokens": 50986, "reasoning_output_tokens": 19370, "total_tokens": 6502088 }, "thread_token_usage": { "input_tokens": 6451102, "cached_input_tokens": 6354176, "cache_write_input_tokens": 0, "output_tokens": 50986, "reasoning_output_tokens": 19370, "total_tokens": 6502088 } } ## Step 121 · agent · 2026-09-25T01:03:56.599Z ### Thinking unavailable — no text recorded ## Step 122 · agent · 2026-09-25T01:04:04.698Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:04:10.222Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntop=np.empty((len(uids),100),np.int32)\\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\\nprint('baseline submission saved',len(uids)*100)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:04:11.390Z Script completed Wall time 1.2 seconds Output: SESSION_ID=59692 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c872549087d29620391d7cde653a", "usage": { "input_tokens": 88164, "cached_input_tokens": 87552, "cache_write_input_tokens": 0, "output_tokens": 1275, "reasoning_output_tokens": 960, "total_tokens": 89439 }, "turn_token_usage": { "input_tokens": 6539266, "cached_input_tokens": 6441728, "cache_write_input_tokens": 0, "output_tokens": 52261, "reasoning_output_tokens": 20330, "total_tokens": 6591527 }, "thread_token_usage": { "input_tokens": 6539266, "cached_input_tokens": 6441728, "cache_write_input_tokens": 0, "output_tokens": 52261, "reasoning_output_tokens": 20330, "total_tokens": 6591527 } } ### exec · 2026-09-25T01:04:13.401Z { "code": "const r = await tools.write_stdin({\"session_id\":59692,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:04:14.909Z Script completed Wall time 1.5 seconds Output: Traceback (most recent call last): File "", line 3, in File "/app/solution/train_ranker.py", line 540, in recent_trend x = load_family(stage, family, cols) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/solution/train_ranker.py", line 56, in load_family frame.sort_values(["uid", "timestamp"], kind="stable", inplace=True) File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py", line 7194, in sort_values keys = [self._get_label_or_level_values(x, axis=axis) for x in by] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py", line 1914, in _get_label_or_level_values raise KeyError(key) KeyError: 'uid' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c88b97d087d287ab94281a1c609d", "usage": { "input_tokens": 89467, "cached_input_tokens": 87936, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 89512 }, "turn_token_usage": { "input_tokens": 6628733, "cached_input_tokens": 6529664, "cache_write_input_tokens": 0, "output_tokens": 52306, "reasoning_output_tokens": 20330, "total_tokens": 6681039 }, "thread_token_usage": { "input_tokens": 6628733, "cached_input_tokens": 6529664, "cache_write_input_tokens": 0, "output_tokens": 52306, "reasoning_output_tokens": 20330, "total_tokens": 6681039 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-125729d5-8fa9-437c-96ab-8b37b47a07af", "process_id": "59692", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n", "exit_code": 1, "duration": { "secs": 4, "nanos": 518335932 }, "formatted_output": "Traceback (most recent call last):\r\n File \"\", line 3, in \r\n File \"/app/solution/train_ranker.py\", line 540, in recent_trend\r\n x = load_family(stage, family, cols)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_ranker.py\", line 56, in load_family\r\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 7194, in sort_values\r\n keys = [self._get_label_or_level_values(x, axis=axis) for x in by]\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/generic.py\", line 1914, in _get_label_or_level_values\r\n raise KeyError(key)\r\nKeyError: 'uid'\r\n" } } ## Step 123 · agent · 2026-09-25T01:04:17.621Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:04:19.356Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n- if stage == \\\"final\\\":\\n+ if stage == \\\"final\\\" and \\\"uid\\\" in frame.columns:\\n frame.sort_values([\\\"uid\\\", \\\"timestamp\\\"], kind=\\\"stable\\\", inplace=True)\\n frame.reset_index(drop=True, inplace=True)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:04:19.513Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-d6cecd2f-56e4-4795-999b-507d8805a9a9", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -54,3 +54,3 @@\n # Each source is uid/time sorted, but concatenating the two windows is not.\n- if stage == \"final\":\n+ if stage == \"final\" and \"uid\" in frame.columns:\n frame.sort_values([\"uid\", \"timestamp\"], kind=\"stable\", inplace=True)\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": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c88f1ac487d2ada589877eb805b3", "usage": { "input_tokens": 89742, "cached_input_tokens": 89344, "cache_write_input_tokens": 0, "output_tokens": 175, "reasoning_output_tokens": 70, "total_tokens": 89917 }, "turn_token_usage": { "input_tokens": 6718475, "cached_input_tokens": 6619008, "cache_write_input_tokens": 0, "output_tokens": 52481, "reasoning_output_tokens": 20400, "total_tokens": 6770956 }, "thread_token_usage": { "input_tokens": 6718475, "cached_input_tokens": 6619008, "cache_write_input_tokens": 0, "output_tokens": 52481, "reasoning_output_tokens": 20400, "total_tokens": 6770956 } } ## Step 124 · agent · 2026-09-25T01:04:21.332Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:04:26.895Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntop=np.empty((len(uids),100),np.int32)\\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\\nprint('baseline submission saved',len(uids)*100)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:04:28.059Z Script completed Wall time 1.2 seconds Output: SESSION_ID=60400 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c894186487d28c5b35fcb68cf716", "usage": { "input_tokens": 89941, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 322, "reasoning_output_tokens": 7, "total_tokens": 90263 }, "turn_token_usage": { "input_tokens": 6808416, "cached_input_tokens": 6708608, "cache_write_input_tokens": 0, "output_tokens": 52803, "reasoning_output_tokens": 20407, "total_tokens": 6861219 }, "thread_token_usage": { "input_tokens": 6808416, "cached_input_tokens": 6708608, "cache_write_input_tokens": 0, "output_tokens": 52803, "reasoning_output_tokens": 20407, "total_tokens": 6861219 } } ### exec · 2026-09-25T01:04:29.904Z { "code": "const r = await tools.write_stdin({\"session_id\":60400,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:04:33.246Z Script completed Wall time 3.3 seconds Output: baseline submission saved 845600 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c89c3f6c87d2be229b2f09bd01b7", "usage": { "input_tokens": 90291, "cached_input_tokens": 89728, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 90336 }, "turn_token_usage": { "input_tokens": 6898707, "cached_input_tokens": 6798336, "cache_write_input_tokens": 0, "output_tokens": 52848, "reasoning_output_tokens": 20407, "total_tokens": 6951555 }, "thread_token_usage": { "input_tokens": 6898707, "cached_input_tokens": 6798336, "cache_write_input_tokens": 0, "output_tokens": 52848, "reasoning_output_tokens": 20407, "total_tokens": 6951555 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4509267e-0cf8-4cc7-94ae-4a8721ce5336", "process_id": "60400", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\ncat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); trend=tr.recent_trend('final',cat.item_id.to_numpy(dtype=np.int64)); order=np.argsort(-trend)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntop=np.empty((len(uids),100),np.int32)\nfor r in range(len(uids)): top[r]=order[mask[r,order]][:100]\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet('submission.parquet',index=False)\nprint('baseline submission saved',len(uids)*100)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "baseline submission saved 845600\r\n", "stderr": "", "aggregated_output": "baseline submission saved 845600\r\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 186188691 }, "formatted_output": "baseline submission saved 845600\r\n" } } ## Step 125 · agent · 2026-09-25T01:04:44.131Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:22.423Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Refit the selected two-tower ensemble and write the hidden-test ranking.\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import copy\\n+import json\\n+import time\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import torch\\n+\\n+import train_ranker as tr\\n+\\n+\\n+DIM = 48\\n+WEEKS = 30\\n+RAW_MIX = 0.35\\n+UNIQUE_MIX = 0.65\\n+# Coefficients selected on all-target activity quintiles in the public backtest.\\n+TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\\n+\\n+\\n+def save_model(model: tr.TwoTower, path, features: np.ndarray, catalog_ids: np.ndarray, mode: str) -> None:\\n+ torch.save(\\n+ {\\n+ \\\"state_dict\\\": model.state_dict(),\\n+ \\\"dim\\\": DIM,\\n+ \\\"item_features\\\": features,\\n+ \\\"catalog_ids\\\": catalog_ids,\\n+ \\\"history_mode\\\": mode,\\n+ \\\"architecture\\\": \\\"history/preference MLP user tower + ID/feature MLP item tower\\\",\\n+ },\\n+ path,\\n+ )\\n+\\n+\\n+def final_activity(targets: np.ndarray) -> np.ndarray:\\n+ \\\"\\\"\\\"Cutoff-correct 90-day conventional-positive counts for calibration.\\\"\\\"\\\"\\n+ columns = [\\\"uid\\\", \\\"timestamp\\\", \\\"played_ratio_pct\\\"]\\n+ x = pd.concat(\\n+ [\\n+ pd.read_parquet(tr.DATA / \\\"train_listens.parquet\\\", columns=columns),\\n+ pd.read_parquet(tr.DATA / \\\"val_listens.parquet\\\", columns=columns),\\n+ ],\\n+ ignore_index=True,\\n+ )\\n+ keep = (\\n+ (x.timestamp.to_numpy() >= tr.VALIDATION_CUTOFF - 90 * 86400)\\n+ & (x.timestamp.to_numpy() < tr.VALIDATION_CUTOFF)\\n+ & (x.played_ratio_pct.to_numpy() >= 50)\\n+ )\\n+ counts = x.loc[keep].groupby(\\\"uid\\\").size()\\n+ return counts.reindex(targets, fill_value=0).to_numpy(dtype=np.float32)\\n+\\n+\\n+def main() -> None:\\n+ started = time.time()\\n+ catalog = pd.read_parquet(tr.DATA / \\\"candidate_catalog.parquet\\\")\\n+ catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\\n+ features = tr.item_features(catalog)\\n+\\n+ raw_cache = tr.OUT / f\\\"snapshots_final_{WEEKS}w_raw.npz\\\"\\n+ if raw_cache.exists():\\n+ raw_snapshots = tr.SnapshotArrays.load(raw_cache)\\n+ else:\\n+ raw_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=False)\\n+ raw_snapshots.save(raw_cache)\\n+ raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\\n+ raw_model = tr.train_model(\\n+ raw_snapshots,\\n+ features,\\n+ DIM,\\n+ 10,\\n+ 20261010,\\n+ initial_model=raw_model,\\n+ learning_rate=1e-3,\\n+ )\\n+ save_model(raw_model, tr.OUT / \\\"two_tower_final_raw.pt\\\", features, catalog_ids, \\\"raw\\\")\\n+ raw_users = tr.build_inference_users(\\\"final\\\", catalog_ids, unique_history=False)\\n+ raw_user_embedding, raw_item_embedding = tr.encode(raw_model, raw_users, features)\\n+ del raw_snapshots\\n+\\n+ unique_cache = tr.OUT / f\\\"snapshots_final_{WEEKS}w_unique.npz\\\"\\n+ if unique_cache.exists():\\n+ unique_snapshots = tr.SnapshotArrays.load(unique_cache)\\n+ else:\\n+ unique_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=True)\\n+ unique_snapshots.save(unique_cache)\\n+ unique_model = copy.deepcopy(raw_model)\\n+ unique_model = tr.train_model(\\n+ unique_snapshots,\\n+ features,\\n+ DIM,\\n+ 10,\\n+ 20261030,\\n+ initial_model=unique_model,\\n+ learning_rate=1e-3,\\n+ )\\n+ save_model(unique_model, tr.OUT / \\\"two_tower_final_unique.pt\\\", features, catalog_ids, \\\"unique\\\")\\n+ unique_users = tr.build_inference_users(\\\"final\\\", catalog_ids, unique_history=True)\\n+ unique_user_embedding, unique_item_embedding = tr.encode(unique_model, unique_users, features)\\n+\\n+ targets = raw_users[0]\\n+ assert np.array_equal(targets, unique_users[0])\\n+ activity = final_activity(targets)\\n+ activity_edges = np.quantile(activity, [0.2, 0.4, 0.6, 0.8])\\n+ activity_bin = np.searchsorted(activity_edges, activity, side=\\\"right\\\")\\n+ trend_weight = TREND_BY_ACTIVITY[activity_bin]\\n+ trend = tr.recent_trend(\\\"final\\\", catalog_ids)\\n+\\n+ # These are the actual request-time/offline MIPS vectors. Their dot product\\n+ # is exactly the deployed relevance score—there is no cross feature or\\n+ # post-retrieval model.\\n+ user_vector = np.concatenate(\\n+ [\\n+ RAW_MIX * raw_user_embedding,\\n+ UNIQUE_MIX * unique_user_embedding,\\n+ trend_weight[:, None],\\n+ ],\\n+ axis=1,\\n+ ).astype(np.float32)\\n+ item_vector = np.concatenate(\\n+ [raw_item_embedding, unique_item_embedding, trend[:, None]], axis=1\\n+ ).astype(np.float32)\\n+ np.savez_compressed(\\n+ tr.OUT / \\\"deployment_vectors.npz\\\",\\n+ uids=targets,\\n+ item_ids=catalog_ids,\\n+ user_vectors=user_vector,\\n+ item_vectors=item_vector,\\n+ )\\n+\\n+ with np.load(tr.DATA / \\\"test_eligible_candidates.npz\\\") as z:\\n+ eligible_uids = z[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = z[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = z[\\\"packed_eligible\\\"].copy()\\n+ assert np.array_equal(targets, eligible_uids)\\n+ assert np.array_equal(catalog_ids, eligible_items)\\n+ masks = np.unpackbits(\\n+ packed, axis=1, count=len(eligible_items), bitorder=\\\"little\\\"\\n+ ).astype(bool)\\n+ top = np.empty((len(targets), 100), dtype=np.int32)\\n+ for start in range(0, len(targets), 128):\\n+ end = min(start + 128, len(targets))\\n+ score = user_vector[start:end] @ item_vector.T\\n+ score[~masks[start:end]] = -np.inf\\n+ part = np.argpartition(score, -100, axis=1)[:, -100:]\\n+ selected_score = np.take_along_axis(score, part, axis=1)\\n+ top[start:end] = np.take_along_axis(\\n+ part, np.argsort(-selected_score, axis=1), axis=1\\n+ )\\n+\\n+ submission = pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(targets, 100).astype(np.int64),\\n+ \\\"item_id\\\": eligible_items[top.reshape(-1)].astype(np.int64),\\n+ \\\"rank\\\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\\n+ }\\n+ )\\n+ # Atomic replacement keeps the previously saved valid baseline intact\\n+ # until the complete refit ranking has been written successfully.\\n+ temporary = tr.OUT / \\\"submission.refit.parquet\\\"\\n+ submission.to_parquet(temporary, index=False)\\n+ temporary.replace(tr.OUT / \\\"submission.parquet\\\")\\n+\\n+ report = {\\n+ \\\"model\\\": \\\"97d concatenated two-tower MIPS ensemble\\\",\\n+ \\\"raw_examples\\\": int(len(raw_snapshots)) if False else None,\\n+ \\\"unique_examples\\\": int(len(unique_snapshots.history)),\\n+ \\\"activity_edges\\\": activity_edges.tolist(),\\n+ \\\"trend_weights\\\": TREND_BY_ACTIVITY.tolist(),\\n+ \\\"rows\\\": int(len(submission)),\\n+ \\\"seconds\\\": round(time.time() - started, 1),\\n+ }\\n+ (tr.OUT / \\\"final_report.json\\\").write_text(json.dumps(report, indent=2) + \\\"\\\\n\\\")\\n+ print(json.dumps(report, indent=2), flush=True)\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:05:22.522Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-4709cbee-594c-4b7c-852f-d277de454bd6", "changes": { "/app/solution/build_submission.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Refit the selected two-tower ensemble and write the hidden-test ranking.\"\"\"\nfrom __future__ import annotations\n\nimport copy\nimport json\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nimport train_ranker as tr\n\n\nDIM = 48\nWEEKS = 30\nRAW_MIX = 0.35\nUNIQUE_MIX = 0.65\n# Coefficients selected on all-target activity quintiles in the public backtest.\nTREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\n\n\ndef save_model(model: tr.TwoTower, path, features: np.ndarray, catalog_ids: np.ndarray, mode: str) -> None:\n torch.save(\n {\n \"state_dict\": model.state_dict(),\n \"dim\": DIM,\n \"item_features\": features,\n \"catalog_ids\": catalog_ids,\n \"history_mode\": mode,\n \"architecture\": \"history/preference MLP user tower + ID/feature MLP item tower\",\n },\n path,\n )\n\n\ndef final_activity(targets: np.ndarray) -> np.ndarray:\n \"\"\"Cutoff-correct 90-day conventional-positive counts for calibration.\"\"\"\n columns = [\"uid\", \"timestamp\", \"played_ratio_pct\"]\n x = pd.concat(\n [\n pd.read_parquet(tr.DATA / \"train_listens.parquet\", columns=columns),\n pd.read_parquet(tr.DATA / \"val_listens.parquet\", columns=columns),\n ],\n ignore_index=True,\n )\n keep = (\n (x.timestamp.to_numpy() >= tr.VALIDATION_CUTOFF - 90 * 86400)\n & (x.timestamp.to_numpy() < tr.VALIDATION_CUTOFF)\n & (x.played_ratio_pct.to_numpy() >= 50)\n )\n counts = x.loc[keep].groupby(\"uid\").size()\n return counts.reindex(targets, fill_value=0).to_numpy(dtype=np.float32)\n\n\ndef main() -> None:\n started = time.time()\n catalog = pd.read_parquet(tr.DATA / \"candidate_catalog.parquet\")\n catalog_ids = catalog.item_id.to_numpy(dtype=np.int64)\n features = tr.item_features(catalog)\n\n raw_cache = tr.OUT / f\"snapshots_final_{WEEKS}w_raw.npz\"\n if raw_cache.exists():\n raw_snapshots = tr.SnapshotArrays.load(raw_cache)\n else:\n raw_snapshots = tr.build_snapshots(\"final\", catalog_ids, WEEKS, unique_history=False)\n raw_snapshots.save(raw_cache)\n raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\n raw_model = tr.train_model(\n raw_snapshots,\n features,\n DIM,\n 10,\n 20261010,\n initial_model=raw_model,\n learning_rate=1e-3,\n )\n save_model(raw_model, tr.OUT / \"two_tower_final_raw.pt\", features, catalog_ids, \"raw\")\n raw_users = tr.build_inference_users(\"final\", catalog_ids, unique_history=False)\n raw_user_embedding, raw_item_embedding = tr.encode(raw_model, raw_users, features)\n del raw_snapshots\n\n unique_cache = tr.OUT / f\"snapshots_final_{WEEKS}w_unique.npz\"\n if unique_cache.exists():\n unique_snapshots = tr.SnapshotArrays.load(unique_cache)\n else:\n unique_snapshots = tr.build_snapshots(\"final\", catalog_ids, WEEKS, unique_history=True)\n unique_snapshots.save(unique_cache)\n unique_model = copy.deepcopy(raw_model)\n unique_model = tr.train_model(\n unique_snapshots,\n features,\n DIM,\n 10,\n 20261030,\n initial_model=unique_model,\n learning_rate=1e-3,\n )\n save_model(unique_model, tr.OUT / \"two_tower_final_unique.pt\", features, catalog_ids, \"unique\")\n unique_users = tr.build_inference_users(\"final\", catalog_ids, unique_history=True)\n unique_user_embedding, unique_item_embedding = tr.encode(unique_model, unique_users, features)\n\n targets = raw_users[0]\n assert np.array_equal(targets, unique_users[0])\n activity = final_activity(targets)\n activity_edges = np.quantile(activity, [0.2, 0.4, 0.6, 0.8])\n activity_bin = np.searchsorted(activity_edges, activity, side=\"right\")\n trend_weight = TREND_BY_ACTIVITY[activity_bin]\n trend = tr.recent_trend(\"final\", catalog_ids)\n\n # These are the actual request-time/offline MIPS vectors. Their dot product\n # is exactly the deployed relevance score—there is no cross feature or\n # post-retrieval model.\n user_vector = np.concatenate(\n [\n RAW_MIX * raw_user_embedding,\n UNIQUE_MIX * unique_user_embedding,\n trend_weight[:, None],\n ],\n axis=1,\n ).astype(np.float32)\n item_vector = np.concatenate(\n [raw_item_embedding, unique_item_embedding, trend[:, None]], axis=1\n ).astype(np.float32)\n np.savez_compressed(\n tr.OUT / \"deployment_vectors.npz\",\n uids=targets,\n item_ids=catalog_ids,\n user_vectors=user_vector,\n item_vectors=item_vector,\n )\n\n with np.load(tr.DATA / \"test_eligible_candidates.npz\") as z:\n eligible_uids = z[\"uids\"].astype(np.int64, copy=True)\n eligible_items = z[\"item_ids\"].astype(np.int64, copy=True)\n packed = z[\"packed_eligible\"].copy()\n assert np.array_equal(targets, eligible_uids)\n assert np.array_equal(catalog_ids, eligible_items)\n masks = np.unpackbits(\n packed, axis=1, count=len(eligible_items), bitorder=\"little\"\n ).astype(bool)\n top = np.empty((len(targets), 100), dtype=np.int32)\n for start in range(0, len(targets), 128):\n end = min(start + 128, len(targets))\n score = user_vector[start:end] @ item_vector.T\n score[~masks[start:end]] = -np.inf\n part = np.argpartition(score, -100, axis=1)[:, -100:]\n selected_score = np.take_along_axis(score, part, axis=1)\n top[start:end] = np.take_along_axis(\n part, np.argsort(-selected_score, axis=1), axis=1\n )\n\n submission = pd.DataFrame(\n {\n \"uid\": np.repeat(targets, 100).astype(np.int64),\n \"item_id\": eligible_items[top.reshape(-1)].astype(np.int64),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), len(targets)),\n }\n )\n # Atomic replacement keeps the previously saved valid baseline intact\n # until the complete refit ranking has been written successfully.\n temporary = tr.OUT / \"submission.refit.parquet\"\n submission.to_parquet(temporary, index=False)\n temporary.replace(tr.OUT / \"submission.parquet\")\n\n report = {\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\n \"raw_examples\": int(len(raw_snapshots)) if False else None,\n \"unique_examples\": int(len(unique_snapshots.history)),\n \"activity_edges\": activity_edges.tolist(),\n \"trend_weights\": TREND_BY_ACTIVITY.tolist(),\n \"rows\": int(len(submission)),\n \"seconds\": round(time.time() - started, 1),\n }\n (tr.OUT / \"final_report.json\").write_text(json.dumps(report, indent=2) + \"\\n\")\n print(json.dumps(report, indent=2), flush=True)\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_submission.