{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "fable51-high-2-1-288-base-r01-20261003T075109Z", "taskChecksum": "171ad6a9edf857032998ad02d525a117a407c5b82d02dd365cf3de7b1db26b41", "source": { "format": "claude-code-session", "sha256": "e0fc5a157505a10b518dfa44f561d6aa68b1f1ef8ee4aec593cd84d15363fd54", "records": 122, "bookkeepingRecords": { "queue-operation": 2, "atis-latch": 7, "last-prompt": 7 }, "usageRecords": [ { "type": "cost-state", "sessionId": "7757d951-ab6c-47d2-a2db-f61a4565dae4", "totalCostUSD": 2.0597032499999997, "totalAPIDuration": 368550, "totalAPIDurationWithoutRetries": 368517, "totalToolDuration": 1649246, "totalLinesAdded": 289, "totalLinesRemoved": 0, "totalDuration": 2018538, "startTime": 1791015182319, "modelUsage": { "claude-fable-5-1": { "inputTokens": 514, "outputTokens": 24114, "thinkingTokens": 7321, "cacheReadInputTokens": 655853, "cacheCreationInputTokens": 34245, "webSearchRequests": 0, "costUSD": 2.0597032499999997 } }, "hasUnknownModelCost": false } ] }, "sessionId": "7757d951-ab6c-47d2-a2db-f61a4565dae4", "agentVersion": "2.1.288", "model": "claude-fable-5-1", "steps": [ { "id": 1, "role": "user", "timestamp": "2026-10-03T08:13:02.677Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T08:13:02.677Z", "text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" } } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "model", "identity": { "modelId": "claude-fable-5-1", "marketingName": "Fable 5.1", "knowledgeCutoff": "June 2026" }, "text": "You are powered by the model named Fable 5.1. The exact model ID is claude-fable-5-1. Assistant knowledge cutoff is June 2026." } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "deferred_tools_delta", "addedNames": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "addedLines": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "removedNames": [], "wireHiddenNames": [], "readdedNames": [], "pendingMcpServers": [], "needsAuthMcpServers": [], "failedMcpServers": [] } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "agent_listing_delta", "addedTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "addedLines": [ "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)", "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)", "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)" ], "builtInTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "removedTypes": [], "isInitial": true, "showConcurrencyNote": true } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "skill_listing", "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. Pass --max-findings to report up to n findings, or --max-findings all for every finding. The choice stays until you pass --max-findings default.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- plugin-authoring: Make a mod: a live pane, band, status line, toast or hook inside Claude Code (terminal or desktop Code tab), written as a plugin of function hooks that hot-reloads in this session. Load before writing or debugging a hooks module.\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch", "skillCount": 14, "isInitial": true, "names": [ "dataviz", "update-config", "keybindings-help", "code-review", "simplify", "fewer-permission-prompts", "loop", "schedule", "claude-api", "workflow-authoring", "run", "plugin-authoring", "init", "security-review" ] } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "auto_mode", "autoModeConsentFlow": false, "bashFirst": true, "bashFirstSteer": "strict", "steerOnly": true, "bypass": true } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.676Z", "data": { "type": "total_tokens_reminder", "text": "15000000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.692Z", "data": { "type": "session_context", "context": {} } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.692Z", "data": { "type": "date", "date": "2026-10-03" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.694Z", "data": { "type": "remote_session_change", "url": null, "commit": "Co-Authored-By: Claude Fable 5.1 ", "pr": "🤖 Generated with [Claude Code](https://claude.com/claude-code)", "sendUserFileHint": false, "managedCommit": false, "managedPr": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:02.694Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "reminderFold": false, "echoWireToolInputs": false, "contextRendering": "announced" } } ] }, { "id": 2, "role": "agent", "timestamp": "2026-10-03T08:13:15.524Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:13:15.524Z", "text": "I should start by exploring the data and environment, keeping in mind I only have 60 minutes so I need to work efficiently.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:13:15.527Z", "text": "I'll start by inspecting the data, the evaluator, and the environment, then build the two-tower model." }, { "kind": "tool", "timestamp": "2026-10-03T08:13:16.644Z", "callId": "toolu_016dncYZAGzmSa8A7EMvYJRQ", "name": "Bash", "input": { "command": "cd /task && ls -la data/ && cat requirements.txt | head -50 && nproc && free -g && cat evaluate_public.py && cat ranking_evaluation.py", "description": "List task data and read evaluator scripts" }, "result": { "timestamp": "2026-10-03T08:13:16.732Z", "content": "total 347392\ndr-xr-xr-x 1 root root 4096 Oct 3 07:55 .