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8a1756887d2982badc4d9f25797", "usage": { "input_tokens": 90366, "cached_input_tokens": 90112, "cache_write_input_tokens": 0, "output_tokens": 2652, "reasoning_output_tokens": 516, "total_tokens": 93018 }, "turn_token_usage": { "input_tokens": 6989073, "cached_input_tokens": 6888448, "cache_write_input_tokens": 0, "output_tokens": 55500, "reasoning_output_tokens": 20923, "total_tokens": 7044573 }, "thread_token_usage": { "input_tokens": 6989073, "cached_input_tokens": 6888448, "cache_write_input_tokens": 0, "output_tokens": 55500, "reasoning_output_tokens": 20923, "total_tokens": 7044573 } } ## Step 126 · agent · 2026-09-25T01:05:25.203Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:27.439Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_submission.py\\n@@\\n else:\\n raw_snapshots = tr.build_snapshots(\\\"final\\\", catalog_ids, WEEKS, unique_history=False)\\n raw_snapshots.save(raw_cache)\\n+ raw_example_count = len(raw_snapshots.history)\\n@@\\n- \\\"raw_examples\\\": int(len(raw_snapshots)) if False else None,\\n+ \\\"raw_examples\\\": int(raw_example_count),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:05:27.575Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ab39b1c0-3924-4685-b699-29bddd6ea8f2", "changes": { "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -68,2 +68,3 @@\n raw_snapshots.save(raw_cache)\n+ raw_example_count = len(raw_snapshots.history)\n raw_model = tr.train_model(raw_snapshots, features, DIM, 15, 20260926)\n@@ -168,3 +169,3 @@\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\n- \"raw_examples\": int(len(raw_snapshots)) if False else None,\n+ \"raw_examples\": int(raw_example_count),\n \"unique_examples\": int(len(unique_snapshots.history)),\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_submission.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8d2d76487d299b583aef049e4e3", "usage": { "input_tokens": 93042, "cached_input_tokens": 90240, "cache_write_input_tokens": 0, "output_tokens": 197, "reasoning_output_tokens": 67, "total_tokens": 93239 }, "turn_token_usage": { "input_tokens": 7082115, "cached_input_tokens": 6978688, "cache_write_input_tokens": 0, "output_tokens": 55697, "reasoning_output_tokens": 20990, "total_tokens": 7137812 }, "thread_token_usage": { "input_tokens": 7082115, "cached_input_tokens": 6978688, "cache_write_input_tokens": 0, "output_tokens": 55697, "reasoning_output_tokens": 20990, "total_tokens": 7137812 } } ### exec · 2026-09-25T01:05:30.287Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:05:30.455Z Script completed Wall time 0.2 seconds Output: -rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8d7e9dc87d28a53dd92c49ada32", "usage": { "input_tokens": 93263, "cached_input_tokens": 92800, "cache_write_input_tokens": 0, "output_tokens": 84, "reasoning_output_tokens": 0, "total_tokens": 93347 }, "turn_token_usage": { "input_tokens": 7175378, "cached_input_tokens": 7071488, "cache_write_input_tokens": 0, "output_tokens": 55781, "reasoning_output_tokens": 20990, "total_tokens": 7231159 }, "thread_token_usage": { "input_tokens": 7175378, "cached_input_tokens": 7071488, "cache_write_input_tokens": 0, "output_tokens": 55781, "reasoning_output_tokens": 20990, "total_tokens": 7231159 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e62a0bb8-b343-4df8-95af-9a5d70d8079a", "process_id": "41275", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_ranker.py /app/solution/build_submission.py && ls -lh /app/solution/submission.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n", "stderr": "", "aggregated_output": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4031 }, "formatted_output": "-rw-r--r-- 1 agent agent 642K Sep 25 01:04 /app/solution/submission.parquet\n" } } ## Step 127 · agent · 2026-09-25T01:05:33.458Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:34.833Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u build_submission.py\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:05:35.999Z Script completed Wall time 1.2 seconds Output: SESSION_ID=54733 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8daaea887d2be15017e12a09c85", "usage": { "input_tokens": 93399, "cached_input_tokens": 93056, "cache_write_input_tokens": 0, "output_tokens": 100, "reasoning_output_tokens": 15, "total_tokens": 93499 }, "turn_token_usage": { "input_tokens": 7268777, "cached_input_tokens": 7164544, "cache_write_input_tokens": 0, "output_tokens": 55881, "reasoning_output_tokens": 21005, "total_tokens": 7324658 }, "thread_token_usage": { "input_tokens": 7268777, "cached_input_tokens": 7164544, "cache_write_input_tokens": 0, "output_tokens": 55881, "reasoning_output_tokens": 21005, "total_tokens": 7324658 } } ## Step 128 · agent · 2026-09-25T01:05:37.488Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:38.164Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:06:08.207Z Script completed Wall time 30.0 seconds Output: loading final events for 30 rolling cutoffs users 4000/9982 examples=39993 users 6000/9982 examples=59796 users 8000/9982 examples=80043 built 100,053 examples, 846,369 relevant pairs ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c8e03d1887d291ada84315ccda15", "usage": { "input_tokens": 93527, "cached_input_tokens": 93184, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93580 }, "turn_token_usage": { "input_tokens": 7362304, "cached_input_tokens": 7257728, "cache_write_input_tokens": 0, "output_tokens": 55934, "reasoning_output_tokens": 21011, "total_tokens": 7418238 }, "thread_token_usage": { "input_tokens": 7362304, "cached_input_tokens": 7257728, "cache_write_input_tokens": 0, "output_tokens": 55934, "reasoning_output_tokens": 21011, "total_tokens": 7418238 } } ## Step 129 · agent · 2026-09-25T01:06:09.545Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:06:10.211Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:06:40.235Z Script completed Wall time 30.0 seconds Output: epoch 1/15 loss=6.0187 scale=11.10 epoch 2/15 loss=5.9142 scale=11.08 epoch 3/15 loss=5.8730 scale=11.08 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c900650487d2851f664b4fbf87c4", "usage": { "input_tokens": 93666, "cached_input_tokens": 93312, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 93721 }, "turn_token_usage": { "input_tokens": 7455970, "cached_input_tokens": 7351040, "cache_write_input_tokens": 0, "output_tokens": 55989, "reasoning_output_tokens": 21019, "total_tokens": 7511959 }, "thread_token_usage": { "input_tokens": 7455970, "cached_input_tokens": 7351040, "cache_write_input_tokens": 0, "output_tokens": 55989, "reasoning_output_tokens": 21019, "total_tokens": 7511959 } } ## Step 130 · agent · 2026-09-25T01:06:41.606Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:06:42.284Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:07:12.294Z Script completed Wall time 30.0 seconds Output: epoch 4/15 loss=5.8222 scale=11.10 epoch 5/15 loss=5.7566 scale=11.16 epoch 6/15 loss=5.6760 scale=11.21 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c92073c087d29486de49af705298", "usage": { "input_tokens": 93795, "cached_input_tokens": 93440, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 93849 }, "turn_token_usage": { "input_tokens": 7549765, "cached_input_tokens": 7444480, "cache_write_input_tokens": 0, "output_tokens": 56043, "reasoning_output_tokens": 21026, "total_tokens": 7605808 }, "thread_token_usage": { "input_tokens": 7549765, "cached_input_tokens": 7444480, "cache_write_input_tokens": 0, "output_tokens": 56043, "reasoning_output_tokens": 21026, "total_tokens": 7605808 } } ## Step 131 · agent · 2026-09-25T01:07:13.587Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:07:14.289Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:07:44.299Z Script completed Wall time 30.0 seconds Output: epoch 7/15 loss=5.5735 scale=11.40 epoch 8/15 loss=5.4604 scale=11.78 epoch 9/15 loss=5.3497 scale=12.54 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c94080e087d281fd393bde56e60e", "usage": { "input_tokens": 93923, "cached_input_tokens": 93568, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 93976 }, "turn_token_usage": { "input_tokens": 7643688, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 56096, "reasoning_output_tokens": 21032, "total_tokens": 7699784 }, "thread_token_usage": { "input_tokens": 7643688, "cached_input_tokens": 7538048, "cache_write_input_tokens": 0, "output_tokens": 56096, "reasoning_output_tokens": 21032, "total_tokens": 7699784 } } ## Step 132 · agent · 2026-09-25T01:07:50.778Z ### Thinking unavailable — no text recorded The final refit has 100,053 rolling history examples and 846,369 future-relevance pairs, including the now-observed validation week. Training is progressing normally; the original trend-only test submission remains safely in place until the 97-dimensional ensemble ranking is atomically complete. ### exec · 2026-09-25T01:07:50.804Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:08:20.811Z Script completed Wall time 30.0 seconds Output: epoch 10/15 loss=5.2422 scale=13.62 epoch 11/15 loss=5.1397 scale=14.86 epoch 12/15 loss=5.0393 scale=16.28 epoch 13/15 loss=4.9384 scale=17.68 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c96082bc87d28cbce5d0f16348b0", "usage": { "input_tokens": 94050, "cached_input_tokens": 93696, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 6, "total_tokens": 94167 }, "turn_token_usage": { "input_tokens": 7737738, "cached_input_tokens": 7631744, "cache_write_input_tokens": 0, "output_tokens": 56213, "reasoning_output_tokens": 21038, "total_tokens": 7793951 }, "thread_token_usage": { "input_tokens": 7737738, "cached_input_tokens": 7631744, "cache_write_input_tokens": 0, "output_tokens": 56213, "reasoning_output_tokens": 21038, "total_tokens": 7793951 } } ## Step 133 · agent · 2026-09-25T01:08:22.204Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:08:22.889Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:08:52.900Z Script completed Wall time 30.0 seconds Output: epoch 14/15 loss=4.8527 scale=18.79 epoch 15/15 loss=4.7554 scale=20.03 epoch 1/10 loss=4.6227 scale=20.94 epoch 2/10 loss=4.5839 scale=21.48 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c98510e087d2a99559f8b8923c12", "usage": { "input_tokens": 94258, "cached_input_tokens": 93824, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 94311 }, "turn_token_usage": { "input_tokens": 7831996, "cached_input_tokens": 7725568, "cache_write_input_tokens": 0, "output_tokens": 56266, "reasoning_output_tokens": 21044, "total_tokens": 7888262 }, "thread_token_usage": { "input_tokens": 7831996, "cached_input_tokens": 7725568, "cache_write_input_tokens": 0, "output_tokens": 56266, "reasoning_output_tokens": 21044, "total_tokens": 7888262 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-76843ead-3fa0-48df-b001-08ca9f2dc248", "process_id": "96937", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\n# variants intrinsic catalog columns\norders={\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\n}\nfor name,order in orders.items():\n out=[]\n for r,u in enumerate(t):\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\n take=order[mask[order]][:100]\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\n print(p)\nPY\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nt=pd.read_parquet(D/'target_users.parquet').uid.astype(np.int64).to_numpy()\ncat=pd.read_parquet(D/'candidate_catalog.parquet')\nz=np.load(D/'validation_eligible_candidates.npz'); assert np.array_equal(t,z['uids'].astype(np.int64))\n# variants intrinsic catalog columns\norders={\n 'pop':np.argsort(cat.popularity_rank.to_numpy()),\n 'organic_pop':np.argsort(-cat.organic_positive_listens.to_numpy()),\n 'organic_share':np.argsort(-(cat.organic_positive_listens.to_numpy()+10)/(cat.positive_listens.to_numpy()+20)),\n}\nfor name,order in orders.items():\n out=[]\n for r,u in enumerate(t):\n mask=np.unpackbits(z['packed_eligible'][r],count=len(cat),bitorder='little').astype(bool)\n take=order[mask[order]][:100]\n out.append(pd.DataFrame({'uid':u,'item_id':z['item_ids'][take].astype(np.int64),'rank':np.arange(1,101,dtype=np.int64)}))\n p=O/f'validation_{name}.parquet'; pd.concat(out,ignore_index=True).to_parquet(p,index=False)\n print(p)\nPY\nfor f in /app/solution/validation_pop.parquet /app/solution/validation_organic_pop.parquet /app/solution/validation_organic_share.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2455, "nanos": 513734558 }, "formatted_output": "/app/solution/validation_pop.parquet\n/app/solution/validation_organic_pop.parquet\n/app/solution/validation_organic_share.parquet\n{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 1.5e-05,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_organic_share.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010695,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 134 · agent · 2026-09-25T01:08:54.403Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:08:55.093Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:09:25.106Z Script completed Wall time 30.0 seconds Output: epoch 3/10 loss=4.5513 scale=22.05 epoch 4/10 loss=4.5253 scale=22.54 epoch 5/10 loss=4.4782 scale=23.09 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9a5236887d28854abf2e3e891fd", "usage": { "input_tokens": 94402, "cached_input_tokens": 94080, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 94458 }, "turn_token_usage": { "input_tokens": 7926398, "cached_input_tokens": 7819648, "cache_write_input_tokens": 0, "output_tokens": 56322, "reasoning_output_tokens": 21053, "total_tokens": 7982720 }, "thread_token_usage": { "input_tokens": 7926398, "cached_input_tokens": 7819648, "cache_write_input_tokens": 0, "output_tokens": 56322, "reasoning_output_tokens": 21053, "total_tokens": 7982720 } } ### exec · 2026-09-25T01:09:29.077Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:09:59.088Z Script completed Wall time 30.0 seconds Output: epoch 6/10 loss=4.4576 scale=23.43 epoch 7/10 loss=4.4255 scale=23.93 epoch 8/10 loss=4.3937 scale=24.35 epoch 9/10 loss=4.3684 scale=24.81 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9c54e5887d2802a9f783052b08a", "usage": { "input_tokens": 94532, "cached_input_tokens": 94208, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 94577 }, "turn_token_usage": { "input_tokens": 8020930, "cached_input_tokens": 7913856, "cache_write_input_tokens": 0, "output_tokens": 56367, "reasoning_output_tokens": 21053, "total_tokens": 8077297 }, "thread_token_usage": { "input_tokens": 8020930, "cached_input_tokens": 7913856, "cache_write_input_tokens": 0, "output_tokens": 56367, "reasoning_output_tokens": 21053, "total_tokens": 8077297 } } ### exec · 2026-09-25T01:10:01.071Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:10:31.083Z Script completed Wall time 30.0 seconds Output: epoch 10/10 loss=4.3407 scale=25.23 loading final events for 30 rolling cutoffs ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5c9e74bcc87d28bb6d18ece5b5112", "usage": { "input_tokens": 94668, "cached_input_tokens": 94336, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 94713 }, "turn_token_usage": { "input_tokens": 8115598, "cached_input_tokens": 8008192, "cache_write_input_tokens": 0, "output_tokens": 56412, "reasoning_output_tokens": 21053, "total_tokens": 8172010 }, "thread_token_usage": { "input_tokens": 8115598, "cached_input_tokens": 8008192, "cache_write_input_tokens": 0, "output_tokens": 56412, "reasoning_output_tokens": 21053, "total_tokens": 8172010 } } ## Step 135 · agent · 2026-09-25T01:10:33.475Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:10:34.165Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:11:04.190Z Script completed Wall time 30.0 seconds Output: users 4000/9982 examples=39993 users 6000/9982 examples=59796 users 8000/9982 examples=80043 built 100,053 examples, 846,369 relevant pairs ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca074cb487d2ad1226acb2f0ea2d", "usage": { "input_tokens": 94763, "cached_input_tokens": 94464, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 31, "total_tokens": 94841 }, "turn_token_usage": { "input_tokens": 8210361, "cached_input_tokens": 8102656, "cache_write_input_tokens": 0, "output_tokens": 56490, "reasoning_output_tokens": 21084, "total_tokens": 8266851 }, "thread_token_usage": { "input_tokens": 8210361, "cached_input_tokens": 8102656, "cache_write_input_tokens": 0, "output_tokens": 56490, "reasoning_output_tokens": 21084, "total_tokens": 8266851 } } ## Step 136 · agent · 2026-09-25T01:11:05.668Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:11:06.360Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:11:36.369Z Script completed Wall time 30.0 seconds Output: epoch 1/10 loss=4.5343 scale=25.23 epoch 2/10 loss=4.4967 scale=25.39 epoch 3/10 loss=4.4480 scale=25.93 epoch 4/10 loss=4.4139 scale=26.21 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca287a4c87d2b841d3e2436f08e8", "usage": { "input_tokens": 94917, "cached_input_tokens": 94592, "cache_write_input_tokens": 0, "output_tokens": 57, "reasoning_output_tokens": 10, "total_tokens": 94974 }, "turn_token_usage": { "input_tokens": 8305278, "cached_input_tokens": 8197248, "cache_write_input_tokens": 0, "output_tokens": 56547, "reasoning_output_tokens": 21094, "total_tokens": 8361825 }, "thread_token_usage": { "input_tokens": 8305278, "cached_input_tokens": 8197248, "cache_write_input_tokens": 0, "output_tokens": 56547, "reasoning_output_tokens": 21094, "total_tokens": 8361825 } } ## Step 137 · agent · 2026-09-25T01:11:37.943Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:11:38.642Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:12:08.653Z Script completed Wall time 30.0 seconds Output: epoch 5/10 loss=4.3815 scale=26.58 epoch 6/10 loss=4.3505 scale=27.12 epoch 7/10 loss=4.3163 scale=27.47 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca48a70887d28bd9680ade0215b6", "usage": { "input_tokens": 95065, "cached_input_tokens": 94720, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 95118 }, "turn_token_usage": { "input_tokens": 8400343, "cached_input_tokens": 8291968, "cache_write_input_tokens": 0, "output_tokens": 56600, "reasoning_output_tokens": 21100, "total_tokens": 8456943 }, "thread_token_usage": { "input_tokens": 8400343, "cached_input_tokens": 8291968, "cache_write_input_tokens": 0, "output_tokens": 56600, "reasoning_output_tokens": 21100, "total_tokens": 8456943 } } ## Step 138 · agent · 2026-09-25T01:12:10.072Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:12:10.753Z { "code": "const r = await tools.write_stdin({\"session_id\":54733,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:12:40.491Z Script completed Wall time 29.7 seconds Output: epoch 8/10 loss=4.2813 scale=27.97 epoch 9/10 loss=4.2593 scale=28.27 epoch 10/10 loss=4.2338 scale=28.76 { "model": "97d concatenated two-tower MIPS ensemble", "raw_examples": 100053, "unique_examples": 100053, "activity_edges": [ 242.0, 580.0, 1116.0, 2021.0 ], "trend_weights": [ 0.09000000357627869, 0.05999999865889549, 0.05999999865889549, 0.05999999865889549, 0.054999999701976776 ], "rows": 845600, "seconds": 421.5 } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca68df5487d297e0dbc104624a38", "usage": { "input_tokens": 95192, "cached_input_tokens": 94848, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 95245 }, "turn_token_usage": { "input_tokens": 8495535, "cached_input_tokens": 8386816, "cache_write_input_tokens": 0, "output_tokens": 56653, "reasoning_output_tokens": 21106, "total_tokens": 8552188 }, "thread_token_usage": { "input_tokens": 8495535, "cached_input_tokens": 8386816, "cache_write_input_tokens": 0, "output_tokens": 56653, "reasoning_output_tokens": 21106, "total_tokens": 8552188 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6bacbb60-086f-4416-abb6-70a74bccbe84", "process_id": "54733", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u build_submission.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u build_submission.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n", "stderr": "", "aggregated_output": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n", "exit_code": 0, "duration": { "secs": 425, "nanos": 492150204 }, "formatted_output": "loading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/15 loss=6.0187 scale=11.10\r\nepoch 2/15 loss=5.9142 scale=11.08\r\nepoch 3/15 loss=5.8730 scale=11.08\r\nepoch 4/15 loss=5.8222 scale=11.10\r\nepoch 5/15 loss=5.7566 scale=11.16\r\nepoch 6/15 loss=5.6760 scale=11.21\r\nepoch 7/15 loss=5.5735 scale=11.40\r\nepoch 8/15 loss=5.4604 scale=11.78\r\nepoch 9/15 loss=5.3497 scale=12.54\r\nepoch 10/15 loss=5.2422 scale=13.62\r\nepoch 11/15 loss=5.1397 scale=14.86\r\nepoch 12/15 loss=5.0393 scale=16.28\r\nepoch 13/15 loss=4.9384 scale=17.68\r\nepoch 14/15 loss=4.8527 scale=18.79\r\nepoch 15/15 loss=4.7554 scale=20.03\r\nepoch 1/10 loss=4.6227 scale=20.94\r\nepoch 2/10 loss=4.5839 scale=21.48\r\nepoch 3/10 loss=4.5513 scale=22.05\r\nepoch 4/10 loss=4.5253 scale=22.54\r\nepoch 5/10 loss=4.4782 scale=23.09\r\nepoch 6/10 loss=4.4576 scale=23.43\r\nepoch 7/10 loss=4.4255 scale=23.93\r\nepoch 8/10 loss=4.3937 scale=24.35\r\nepoch 9/10 loss=4.3684 scale=24.81\r\nepoch 10/10 loss=4.3407 scale=25.23\r\nloading final events for 30 rolling cutoffs\r\n users 4000/9982 examples=39993\r\n users 6000/9982 examples=59796\r\n users 8000/9982 examples=80043\r\nbuilt 100,053 examples, 846,369 relevant pairs\r\nepoch 1/10 loss=4.5343 scale=25.23\r\nepoch 2/10 loss=4.4967 scale=25.39\r\nepoch 3/10 loss=4.4480 scale=25.93\r\nepoch 4/10 loss=4.4139 scale=26.21\r\nepoch 5/10 loss=4.3815 scale=26.58\r\nepoch 6/10 loss=4.3505 scale=27.12\r\nepoch 7/10 loss=4.3163 scale=27.47\r\nepoch 8/10 loss=4.2813 scale=27.97\r\nepoch 9/10 loss=4.2593 scale=28.27\r\nepoch 10/10 loss=4.2338 scale=28.76\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.05999999865889549,\r\n 0.054999999701976776\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5\r\n}\r\n" } } ## Step 139 · agent · 2026-09-25T01:12:48.817Z ### Thinking unavailable — no text recorded The train+validation refit is complete, and the final 845,600-row submission has replaced the fallback atomically. I’m doing the last acceptance checks now: schema/ranks, per-user uniqueness, packed eligibility, and exact reproduction of every submitted order from the saved user/item dot-product vectors. ### exec · 2026-09-25T01:13:02.365Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task')\\nfrom ranking_evaluation import validate_ranking\\nD=Path('/task/data'); S=Path('/app/solution')\\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\\nwith np.load(D/'test_eligible_candidates.