\ndr-xr-xr-x 1 root root 4096 Oct 3 08:08 ..\n-r--r--r-- 1 root root 349352 Oct 3 07:55 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Oct 3 07:55 target_users.parquet\n-r--r--r-- 1 root root 7736341 Oct 3 07:55 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Oct 3 07:55 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Oct 3 07:55 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Oct 3 07:55 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Oct 3 07:55 train_listens.parquet\n-r--r--r-- 1 root root 155988 Oct 3 07:55 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Oct 3 07:55 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Oct 3 07:55 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Oct 3 07:55 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Oct 3 07:55 val_listens.parquet\n-r--r--r-- 1 root root 7845 Oct 3 07:55 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Oct 3 07:55 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Oct 3 07:55 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Oct 3 07:55 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\n8\n total used free shared buff/cache available\nMem: 15 1 0 0 13 13\nSwap: 0 0 0\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 }\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:16.734Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "tools": [ { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "schema": { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "description": { "description": "A short (3-5 word) description of the task", "type": "string" }, "prompt": { "description": "The task for the agent to perform", "type": "string" }, "subagent_type": { "description": "The type of specialized agent to use for this task", "type": "string" }, "model": { "description": "Optional model override for this agent. Takes precedence over the agent definition's model frontmatter and the configured default subagent model. If omitted, uses the agent definition's model, else the default (inherits from the parent unless a default subagent model is configured). Ignored for subagent_type: \"fork\" — forks always inherit the parent model.", "type": "string", "enum": [ "sonnet", "opus", "haiku", "fable" ] }, "run_in_background": { "description": "Agents run in the background by default; you will be notified when one completes. Set to false only when your very next action depends on this agent's result and nothing else could usefully happen while it runs — otherwise leave it in the background so the user can hand you other work.", "type": "boolean" }, "isolation": { "description": "Isolation mode. \"worktree\" creates a temporary git worktree so the agent works on an isolated copy of the repo. \"remote\" launches the agent in a remote cloud environment (always runs in background; availability is gated).", "type": "string", "enum": [ "worktree", "remote" ] } }, "required": [ "description", "prompt" ], "additionalProperties": false } } }, { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "schema": { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "command": { "description": "The command to execute", "type": "string" }, "timeout": { "description": "Optional timeout in milliseconds (max 600000 for a foreground command)", "type": "number" }, "description": { "description": "Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"", "type": "string" }, "run_in_background": { "description": "Set to true to run this command in the background. With it, `timeout` limits how long the command may run in the background before it is stopped (default 1800000 ms, max 7200000 ms).", "type": "boolean" }, "dangerouslyDisableSandbox": { "description": "Set this to true to dangerously override sandbox mode and run commands without sandboxing.", "type": "boolean" } }, "required": [ "command" ], "additionalProperties": false } } }, { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "schema": { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to modify", "type": "string" }, "old_string": { "description": "The text to replace", "type": "string" }, "new_string": { "description": "The text to replace it with (must be different from old_string)", "type": "string" }, "replace_all": { "description": "Replace all occurrences of old_string (default false)", "default": false, "type": "boolean" } }, "required": [ "file_path", "old_string", "new_string" ], "additionalProperties": false } } }, { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "schema": { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "channel": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 }, "q": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 } }, "additionalProperties": false } } }, { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "schema": { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to read", "type": "string" }, "offset": { "description": "The line number to start reading from. Only provide if the file is too large to read at once", "type": "integer", "minimum": 0, "maximum": 9007199254740991 }, "limit": { "description": "The number of lines to read. Only provide if the file is too large to read at once.", "type": "integer", "exclusiveMinimum": 0, "maximum": 9007199254740991 }, "pages": { "description": "Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.", "type": "string" } }, "required": [ "file_path" ], "additionalProperties": false } } }, { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "schema": { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "level": { "description": "Effort level the review ran at", "type": "string", "enum": [ "low", "medium", "high", "xhigh", "max" ] }, "findings": { "description": "Verified findings, most-severe first; empty if none survived", "maxItems": 32, "type": "array", "items": { "type": "object", "properties": { "file": { "description": "Repo-relative path of the file the finding is in", "type": "string" }, "line": { "description": "1-indexed line the finding anchors to", "type": "integer", "minimum": -9007199254740991, "maximum": 9007199254740991 }, "summary": { "description": "One-sentence statement of the defect", "type": "string" }, "short_summary": { "description": "Compressed label for compact UI (≤60 chars): the claim alone, no rationale or consequence clause", "type": "string", "maxLength": 60 }, "failure_scenario": { "description": "Concrete inputs/state → wrong output/crash", "type": "string" }, "category": { "description": "Short kebab-case slug of the finding type, e.g. \"correctness\", \"simplification\", \"efficiency\", \"test-coverage\"", "type": "string", "maxLength": 40 }, "verdict": { "description": "Set when a verify pass ran; absent on inline-only reviews", "type": "string", "enum": [ "CONFIRMED", "PLAUSIBLE" ] }, "outcome": { "description": "Set ONLY when re-reporting after applying fixes: what happened to this finding", "type": "string", "enum": [ "fixed", "skipped", "no_change_needed" ] } }, "required": [ "file", "summary", "failure_scenario" ], "additionalProperties": false } } }, "required": [ "findings" ], "additionalProperties": false } } }, { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "schema": { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "delaySeconds": { "description": "Seconds from now to wake up. Clamped to [60, 3600] by the runtime. Required unless `stop` is true.", "type": "number" }, "reason": { "description": "One short sentence explaining the chosen delay. Goes to telemetry and is shown to the user. Be specific. Required unless `stop` is true.", "type": "string" }, "prompt": { "description": "The /loop input to fire on wake-up. Pass the same /loop input verbatim each turn so the next firing re-enters the skill and continues the loop. For autonomous /loop (no user prompt), pass the literal sentinel `<>` instead (the dynamic-pacing variant, not the CronCreate-mode `<>`). Required unless `stop` is true.", "type": "string" }, "stop": { "description": "Set to true to end the dynamic loop immediately instead of scheduling another wakeup. When true, all other fields are ignored and no further wakeups fire.", "type": "boolean" }, "noop": { "description": "true = nothing changed (you checked and there is nothing to report). false = something happened worth keeping (edited a file, posted a message, advanced state, surfaced a finding). Consecutive noop:true ticks are collapsed in the user's terminal view and tracked as a streak. Required unless `stop` is true.", "type": "boolean" } }, "additionalProperties": false } } }, { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "schema": { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "skill": { "description": "The name of a skill from the available-skills list. Do not guess names.", "type": "string" }, "args": { "description": "Optional arguments for the skill", "type": "string" } }, "required": [ "skill" ], "additionalProperties": false } } }, { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "schema": { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "query": { "description": "Query to find deferred tools. Use \"select:\" for direct selection, or keywords to search.", "type": "string" }, "max_results": { "description": "Maximum number of results to return (default: 5)", "default": 5, "type": "number" } }, "required": [ "query", "max_results" ], "additionalProperties": false } } }, { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "schema": { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "script": { "description": "Self-contained workflow script. Must begin with `export const meta = { name, description, phases }` (pure literal, no computed values) followed by the script body using agent()/parallel()/pipeline()/phase().", "type": "string", "maxLength": 524288 }, "name": { "description": "Name of a predefined workflow (built-in or from .claude/workflows/). Resolves to a self-contained script.", "type": "string" }, "description": { "description": "Ignored — set the workflow description in the script's `meta` block.", "type": "string" }, "title": { "description": "Ignored — set the workflow title in the script's `meta` block.", "type": "string" }, "args": { "description": "Optional