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\\nassert eligible.all()\\nwith np.load(S/'deployment_vectors.npz') as v:\\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\\nassert np.array_equal(submitted,reproduced)\\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\nls -lh /app/solution | sort -k5h | tail -25\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:13:03.537Z Script completed Wall time 1.2 seconds Output: SESSION_ID=45802 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca88af5c87d29644c14972a8fdab", "usage": { "input_tokens": 95469, "cached_input_tokens": 94976, "cache_write_input_tokens": 0, "output_tokens": 1151, "reasoning_output_tokens": 396, "total_tokens": 96620 }, "turn_token_usage": { "input_tokens": 8591004, "cached_input_tokens": 8481792, "cache_write_input_tokens": 0, "output_tokens": 57804, "reasoning_output_tokens": 21502, "total_tokens": 8648808 }, "thread_token_usage": { "input_tokens": 8591004, "cached_input_tokens": 8481792, "cache_write_input_tokens": 0, "output_tokens": 57804, "reasoning_output_tokens": 21502, "total_tokens": 8648808 } } ### exec · 2026-09-25T01:13:06.068Z { "code": "const r = await tools.write_stdin({\"session_id\":45802,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:13:09.584Z Script completed Wall time 3.5 seconds Output: {'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97} { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.040776, "quality_users": 4263 } } -rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet -rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet -rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy -rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy -rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz -rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz -rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz -rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz -rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz -rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz -rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz -rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt -rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt -rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt -rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt -rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt -rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt -rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt -rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt -rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz -rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz -rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz -rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ca9fb77c87d2babe39626660a22d", "usage": { "input_tokens": 96648, "cached_input_tokens": 95232, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 96693 }, "turn_token_usage": { "input_tokens": 8687652, "cached_input_tokens": 8577024, "cache_write_input_tokens": 0, "output_tokens": 57849, "reasoning_output_tokens": 21502, "total_tokens": 8745501 }, "thread_token_usage": { "input_tokens": 8687652, "cached_input_tokens": 8577024, "cache_write_input_tokens": 0, "output_tokens": 57849, "reasoning_output_tokens": 21502, "total_tokens": 8745501 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3d95be46-c8aa-4d97-9a6b-ed3eec77ccfe", "process_id": "45802", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); S=Path('/app/solution')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\nassert eligible.all()\nwith np.load(S/'deployment_vectors.npz') as v:\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -25" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_activity.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); S=Path('/app/solution')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(pd.read_parquet(S/'submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\nr=np.fromiter((uidrow[int(x)] for x in f.uid),dtype=np.int32,count=len(f)); c=np.fromiter((itemcol[int(x)] for x in f.item_id),dtype=np.int32,count=len(f)); eligible=((packed[r,c//8]>>(c%8))&1).astype(bool)\nassert eligible.all()\nwith np.load(S/'deployment_vectors.npz') as v:\n vu=v['user_vectors']; vi=v['item_vectors']; vuids=v['uids']; viids=v['item_ids']\nassert np.array_equal(vuids,uids) and np.array_equal(viids,items)\n# Reproduce top 100 from the exact saved MIPS vectors, in blocks.\nsubmitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':bool(eligible.all()),'exact_dot_product':True,'vector_dim':vi.shape[1]})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\nls -lh /app/solution | sort -k5h | tail -25" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 50051700 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97}\r\n{\r\n \"model\": \"/app/solution/validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.040776,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:01 validation_activity.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:03 validation_activity_ensemble.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:12 submission.parquet\r\n-rw-r--r-- 1 agent agent 1.6M Sep 25 01:13 validation.parquet\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_likes.npy\r\n-rw-r--r-- 1 agent agent 3.3M Sep 25 00:41 daily_listens.npy\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:40 validation_embeddings.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:46 validation_embeddings_15e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:48 validation_embeddings_25e.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:51 validation_embeddings_masked.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:54 validation_embeddings_unique.npz\r\n-rw-r--r-- 1 agent agent 6.6M Sep 25 00:56 validation_embeddings_unique18.npz\r\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 deployment_vectors.npz\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:36 two_tower_validation.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:46 two_tower_validation_15e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:48 two_tower_validation_25e.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:51 two_tower_validation_masked.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:54 two_tower_validation_unique.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 00:56 two_tower_validation_unique18.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:10 two_tower_final_raw.pt\r\n-rw-r--r-- 1 agent agent 24M Sep 25 01:12 two_tower_final_unique.pt\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:35 snapshots_validation_30w.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 00:53 snapshots_validation_30w_unique.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:06 snapshots_final_30w_raw.npz\r\n-rw-r--r-- 1 agent agent 30M Sep 25 01:10 snapshots_final_30w_unique.npz\r\n" } } ## Step 140 · agent · 2026-09-25T01:13:20.081Z ### Thinking unavailable — no text recorded ## Step 141 · agent · 2026-09-25T01:13:26.921Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:13:51.870Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n class TwoTower(nn.Module):\\n@@\\n self.item_tower = ItemTower(n_items, feature_dim, dim)\\n self.logit_scale = nn.Parameter(torch.tensor(math.log(12.0)))\\n+\\n+\\n+class RetrievalUserTower(nn.Module):\\n+ \\\"\\\"\\\"The deployed user tower, combining two trained history resolutions.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: UserTower, unique: UserTower, raw_mix: float = 0.35):\\n+ super().__init__()\\n+ self.raw = raw\\n+ self.unique = unique\\n+ self.raw_mix = float(raw_mix)\\n+\\n+ def forward(\\n+ self,\\n+ raw_inputs: tuple[torch.Tensor, ...],\\n+ unique_inputs: tuple[torch.Tensor, ...],\\n+ trend_weight: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ raw_vector = self.raw(*raw_inputs)\\n+ unique_vector = self.unique(*unique_inputs)\\n+ return torch.cat(\\n+ [\\n+ self.raw_mix * raw_vector,\\n+ (1.0 - self.raw_mix) * unique_vector,\\n+ trend_weight.reshape(-1, 1),\\n+ ],\\n+ dim=1,\\n+ )\\n+\\n+\\n+class RetrievalItemTower(nn.Module):\\n+ \\\"\\\"\\\"The independently precomputable deployed catalog-item tower.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: ItemTower, unique: ItemTower):\\n+ super().__init__()\\n+ self.raw = raw\\n+ self.unique = unique\\n+\\n+ def forward(\\n+ self,\\n+ item: torch.Tensor,\\n+ features: torch.Tensor,\\n+ trend: torch.Tensor,\\n+ ) -> torch.Tensor:\\n+ return torch.cat(\\n+ [\\n+ self.raw(item, features),\\n+ self.unique(item, features),\\n+ trend.reshape(-1, 1),\\n+ ],\\n+ dim=1,\\n+ )\\n+\\n+\\n+class RetrievalTwoTower(nn.Module):\\n+ \\\"\\\"\\\"Deployment contract: relevance is exactly ``user_vector @ item_vector``.\\\"\\\"\\\"\\n+\\n+ def __init__(self, raw: TwoTower, unique: TwoTower, raw_mix: float = 0.35):\\n+ super().__init__()\\n+ self.user_tower = RetrievalUserTower(raw.user_tower, unique.user_tower, raw_mix)\\n+ self.item_tower = RetrievalItemTower(raw.item_tower, unique.item_tower)\\n+\\n+ @staticmethod\\n+ def score(user_vector: torch.Tensor, item_vector: torch.Tensor) -> torch.Tensor:\\n+ return user_vector @ item_vector.T\\n*** Add File: /app/solution/README.md\\n+# Made for You two-tower ranker\\n+\\n+`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\\n+each of 8,456 users), and was checked for schema, unique ranks/items, packed\\n+test eligibility, and exact reproduction from the saved MIPS vectors.\\n+\\n+## Model\\n+\\n+The deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\\n+user-ID lookup and never builds or factorizes a user-item matrix.\\n+\\n+- The user tower pools the last 160 conventional-positive catalog events with\\n+ recency, organic, and completion weights. A second view pools the last 160\\n+ *distinct* positive tracks. Active liked and disliked tracks are separate\\n+ embedding pools sourced from the cutoff-correct preference state. Each view\\n+ passes its pooled history and four history statistics through a two-layer\\n+ GELU MLP and produces a normalized 48-dimensional vector.\\n+- The item tower combines a trainable catalog-ID embedding with a residual MLP\\n+ over log-popularity and organic-share features, producing a normalized\\n+ 48-dimensional vector for each history view.\\n+- The two 48-dimensional spaces are concatenated with one offline item-trend\\n+ dimension. Trend uses organic 80%-complete listens and organic likes with a\\n+ three-day half-life. Its user-side coefficient depends only on 90-day\\n+ history activity. Thus the final 97-dimensional relevance score is exactly\\n+ one user-vector/item-vector dot product. Candidate vectors can be computed\\n+ offline; request time needs one history-derived user vector and MIPS.\\n+\\n+`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\\n+`train_ranker.py` define the deployment contract. The exact fitted vectors are\\n+saved in `deployment_vectors.npz`; the two fitted checkpoints are\\n+`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\\n+\\n+## Training\\n+\\n+Training examples use 30 rolling cutoff-correct backtests. A history predicts\\n+organic first-discovery tracks in the next seven days after a 30-minute gap.\\n+The loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\\n+3.5 times as often as grade-1 strong listens. This is event-sampled and scales\\n+linearly with interaction records and minibatches. The final models are refit\\n+on `train_* + val_*`; validation events are then history/training data, never\\n+hidden outcomes.\\n+\\n+Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\\n+with `/task/evaluate_public.py`. The corresponding ranking is\\n+`validation.parquet`.\\n+\\n+## Reproduce the final submission\\n+\\n+From `/app/solution` with the supplied environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python build_submission.py\\n+```\\n+\\n+This writes checkpoints and vectors under `/app/solution/`, then atomically\\n+replaces `/app/solution/submission.parquet` only after ranking succeeds.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:13:51.988Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-0ce43e12-2a12-4b38-949b-3dc007fa3fe9", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Made for You two-tower ranker\n\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\ntest eligibility, and exact reproduction from the saved MIPS vectors.\n\n## Model\n\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\nuser-ID lookup and never builds or factorizes a user-item matrix.\n\n- The user tower pools the last 160 conventional-positive catalog events with\n recency, organic, and completion weights. A second view pools the last 160\n *distinct* positive tracks. Active liked and disliked tracks are separate\n embedding pools sourced from the cutoff-correct preference state. Each view\n passes its pooled history and four history statistics through a two-layer\n GELU MLP and produces a normalized 48-dimensional vector.\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\n over log-popularity and organic-share features, producing a normalized\n 48-dimensional vector for each history view.\n- The two 48-dimensional spaces are concatenated with one offline item-trend\n dimension. Trend uses organic 80%-complete listens and organic likes with a\n three-day half-life. Its user-side coefficient depends only on 90-day\n history activity. Thus the final 97-dimensional relevance score is exactly\n one user-vector/item-vector dot product. Candidate vectors can be computed\n offline; request time needs one history-derived user vector and MIPS.\n\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\n\n## Training\n\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\norganic first-discovery tracks in the next seven days after a 30-minute gap.\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\nlinearly with interaction records and minibatches. The final models are refit\non `train_* + val_*`; validation events are then history/training data, never\nhidden outcomes.\n\nPublic validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\nwith `/task/evaluate_public.py`. The corresponding ranking is\n`validation.parquet`.\n\n## Reproduce the final submission\n\nFrom `/app/solution` with the supplied environment:\n\n```sh\n/opt/task-venv/bin/python build_submission.py\n```\n\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\n" }, "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -378,2 +378,66 @@\n \n+class RetrievalUserTower(nn.Module):\n+ \"\"\"The deployed user tower, combining two trained history resolutions.\"\"\"\n+\n+ def __init__(self, raw: UserTower, unique: UserTower, raw_mix: float = 0.35):\n+ super().__init__()\n+ self.raw = raw\n+ self.unique = unique\n+ self.raw_mix = float(raw_mix)\n+\n+ def forward(\n+ self,\n+ raw_inputs: tuple[torch.Tensor, ...],\n+ unique_inputs: tuple[torch.Tensor, ...],\n+ trend_weight: torch.Tensor,\n+ ) -> torch.Tensor:\n+ raw_vector = self.raw(*raw_inputs)\n+ unique_vector = self.unique(*unique_inputs)\n+ return torch.cat(\n+ [\n+ self.raw_mix * raw_vector,\n+ (1.0 - self.raw_mix) * unique_vector,\n+ trend_weight.reshape(-1, 1),\n+ ],\n+ dim=1,\n+ )\n+\n+\n+class RetrievalItemTower(nn.Module):\n+ \"\"\"The independently precomputable deployed catalog-item tower.\"\"\"\n+\n+ def __init__(self, raw: ItemTower, unique: ItemTower):\n+ super().__init__()\n+ self.raw = raw\n+ self.unique = unique\n+\n+ def forward(\n+ self,\n+ item: torch.Tensor,\n+ features: torch.Tensor,\n+ trend: torch.Tensor,\n+ ) -> torch.Tensor:\n+ return torch.cat(\n+ [\n+ self.raw(item, features),\n+ self.unique(item, features),\n+ trend.reshape(-1, 1),\n+ ],\n+ dim=1,\n+ )\n+\n+\n+class RetrievalTwoTower(nn.Module):\n+ \"\"\"Deployment contract: relevance is exactly ``user_vector @ item_vector``.\"\"\"\n+\n+ def __init__(self, raw: TwoTower, unique: TwoTower, raw_mix: float = 0.35):\n+ super().__init__()\n+ self.user_tower = RetrievalUserTower(raw.user_tower, unique.user_tower, raw_mix)\n+ self.item_tower = RetrievalItemTower(raw.item_tower, unique.item_tower)\n+\n+ @staticmethod\n+ def score(user_vector: torch.Tensor, item_vector: torch.Tensor) -> torch.Tensor:\n+ return user_vector @ item_vector.T\n+\n+\n def train_model(\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_ranker.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5caa5c76487d280fc405cc0056a09", "usage": { "input_tokens": 97503, "cached_input_tokens": 96512, "cache_write_input_tokens": 0, "output_tokens": 2284, "reasoning_output_tokens": 889, "total_tokens": 99787 }, "turn_token_usage": { "input_tokens": 8785155, "cached_input_tokens": 8673536, "cache_write_input_tokens": 0, "output_tokens": 60133, "reasoning_output_tokens": 22391, "total_tokens": 8845288 }, "thread_token_usage": { "input_tokens": 8785155, "cached_input_tokens": 8673536, "cache_write_input_tokens": 0, "output_tokens": 60133, "reasoning_output_tokens": 22391, "total_tokens": 8845288 } } ## Step 142 · agent · 2026-09-25T01:14:02.719Z ### Thinking unavailable — no text recorded ## Step 143 · agent · 2026-09-25T01:14:12.044Z ### Thinking unavailable — no text recorded ## Step 144 · agent · 2026-09-25T01:14:17.024Z ### Thinking unavailable — no text recorded All acceptance checks pass: 8,456 users, 100 unique eligible items each, and the saved 97-dimensional vectors reproduce the submission exactly by dot product. I’m doing one final low-risk check on whether distinct-listener trend counts outperform raw event counts; the validated submission will only change if this improves the public backtest. ### exec · 2026-09-25T01:14:30.793Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\\nfor fam in ['listens','likes']:\\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\\n a=np.zeros((nd,len(ids)),np.float32)\\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\\n# public global score helper\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\\ndef ev(s):\\n order=np.argsort(-s,kind='stable')[:400]; q=[]\\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\\n return np.mean(q)\\nres=[]\\nfor half in [1,1.5,2,3,4,5,7,10]:\\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\\n for aw in [7,15,25,35,50]:\\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\\nprint(*sorted(res,reverse=True)[:25],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:14:31.973Z Script completed Wall time 1.2 seconds Output: SESSION_ID=63104 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cad06ecc87d28211d7d6646a8896", "usage": { "input_tokens": 99811, "cached_input_tokens": 97280, "cache_write_input_tokens": 0, "output_tokens": 2078, "reasoning_output_tokens": 1310, "total_tokens": 101889 }, "turn_token_usage": { "input_tokens": 8884966, "cached_input_tokens": 8770816, "cache_write_input_tokens": 0, "output_tokens": 62211, "reasoning_output_tokens": 23701, "total_tokens": 8947177 }, "thread_token_usage": { "input_tokens": 8884966, "cached_input_tokens": 8770816, "cache_write_input_tokens": 0, "output_tokens": 62211, "reasoning_output_tokens": 23701, "total_tokens": 8947177 } } ### exec · 2026-09-25T01:14:33.847Z { "code": "const r = await tools.write_stdin({\"session_id\":63104,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:14:57.922Z Script completed Wall time 24.1 seconds Output: listens events 1170755 unique-user-days 912916 likes events 41623 unique-user-days 40752 (np.float64(0.03453179869211387), 1, 7, 0) (np.float64(0.03443231486390028), 1, 50, 0.1) (np.float64(0.034318296320975), 1, 25, 0.03) (np.float64(0.034280755978172824), 1.5, 15, 0.03) (np.float64(0.034252785554527695), 1, 50, 0) (np.float64(0.03422815481954311), 1, 35, 0) (np.float64(0.03421940404642105), 1.5, 35, 0.1) (np.float64(0.034184128607391555), 1, 35, 0.03) (np.float64(0.03414228669463759), 1.5, 35, 0.03) (np.float64(0.03410334103248491), 1, 25, 0) (np.float64(0.03409867486731631), 1, 15, 0) (np.float64(0.0340924512701297), 1.5, 25, 0) (np.float64(0.034091023152258655), 1, 50, 0.03) (np.float64(0.034067306376876626), 2, 35, 0.1) (np.float64(0.03402062629991203), 1.5, 15, 0) (np.float64(0.03401120675221531), 1.5, 50, 0.03) (np.float64(0.034008671809672444), 1.5, 25, 0.03) (np.float64(0.03400339456812733), 2, 15, 0) (np.float64(0.03394011179342464), 1.5, 50, 0.1) (np.float64(0.033926708312581086), 2, 7, 0.03) (np.float64(0.033924229192499815), 1.5, 7, 0.03) (np.float64(0.0339231782018535), 1.5, 35, 0) (np.float64(0.033839024697309254), 1.5, 25, 0.1) (np.float64(0.03383716461999077), 1.5, 7, 0) (np.float64(0.0338004343101169), 1, 15, 0.03) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5caf8314487d28435af1ce9b44ab3", "usage": { "input_tokens": 101917, "cached_input_tokens": 99584, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 101962 }, "turn_token_usage": { "input_tokens": 8986883, "cached_input_tokens": 8870400, "cache_write_input_tokens": 0, "output_tokens": 62256, "reasoning_output_tokens": 23701, "total_tokens": 9049139 }, "thread_token_usage": { "input_tokens": 8986883, "cached_input_tokens": 8870400, "cache_write_input_tokens": 0, "output_tokens": 62256, "reasoning_output_tokens": 23701, "total_tokens": 9049139 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c9e5b42f-4425-472e-9b49-4be5e8fa9fb9", "process_id": "63104", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\n a=np.zeros((nd,len(ids)),np.float32)\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\n# public global score helper\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\ndef ev(s):\n order=np.argsort(-s,kind='stable')[:400]; q=[]\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\n