input value exposed to the script as the global `args`, verbatim. Pass arrays/objects as actual JSON values, NOT as a JSON-encoded string — a stringified list breaks `args.filter`/`args.map` in the script. Use for parameterized named workflows (e.g. a research question)." }, "scriptPath": { "description": "Path to a workflow script file on disk. Every Workflow invocation persists its script under the session directory and returns the path in the tool result. To iterate, edit that file with Write/Edit and re-invoke Workflow with the same `scriptPath` instead of re-sending the full script. Takes precedence over `script` and `name`.", "type": "string" }, "resumeFromRunId": { "description": "Run ID of a prior Workflow invocation to resume from. Completed agent() calls with unchanged (prompt, opts) return their cached results instantly; only edited or new calls re-run. Same-session only. Stop the prior run first (TaskStop) before resuming.", "type": "string", "pattern": "^wf_[a-z0-9-]{6,}$" } }, "additionalProperties": false } } }, { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "schema": { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to write (must be absolute, not relative)", "type": "string" }, "content": { "description": "The content to write to the file", "type": "string" } }, "required": [ "file_path", "content" ], "additionalProperties": false } } } ], "cliPrefix": "You are a Claude agent, built on Anthropic's Claude Agent SDK.", "reminderFold": false, "systemTurns": true, "toolChangeHeader": true, "inlineTools": false, "keptReminders": true, "echoWireToolInputs": false, "contextRendering": "announced" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:16.737Z", "data": { "type": "total_tokens_reminder", "text": "14980364 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:16.740Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 3, "role": "agent", "timestamp": "2026-10-03T08:13:32.746Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T08:13:32.746Z", "callId": "toolu_01FcygTmrD8RAY3mAa3KgVx6", "name": "Bash", "input": { "command": "mkdir -p solution && python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\nD='/task/data/'\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_likes','train_dislikes','val_likes','val_listens']:\n df=pd.read_parquet(D+f+'.parquet'); print(f, df.shape); print(df.head(3)); print()\ntl=pd.read_parquet(D+'train_listens.parquet'); print('train_listens',tl.shape, tl.dtypes); print(tl.head()); \nprint('ts range', tl.timestamp.min(), tl.timestamp.max(), 'users', tl.uid.nunique(), 'items', tl.item_id.nunique())\nprint('organic frac', tl.is_organic.mean(), 'ratio>=80 frac', (tl.played_ratio_pct>=80).mean())\ntu=pd.read_parquet(D+'target_users.parquet'); print('targets', len(tu))\nprint('target listens frac', tl.uid.isin(tu.uid).mean())\nz=np.load(D+'validation_eligible_candidates.npz'); print({k:z[k].shape for k in z})\nm=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little'); print('elig per user mean', m.sum(1).mean(), m.sum(1).min(), m.sum(1).max())\nzt=np.load(D+'test_eligible_candidates.npz'); mt=np.unpackbits(zt['packed_eligible'],axis=1,count=len(zt['item_ids']),bitorder='little'); print('test elig per user mean', mt.sum(1).mean())\nprint('same uids order', (z['uids']==zt['uids']).all(), (z['item_ids']==zt['item_ids']).all())\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); print('catalog==item_ids', (cat.item_id.values==z['item_ids']).all())\nvl=pd.read_parquet(D+'val_listens.parquet'); print('val ts', vl.timestamp.min(), vl.timestamp.max())\nprint(time.time()-t)\n\"\n", "description": "Inspect data shapes, columns, and eligibility structure" }, "result": { "timestamp": "2026-10-03T08:13:36.540Z", "content": "candidate_catalog (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n\ntarget_users (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n\nvalidation_preference_state (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n\ntest_preference_state (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n\ntrain_likes (814831, 4)\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n\ntrain_dislikes (101075, 4)\n uid timestamp item_id is_organic\n0 100 3087560 9170134 1\n1 100 3936560 8661238 1\n2 100 20432825 4927727 1\n\nval_likes (37795, 4)\n uid timestamp item_id is_organic\n0 100 25249855 3526521 1\n1 300 25046245 4867327 1\n2 300 25046970 2674136 1\n\nval_listens (1501697, 6)\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n\ntrain_listens (43484159, 6) uid uint32\ntimestamp uint32\nitem_id uint32\nis_organic uint8\nplayed_ratio_pct uint16\ntrack_length_seconds uint32\ndtype: object\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\nts range 0 24786795 users 9238 items 845395\norganic frac 0.5169151828370419 ratio>=80 frac 0.5922891828263254\ntargets 8456\ntarget listens frac 0.9939779909276847\n{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}\nelig per user mean 29067.306291390727 24657 30000\ntest elig per user mean 29041.3178807947\nsame uids order True True\ncatalog==item_ids True\nval ts 24788600 25393395\n2.1090288162231445", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:36.543Z", "data": { "type": "total_tokens_reminder", "text": "14972732 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:36.545Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 4, "role": "agent", "timestamp": "2026-10-03T08:14:11.253Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:14:11.253Z", "text": "I'm figuring out that eligibility likely excludes items the user already interacted with, since the ~933 excluded per user roughly matches history size. For the architecture, I'm leaning toward a two-tower design: a user tower built from pooled or attention-weighted interaction history embeddings, and an item tower based on item ID embeddings.