return np.mean(q)\nres=[]\nfor half in [1,1.5,2,3,4,5,7,10]:\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\n for aw in [7,15,25,35,50]:\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\nprint(*sorted(res,reverse=True)[:25],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); nd=28; arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'day':age[keep]}).drop_duplicates(['uid','ci','day'])\n a=np.zeros((nd,len(ids)),np.float32)\n for d,g in y.groupby('day'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_{fam}.npy',a); print(fam,'events',keep.sum(),'unique-user-days',len(y),flush=True)\n# public global score helper\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([r for r,u in enumerate(uids) if int(u) in truth]); pop=cat.positive_listens.to_numpy(float)\ndef ev(s):\n order=np.argsort(-s,kind='stable')[:400]; q=[]\n for r in rows: q.append(tr.ndcg10(items[order[masks[r,order]][:10]],truth[int(uids[r])]))\n return np.mean(q)\nres=[]\nfor half in [1,1.5,2,3,4,5,7,10]:\n d=2**(-np.arange(nd)/half); L=d@arr['listens']; A=d@arr['likes']\n for aw in [7,15,25,35,50]:\n for prior in [0,.03,.1,.25]: res.append((ev(L+aw*A+prior*pop**.75),half,aw,prior))\nprint(*sorted(res,reverse=True)[:25],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n", "stderr": "", "aggregated_output": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 948388009 }, "formatted_output": "listens events 1170755 unique-user-days 912916\r\nlikes events 41623 unique-user-days 40752\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03443231486390028), 1, 50, 0.1)\r\n(np.float64(0.034318296320975), 1, 25, 0.03)\r\n(np.float64(0.034280755978172824), 1.5, 15, 0.03)\r\n(np.float64(0.034252785554527695), 1, 50, 0)\r\n(np.float64(0.03422815481954311), 1, 35, 0)\r\n(np.float64(0.03421940404642105), 1.5, 35, 0.1)\r\n(np.float64(0.034184128607391555), 1, 35, 0.03)\r\n(np.float64(0.03414228669463759), 1.5, 35, 0.03)\r\n(np.float64(0.03410334103248491), 1, 25, 0)\r\n(np.float64(0.03409867486731631), 1, 15, 0)\r\n(np.float64(0.0340924512701297), 1.5, 25, 0)\r\n(np.float64(0.034091023152258655), 1, 50, 0.03)\r\n(np.float64(0.034067306376876626), 2, 35, 0.1)\r\n(np.float64(0.03402062629991203), 1.5, 15, 0)\r\n(np.float64(0.03401120675221531), 1.5, 50, 0.03)\r\n(np.float64(0.034008671809672444), 1.5, 25, 0.03)\r\n(np.float64(0.03400339456812733), 2, 15, 0)\r\n(np.float64(0.03394011179342464), 1.5, 50, 0.1)\r\n(np.float64(0.033926708312581086), 2, 7, 0.03)\r\n(np.float64(0.033924229192499815), 1.5, 7, 0.03)\r\n(np.float64(0.0339231782018535), 1.5, 35, 0)\r\n(np.float64(0.033839024697309254), 1.5, 25, 0.1)\r\n(np.float64(0.03383716461999077), 1.5, 7, 0)\r\n(np.float64(0.0338004343101169), 1, 15, 0.03)\r\n" } } ## Step 145 · agent · 2026-09-25T01:15:02.039Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:15:07.754Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\\nnp.save('validation_unique_trend.npy',trend)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:15:08.919Z Script completed Wall time 1.2 seconds Output: SESSION_ID=6330 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb1222f487d2b8dc890ab098acf0", "usage": { "input_tokens": 102614, "cached_input_tokens": 101760, "cache_write_input_tokens": 0, "output_tokens": 480, "reasoning_output_tokens": 154, "total_tokens": 103094 }, "turn_token_usage": { "input_tokens": 9089497, "cached_input_tokens": 8972160, "cache_write_input_tokens": 0, "output_tokens": 62736, "reasoning_output_tokens": 23855, "total_tokens": 9152233 }, "thread_token_usage": { "input_tokens": 9089497, "cached_input_tokens": 8972160, "cache_write_input_tokens": 0, "output_tokens": 62736, "reasoning_output_tokens": 23855, "total_tokens": 9152233 } } ### exec · 2026-09-25T01:15:10.841Z { "code": "const r = await tools.write_stdin({\"session_id\":6330,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:15:24.469Z Script completed Wall time 13.6 seconds Output: blend trend_weight=0 public_ndcg@10=0.002883 blend trend_weight=0.035 public_ndcg@10=0.036911 blend trend_weight=0.045 public_ndcg@10=0.039377 blend trend_weight=0.055 public_ndcg@10=0.041111 blend trend_weight=0.065 public_ndcg@10=0.041459 blend trend_weight=0.075 public_ndcg@10=0.041449 blend trend_weight=0.085 public_ndcg@10=0.041221 blend trend_weight=0.095 public_ndcg@10=0.041198 blend trend_weight=0.11 public_ndcg@10=0.040666 blend trend_weight=0.13 public_ndcg@10=0.039786 blend trend_weight=0.16 public_ndcg@10=0.039111 blend trend_weight=0.22 public_ndcg@10=0.038044 blend trend_weight=0.35 public_ndcg@10=0.036711 blend trend_weight=0.65 public_ndcg@10=0.035678 blend trend_weight=1.5 public_ndcg@10=0.034915 blend trend_weight=4 public_ndcg@10=0.034539 (0.04145860428851774, 0.065) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb1d1de087d28a4365a2a1484f0a", "usage": { "input_tokens": 103122, "cached_input_tokens": 102400, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 103167 }, "turn_token_usage": { "input_tokens": 9192619, "cached_input_tokens": 9074560, "cache_write_input_tokens": 0, "output_tokens": 62781, "reasoning_output_tokens": 23855, "total_tokens": 9255400 }, "thread_token_usage": { "input_tokens": 9192619, "cached_input_tokens": 9074560, "cache_write_input_tokens": 0, "output_tokens": 62781, "reasoning_output_tokens": 23855, "total_tokens": 9255400 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07b1c341-8751-4e6e-a1f0-ca449b56154c", "process_id": "6330", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\nnp.save('validation_unique_trend.npy',trend)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/1.0); raw=d@L+7*(d@A); order=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[order]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_unique_trend.parquet',None))\nnp.save('validation_unique_trend.npy',trend)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 550098409 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036911\r\nblend trend_weight=0.045 public_ndcg@10=0.039377\r\nblend trend_weight=0.055 public_ndcg@10=0.041111\r\nblend trend_weight=0.065 public_ndcg@10=0.041459\r\nblend trend_weight=0.075 public_ndcg@10=0.041449\r\nblend trend_weight=0.085 public_ndcg@10=0.041221\r\nblend trend_weight=0.095 public_ndcg@10=0.041198\r\nblend trend_weight=0.11 public_ndcg@10=0.040666\r\nblend trend_weight=0.13 public_ndcg@10=0.039786\r\nblend trend_weight=0.16 public_ndcg@10=0.039111\r\nblend trend_weight=0.22 public_ndcg@10=0.038044\r\nblend trend_weight=0.35 public_ndcg@10=0.036711\r\nblend trend_weight=0.65 public_ndcg@10=0.035678\r\nblend trend_weight=1.5 public_ndcg@10=0.034915\r\nblend trend_weight=4 public_ndcg@10=0.034539\r\n(0.04145860428851774, 0.065)\r\n" } } ## Step 146 · agent · 2026-09-25T01:15:28.477Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:15:36.099Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\\nprint(*sorted(res,reverse=True)[:40],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:15:37.276Z Script completed Wall time 1.2 seconds Output: SESSION_ID=31638 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb2ca95c87d29ccf057b78a3d27b", "usage": { "input_tokens": 103489, "cached_input_tokens": 102912, "cache_write_input_tokens": 0, "output_tokens": 568, "reasoning_output_tokens": 136, "total_tokens": 104057 }, "turn_token_usage": { "input_tokens": 9296108, "cached_input_tokens": 9177472, "cache_write_input_tokens": 0, "output_tokens": 63349, "reasoning_output_tokens": 23991, "total_tokens": 9359457 }, "thread_token_usage": { "input_tokens": 9296108, "cached_input_tokens": 9177472, "cache_write_input_tokens": 0, "output_tokens": 63349, "reasoning_output_tokens": 23991, "total_tokens": 9359457 } } ### exec · 2026-09-25T01:15:39.187Z { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:16:09.195Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb39823887d2bb9cbe69b6f96adc", "usage": { "input_tokens": 104085, "cached_input_tokens": 103296, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 104130 }, "turn_token_usage": { "input_tokens": 9400193, "cached_input_tokens": 9280768, "cache_write_input_tokens": 0, "output_tokens": 63394, "reasoning_output_tokens": 23991, "total_tokens": 9463587 }, "thread_token_usage": { "input_tokens": 9400193, "cached_input_tokens": 9280768, "cache_write_input_tokens": 0, "output_tokens": 63394, "reasoning_output_tokens": 23991, "total_tokens": 9463587 } } ## Step 147 · agent · 2026-09-25T01:16:10.657Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:16:11.355Z { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:16:41.363Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb59673c87d2a556d48867f9fe1f", "usage": { "input_tokens": 104153, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 104206 }, "turn_token_usage": { "input_tokens": 9504346, "cached_input_tokens": 9384704, "cache_write_input_tokens": 0, "output_tokens": 63447, "reasoning_output_tokens": 23997, "total_tokens": 9567793 }, "thread_token_usage": { "input_tokens": 9504346, "cached_input_tokens": 9384704, "cache_write_input_tokens": 0, "output_tokens": 63447, "reasoning_output_tokens": 23997, "total_tokens": 9567793 } } ## Step 148 · agent · 2026-09-25T01:16:43.529Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:16:44.231Z { "code": "const r = await tools.write_stdin({\"session_id\":31638,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:16:59.238Z Script completed Wall time 15.0 seconds Output: (np.float64(0.03520480844257275), 0.4, 4, 0) (np.float64(0.03503369282950995), 0.55, 4, 0) (np.float64(0.03502223888855064), 0.55, 6, 0) (np.float64(0.03499294910132534), 0.4, 6, 0) (np.float64(0.03497055975199991), 0.4, 7, 0) (np.float64(0.03492307949567489), 0.55, 7, 0) (np.float64(0.034879412801696104), 0.85, 6, 0) (np.float64(0.03485539596909437), 0.7, 4, 0) (np.float64(0.03484229945616528), 0.25, 4, 0) (np.float64(0.034841446363221805), 0.7, 6, 0) (np.float64(0.03483888997440489), 1.2, 8, 0.01) (np.float64(0.0348087619021521), 0.85, 7, 0) (np.float64(0.034748880425747836), 0.7, 7, 0) (np.float64(0.034701748159511284), 0.85, 20, 0.03) (np.float64(0.03469134251635742), 1, 10, 0.01) (np.float64(0.03468546354129284), 0.7, 12, 0.01) (np.float64(0.03468378061787328), 0.85, 12, 0.01) (np.float64(0.034682499011860254), 0.7, 8, 0) (np.float64(0.03467125328354198), 1, 8, 0) (np.float64(0.03465612430180013), 0.85, 4, 0) (np.float64(0.03464078778693491), 1, 12, 0.01) (np.float64(0.034629704726101565), 0.85, 10, 0.01) (np.float64(0.03462828331698332), 0.55, 8, 0) (np.float64(0.03460415058618136), 1.5, 8, 0.01) (np.float64(0.03460374111315636), 0.4, 8, 0) (np.float64(0.03456096451621286), 1, 4, 0) (np.float64(0.03455144059407113), 1, 10, 0) (np.float64(0.03454478942555659), 0.85, 8, 0) (np.float64(0.03453365746593463), 1, 20, 0.03) (np.float64(0.03453179869211387), 1, 7, 0) (np.float64(0.03450592591175055), 1.2, 8, 0) (np.float64(0.03449985645761904), 0.85, 10, 0) (np.float64(0.03448149966852392), 1.2, 30, 0.03) (np.float64(0.034455745227545985), 1.2, 10, 0) (np.float64(0.034455059058202016), 1.2, 10, 0.01) (np.float64(0.03445079038658426), 1, 10, 0.03) (np.float64(0.034444913265577866), 0.55, 10, 0) (np.float64(0.034417653194543624), 0.7, 15, 0.01) (np.float64(0.03440648387823677), 1, 30, 0.03) (np.float64(0.03439880588324988), 0.25, 6, 0) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb79907087d285d70ab411050501", "usage": { "input_tokens": 104229, "cached_input_tokens": 103936, "cache_write_input_tokens": 0, "output_tokens": 67, "reasoning_output_tokens": 20, "total_tokens": 104296 }, "turn_token_usage": { "input_tokens": 9608575, "cached_input_tokens": 9488640, "cache_write_input_tokens": 0, "output_tokens": 63514, "reasoning_output_tokens": 24017, "total_tokens": 9672089 }, "thread_token_usage": { "input_tokens": 9608575, "cached_input_tokens": 9488640, "cache_write_input_tokens": 0, "output_tokens": 63514, "reasoning_output_tokens": 24017, "total_tokens": 9672089 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f918c2de-254e-4569-97b9-2467f2015d7c", "process_id": "31638", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\nprint(*sorted(res,reverse=True)[:40],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nL=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); pop=cat.positive_listens.to_numpy(float)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=np.array([i for i,x in enumerate(uids) if int(x) in truth])\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,2.5,3]:\n d=2**(-np.arange(28)/h); l=d@L; a=d@A\n for aw in [0,2,4,6,7,8,10,12,15,20,30]:\n for p in [0,.01,.03,.06,.1]: res.append((ev(l+aw*a+p*pop**.75),h,aw,p))\nprint(*sorted(res,reverse=True)[:40],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.03520480844257275), 0.4, 4, 0)\r\n(np.float64(0.03503369282950995), 0.55, 4, 0)\r\n(np.float64(0.03502223888855064), 0.55, 6, 0)\r\n(np.float64(0.03499294910132534), 0.4, 6, 0)\r\n(np.float64(0.03497055975199991), 0.4, 7, 0)\r\n(np.float64(0.03492307949567489), 0.55, 7, 0)\r\n(np.float64(0.034879412801696104), 0.85, 6, 0)\r\n(np.float64(0.03485539596909437), 0.7, 4, 0)\r\n(np.float64(0.03484229945616528), 0.25, 4, 0)\r\n(np.float64(0.034841446363221805), 0.7, 6, 0)\r\n(np.float64(0.03483888997440489), 1.2, 8, 0.01)\r\n(np.float64(0.0348087619021521), 0.85, 7, 0)\r\n(np.float64(0.034748880425747836), 0.7, 7, 0)\r\n(np.float64(0.034701748159511284), 0.85, 20, 0.03)\r\n(np.float64(0.03469134251635742), 1, 10, 0.01)\r\n(np.float64(0.03468546354129284), 0.7, 12, 0.01)\r\n(np.float64(0.03468378061787328), 0.85, 12, 0.01)\r\n(np.float64(0.034682499011860254), 0.7, 8, 0)\r\n(np.float64(0.03467125328354198), 1, 8, 0)\r\n(np.float64(0.03465612430180013), 0.85, 4, 0)\r\n(np.float64(0.03464078778693491), 1, 12, 0.01)\r\n(np.float64(0.034629704726101565), 0.85, 10, 0.01)\r\n(np.float64(0.03462828331698332), 0.55, 8, 0)\r\n(np.float64(0.03460415058618136), 1.5, 8, 0.01)\r\n(np.float64(0.03460374111315636), 0.4, 8, 0)\r\n(np.float64(0.03456096451621286), 1, 4, 0)\r\n(np.float64(0.03455144059407113), 1, 10, 0)\r\n(np.float64(0.03454478942555659), 0.85, 8, 0)\r\n(np.float64(0.03453365746593463), 1, 20, 0.03)\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03450592591175055), 1.2, 8, 0)\r\n(np.float64(0.03449985645761904), 0.85, 10, 0)\r\n(np.float64(0.03448149966852392), 1.2, 30, 0.03)\r\n(np.float64(0.034455745227545985), 1.2, 10, 0)\r\n(np.float64(0.034455059058202016), 1.2, 10, 0.01)\r\n(np.float64(0.03445079038658426), 1, 10, 0.03)\r\n(np.float64(0.034444913265577866), 0.55, 10, 0)\r\n(np.float64(0.034417653194543624), 0.7, 15, 0.01)\r\n(np.float64(0.03440648387823677), 1, 30, 0.03)\r\n(np.float64(0.03439880588324988), 0.25, 6, 0)\r\n", "stderr": "", "aggregated_output": "(np.float64(0.03520480844257275), 0.4, 4, 0)\r\n(np.float64(0.03503369282950995), 0.55, 4, 0)\r\n(np.float64(0.03502223888855064), 0.55, 6, 0)\r\n(np.float64(0.03499294910132534), 0.4, 6, 0)\r\n(np.float64(0.03497055975199991), 0.4, 7, 0)\r\n(np.float64(0.03492307949567489), 0.55, 7, 0)\r\n(np.float64(0.034879412801696104), 0.85, 6, 0)\r\n(np.float64(0.03485539596909437), 0.7, 4, 0)\r\n(np.float64(0.03484229945616528), 0.25, 4, 0)\r\n(np.float64(0.034841446363221805), 0.7, 6, 0)\r\n(np.float64(0.03483888997440489), 1.2, 8, 0.01)\r\n(np.float64(0.0348087619021521), 0.85, 7, 0)\r\n(np.float64(0.034748880425747836), 0.7, 7, 0)\r\n(np.float64(0.034701748159511284), 0.85, 20, 0.03)\r\n(np.float64(0.03469134251635742), 1, 10, 0.01)\r\n(np.float64(0.03468546354129284), 0.7, 12, 0.01)\r\n(np.float64(0.03468378061787328), 0.85, 12, 0.01)\r\n(np.float64(0.034682499011860254), 0.7, 8, 0)\r\n(np.float64(0.03467125328354198), 1, 8, 0)\r\n(np.float64(0.03465612430180013), 0.85, 4, 0)\r\n(np.float64(0.03464078778693491), 1, 12, 0.01)\r\n(np.float64(0.034629704726101565), 0.85, 10, 0.01)\r\n(np.float64(0.03462828331698332), 0.55, 8, 0)\r\n(np.float64(0.03460415058618136), 1.5, 8, 0.01)\r\n(np.float64(0.03460374111315636), 0.4, 8, 0)\r\n(np.float64(0.03456096451621286), 1, 4, 0)\r\n(np.float64(0.03455144059407113), 1, 10, 0)\r\n(np.float64(0.03454478942555659), 0.85, 8, 0)\r\n(np.float64(0.03453365746593463), 1, 20, 0.03)\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03450592591175055), 1.2, 8, 0)\r\n(np.float64(0.03449985645761904), 0.85, 10, 0)\r\n(np.float64(0.03448149966852392), 1.2, 30, 0.03)\r\n(np.float64(0.034455745227545985), 1.2, 10, 0)\r\n(np.float64(0.034455059058202016), 1.2, 10, 0.01)\r\n(np.float64(0.03445079038658426), 1, 10, 0.03)\r\n(np.float64(0.034444913265577866), 0.55, 10, 0)\r\n(np.float64(0.034417653194543624), 0.7, 15, 0.01)\r\n(np.float64(0.03440648387823677), 1, 30, 0.03)\r\n(np.float64(0.03439880588324988), 0.25, 6, 0)\r\n", "exit_code": 0, "duration": { "secs": 82, "nanos": 961162379 }, "formatted_output": "(np.float64(0.03520480844257275), 0.4, 4, 0)\r\n(np.float64(0.03503369282950995), 0.55, 4, 0)\r\n(np.float64(0.03502223888855064), 0.55, 6, 0)\r\n(np.float64(0.03499294910132534), 0.4, 6, 0)\r\n(np.float64(0.03497055975199991), 0.4, 7, 0)\r\n(np.float64(0.03492307949567489), 0.55, 7, 0)\r\n(np.float64(0.034879412801696104), 0.85, 6, 0)\r\n(np.float64(0.03485539596909437), 0.7, 4, 0)\r\n(np.float64(0.03484229945616528), 0.25, 4, 0)\r\n(np.float64(0.034841446363221805), 0.7, 6, 0)\r\n(np.float64(0.03483888997440489), 1.2, 8, 0.01)\r\n(np.float64(0.0348087619021521), 0.85, 7, 0)\r\n(np.float64(0.034748880425747836), 0.7, 7, 0)\r\n(np.float64(0.034701748159511284), 0.85, 20, 0.03)\r\n(np.float64(0.03469134251635742), 1, 10, 0.01)\r\n(np.float64(0.03468546354129284), 0.7, 12, 0.01)\r\n(np.float64(0.03468378061787328), 0.85, 12, 0.01)\r\n(np.float64(0.034682499011860254), 0.7, 8, 0)\r\n(np.float64(0.03467125328354198), 1, 8, 0)\r\n(np.float64(0.03465612430180013), 0.85, 4, 0)\r\n(np.float64(0.03464078778693491), 1, 12, 0.01)\r\n(np.float64(0.034629704726101565), 0.85, 10, 0.01)\r\n(np.float64(0.03462828331698332), 0.55, 8, 0)\r\n(np.float64(0.03460415058618136), 1.5, 8, 0.01)\r\n(np.float64(0.03460374111315636), 0.4, 8, 0)\r\n(np.float64(0.03456096451621286), 1, 4, 0)\r\n(np.float64(0.03455144059407113), 1, 10, 0)\r\n(np.float64(0.03454478942555659), 0.85, 8, 0)\r\n(np.float64(0.03453365746593463), 1, 20, 0.03)\r\n(np.float64(0.03453179869211387), 1, 7, 0)\r\n(np.float64(0.03450592591175055), 1.2, 8, 0)\r\n(np.float64(0.03449985645761904), 0.85, 10, 0)\r\n(np.float64(0.03448149966852392), 1.2, 30, 0.03)\r\n(np.float64(0.034455745227545985), 1.2, 10, 0)\r\n(np.float64(0.034455059058202016), 1.2, 10, 0.01)\r\n(np.float64(0.03445079038658426), 1, 10, 0.03)\r\n(np.float64(0.034444913265577866), 0.55, 10, 0)\r\n(np.float64(0.034417653194543624), 0.7, 15, 0.01)\r\n(np.float64(0.03440648387823677), 1, 30, 0.03)\r\n(np.float64(0.03439880588324988), 0.25, 6, 0)\r\n" } } ## Step 149 · agent · 2026-09-25T01:17:01.877Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:17:03.089Z { "code": "const r = await tools.exec_command({\"cmd\":\"python - <<'PY'\\nprint(24786800%86400,25393400%86400)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:17:03.236Z Script completed Wall time 0.0 seconds Output: 76400 78200 ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-099ac669-8603-4ea2-9964-960970a7a8c4", "process_id": "77731", "command": [ "/bin/sh", "-lc", "python - <<'PY'\nprint(24786800%86400,25393400%86400)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "python - <<'PY'\nprint(24786800%86400,25393400%86400)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "76400 78200\n", "stderr": "", "aggregated_output": "76400 78200\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 7870 }, "formatted_output": "76400 78200\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb8b700087d2a92fdb26078cbc7f", "usage": { "input_tokens": 105289, "cached_input_tokens": 104064, "cache_write_input_tokens": 0, "output_tokens": 153, "reasoning_output_tokens": 81, "total_tokens": 105442 }, "turn_token_usage": { "input_tokens": 9713864, "cached_input_tokens": 9592704, "cache_write_input_tokens": 0, "output_tokens": 63667, "reasoning_output_tokens": 24098, "total_tokens": 9777531 }, "thread_token_usage": { "input_tokens": 9713864, "cached_input_tokens": 9592704, "cache_write_input_tokens": 0, "output_tokens": 63667, "reasoning_output_tokens": 24098, "total_tokens": 9777531 } } ## Step 150 · agent · 2026-09-25T01:17:05.110Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:17:10.772Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:17:11.937Z Script completed Wall time 1.2 seconds Output: SESSION_ID=2261 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb8f895c87d285f65f9bcbfe8a2e", "usage": { "input_tokens": 105471, "cached_input_tokens": 105088, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 34, "total_tokens": 105829 }, "turn_token_usage": { "input_tokens": 9819335, "cached_input_tokens": 9697792, "cache_write_input_tokens": 0, "output_tokens": 64025, "reasoning_output_tokens": 24132, "total_tokens": 9883360 }, "thread_token_usage": { "input_tokens": 9819335, "cached_input_tokens": 9697792, "cache_write_input_tokens": 0, "output_tokens": 64025, "reasoning_output_tokens": 24132, "total_tokens": 9883360 } } ### exec · 2026-09-25T01:17:14.068Z { "code": "const r = await tools.write_stdin({\"session_id\":2261,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:17:27.528Z Script completed Wall time 13.5 seconds Output: blend trend_weight=0 public_ndcg@10=0.002883 blend trend_weight=0.035 public_ndcg@10=0.036774 blend trend_weight=0.045 public_ndcg@10=0.039178 blend trend_weight=0.055 public_ndcg@10=0.040362 blend trend_weight=0.065 public_ndcg@10=0.040667 blend trend_weight=0.075 public_ndcg@10=0.041008 blend trend_weight=0.085 public_ndcg@10=0.040957 blend trend_weight=0.095 public_ndcg@10=0.040790 blend trend_weight=0.11 public_ndcg@10=0.040268 blend trend_weight=0.13 public_ndcg@10=0.039697 blend trend_weight=0.16 public_ndcg@10=0.038925 blend trend_weight=0.22 public_ndcg@10=0.038021 blend trend_weight=0.35 public_ndcg@10=0.036518 blend trend_weight=0.65 public_ndcg@10=0.035770 blend trend_weight=1.5 public_ndcg@10=0.035399 blend trend_weight=4 public_ndcg@10=0.035240 (0.04100757769872118, 0.075) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cb982d9087d28bb2bbf2997e6e81", "usage": { "input_tokens": 105857, "cached_input_tokens": 105344, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 105902 }, "turn_token_usage": { "input_tokens": 9925192, "cached_input_tokens": 9803136, "cache_write_input_tokens": 0, "output_tokens": 64070, "reasoning_output_tokens": 24132, "total_tokens": 9989262 }, "thread_token_usage": { "input_tokens": 9925192, "cached_input_tokens": 9803136, "cache_write_input_tokens": 0, "output_tokens": 64070, "reasoning_output_tokens": 24132, "total_tokens": 9989262 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0766c744-25fc-462a-81ca-0f4da3ca179e", "process_id": "2261", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listens.npy'); A=np.load('daily_unique_likes.npy'); d=2**(-np.arange(28)/.4); raw=d@L+4*(d@A); o=np.argsort(-raw,kind='stable'); rank=np.empty(len(raw),int); rank[o]=np.arange(1,len(raw)+1); trend=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,trend,tr.OUT/'validation_distinct_trend.parquet',None)); np.save('validation_distinct_trend.npy',trend)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 591164042 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.036774\r\nblend trend_weight=0.045 public_ndcg@10=0.039178\r\nblend trend_weight=0.055 public_ndcg@10=0.040362\r\nblend trend_weight=0.065 public_ndcg@10=0.040667\r\nblend trend_weight=0.075 public_ndcg@10=0.041008\r\nblend trend_weight=0.085 public_ndcg@10=0.040957\r\nblend trend_weight=0.095 public_ndcg@10=0.040790\r\nblend trend_weight=0.11 public_ndcg@10=0.040268\r\nblend trend_weight=0.13 public_ndcg@10=0.039697\r\nblend trend_weight=0.16 public_ndcg@10=0.038925\r\nblend trend_weight=0.22 public_ndcg@10=0.038021\r\nblend trend_weight=0.35 public_ndcg@10=0.036518\r\nblend trend_weight=0.65 public_ndcg@10=0.035770\r\nblend trend_weight=1.5 public_ndcg@10=0.035399\r\nblend trend_weight=4 public_ndcg@10=0.035240\r\n(0.04100757769872118, 0.075)\r\n" } } ## Step 151 · agent · 2026-09-25T01:17:32.901Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:17:43.274Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\\n for a in aa:\\n s=base+a*t1[cand]\\n for b in bb:\\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\\nprint(*best[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:17:44.438Z Script completed Wall time 1.2 seconds Output: SESSION_ID=9914 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cba7bdac87d28d2e8a8c78504e43", "usage": { "input_tokens": 106224, "cached_input_tokens": 105728, "cache_write_input_tokens": 0, "output_tokens": 810, "reasoning_output_tokens": 225, "total_tokens": 107034 }, "turn_token_usage": { "input_tokens": 10031416, "cached_input_tokens": 9908864, "cache_write_input_tokens": 0, "output_tokens": 64880, "reasoning_output_tokens": 24357, "total_tokens": 10096296 }, "thread_token_usage": { "input_tokens": 10031416, "cached_input_tokens": 9908864, "cache_write_input_tokens": 0, "output_tokens": 64880, "reasoning_output_tokens": 24357, "total_tokens": 10096296 } } ## Step 152 · agent · 2026-09-25T01:17:45.982Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:17:46.705Z { "code": "const r = await tools.write_stdin({\"session_id\":9914,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:18:12.960Z Script completed Wall time 26.3 seconds Output: (np.float64(0.0415435147667795), 0.06, 0) (np.float64(0.04154312445895421), 0.06, 0.01) (np.float64(0.041509027920528595), 0.05, 0.02) (np.float64(0.04147371154168359), 0.055, 0.02) (np.float64(0.041471609467006135), 0.055, 0.01) (np.float64(0.04147030477830557), 0.075, -0.015) (np.float64(0.04147005653952609), 0.085, -0.025) (np.float64(0.04146680499019699), 0.065, 0.01) (np.float64(0.04145989484743894), 0.07, -0.015) (np.float64(0.04145860428851774), 0.065, 0) (np.float64(0.04144916324061703), 0.075, 0) (np.float64(0.041435895444229584), 0.045, 0.02) (np.float64(0.04140796574407501), 0.07, 0) (np.float64(0.04137283172964032), 0.045, 0.03) (np.float64(0.04134979546605336), 0.05, 0.01) (np.float64(0.04133916464071807), 0.07, 0.01) (np.float64(0.04132163384583767), 0.065, 0.02) (np.float64(0.04130669472109521), 0.075, 0.01) (np.float64(0.04128729230228596), 0.035, 0.03) (np.float64(0.041261208090429824), 0.085, 0.01) (np.float64(0.0412554836717363), 0.075, 0.02) (np.float64(0.041244424344536006), 0.07, 0.02) (np.float64(0.041234557189908184), 0.065, 0.03) (np.float64(0.041224807584757876), 0.085, -0.015) (np.float64(0.041221144732019724), 0.085, 0) (np.float64(0.04120410431831924), 0.06, 0.02) (np.float64(0.041185439362510186), 0.05, 0.03) (np.float64(0.0411652025443005), 0.045, 0.01) (np.float64(0.041142909141608536), 0.06, 0.03) (np.float64(0.04111283054074914), 0.035, 0.045) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbb8a97087d2985dcc74950f9112", "usage": { "input_tokens": 107062, "cached_input_tokens": 105984, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 107115 }, "turn_token_usage": { "input_tokens": 10138478, "cached_input_tokens": 10014848, "cache_write_input_tokens": 0, "output_tokens": 64933, "reasoning_output_tokens": 24363, "total_tokens": 10203411 }, "thread_token_usage": { "input_tokens": 10138478, "cached_input_tokens": 10014848, "cache_write_input_tokens": 0, "output_tokens": 64933, "reasoning_output_tokens": 24363, "total_tokens": 10203411 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7b77abf8-3f54-4e08-b402-a9390e5ac4b7", "process_id": "9914", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t1[cand]\n for b in bb:\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nprint(*best[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t1=np.load('validation_unique_trend.npy'); t2=np.load('validation_distinct_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.035,.045,.05,.055,.06,.065,.07,.075,.085]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045,.06]; vals={(a,b):[] for a in aa for b in bb}; o1=np.argsort(-t1); o2=np.argsort(-t2)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],o1[masks[row,o1]][:600]),o2[masks[row,o2]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t1[cand]\n for b in bb:\n top=cand[np.argsort(-(s+b*t2[cand]))[:10]]; vals[(a,b)].append(tr.ndcg10(items[top],gold))\nbest=sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)\nprint(*best[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n", "stderr": "", "aggregated_output": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n", "exit_code": 0, "duration": { "secs": 29, "nanos": 520916244 }, "formatted_output": "(np.float64(0.0415435147667795), 0.06, 0)\r\n(np.float64(0.04154312445895421), 0.06, 0.01)\r\n(np.float64(0.041509027920528595), 0.05, 0.02)\r\n(np.float64(0.04147371154168359), 0.055, 0.02)\r\n(np.float64(0.041471609467006135), 0.055, 0.01)\r\n(np.float64(0.04147030477830557), 0.075, -0.015)\r\n(np.float64(0.04147005653952609), 0.085, -0.025)\r\n(np.float64(0.04146680499019699), 0.065, 0.01)\r\n(np.float64(0.04145989484743894), 0.07, -0.015)\r\n(np.float64(0.04145860428851774), 0.065, 0)\r\n(np.float64(0.04144916324061703), 0.075, 0)\r\n(np.float64(0.041435895444229584), 0.045, 0.02)\r\n(np.float64(0.04140796574407501), 0.07, 0)\r\n(np.float64(0.04137283172964032), 0.045, 0.03)\r\n(np.float64(0.04134979546605336), 0.05, 0.01)\r\n(np.float64(0.04133916464071807), 0.07, 0.01)\r\n(np.float64(0.04132163384583767), 0.065, 0.02)\r\n(np.float64(0.04130669472109521), 0.075, 0.01)\r\n(np.float64(0.04128729230228596), 0.035, 0.03)\r\n(np.float64(0.041261208090429824), 0.085, 0.01)\r\n(np.float64(0.0412554836717363), 0.075, 0.02)\r\n(np.float64(0.041244424344536006), 0.07, 0.02)\r\n(np.float64(0.041234557189908184), 0.065, 0.03)\r\n(np.float64(0.041224807584757876), 0.085, -0.015)\r\n(np.float64(0.041221144732019724), 0.085, 0)\r\n(np.float64(0.04120410431831924), 0.06, 0.02)\r\n(np.float64(0.041185439362510186), 0.05, 0.03)\r\n(np.float64(0.0411652025443005), 0.045, 0.01)\r\n(np.float64(0.041142909141608536), 0.06, 0.03)\r\n(np.float64(0.04111283054074914), 0.035, 0.045)\r\n" } } ## Step 153 · agent · 2026-09-25T01:18:16.591Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:18:20.977Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:18:22.156Z Script completed Wall time 1.2 seconds Output: SESSION_ID=43374 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbd5263087d2a1959b5230829beb", "usage": { "input_tokens": 107818, "cached_input_tokens": 106880, "cache_write_input_tokens": 0, "output_tokens": 389, "reasoning_output_tokens": 141, "total_tokens": 108207 }, "turn_token_usage": { "input_tokens": 10246296, "cached_input_tokens": 10121728, "cache_write_input_tokens": 0, "output_tokens": 65322, "reasoning_output_tokens": 24504, "total_tokens": 10311618 }, "thread_token_usage": { "input_tokens": 10246296, "cached_input_tokens": 10121728, "cache_write_input_tokens": 0, "output_tokens": 65322, "reasoning_output_tokens": 24504, "total_tokens": 10311618 } } ### exec · 2026-09-25T01:18:24.252Z { "code": "const r = await tools.write_stdin({\"session_id\":43374,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:18:30.641Z Script completed Wall time 6.4 seconds Output: (0.0415435147667795, 0.06) { "model": "/app/solution/validation_distinct_fixed.parquet", "scores": { "quality_ndcg@10": 0.041544, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbde81fc87d2a2e252660ad3cdd7", "usage": { "input_tokens": 108235, "cached_input_tokens": 107648, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 108280 }, "turn_token_usage": { "input_tokens": 10354531, "cached_input_tokens": 10229376, "cache_write_input_tokens": 0, "output_tokens": 65367, "reasoning_output_tokens": 24504, "total_tokens": 10419898 }, "thread_token_usage": { "input_tokens": 10354531, "cached_input_tokens": 10229376, "cache_write_input_tokens": 0, "output_tokens": 65367, "reasoning_output_tokens": 24504, "total_tokens": 10419898 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-31d48d97-5ad2-46d0-ae8a-cd68bc7c80c2", "process_id": "43374", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65\nue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nprint(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_distinct_fixed.parquet',.06))\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_fixed.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 484273264 }, "formatted_output": "(0.0415435147667795, 0.06)\r\n{\r\n \"model\": \"/app/solution/validation_distinct_fixed.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041544,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 154 · agent · 2026-09-25T01:18:33.047Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:18:44.256Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\\nfor b in range(5):\\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\\n# best schedule total\\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:18:45.422Z Script completed Wall time 1.2 seconds Output: SESSION_ID=64744 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbe6d8c087d2b8819062b9f2a31e", "usage": { "input_tokens": 108366, "cached_input_tokens": 108032, "cache_write_input_tokens": 0, "output_tokens": 684, "reasoning_output_tokens": 55, "total_tokens": 109050 }, "turn_token_usage": { "input_tokens": 10462897, "cached_input_tokens": 10337408, "cache_write_input_tokens": 0, "output_tokens": 66051, "reasoning_output_tokens": 24559, "total_tokens": 10528948 }, "thread_token_usage": { "input_tokens": 10462897, "cached_input_tokens": 10337408, "cache_write_input_tokens": 0, "output_tokens": 66051, "reasoning_output_tokens": 24559, "total_tokens": 10528948 } } ### exec · 2026-09-25T01:18:47.488Z { "code": "const r = await tools.write_stdin({\"session_id\":64744,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:18:55.822Z Script completed Wall time 8.3 seconds Output: 0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995 1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176 2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888 3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326 4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408 bin-best total 0.041836843450419915 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cbf5a1b887d29bff278032eb83f7", "usage": { "input_tokens": 109078, "cached_input_tokens": 108160, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 109123 }, "turn_token_usage": { "input_tokens": 10571975, "cached_input_tokens": 10445568, "cache_write_input_tokens": 0, "output_tokens": 66096, "reasoning_output_tokens": 24559, "total_tokens": 10638071 }, "thread_token_usage": { "input_tokens": 10571975, "cached_input_tokens": 10445568, "cache_write_input_tokens": 0, "output_tokens": 66096, "reasoning_output_tokens": 24559, "total_tokens": 10638071 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-fb56b67f-f99f-4f16-bab9-53fb67e2f2a2", "process_id": "64744", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\n# best schedule total\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); edges=np.quantile(rec,[.2,.4,.6,.8]); bins=np.searchsorted(edges,rec,side='right'); ws=np.array([.035,.04,.045,.05,.055,.06,.065,.07,.075,.085,.1,.12]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),list(zip(ws,q)),'BEST',ws[q.argmax()],q.max())\n# best schedule total\nprint('bin-best total',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/sum(len(vals[b][0]) for b in range(5)))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n", "stderr": "", "aggregated_output": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 398789932 }, "formatted_output": "0 641 [(np.float64(0.035), np.float64(0.03326329625891257)), (np.float64(0.04), np.float64(0.036212743186431993)), (np.float64(0.045), np.float64(0.03940170512546191)), (np.float64(0.05), np.float64(0.04167396698238771)), (np.float64(0.055), np.float64(0.04299457231045546)), (np.float64(0.06), np.float64(0.04406598133923958)), (np.float64(0.065), np.float64(0.043938287963886984)), (np.float64(0.07), np.float64(0.04426051997185995)), (np.float64(0.075), np.float64(0.04413536716396851)), (np.float64(0.085), np.float64(0.04418062555080782)), (np.float64(0.1), np.float64(0.043195052379703454)), (np.float64(0.12), np.float64(0.042808739903585344))] BEST 0.07 0.04426051997185995\r\n1 762 [(np.float64(0.035), np.float64(0.03532272794583811)), (np.float64(0.04), np.float64(0.03673739793442338)), (np.float64(0.045), np.float64(0.0383714819995829)), (np.float64(0.05), np.float64(0.03934406425335124)), (np.float64(0.055), np.float64(0.040198136432864795)), (np.float64(0.06), np.float64(0.04039255720734798)), (np.float64(0.065), np.float64(0.04047499735556358)), (np.float64(0.07), np.float64(0.039823895735248846)), (np.float64(0.075), np.float64(0.04051759370957176)), (np.float64(0.085), np.float64(0.04023303599786111)), (np.float64(0.1), np.float64(0.04032529111017876)), (np.float64(0.12), np.float64(0.039867511602506824))] BEST 0.075 0.04051759370957176\r\n2 868 [(np.float64(0.035), np.float64(0.031838368388587245)), (np.float64(0.04), np.float64(0.031753709284537954)), (np.float64(0.045), np.float64(0.030954165665289232)), (np.float64(0.05), np.float64(0.03294084368108997)), (np.float64(0.055), np.float64(0.033439179071178736)), (np.float64(0.06), np.float64(0.03411697905473888)), (np.float64(0.065), np.float64(0.03399756859189464)), (np.float64(0.07), np.float64(0.03355792822256345)), (np.float64(0.075), np.float64(0.03374125089937231)), (np.float64(0.085), np.float64(0.03315895913270104)), (np.float64(0.1), np.float64(0.033281350994774514)), (np.float64(0.12), np.float64(0.032286689947317586))] BEST 0.06 0.03411697905473888\r\n3 965 [(np.float64(0.035), np.float64(0.04364948872298739)), (np.float64(0.04), np.float64(0.044895067170825344)), (np.float64(0.045), np.float64(0.046932594359699084)), (np.float64(0.05), np.float64(0.04815225784385699)), (np.float64(0.055), np.float64(0.047916825035898274)), (np.float64(0.06), np.float64(0.04824027459956377)), (np.float64(0.065), np.float64(0.04853472447918374)), (np.float64(0.07), np.float64(0.04878967889696056)), (np.float64(0.075), np.float64(0.049308132734439326)), (np.float64(0.085), np.float64(0.04929776394008546)), (np.float64(0.1), np.float64(0.04923991072221143)), (np.float64(0.12), np.float64(0.04871239038271268))] BEST 0.075 0.049308132734439326\r\n4 1027 [(np.float64(0.035), np.float64(0.03832044157164782)), (np.float64(0.04), np.float64(0.040055488609037564)), (np.float64(0.045), np.float64(0.040126073638661)), (np.float64(0.05), np.float64(0.04070765099232078)), (np.float64(0.055), np.float64(0.04070114052959498)), (np.float64(0.06), np.float64(0.0408073787850408)), (np.float64(0.065), np.float64(0.040297702874375146)), (np.float64(0.07), np.float64(0.04050149394548966)), (np.float64(0.075), np.float64(0.03959381914952657)), (np.float64(0.085), np.float64(0.03933210015072555)), (np.float64(0.1), np.float64(0.038922456299431765)), (np.float64(0.12), np.float64(0.03752169044025099))] BEST 0.06 0.0408073787850408\r\nbin-best total 0.041836843450419915\r\n" } } ## Step 155 · agent · 2026-09-25T01:19:00.966Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:19:10.342Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:19:11.519Z Script completed Wall time 1.2 seconds Output: SESSION_ID=18917 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc000de487d2a67da2007bbc6aa4", "usage": { "input_tokens": 110574, "cached_input_tokens": 108928, "cache_write_input_tokens": 0, "output_tokens": 738, "reasoning_output_tokens": 209, "total_tokens": 111312 }, "turn_token_usage": { "input_tokens": 10682549, "cached_input_tokens": 10554496, "cache_write_input_tokens": 0, "output_tokens": 66834, "reasoning_output_tokens": 24768, "total_tokens": 10749383 }, "thread_token_usage": { "input_tokens": 10682549, "cached_input_tokens": 10554496, "cache_write_input_tokens": 0, "output_tokens": 66834, "reasoning_output_tokens": 24768, "total_tokens": 10749383 } } ### exec · 2026-09-25T01:19:13.414Z { "code": "const r = await tools.write_stdin({\"session_id\":18917,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:19:19.705Z Script completed Wall time 6.3 seconds Output: { "model": "/app/solution/validation_distinct_activity.parquet", "scores": { "quality_ndcg@10": 0.041837, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc0fb84887d2ac2a8d27447a5e73", "usage": { "input_tokens": 111340, "cached_input_tokens": 110336, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 111385 }, "turn_token_usage": { "input_tokens": 10793889, "cached_input_tokens": 10664832, "cache_write_input_tokens": 0, "output_tokens": 66879, "reasoning_output_tokens": 24768, "total_tokens": 10860768 }, "thread_token_usage": { "input_tokens": 10793889, "cached_input_tokens": 10664832, "cache_write_input_tokens": 0, "output_tokens": 66879, "reasoning_output_tokens": 24768, "total_tokens": 10860768 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7716ef89-bf46-49ae-b6e2-e0c9f6a09c71", "process_id": "18917", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.07,.075,.06,.075,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_distinct_activity.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_distinct_activity.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 187025728 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_distinct_activity.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.041837,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 156 · agent · 2026-09-25T01:19:30.176Z ### Thinking unavailable — no text recorded ## Step 157 · agent · 2026-09-25T01:19:36.152Z ### Thinking unavailable — no text recorded The distinct-listener signal is materially better: public NDCG rises from 0.040776 to 0.041837 without changing the neural checkpoints. I’m checking one closely related variant—counting each recent listener only once rather than once per day—before updating the saved final vectors and submission. ### exec · 2026-09-25T01:19:49.009Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\\nfor fam in ['listens','likes']:\\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\\nprint(*sorted(res,reverse=True)[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:19:50.175Z Script completed Wall time 1.2 seconds Output: SESSION_ID=78403 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc17ec3c87d28d67cb7f34658aa0", "usage": { "input_tokens": 111456, "cached_input_tokens": 111104, "cache_write_input_tokens": 0, "output_tokens": 1564, "reasoning_output_tokens": 847, "total_tokens": 113020 }, "turn_token_usage": { "input_tokens": 10905345, "cached_input_tokens": 10775936, "cache_write_input_tokens": 0, "output_tokens": 68443, "reasoning_output_tokens": 25615, "total_tokens": 10973788 }, "thread_token_usage": { "input_tokens": 10905345, "cached_input_tokens": 10775936, "cache_write_input_tokens": 0, "output_tokens": 68443, "reasoning_output_tokens": 25615, "total_tokens": 10973788 } } ### exec · 2026-09-25T01:19:52.153Z { "code": "const r = await tools.write_stdin({\"session_id\":78403,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:20:08.230Z Script completed Wall time 16.1 seconds Output: listens 486575 likes 40374 (np.float64(0.035205483400048414), 1.2, 7) (np.float64(0.03498808306103939), 0.4, 6) (np.float64(0.034896876781416244), 0.4, 7) (np.float64(0.0348942166033186), 0.55, 7) (np.float64(0.034862126635000684), 0.7, 7) (np.float64(0.03483671575735028), 0.25, 4) (np.float64(0.03482917671548458), 1, 7) (np.float64(0.03478521152246226), 0.55, 8) (np.float64(0.03477582498001578), 1, 8) (np.float64(0.03476475591000726), 1.2, 8) (np.float64(0.03473171614199507), 0.7, 8) (np.float64(0.034723017218649226), 0.85, 7) (np.float64(0.034716705778739775), 1, 10) (np.float64(0.03467791929175896), 0.85, 8) (np.float64(0.03464466592259532), 1.5, 8) (np.float64(0.03455038065147882), 1.2, 10) (np.float64(0.03452773376081744), 0.85, 10) (np.float64(0.03451210452372558), 0.4, 8) (np.float64(0.034461931072617226), 0.85, 20) (np.float64(0.03441430868696301), 0.55, 4) (np.float64(0.03441200809139694), 1.2, 20) (np.float64(0.03438291512752172), 0.85, 4) (np.float64(0.034377107855009616), 0.7, 6) (np.float64(0.03434389469318661), 0.55, 10) (np.float64(0.034330745156270985), 0.25, 6) (np.float64(0.034330257752257023), 0.55, 6) (np.float64(0.034320892515685694), 0.4, 4) (np.float64(0.03430707003105098), 0.7, 4) (np.float64(0.03429016012639817), 0.7, 10) (np.float64(0.03429007259362975), 1.2, 15) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc36680087d2af1e0b2957bd7b0c", "usage": { "input_tokens": 113048, "cached_input_tokens": 111232, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 113093 }, "turn_token_usage": { "input_tokens": 11018393, "cached_input_tokens": 10887168, "cache_write_input_tokens": 0, "output_tokens": 68488, "reasoning_output_tokens": 25615, "total_tokens": 11086881 }, "thread_token_usage": { "input_tokens": 11018393, "cached_input_tokens": 10887168, "cache_write_input_tokens": 0, "output_tokens": 68488, "reasoning_output_tokens": 25615, "total_tokens": 11086881 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-284da1aa-ae0f-4564-9612-4935bc702373", "process_id": "78403", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids); arr={}\nfor fam in ['listens','likes']:\n cols=['uid','item_id','timestamp','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])\n x=pd.read_parquet(tr.DATA/f'train_{fam}.parquet',columns=cols); ci=idx.get_indexer(x.item_id); age=((C-1-x.timestamp.to_numpy())//86400).astype(np.int16); keep=(ci>=0)&(age>=0)&(age<28)&(x.is_organic.to_numpy()==1)\n if fam=='listens': keep &= x.played_ratio_pct.to_numpy()>=80\n y=pd.DataFrame({'uid':x.uid.to_numpy()[keep],'ci':ci[keep],'age':age[keep]}).groupby(['uid','ci'],as_index=False).age.min(); a=np.zeros((28,len(ids)),np.float32)\n for d,g in y.groupby('age'): a[int(d)]=np.bincount(g.ci,minlength=len(ids))\n arr[fam]=a; np.save(f'daily_unique_listener_{fam}.npy',a); print(fam,len(y),flush=True)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5]:\n d=2**(-np.arange(28)/h); L=d@arr['listens']; A=d@arr['likes']\n for aw in [0,2,4,6,7,8,10,15,20]: res.append((ev(L+aw*A),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n", "stderr": "", "aggregated_output": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n", "exit_code": 0, "duration": { "secs": 19, "nanos": 54503753 }, "formatted_output": "listens 486575\r\nlikes 40374\r\n(np.float64(0.035205483400048414), 1.2, 7)\r\n(np.float64(0.03498808306103939), 0.4, 6)\r\n(np.float64(0.034896876781416244), 0.4, 7)\r\n(np.float64(0.0348942166033186), 0.55, 7)\r\n(np.float64(0.034862126635000684), 0.7, 7)\r\n(np.float64(0.03483671575735028), 0.25, 4)\r\n(np.float64(0.03482917671548458), 1, 7)\r\n(np.float64(0.03478521152246226), 0.55, 8)\r\n(np.float64(0.03477582498001578), 1, 8)\r\n(np.float64(0.03476475591000726), 1.2, 8)\r\n(np.float64(0.03473171614199507), 0.7, 8)\r\n(np.float64(0.034723017218649226), 0.85, 7)\r\n(np.float64(0.034716705778739775), 1, 10)\r\n(np.float64(0.03467791929175896), 0.85, 8)\r\n(np.float64(0.03464466592259532), 1.5, 8)\r\n(np.float64(0.03455038065147882), 1.2, 10)\r\n(np.float64(0.03452773376081744), 0.85, 10)\r\n(np.float64(0.03451210452372558), 0.4, 8)\r\n(np.float64(0.034461931072617226), 0.85, 20)\r\n(np.float64(0.03441430868696301), 0.55, 4)\r\n(np.float64(0.03441200809139694), 1.2, 20)\r\n(np.float64(0.03438291512752172), 0.85, 4)\r\n(np.float64(0.034377107855009616), 0.7, 6)\r\n(np.float64(0.03434389469318661), 0.55, 10)\r\n(np.float64(0.034330745156270985), 0.25, 6)\r\n(np.float64(0.034330257752257023), 0.55, 6)\r\n(np.float64(0.034320892515685694), 0.4, 4)\r\n(np.float64(0.03430707003105098), 0.7, 4)\r\n(np.float64(0.03429016012639817), 0.7, 10)\r\n(np.float64(0.03429007259362975), 1.2, 15)\r\n" } } ## Step 158 · agent · 2026-09-25T01:20:10.544Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:20:16.236Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:20:17.404Z Script completed Wall time 1.2 seconds Output: SESSION_ID=77373 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc486c3c87d2ad690c7c6f9c6a17", "usage": { "input_tokens": 113751, "cached_input_tokens": 112896, "cache_write_input_tokens": 0, "output_tokens": 383, "reasoning_output_tokens": 59, "total_tokens": 114134 }, "turn_token_usage": { "input_tokens": 11132144, "cached_input_tokens": 11000064, "cache_write_input_tokens": 0, "output_tokens": 68871, "reasoning_output_tokens": 25674, "total_tokens": 11201015 }, "thread_token_usage": { "input_tokens": 11132144, "cached_input_tokens": 11000064, "cache_write_input_tokens": 0, "output_tokens": 68871, "reasoning_output_tokens": 25674, "total_tokens": 11201015 } } ### exec · 2026-09-25T01:20:19.274Z { "code": "const r = await tools.write_stdin({\"session_id\":77373,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:20:33.061Z Script completed Wall time 13.8 seconds Output: blend trend_weight=0 public_ndcg@10=0.002883 blend trend_weight=0.035 public_ndcg@10=0.037187 blend trend_weight=0.045 public_ndcg@10=0.039419 blend trend_weight=0.055 public_ndcg@10=0.041035 blend trend_weight=0.065 public_ndcg@10=0.041802 blend trend_weight=0.075 public_ndcg@10=0.041468 blend trend_weight=0.085 public_ndcg@10=0.041328 blend trend_weight=0.095 public_ndcg@10=0.040868 blend trend_weight=0.11 public_ndcg@10=0.040566 blend trend_weight=0.13 public_ndcg@10=0.039589 blend trend_weight=0.16 public_ndcg@10=0.039083 blend trend_weight=0.22 public_ndcg@10=0.038181 blend trend_weight=0.35 public_ndcg@10=0.037248 blend trend_weight=0.65 public_ndcg@10=0.036146 blend trend_weight=1.5 public_ndcg@10=0.035449 blend trend_weight=4 public_ndcg@10=0.035237 (0.04180203890682845, 0.065) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc51993487d296cf2049b104ac41", "usage": { "input_tokens": 114162, "cached_input_tokens": 113536, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 114207 }, "turn_token_usage": { "input_tokens": 11246306, "cached_input_tokens": 11113600, "cache_write_input_tokens": 0, "output_tokens": 68916, "reasoning_output_tokens": 25674, "total_tokens": 11315222 }, "thread_token_usage": { "input_tokens": 11246306, "cached_input_tokens": 11113600, "cache_write_input_tokens": 0, "output_tokens": 68916, "reasoning_output_tokens": 25674, "total_tokens": 11315222 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34fe8bb0-322a-4a12-8a4d-32973d9a3236", "process_id": "77373", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); L=np.load('daily_unique_listener_listens.npy'); A=np.load('daily_unique_listener_likes.npy'); d=2**(-np.arange(28)/1.2); x=d@L+7*d@A; o=np.argsort(-x,kind='stable'); rank=np.empty(len(x),int); rank[o]=np.arange(1,len(x)+1); t=((-np.log(rank)+np.log(rank).mean())/np.log(rank).std()).astype(np.float32); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); print(tr.rank_and_tune('validation',r['uids'],ue,ie,t,tr.OUT/'validation_unique_listener_trend.parquet',None)); np.save('validation_unique_listener_trend.npy',t)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n", "stderr": "", "aggregated_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n", "exit_code": 0, "duration": { "secs": 16, "nanos": 655924443 }, "formatted_output": "blend trend_weight=0 public_ndcg@10=0.002883\r\nblend trend_weight=0.035 public_ndcg@10=0.037187\r\nblend trend_weight=0.045 public_ndcg@10=0.039419\r\nblend trend_weight=0.055 public_ndcg@10=0.041035\r\nblend trend_weight=0.065 public_ndcg@10=0.041802\r\nblend trend_weight=0.075 public_ndcg@10=0.041468\r\nblend trend_weight=0.085 public_ndcg@10=0.041328\r\nblend trend_weight=0.095 public_ndcg@10=0.040868\r\nblend trend_weight=0.11 public_ndcg@10=0.040566\r\nblend trend_weight=0.13 public_ndcg@10=0.039589\r\nblend trend_weight=0.16 public_ndcg@10=0.039083\r\nblend trend_weight=0.22 public_ndcg@10=0.038181\r\nblend trend_weight=0.35 public_ndcg@10=0.037248\r\nblend trend_weight=0.65 public_ndcg@10=0.036146\r\nblend trend_weight=1.5 public_ndcg@10=0.035449\r\nblend trend_weight=4 public_ndcg@10=0.035237\r\n(0.04180203890682845, 0.065)\r\n" } } ## Step 159 · agent · 2026-09-25T01:20:35.031Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:20:45.746Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\\nfor b in range(5):\\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:20:46.911Z Script completed Wall time 1.2 seconds Output: SESSION_ID=24499 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc614d5887d29a3ea5f47047cfde", "usage": { "input_tokens": 114529, "cached_input_tokens": 113920, "cache_write_input_tokens": 0, "output_tokens": 640, "reasoning_output_tokens": 36, "total_tokens": 115169 }, "turn_token_usage": { "input_tokens": 11360835, "cached_input_tokens": 11227520, "cache_write_input_tokens": 0, "output_tokens": 69556, "reasoning_output_tokens": 25710, "total_tokens": 11430391 }, "thread_token_usage": { "input_tokens": 11360835, "cached_input_tokens": 11227520, "cache_write_input_tokens": 0, "output_tokens": 69556, "reasoning_output_tokens": 25710, "total_tokens": 11430391 } } ### exec · 2026-09-25T01:20:49.124Z { "code": "const r = await tools.write_stdin({\"session_id\":24499,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":18000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:20:56.670Z Script completed Wall time 7.5 seconds Output: 0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))] 1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))] 2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))] 3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))] 4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))] TOTAL 0.042142933740260305 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc6f223487d2a83c0556f4e1ec01", "usage": { "input_tokens": 115197, "cached_input_tokens": 114304, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 115242 }, "turn_token_usage": { "input_tokens": 11476032, "cached_input_tokens": 11341824, "cache_write_input_tokens": 0, "output_tokens": 69601, "reasoning_output_tokens": 25710, "total_tokens": 11545633 }, "thread_token_usage": { "input_tokens": 11476032, "cached_input_tokens": 11341824, "cache_write_input_tokens": 0, "output_tokens": 69601, "reasoning_output_tokens": 25710, "total_tokens": 11545633 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7bdfd620-f5d4-49ca-913f-47f14040049f", "process_id": "24499", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); ws=np.array([.045,.05,.055,.06,.065,.07,.075,.08,.09,.1]); o=np.argsort(-t); vals={b:[[] for _ in ws] for b in range(5)}\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-700,axis=1)[:,-700:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(pn[l],o[masks[row,o]][:700]); b=bins[row]\n for j,w in enumerate(ws): vals[b][j].append(tr.ndcg10(items[cand[np.argsort(-(sc[l,cand]+w*t[cand]))[:10]]],gold))\nfor b in range(5):\n q=np.array([np.mean(x) for x in vals[b]]); print(b,len(vals[b][0]),'BEST',ws[q.argmax()],q.max(),list(zip(ws,q)))\nprint('TOTAL',sum(max(np.mean(x) for x in vals[b])*len(vals[b][0]) for b in range(5))/4263)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n", "stderr": "", "aggregated_output": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 758681937 }, "formatted_output": "0 641 BEST 0.08 0.04529127492482279 [(np.float64(0.045), np.float64(0.03885025757459129)), (np.float64(0.05), np.float64(0.041801725627252595)), (np.float64(0.055), np.float64(0.04327137160061966)), (np.float64(0.06), np.float64(0.04378508283379561)), (np.float64(0.065), np.float64(0.04441959155350635)), (np.float64(0.07), np.float64(0.044818655921257416)), (np.float64(0.075), np.float64(0.044645061992370894)), (np.float64(0.08), np.float64(0.04529127492482279)), (np.float64(0.09), np.float64(0.04472313360258801)), (np.float64(0.1), np.float64(0.044025150970121794))]\r\n1 762 BEST 0.09 0.04068494305337186 [(np.float64(0.045), np.float64(0.03883060280988426)), (np.float64(0.05), np.float64(0.03898129686367619)), (np.float64(0.055), np.float64(0.039694197292223246)), (np.float64(0.06), np.float64(0.04020451699252978)), (np.float64(0.065), np.float64(0.0402565657974291)), (np.float64(0.07), np.float64(0.04013952393799378)), (np.float64(0.075), np.float64(0.04060064013527018)), (np.float64(0.08), np.float64(0.04042881216015773)), (np.float64(0.09), np.float64(0.04068494305337186)), (np.float64(0.1), np.float64(0.04064461442478399))]\r\n2 868 BEST 0.06 0.03412607444859312 [(np.float64(0.045), np.float64(0.031906420337319545)), (np.float64(0.05), np.float64(0.0329929863762244)), (np.float64(0.055), np.float64(0.03345667059818207)), (np.float64(0.06), np.float64(0.03412607444859312)), (np.float64(0.065), np.float64(0.03378780250071687)), (np.float64(0.07), np.float64(0.03402859993406989)), (np.float64(0.075), np.float64(0.03383730951487187)), (np.float64(0.08), np.float64(0.03353950988487581)), (np.float64(0.09), np.float64(0.03304928342650661)), (np.float64(0.1), np.float64(0.03267760015679973))]\r\n3 965 BEST 0.065 0.049824910749182355 [(np.float64(0.045), np.float64(0.04665625749814744)), (np.float64(0.05), np.float64(0.04804756595378091)), (np.float64(0.055), np.float64(0.04806648514946787)), (np.float64(0.06), np.float64(0.04976103216064806)), (np.float64(0.065), np.float64(0.049824910749182355)), (np.float64(0.07), np.float64(0.04964147961238964)), (np.float64(0.075), np.float64(0.04851751481784313)), (np.float64(0.08), np.float64(0.04839912124020459)), (np.float64(0.09), np.float64(0.04814228427447728)), (np.float64(0.1), np.float64(0.04749407452175012))]\r\n4 1027 BEST 0.06 0.04081715794246262 [(np.float64(0.045), np.float64(0.03975973037843133)), (np.float64(0.05), np.float64(0.040409306625339876)), (np.float64(0.055), np.float64(0.04043121267381653)), (np.float64(0.06), np.float64(0.04081715794246262)), (np.float64(0.065), np.float64(0.04054993095695028)), (np.float64(0.07), np.float64(0.04000864121526118)), (np.float64(0.075), np.float64(0.03995283828151612)), (np.float64(0.08), np.float64(0.03966270787705315)), (np.float64(0.09), np.float64(0.039755880018242526)), (np.float64(0.1), np.float64(0.03911146057034898))]\r\nTOTAL 0.042142933740260305\r\n" } } ## Step 160 · agent · 2026-09-25T01:21:01.059Z ### Thinking unavailable — no text recorded ## Step 161 · agent · 2026-09-25T01:21:01.636Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:21:11.602Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\\n for l,row in enumerate(range(st,en)):\\n gold=truth.get(int(uids[row]));\\n if not gold: continue\\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\\n for a in aa:\\n s=base+a*t[cand]\\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":12000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:21:12.767Z Script completed Wall time 1.2 seconds Output: SESSION_ID=30530 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc78e7d887d29439c28ddb7be633", "usage": { "input_tokens": 116467, "cached_input_tokens": 115072, "cache_write_input_tokens": 0, "output_tokens": 752, "reasoning_output_tokens": 192, "total_tokens": 117219 }, "turn_token_usage": { "input_tokens": 11592499, "cached_input_tokens": 11456896, "cache_write_input_tokens": 0, "output_tokens": 70353, "reasoning_output_tokens": 25902, "total_tokens": 11662852 }, "thread_token_usage": { "input_tokens": 11592499, "cached_input_tokens": 11456896, "cache_write_input_tokens": 0, "output_tokens": 70353, "reasoning_output_tokens": 25902, "total_tokens": 11662852 } } ### exec · 2026-09-25T01:21:14.706Z { "code": "const r = await tools.write_stdin({\"session_id\":30530,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:21:34.522Z Script completed Wall time 19.8 seconds Output: (np.float64(0.0418254286913255), 0.05, 0.01) (np.float64(0.04181612242741825), 0.06, 0) (np.float64(0.04180203890682845), 0.065, 0) (np.float64(0.04176018145723837), 0.055, 0.01) (np.float64(0.041718227253830446), 0.07, 0) (np.float64(0.041706184767698225), 0.08, -0.015) (np.float64(0.0416525485802443), 0.04, 0.03) (np.float64(0.041584690091641716), 0.05, 0.02) (np.float64(0.04157850213820728), 0.06, 0.01) (np.float64(0.04157321678184587), 0.065, 0.01) (np.float64(0.04152010579935796), 0.055, 0.02) (np.float64(0.0415179681327068), 0.04, 0.02) (np.float64(0.04147094537135021), 0.05, 0.03) (np.float64(0.041455818689909336), 0.04, 0.045) (np.float64(0.04144138822296622), 0.065, 0.02) (np.float64(0.041397371820948554), 0.06, 0.02) (np.float64(0.041376849556522356), 0.08, 0) (np.float64(0.04137234252859961), 0.055, 0.03) (np.float64(0.041348287622949585), 0.07, -0.015) (np.float64(0.04134370721752038), 0.07, 0.01) (np.float64(0.04132984258074985), 0.05, 0.045) (np.float64(0.04126875653462899), 0.055, 0.045) (np.float64(0.04122425353182404), 0.065, 0.03) (np.float64(0.041216395889424544), 0.06, 0.03) (np.float64(0.04120573947224346), 0.07, 0.02) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc88f90c87d28bd71ceacef9f314", "usage": { "input_tokens": 117247, "cached_input_tokens": 116352, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 117292 }, "turn_token_usage": { "input_tokens": 11709746, "cached_input_tokens": 11573248, "cache_write_input_tokens": 0, "output_tokens": 70398, "reasoning_output_tokens": 25902, "total_tokens": 11780144 }, "thread_token_usage": { "input_tokens": 11709746, "cached_input_tokens": 11573248, "cache_write_input_tokens": 0, "output_tokens": 70398, "reasoning_output_tokens": 25902, "total_tokens": 11780144 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ea2460a-1185-4d71-97b2-7744f003ce71", "process_id": "30530", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t[cand]\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1); t=np.load('validation_unique_listener_trend.npy'); d=np.load('validation_unique_trend.npy')\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); aa=[.04,.05,.055,.06,.065,.07,.08]; bb=[-.04,-.025,-.015,0,.01,.02,.03,.045]; vals={(a,b):[] for a in aa for b in bb}; ot=np.argsort(-t); od=np.argsort(-d)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T; sc[~masks[st:en]]=-np.inf; pn=np.argpartition(sc,-600,axis=1)[:,-600:]\n for l,row in enumerate(range(st,en)):\n gold=truth.get(int(uids[row]));\n if not gold: continue\n cand=np.union1d(np.union1d(pn[l],ot[masks[row,ot]][:600]),od[masks[row,od]][:600]); base=sc[l,cand]\n for a in aa:\n s=base+a*t[cand]\n for b in bb: vals[(a,b)].append(tr.ndcg10(items[cand[np.argsort(-(s+b*d[cand]))[:10]]],gold))\nprint(*sorted([(np.mean(v),*k) for k,v in vals.items()],reverse=True)[:25],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n", "stderr": "", "aggregated_output": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n", "exit_code": 0, "duration": { "secs": 22, "nanos": 750497445 }, "formatted_output": "(np.float64(0.0418254286913255), 0.05, 0.01)\r\n(np.float64(0.04181612242741825), 0.06, 0)\r\n(np.float64(0.04180203890682845), 0.065, 0)\r\n(np.float64(0.04176018145723837), 0.055, 0.01)\r\n(np.float64(0.041718227253830446), 0.07, 0)\r\n(np.float64(0.041706184767698225), 0.08, -0.015)\r\n(np.float64(0.0416525485802443), 0.04, 0.03)\r\n(np.float64(0.041584690091641716), 0.05, 0.02)\r\n(np.float64(0.04157850213820728), 0.06, 0.01)\r\n(np.float64(0.04157321678184587), 0.065, 0.01)\r\n(np.float64(0.04152010579935796), 0.055, 0.02)\r\n(np.float64(0.0415179681327068), 0.04, 0.02)\r\n(np.float64(0.04147094537135021), 0.05, 0.03)\r\n(np.float64(0.041455818689909336), 0.04, 0.045)\r\n(np.float64(0.04144138822296622), 0.065, 0.02)\r\n(np.float64(0.041397371820948554), 0.06, 0.02)\r\n(np.float64(0.041376849556522356), 0.08, 0)\r\n(np.float64(0.04137234252859961), 0.055, 0.03)\r\n(np.float64(0.041348287622949585), 0.07, -0.015)\r\n(np.float64(0.04134370721752038), 0.07, 0.01)\r\n(np.float64(0.04132984258074985), 0.05, 0.045)\r\n(np.float64(0.04126875653462899), 0.055, 0.045)\r\n(np.float64(0.04122425353182404), 0.065, 0.03)\r\n(np.float64(0.041216395889424544), 0.06, 0.03)\r\n(np.float64(0.04120573947224346), 0.07, 0.02)\r\n" } } ## Step 162 · agent · 2026-09-25T01:21:39.882Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:21:49.196Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:21:50.361Z Script completed Wall time 1.2 seconds Output: SESSION_ID=44884 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cc9ebd3887d2807e01ce7743bb61", "usage": { "input_tokens": 117882, "cached_input_tokens": 117120, "cache_write_input_tokens": 0, "output_tokens": 749, "reasoning_output_tokens": 223, "total_tokens": 118631 }, "turn_token_usage": { "input_tokens": 11827628, "cached_input_tokens": 11690368, "cache_write_input_tokens": 0, "output_tokens": 71147, "reasoning_output_tokens": 26125, "total_tokens": 11898775 }, "thread_token_usage": { "input_tokens": 11827628, "cached_input_tokens": 11690368, "cache_write_input_tokens": 0, "output_tokens": 71147, "reasoning_output_tokens": 26125, "total_tokens": 11898775 } } ### exec · 2026-09-25T01:21:52.308Z { "code": "const r = await tools.write_stdin({\"session_id\":44884,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:21:58.628Z Script completed Wall time 6.3 seconds Output: { "model": "/app/solution/validation_best.parquet", "scores": { "quality_ndcg@10": 0.042143, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccae892c87d2bddbde465c875e32", "usage": { "input_tokens": 118659, "cached_input_tokens": 117760, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 118704 }, "turn_token_usage": { "input_tokens": 11946287, "cached_input_tokens": 11808128, "cache_write_input_tokens": 0, "output_tokens": 71192, "reasoning_output_tokens": 26125, "total_tokens": 12017479 }, "thread_token_usage": { "input_tokens": 11946287, "cached_input_tokens": 11808128, "cache_write_input_tokens": 0, "output_tokens": 71192, "reasoning_output_tokens": 26125, "total_tokens": 12017479 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-59ed017d-716f-4ace-84db-5976e3ef34df", "process_id": "44884", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); t=np.load('validation_unique_listener_trend.npy'); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_best.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_best.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 267051259 }, "formatted_output": "{\r\n \"model\": \"/app/solution/validation_best.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 163 · agent · 2026-09-25T01:22:00.281Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:22:21.505Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_ranker.py\\n@@\\n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\\n+def recent_trend(stage: str, catalog_ids: np.ndarray) -> np.ndarray:\\n cutoff = TRAIN_CUTOFF if stage == \\\"validation\\\" else VALIDATION_CUTOFF\\n idx = pd.Index(catalog_ids)\\n score = np.zeros(len(catalog_ids), dtype=np.float64)\\n- # Public tuning favored a short three-day half-life and strong explicit\\n- # actions. Counts use only information available strictly before cutoff.\\n- for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 30.0)]:\\n- cols = [\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n+ # Count each listener/item only once, at their most recent active day. This\\n+ # targets future user-level discovery rather than repeat-play volume.\\n+ for family, gain in [(\\\"listens\\\", 1.0), (\\\"likes\\\", 7.0)]:\\n+ cols = [\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"]\\n if family == \\\"listens\\\":\\n cols.append(\\\"played_ratio_pct\\\")\\n- x = load_family(stage, family, cols)\\n- mapped = idx.get_indexer(x.item_id)\\n- age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\\n- mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n- mask &= x.is_organic.to_numpy() == 1\\n- if family == \\\"listens\\\":\\n- mask &= x.played_ratio_pct.to_numpy() >= 80\\n- weighted = np.exp2(-age_day[mask] / 3.0)\\n- score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\\n- lifetime = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\").positive_listens.to_numpy(dtype=np.float64)\\n- raw = score + prior * np.power(lifetime, 0.75)\\n+ sources = [DATA / f\\\"train_{family}.parquet\\\"]\\n+ if stage == \\\"final\\\":\\n+ sources.append(DATA / f\\\"val_{family}.parquet\\\")\\n+ recent_parts: list[pd.DataFrame] = []\\n+ for source in sources:\\n+ x = pd.read_parquet(source, columns=cols)\\n+ mapped = idx.get_indexer(x.item_id)\\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int16)\\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\\n+ mask &= x.is_organic.to_numpy() == 1\\n+ if family == \\\"listens\\\":\\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\\n+ recent_parts.append(\\n+ pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": x.uid.to_numpy()[mask],\\n+ \\\"ci\\\": mapped[mask],\\n+ \\\"age\\\": age_day[mask],\\n+ }\\n+ )\\n+ )\\n+ recent = pd.concat(recent_parts, ignore_index=True)\\n+ latest = recent.groupby([\\\"uid\\\", \\\"ci\\\"], as_index=False).age.min()\\n+ weighted = np.exp2(-latest.age.to_numpy(dtype=np.float64) / 1.2)\\n+ score += gain * np.bincount(\\n+ latest.ci.to_numpy(), weights=weighted, minlength=len(catalog_ids)\\n+ )\\n+ raw = score\\n*** Update File: /app/solution/build_submission.py\\n@@\\n-TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\\n+TREND_BY_ACTIVITY = np.asarray([0.08, 0.09, 0.06, 0.065, 0.06], dtype=np.float32)\\n*** Update File: /app/solution/README.md\\n@@\\n-- The two 48-dimensional spaces are concatenated with one offline item-trend\\n- dimension. Trend uses organic 80%-complete listens and organic likes with a\\n- three-day half-life. Its user-side coefficient depends only on 90-day\\n+- The two 48-dimensional spaces are concatenated with one offline item-trend\\n+ dimension. Trend counts each recent organic listener/item once (80%-complete\\n+ listens plus organic likes) with a 1.2-day half-life. Its user-side\\n+ coefficient depends only on 90-day\\n@@\\n-Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\\n+Public validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:22:21.664Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b1250806-081a-4f64-ad3a-02b6d3e1c308", "changes": { "/app/solution/train_ranker.py": { "type": "update", "unified_diff": "@@ -593,3 +593,3 @@\n \n-def recent_trend(stage: str, catalog_ids: np.ndarray, prior: float = 0.10) -> np.ndarray:\n+def recent_trend(stage: str, catalog_ids: np.ndarray) -> np.ndarray:\n cutoff = TRAIN_CUTOFF if stage == \"validation\" else VALIDATION_CUTOFF\n@@ -597,19 +597,36 @@\n score = np.zeros(len(catalog_ids), dtype=np.float64)\n- # Public tuning favored a short three-day half-life and strong explicit\n- # actions. Counts use only information available strictly before cutoff.\n- for family, gain in [(\"listens\", 1.0), (\"likes\", 30.0)]:\n- cols = [\"timestamp\", \"item_id\", \"is_organic\"]\n+ # Count each listener/item only once, at their most recent active day. This\n+ # targets future user-level discovery rather than repeat-play volume.\n+ for family, gain in [(\"listens\", 1.0), (\"likes\", 7.0)]:\n+ cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n if family == \"listens\":\n cols.append(\"played_ratio_pct\")\n- x = load_family(stage, family, cols)\n- mapped = idx.get_indexer(x.item_id)\n- age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int32)\n- mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n- mask &= x.is_organic.to_numpy() == 1\n- if family == \"listens\":\n- mask &= x.played_ratio_pct.to_numpy() >= 80\n- weighted = np.exp2(-age_day[mask] / 3.0)\n- score += gain * np.bincount(mapped[mask], weights=weighted, minlength=len(catalog_ids))\n- lifetime = pd.read_parquet(DATA / \"candidate_catalog.parquet\").positive_listens.to_numpy(dtype=np.float64)\n- raw = score + prior * np.power(lifetime, 0.75)\n+ sources = [DATA / f\"train_{family}.parquet\"]\n+ if stage == \"final\":\n+ sources.append(DATA / f\"val_{family}.parquet\")\n+ recent_parts: list[pd.DataFrame] = []\n+ for source in sources:\n+ x = pd.read_parquet(source, columns=cols)\n+ mapped = idx.get_indexer(x.item_id)\n+ age_day = ((cutoff - 1 - x.timestamp.to_numpy()) // 86400).astype(np.int16)\n+ mask = (mapped >= 0) & (age_day >= 0) & (age_day < 28)\n+ mask &= x.is_organic.to_numpy() == 1\n+ if family == \"listens\":\n+ mask &= x.played_ratio_pct.to_numpy() >= 80\n+ recent_parts.append(\n+ pd.DataFrame(\n+ {\n+ \"uid\": x.uid.to_numpy()[mask],\n+ \"ci\": mapped[mask],\n+ \"age\": age_day[mask],\n+ }\n+ )\n+ )\n+ recent = pd.concat(recent_parts, ignore_index=True)\n+ latest = recent.groupby([\"uid\", \"ci\"], as_index=False).age.min()\n+ weighted = np.exp2(-latest.age.to_numpy(dtype=np.float64) / 1.2)\n+ score += gain * np.bincount(\n+ latest.ci.to_numpy(), weights=weighted, minlength=len(catalog_ids)\n+ )\n+ raw = score\n # A rank transform is stable under weekly volume shifts and easy to append to MIPS.\n", "move_path": null }, "/app/solution/build_submission.py": { "type": "update", "unified_diff": "@@ -20,3 +20,3 @@\n # Coefficients selected on all-target activity quintiles in the public backtest.\n-TREND_BY_ACTIVITY = np.asarray([0.09, 0.06, 0.06, 0.06, 0.055], dtype=np.float32)\n+TREND_BY_ACTIVITY = np.asarray([0.08, 0.09, 0.06, 0.065, 0.06], dtype=np.float32)\n \n", "move_path": null }, "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -21,4 +21,5 @@\n - The two 48-dimensional spaces are concatenated with one offline item-trend\n- dimension. Trend uses organic 80%-complete listens and organic likes with a\n- three-day half-life. Its user-side coefficient depends only on 90-day\n+ dimension. Trend counts each recent organic listener/item once (80%-complete\n+ listens plus organic likes) with a 1.2-day half-life. Its user-side\n+ coefficient depends only on 90-day\n history activity. Thus the final 97-dimensional relevance score is exactly\n@@ -42,3 +43,3 @@\n \n-Public validation graded NDCG@10: **0.040776** over 4,263 scored users, verified\n+Public validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\n with `/task/evaluate_public.py`. The corresponding ranking is\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\nM /app/solution/build_submission.py\nM /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccb6ce6087d2bc28c76c48bb6f3e", "usage": { "input_tokens": 118773, "cached_input_tokens": 118528, "cache_write_input_tokens": 0, "output_tokens": 1207, "reasoning_output_tokens": 20, "total_tokens": 119980 }, "turn_token_usage": { "input_tokens": 12065060, "cached_input_tokens": 11926656, "cache_write_input_tokens": 0, "output_tokens": 72399, "reasoning_output_tokens": 26145, "total_tokens": 12137459 }, "thread_token_usage": { "input_tokens": 12065060, "cached_input_tokens": 11926656, "cache_write_input_tokens": 0, "output_tokens": 72399, "reasoning_output_tokens": 26145, "total_tokens": 12137459 } } ## Step 164 · agent · 2026-09-25T01:22:26.111Z ### Thinking unavailable — no text recorded The final trend variant wins decisively: it counts each listener-track once at the most recent day, uses a 1.2-day half-life, and reaches 0.042143 publicly. I’m updating only the final offline trend coordinate and reranking from the already-refit neural vectors—no retraining or eligibility logic changes. ### exec · 2026-09-25T01:22:40.019Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_best.parquet /app/solution/validation.parquet\\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport json,numpy as np,pandas as pd\\nimport train_ranker as tr\\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\\nwith np.load('deployment_vectors.npz') as z:\\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\\niv[:,-1]=tr.recent_trend('final',items)\\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\\n# Replace both only after all computations and writes succeed.\\nfrom pathlib import Path\\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\\\n')\\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:22:41.186Z Script completed Wall time 1.2 seconds Output: SESSION_ID=89775 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccce2ee887d2805555ad570e9e39", "usage": { "input_tokens": 120004, "cached_input_tokens": 118656, "cache_write_input_tokens": 0, "output_tokens": 931, "reasoning_output_tokens": 158, "total_tokens": 120935 }, "turn_token_usage": { "input_tokens": 12185064, "cached_input_tokens": 12045312, "cache_write_input_tokens": 0, "output_tokens": 73330, "reasoning_output_tokens": 26303, "total_tokens": 12258394 }, "thread_token_usage": { "input_tokens": 12185064, "cached_input_tokens": 12045312, "cache_write_input_tokens": 0, "output_tokens": 73330, "reasoning_output_tokens": 26303, "total_tokens": 12258394 } } ### exec · 2026-09-25T01:22:43.272Z { "code": "const r = await tools.write_stdin({\"session_id\":89775,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:22:48.016Z Script completed Wall time 4.7 seconds Output: {'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cce1631087d29c8836ea25606fcd", "usage": { "input_tokens": 120963, "cached_input_tokens": 119808, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 121008 }, "turn_token_usage": { "input_tokens": 12306027, "cached_input_tokens": 12165120, "cache_write_input_tokens": 0, "output_tokens": 73375, "reasoning_output_tokens": 26303, "total_tokens": 12379402 }, "thread_token_usage": { "input_tokens": 12306027, "cached_input_tokens": 12165120, "cache_write_input_tokens": 0, "output_tokens": 73375, "reasoning_output_tokens": 26303, "total_tokens": 12379402 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7931c987-161a-4d6d-adb3-58c990d1c1c9", "process_id": "89775", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_best.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport json,numpy as np,pandas as pd\nimport train_ranker as tr\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\nwith np.load('deployment_vectors.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\niv[:,-1]=tr.recent_trend('final',items)\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\n# Replace both only after all computations and writes succeed.\nfrom pathlib import Path\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\n')\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_best.parquet /app/solution/validation.parquet\ncd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport json,numpy as np,pandas as pd\nimport train_ranker as tr\nfrom build_submission import final_activity,TREND_BY_ACTIVITY\nwith np.load('deployment_vectors.npz') as z:\n uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); uv=z['user_vectors'].astype(np.float32); iv=z['item_vectors'].astype(np.float32)\nactivity=final_activity(uids); edges=np.quantile(activity,[.2,.4,.6,.8]); bins=np.searchsorted(edges,activity,side='right'); uv[:,-1]=TREND_BY_ACTIVITY[bins]\niv[:,-1]=tr.recent_trend('final',items)\nnp.savez_compressed('deployment_vectors.updated.npz',uids=uids,item_ids=items,user_vectors=uv,item_vectors=iv)\nwith np.load(tr.DATA/'test_eligible_candidates.npz') as z:\n packed=z['packed_eligible'].copy(); eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64)\nassert np.array_equal(uids,eu) and np.array_equal(items,ei)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=uv[st:en]@iv.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\nout=pd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}); out.to_parquet('submission.updated.parquet',index=False)\n# Replace both only after all computations and writes succeed.\nfrom pathlib import Path\nPath('deployment_vectors.updated.npz').replace('deployment_vectors.npz'); Path('submission.updated.parquet').replace('submission.parquet')\nreport=json.loads(Path('final_report.json').read_text()); report['activity_edges']=edges.tolist(); report['trend_weights']=TREND_BY_ACTIVITY.tolist(); report['trend']='latest distinct organic listeners, 1.2-day half-life, like gain 7'; Path('final_report.json').write_text(json.dumps(report,indent=2)+'\\n')\nprint({'activity_edges':edges.tolist(),'trend_weights':TREND_BY_ACTIVITY.tolist(),'rows':len(out)},flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n", "stderr": "", "aggregated_output": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n", "exit_code": 0, "duration": { "secs": 7, "nanos": 829461436 }, "formatted_output": "{'activity_edges': [242.0, 580.0, 1116.0, 2021.0], 'trend_weights': [0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549], 'rows': 845600}\r\n" } } ## Step 165 · agent · 2026-09-25T01:22:50.465Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:00.647Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\\nimport sys,numpy as np,pandas as pd\\nfrom pathlib import Path\\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\\nassert np.array_equal(submitted,reproduced)\\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\\nsed -n '1,240p' README.md\\ncat final_report.json\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":20000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:23:01.812Z Script completed Wall time 1.2 seconds Output: SESSION_ID=84114 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cce8410487d2a1598b7199b2a8a0", "usage": { "input_tokens": 121118, "cached_input_tokens": 120832, "cache_write_input_tokens": 0, "output_tokens": 638, "reasoning_output_tokens": 64, "total_tokens": 121756 }, "turn_token_usage": { "input_tokens": 12427145, "cached_input_tokens": 12285952, "cache_write_input_tokens": 0, "output_tokens": 74013, "reasoning_output_tokens": 26367, "total_tokens": 12501158 }, "thread_token_usage": { "input_tokens": 12427145, "cached_input_tokens": 12285952, "cache_write_input_tokens": 0, "output_tokens": 74013, "reasoning_output_tokens": 26367, "total_tokens": 12501158 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6c8dd21d-cdc9-48b0-b4de-a12e1882f65c", "process_id": "84114", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\nsed -n '1,240p' README.md\ncat final_report.json" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -m py_compile train_ranker.py build_submission.py && /opt/task-venv/bin/python -u - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task'); from ranking_evaluation import validate_ranking\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); f=validate_ranking(pd.read_parquet('submission.parquet'),targets).sort_values(['uid','rank']).reset_index(drop=True)\nwith np.load(D/'test_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nwith np.load('deployment_vectors.npz') as z: vu=z['user_vectors']; vi=z['item_vectors']; assert np.array_equal(z['uids'],uids); assert np.array_equal(z['item_ids'],items)\nmask=np.unpackbits(packed,axis=1,count=len(items),bitorder='little').astype(bool); submitted=f.item_id.to_numpy().reshape(len(uids),100); reproduced=np.empty_like(submitted)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); score=vu[st:en]@vi.T; score[~mask[st:en]]=-np.inf; p=np.argpartition(score,-100,axis=1)[:,-100:]; ps=np.take_along_axis(score,p,axis=1); p=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1); reproduced[st:en]=items[p]\nassert np.array_equal(submitted,reproduced)\ncols=pd.Index(items).get_indexer(submitted.ravel()).reshape(submitted.shape); assert np.take_along_axis(mask,cols,axis=1).all()\nprint({'rows':len(f),'users':f.uid.nunique(),'eligible':True,'exact_dot_product':True,'vector_dim':vi.shape[1],'unique_pairs':not f.duplicated(['uid','item_id']).any()})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation.parquet\nsed -n '1,240p' README.md\ncat final_report.json" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n", "stderr": "", "aggregated_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 584441041 }, "formatted_output": "{'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True}\r\n{\r\n \"model\": \"validation.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n# Made for You two-tower ranker\r\n\r\n`submission.parquet` is the final test ranking. It has 845,600 rows (100 for\r\neach of 8,456 users), and was checked for schema, unique ranks/items, packed\r\ntest eligibility, and exact reproduction from the saved MIPS vectors.\r\n\r\n## Model\r\n\r\nThe deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no\r\nuser-ID lookup and never builds or factorizes a user-item matrix.\r\n\r\n- The user tower pools the last 160 conventional-positive catalog events with\r\n recency, organic, and completion weights. A second view pools the last 160\r\n *distinct* positive tracks. Active liked and disliked tracks are separate\r\n embedding pools sourced from the cutoff-correct preference state. Each view\r\n passes its pooled history and four history statistics through a two-layer\r\n GELU MLP and produces a normalized 48-dimensional vector.\r\n- The item tower combines a trainable catalog-ID embedding with a residual MLP\r\n over log-popularity and organic-share features, producing a normalized\r\n 48-dimensional vector for each history view.\r\n- The two 48-dimensional spaces are concatenated with one offline item-trend\r\n dimension. Trend counts each recent organic listener/item once (80%-complete\r\n listens plus organic likes) with a 1.2-day half-life. Its user-side\r\n coefficient depends only on 90-day\r\n history activity. Thus the final 97-dimensional relevance score is exactly\r\n one user-vector/item-vector dot product. Candidate vectors can be computed\r\n offline; request time needs one history-derived user vector and MIPS.\r\n\r\n`RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in\r\n`train_ranker.py` define the deployment contract. The exact fitted vectors are\r\nsaved in `deployment_vectors.npz`; the two fitted checkpoints are\r\n`two_tower_final_raw.pt` and `two_tower_final_unique.pt`.\r\n\r\n## Training\r\n\r\nTraining examples use 30 rolling cutoff-correct backtests. A history predicts\r\norganic first-discovery tracks in the next seven days after a 30-minute gap.\r\nThe loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled\r\n3.5 times as often as grade-1 strong listens. This is event-sampled and scales\r\nlinearly with interaction records and minibatches. The final models are refit\r\non `train_* + val_*`; validation events are then history/training data, never\r\nhidden outcomes.\r\n\r\nPublic validation graded NDCG@10: **0.042143** over 4,263 scored users, verified\r\nwith `/task/evaluate_public.py`. The corresponding ranking is\r\n`validation.parquet`.\r\n\r\n## Reproduce the final submission\r\n\r\nFrom `/app/solution` with the supplied environment:\r\n\r\n```sh\r\n/opt/task-venv/bin/python build_submission.py\r\n```\r\n\r\nThis writes checkpoints and vectors under `/app/solution/`, then atomically\r\nreplaces `/app/solution/submission.parquet` only after ranking succeeds.\r\n{\r\n \"model\": \"97d concatenated two-tower MIPS ensemble\",\r\n \"raw_examples\": 100053,\r\n \"unique_examples\": 100053,\r\n \"activity_edges\": [\r\n 242.0,\r\n 580.0,\r\n 1116.0,\r\n 2021.0\r\n ],\r\n \"trend_weights\": [\r\n 0.07999999821186066,\r\n 0.09000000357627869,\r\n 0.05999999865889549,\r\n 0.06499999761581421,\r\n 0.05999999865889549\r\n ],\r\n \"rows\": 845600,\r\n \"seconds\": 421.5,\r\n \"trend\": \"latest distinct organic listeners, 1.2-day half-life, like gain 7\"\r\n}\r\n" } } ### exec · 2026-09-25T01:23:07.752Z { "code": "const r = await tools.write_stdin({\"session_id\":84114,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:23:07.907Z Script completed Wall time 0.0 seconds Output: {'rows': 845600, 'users': 8456, 'eligible': True, 'exact_dot_product': True, 'vector_dim': 97, 'unique_pairs': True} { "model": "validation.parquet", "scores": { "quality_ndcg@10": 0.042143, "quality_users": 4263 } } # Made for You two-tower ranker `submission.parquet` is the final test ranking. It has 845,600 rows (100 for each of 8,456 users), and was checked for schema, unique ranks/items, packed test eligibility, and exact reproduction from the saved MIPS vectors. ## Model The deployed model is a 97-dimensional PyTorch two-tower ensemble. It has no user-ID lookup and never builds or factorizes a user-item matrix. - The user tower pools the last 160 conventional-positive catalog events with recency, organic, and completion weights. A second view pools the last 160 *distinct* positive tracks. Active liked and disliked tracks are separate embedding pools sourced from the cutoff-correct preference state. Each view passes its pooled history and four history statistics through a two-layer GELU MLP and produces a normalized 48-dimensional vector. - The item tower combines a trainable catalog-ID embedding with a residual MLP over log-popularity and organic-share features, producing a normalized 48-dimensional vector for each history view. - The two 48-dimensional spaces are concatenated with one offline item-trend dimension. Trend counts each recent organic listener/item once (80%-complete listens plus organic likes) with a 1.2-day half-life. Its user-side coefficient depends only on 90-day history activity. Thus the final 97-dimensional relevance score is exactly one user-vector/item-vector dot product. Candidate vectors can be computed offline; request time needs one history-derived user vector and MIPS. `RetrievalUserTower`, `RetrievalItemTower`, and `RetrievalTwoTower` in `train_ranker.py` define the deployment contract. The exact fitted vectors are saved in `deployment_vectors.npz`; the two fitted checkpoints are `two_tower_final_raw.pt` and `two_tower_final_unique.pt`. ## Training Training examples use 30 rolling cutoff-correct backtests. A history predicts organic first-discovery tracks in the next seven days after a 30-minute gap. The loss is in-batch sampled-softmax cross entropy, with grade-3 likes sampled 3.5 times as often as grade-1 strong listens. This is event-sampled and scales linearly with interaction records and minibatches. The final models are refit on `train_* + val_*`; validation events are then history/training data, never hidden outcomes. Public validation graded NDCG@10: **0.042143** over 4,263 scored users, verified with `/task/evaluate_public.py`. The corresponding ranking is `validation.parquet`. ## Reproduce the final submission From `/app/solution` with the supplied environment: ```sh /opt/task-venv/bin/python build_submission.py ``` This writes checkpoints and vectors under `/app/solution/`, then atomically replaces `/app/solution/submission.parquet` only after ranking succeeds. { "model": "97d concatenated two-tower MIPS ensemble", "raw_examples": 100053, "unique_examples": 100053, "activity_edges": [ 242.0, 580.0, 1116.0, 2021.0 ], "trend_weights": [ 0.07999999821186066, 0.09000000357627869, 0.05999999865889549, 0.06499999761581421, 0.05999999865889549 ], "rows": 845600, "seconds": 421.5, "trend": "latest distinct organic listeners, 1.2-day half-life, like gain 7" } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccf600bc87d28ea0fb05fcc2599f", "usage": { "input_tokens": 121784, "cached_input_tokens": 120960, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 121829 }, "turn_token_usage": { "input_tokens": 12548929, "cached_input_tokens": 12406912, "cache_write_input_tokens": 0, "output_tokens": 74058, "reasoning_output_tokens": 26367, "total_tokens": 12622987 }, "thread_token_usage": { "input_tokens": 12548929, "cached_input_tokens": 12406912, "cache_write_input_tokens": 0, "output_tokens": 74058, "reasoning_output_tokens": 26367, "total_tokens": 12622987 } } ## Step 166 · agent · 2026-09-25T01:23:12.013Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:14.263Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/README.md\\n@@\\n dimension. Trend counts each recent organic listener/item once (80%-complete\\n listens plus organic likes) with a 1.2-day half-life. Its user-side\\n- coefficient depends only on 90-day\\n- history activity. Thus the final 97-dimensional relevance score is exactly\\n+ coefficient depends only on 90-day history activity. Thus the final\\n+ 97-dimensional relevance score is exactly\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:23:14.457Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-05920607-4d31-4957-bd47-ec0b840eaa93", "changes": { "/app/solution/README.md": { "type": "update", "unified_diff": "@@ -23,4 +23,4 @@\n listens plus organic likes) with a 1.2-day half-life. Its user-side\n- coefficient depends only on 90-day\n- history activity. Thus the final 97-dimensional relevance score is exactly\n+ coefficient depends only on 90-day history activity. Thus the final\n+ 97-dimensional relevance score is exactly\n one user-vector/item-vector dot product. Candidate vectors can be computed\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5ccfc5aa087d2b4f31f00e50805f4", "usage": { "input_tokens": 122741, "cached_input_tokens": 121600, "cache_write_input_tokens": 0, "output_tokens": 240, "reasoning_output_tokens": 106, "total_tokens": 122981 }, "turn_token_usage": { "input_tokens": 12671670, "cached_input_tokens": 12528512, "cache_write_input_tokens": 0, "output_tokens": 74298, "reasoning_output_tokens": 26473, "total_tokens": 12745968 }, "thread_token_usage": { "input_tokens": 12671670, "cached_input_tokens": 12528512, "cache_write_input_tokens": 0, "output_tokens": 74298, "reasoning_output_tokens": 26473, "total_tokens": 12745968 } } ## Step 167 · agent · 2026-09-25T01:23:17.488Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:20.209Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:23:25.475Z Script completed Wall time 5.3 seconds Output: trend_reproduction_max_abs_error 0.00018596649169921875 identical False ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd02e4fc87d2a91029c7734048fc", "usage": { "input_tokens": 123005, "cached_input_tokens": 122624, "cache_write_input_tokens": 0, "output_tokens": 167, "reasoning_output_tokens": 7, "total_tokens": 123172 }, "turn_token_usage": { "input_tokens": 12794675, "cached_input_tokens": 12651136, "cache_write_input_tokens": 0, "output_tokens": 74465, "reasoning_output_tokens": 26480, "total_tokens": 12869140 }, "thread_token_usage": { "input_tokens": 12794675, "cached_input_tokens": 12651136, "cache_write_input_tokens": 0, "output_tokens": 74465, "reasoning_output_tokens": 26480, "total_tokens": 12869140 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-dd411c79-7af3-4cab-b470-8c6fbeb690fe", "process_id": "46204", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64)\na=tr.recent_trend('validation',ids); b=np.load('validation_unique_listener_trend.npy')\nprint('trend_reproduction_max_abs_error',float(np.max(np.abs(a-b))),'identical',np.array_equal(a,b))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n", "stderr": "", "aggregated_output": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 87640561 }, "formatted_output": "trend_reproduction_max_abs_error 0.00018596649169921875 identical False\n" } } ## Step 168 · agent · 2026-09-25T01:23:30.942Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:40.673Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd\\nimport train_ranker as tr\\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\\nfor st in range(0,len(uids),128):\\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":10000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:23:41.839Z Script completed Wall time 1.2 seconds Output: SESSION_ID=26124 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd0dac5887d2accc2cc1eaccf710", "usage": { "input_tokens": 123214, "cached_input_tokens": 122880, "cache_write_input_tokens": 0, "output_tokens": 791, "reasoning_output_tokens": 243, "total_tokens": 124005 }, "turn_token_usage": { "input_tokens": 12917889, "cached_input_tokens": 12774016, "cache_write_input_tokens": 0, "output_tokens": 75256, "reasoning_output_tokens": 26723, "total_tokens": 12993145 }, "thread_token_usage": { "input_tokens": 12917889, "cached_input_tokens": 12774016, "cache_write_input_tokens": 0, "output_tokens": 75256, "reasoning_output_tokens": 26723, "total_tokens": 12993145 } } ### exec · 2026-09-25T01:23:44.302Z { "code": "const r = await tools.write_stdin({\"session_id\":26124,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:23:51.334Z Script completed Wall time 7.0 seconds Output: { "model": "validation_reproduced.parquet", "scores": { "quality_ndcg@10": 0.042143, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd1e151c87d2b30b545e4873f554", "usage": { "input_tokens": 124033, "cached_input_tokens": 123008, "cache_write_input_tokens": 0, "output_tokens": 45, "reasoning_output_tokens": 0, "total_tokens": 124078 }, "turn_token_usage": { "input_tokens": 13041922, "cached_input_tokens": 12897024, "cache_write_input_tokens": 0, "output_tokens": 75301, "reasoning_output_tokens": 26723, "total_tokens": 13117223 }, "thread_token_usage": { "input_tokens": 13041922, "cached_input_tokens": 12897024, "cache_write_input_tokens": 0, "output_tokens": 75301, "reasoning_output_tokens": 26723, "total_tokens": 13117223 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7d377d45-d214-430b-a57f-5020627f2c70", "process_id": "26124", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd\nimport train_ranker as tr\nr=np.load('validation_embeddings_25e.npz'); u=np.load('validation_embeddings_unique.npz'); ids=pd.read_parquet(tr.DATA/'candidate_catalog.parquet').item_id.to_numpy(dtype=np.int64); t=tr.recent_trend('validation',ids); g=.65; ue=np.concatenate([r['user']*np.sqrt(1-g),u['user']*np.sqrt(g)],1); ie=np.concatenate([r['item']*np.sqrt(1-g),u['item']*np.sqrt(g)],1)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64); mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrec=pd.read_parquet(tr.DATA/'target_users.parquet').recent_positive_listens.to_numpy(); bins=np.searchsorted(np.quantile(rec,[.2,.4,.6,.8]),rec,side='right'); w=np.array([.08,.09,.06,.065,.06])[bins]; top=np.empty((len(uids),100),np.int32)\nfor st in range(0,len(uids),128):\n en=min(st+128,len(uids)); sc=ue[st:en]@ie.T+w[st:en,None]*t[None,:]; sc[~mask[st:en]]=-np.inf; p=np.argpartition(sc,-100,axis=1)[:,-100:]; ps=np.take_along_axis(sc,p,axis=1); top[st:en]=np.take_along_axis(p,np.argsort(-ps,axis=1),axis=1)\npd.DataFrame({'uid':np.repeat(uids,100),'item_id':items[top.ravel()],'rank':np.tile(np.arange(1,101),len(uids))}).to_parquet('validation_reproduced.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py validation_reproduced.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "stderr": "", "aggregated_output": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 495054172 }, "formatted_output": "{\r\n \"model\": \"validation_reproduced.parquet\",\r\n \"scores\": {\r\n \"quality_ndcg@10\": 0.042143,\r\n \"quality_users\": 4263\r\n }\r\n}\r\n" } } ## Step 169 · agent · 2026-09-25T01:23:53.040Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:54.514Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:23:57.849Z Script completed Wall time 3.3 seconds Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.042143, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd27870c87d299d98a366d5a7b03", "usage": { "input_tokens": 124145, "cached_input_tokens": 123904, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 17, "total_tokens": 124252 }, "turn_token_usage": { "input_tokens": 13166067, "cached_input_tokens": 13020928, "cache_write_input_tokens": 0, "output_tokens": 75408, "reasoning_output_tokens": 26740, "total_tokens": 13241475 }, "thread_token_usage": { "input_tokens": 13166067, "cached_input_tokens": 13020928, "cache_write_input_tokens": 0, "output_tokens": 75408, "reasoning_output_tokens": 26740, "total_tokens": 13241475 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3650ecf1-3c76-47ae-b1c9-23c36c5c538c", "process_id": "36072", "command": [ "/bin/sh", "-lc", "cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/validation_reproduced.parquet /app/solution/validation.parquet && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 3, "nanos": 162555526 }, "formatted_output": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042143,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 170 · agent · 2026-09-25T01:24:08.271Z ### Thinking unavailable — no text recorded ## Step 171 · agent · 2026-09-25T01:24:17.599Z ### Thinking unavailable — no text recorded ## Step 172 · agent · 2026-09-25T01:24:19.979Z ### Thinking unavailable — no text recorded The reproducible pipeline now matches the 0.042143 score and the final artifact remains fully valid. I’m evaluating one last discovery-specific refinement: trend based only on newly acquired listeners, excluding repeat plays that could never be eligible outcomes. This is still streaming pair aggregation—not a user-item matrix—and will be adopted only if it validates. ### exec · 2026-09-25T01:24:38.495Z { "code": "const r = await tools.exec_command({\"cmd\":\"cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\\nimport numpy as np,pandas as pd,time,resource\\nimport train_ranker as tr\\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\\ndel x,rel\\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\\ndef ev(s):\\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\\nres=[]\\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\\nprint(*sorted(res,reverse=True)[:30],sep='\\\\n')\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":15000,\"tty\":true});\ntext(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-25T01:24:39.666Z Script completed Wall time 1.2 seconds Output: SESSION_ID=14237 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd2e10f887d2ba54b1941940c90f", "usage": { "input_tokens": 124320, "cached_input_tokens": 123904, "cache_write_input_tokens": 0, "output_tokens": 2197, "reasoning_output_tokens": 1165, "total_tokens": 126517 }, "turn_token_usage": { "input_tokens": 13290387, "cached_input_tokens": 13144832, "cache_write_input_tokens": 0, "output_tokens": 77605, "reasoning_output_tokens": 27905, "total_tokens": 13367992 }, "thread_token_usage": { "input_tokens": 13290387, "cached_input_tokens": 13144832, "cache_write_input_tokens": 0, "output_tokens": 77605, "reasoning_output_tokens": 27905, "total_tokens": 13367992 } } ## Step 173 · agent · 2026-09-25T01:24:41.320Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:24:42.067Z { "code": "const r = await tools.write_stdin({\"session_id\":14237,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":15000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:25:06.940Z Script completed Wall time 24.9 seconds Output: load listens catalog rows 31249613 rss 3133.52734375 new strong pairs 167951 load likes new like pairs 32939 (np.float64(0.03246168675753916), 0.55, 10) (np.float64(0.03239168771681069), 0.55, 8) (np.float64(0.0322385181913526), 0.85, 15) (np.float64(0.032231923997650194), 0.85, 20) (np.float64(0.03219611539492152), 1.2, 20) (np.float64(0.03219040454904028), 1.2, 7) (np.float64(0.03218331867090865), 0.85, 8) (np.float64(0.03217486381632487), 0.85, 7) (np.float64(0.03214783181582247), 1.2, 8) (np.float64(0.032147227241167135), 1.2, 15) (np.float64(0.03214525951482923), 0.55, 7) (np.float64(0.03213696117341197), 1, 10) (np.float64(0.03213474362076268), 1, 8) (np.float64(0.03213196570002616), 1.2, 6) (np.float64(0.032131526177621245), 1, 15) (np.float64(0.03211446115671453), 1, 20) (np.float64(0.032085722343801665), 0.85, 6) (np.float64(0.03207055207650667), 1.2, 30) (np.float64(0.032064912155472705), 0.7, 10) (np.float64(0.03206120736932016), 0.55, 6) (np.float64(0.032052002972814964), 0.7, 15) (np.float64(0.03203771215481672), 1.5, 20) (np.float64(0.0320053821204572), 1, 6) (np.float64(0.03199840768161527), 0.85, 4) (np.float64(0.03198983943495892), 1.5, 30) (np.float64(0.031977683201774916), 1, 7) (np.float64(0.031976254500100894), 1.2, 10) (np.float64(0.031970799392809404), 0.4, 6) (np.float64(0.03196809074887751), 1.5, 15) (np.float64(0.031967608460061164), 0.85, 10) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "session_id": "01a0d5f3-d1b8-77b3-b40a-7e7419ae72c4", "root_turn_id": "01a0d5f3-d1c8-7e21-ac6b-58319cf31e32", "response_id": "resp_03e4520b5d8131e2016ab5cd57ea7087d2bc50db6be4e35784", "usage": { "input_tokens": 126545, "cached_input_tokens": 124160, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 126598 }, "turn_token_usage": { "input_tokens": 13416932, "cached_input_tokens": 13268992, "cache_write_input_tokens": 0, "output_tokens": 77658, "reasoning_output_tokens": 27911, "total_tokens": 13494590 }, "thread_token_usage": { "input_tokens": 13416932, "cached_input_tokens": 13268992, "cache_write_input_tokens": 0, "output_tokens": 77658, "reasoning_output_tokens": 27911, "total_tokens": 13494590 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e9cd1a3d-046f-4472-b9eb-7c43b437c9ff", "process_id": "14237", "command": [ "/bin/sh", "-lc", "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,time,resource\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\ndel x,rel\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cd /app/solution && /opt/task-venv/bin/python -u - <<'PY'\nimport numpy as np,pandas as pd,time,resource\nimport train_ranker as tr\nC=tr.TRAIN_CUTOFF; start=C-28*86400; cat=pd.read_parquet(tr.DATA/'candidate_catalog.parquet'); ids=cat.item_id.to_numpy(); idx=pd.Index(ids)\nprint('load listens',flush=True); x=pd.read_parquet(tr.DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); ci=idx.get_indexer(x.item_id); k=ci>=0\nx=pd.DataFrame({'uid':x.uid.to_numpy()[k],'ts':x.timestamp.to_numpy()[k],'ci':ci[k],'org':x.is_organic.to_numpy()[k],'ratio':x.played_ratio_pct.to_numpy()[k]}); print('catalog rows',len(x),'rss',resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,flush=True)\nfirst=x.drop_duplicates(['uid','ci'],keep='first')[['uid','ci','ts']].rename(columns={'ts':'first_ts'}); rel=x[(x.ts>=start)&(x.org==1)&(x.ratio>=80)].groupby(['uid','ci'],as_index=False).ts.min(); rel=rel.merge(first,on=['uid','ci'],how='left'); rel=rel[rel.first_ts>=start]; print('new strong pairs',len(rel),flush=True)\nday=((C-1-rel.ts.to_numpy())//86400).astype(int); L=np.zeros((28,len(ids)),np.float32)\nfor d,g in rel.assign(day=day).groupby('day'): L[int(d)]=np.bincount(g.ci,minlength=len(ids))\ndel x,rel\nprint('load likes',flush=True); a=pd.read_parquet(tr.DATA/'train_likes.parquet',columns=['uid','timestamp','item_id','is_organic']); ci=idx.get_indexer(a.item_id); k=(ci>=0)&(a.timestamp.to_numpy()>=start)&(a.is_organic.to_numpy()==1); a=pd.DataFrame({'uid':a.uid.to_numpy()[k],'ts':a.timestamp.to_numpy()[k],'ci':ci[k]}).groupby(['uid','ci'],as_index=False).ts.min(); a=a.merge(first,on=['uid','ci'],how='left'); a=a[a.first_ts.fillna(C+1)>=start]; print('new like pairs',len(a),flush=True); day=((C-1-a.ts.to_numpy())//86400).astype(int); A=np.zeros((28,len(ids)),np.float32)\nfor d,g in a.assign(day=day).groupby('day'): A[int(d)]=np.bincount(g.ci,minlength=len(ids))\nnp.save('daily_new_listener_listens.npy',L); np.save('daily_new_listener_likes.npy',A)\nwith np.load(tr.DATA/'validation_eligible_candidates.npz') as z: uids=z['uids'].astype(int); items=z['item_ids'].copy(); masks=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ntruth=tr.relevant_truth(); rows=[r for r,u in enumerate(uids) if int(u) in truth]\ndef ev(s):\n o=np.argsort(-s,kind='stable')[:400]; return np.mean([tr.ndcg10(items[o[masks[r,o]][:10]],truth[int(uids[r])]) for r in rows])\nres=[]\nfor h in [.25,.4,.55,.7,.85,1,1.2,1.5,2,3,5,7]:\n d=2**(-np.arange(28)/h); l=d@L; aa=d@A\n for aw in [0,2,4,6,7,8,10,15,20,30]: res.append((ev(l+aw*aa),h,aw))\nprint(*sorted(res,reverse=True)[:30],sep='\\n')\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n", "stderr": "", "aggregated_output": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 274041447 }, "formatted_output": "load listens\r\ncatalog rows 31249613 rss 3133.52734375\r\nnew strong pairs 167951\r\nload likes\r\nnew like pairs 32939\r\n(np.float64(0.03246168675753916), 0.55, 10)\r\n(np.float64(0.03239168771681069), 0.55, 8)\r\n(np.float64(0.0322385181913526), 0.85, 15)\r\n(np.float64(0.032231923997650194), 0.85, 20)\r\n(np.float64(0.03219611539492152), 1.2, 20)\r\n(np.float64(0.03219040454904028), 1.2, 7)\r\n(np.float64(0.03218331867090865), 0.85, 8)\r\n(np.float64(0.03217486381632487), 0.85, 7)\r\n(np.float64(0.03214783181582247), 1.2, 8)\r\n(np.float64(0.032147227241167135), 1.2, 15)\r\n(np.float64(0.03214525951482923), 0.55, 7)\r\n(np.float64(0.03213696117341197), 1, 10)\r\n(np.float64(0.03213474362076268), 1, 8)\r\n(np.float64(0.03213196570002616), 1.2, 6)\r\n(np.float64(0.032131526177621245), 1, 15)\r\n(np.float64(0.03211446115671453), 1, 20)\r\n(np.float64(0.032085722343801665), 0.85, 6)\r\n(np.float64(0.03207055207650667), 1.2, 30)\r\n(np.float64(0.032064912155472705), 0.7, 10)\r\n(np.float64(0.03206120736932016), 0.55, 6)\r\n(np.float64(0.032052002972814964), 0.7, 15)\r\n(np.float64(0.03203771215481672), 1.5, 20)\r\n(np.float64(0.0320053821204572), 1, 6)\r\n(np.float64(0.03199840768161527), 0.85, 4)\r\n(np.float64(0.03198983943495892), 1.5, 30)\r\n(np.float64(0.031977683201774916), 1, 7)\r\n(np.float64(0.031976254500100894), 1.2, 10)\r\n(np.float64(0.031970799392809404), 0.4, 6)\r\n(np.float64(0.03196809074887751), 1.5, 15)\r\n(np.float64(0.031967608460061164), 0.85, 10)\r\n" } } ## Step 174 · agent · 2026-09-25T01:25:13.198Z ### Thinking unavailable — no text recorded