\n\nFor training, I'm planning to use sampled softmax or in-batch negatives, with positives drawn from organic strong listens or likes in a target window — using a cutoff before the final date so I can predict forward 7 days, and reusing earlier cutoffs to generate more training examples. For the final submission, I'd train on the full train+val history with the actual cutoff date and val labels as targets. Time is tight (already 3 of 60 minutes used), so I need to move fast — loading the listens data is quick, but with 43M rows I should restrict to target users and catalog items when building the history representations.\n\nI'm restricting user history to catalog items only (30k items with embeddings), accepting that listens of non-catalog items get dropped since the catalog likely covers most popular tracks. The scoring target is organic likes/strong listens on unseen items, so a popularity or co-occurrence baseline would work, but I need a two-tower neural net. My plan: first preprocess by filtering train listens to target users and catalog items, building per-user history arrays of item index, timestamp, and weight, saved as npz files; then build the model with an item tower combining item embeddings and popularity features through an MLP, and a user tower that embeds history items through a shared or separate embedding table.\n\nFor pooling, I'm weighting by recency and event type (likes > organic listens > non-organic listens), with disliked items contributing a separate negative-weighted pooled vector concatenated before a final MLP. The score is a dot product between towers, and I'm training with full softmax over all 30k catalog items since that's cheap on CPU, masking out history items from the softmax by setting their logits to -inf since those are ineligible for scoring.\n\nFor the loss, I want cross entropy with target weights based on interaction grade, and since users can have multiple positives per step, I'll treat each (user, positive) pair as a separate weighted training example rather than trying multi-label softmax.\n\nFor constructing training examples, I'm planning multiple rolling cutoffs across the train period (e.g., weekly cutoffs going back 8 weeks), with history being everything before the cutoff and targets being organic likes/high-listens in the following week among catalog items not already in that user's history — though I should verify against the actual data whether eligibility truly excludes all previously-interacted items.\n\nFor the time-aware user representation, I'm storing per-user sorted history arrays and, for each cutoff, slicing out items before that cutoff, keeping the most recent 200 with recency weights, packed into a padded tensor with weights — this should be efficient enough.\n\nNow I'm moving to write the preprocessing code, plus a simple popularity-among-eligible baseline to sanity-check the evaluator and have a safe submission ready early, given I have roughly 55 minutes left and need to budget time carefully across preprocessing, modeling, training, and refitting.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:14:16.615Z", "callId": "toolu_01EmWCX31ZGUkKxPmDS7jqZA", "name": "Bash", "input": { "command": "cd /app/solution && python -c \"\nimport pandas as pd, numpy as np\nD='/task/data/'\nz=np.load(D+'validation_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)\nitem_ids=z['item_ids']; col={int(i):c for c,i in enumerate(item_ids)}\ntl=pd.read_parquet(D+'train_listens.parquet',columns=['uid','item_id','played_ratio_pct'])\nu=int(z['uids'][5]); r=5\nh=tl[tl.uid==u]\nhist=set(h.item_id); histpos=set(h[h.played_ratio_pct>=50].item_id)\ninel=set(item_ids[~m[r]])\nprint('inel',len(inel),'hist in catalog',len(hist&set(item_ids)),'inel subset hist',len(inel-hist),'histpos cat', len(histpos&set(item_ids)), 'inel - histpos', len(inel-histpos))\nlk=pd.read_parquet(D+'train_likes.parquet'); dk=pd.read_parquet(D+'train_dislikes.parquet')\nprint('inel - (hist|likes|dislikes)', len(inel-hist-set(lk[lk.uid==u].item_id)-set(dk[dk.uid==u].item_id)))\n# popularity baseline on validation\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nscore=-np.arange(30000)[None,:].repeat(len(m),0).astype(float); score[~m]=-1e9\ntop=np.argsort(-score,axis=1)[:,:100]\nsub=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':item_ids[top.ravel()],'rank':np.tile(np.arange(1,101),len(m))})\nsub.to_parquet('pop_val.parquet'); \n\" && python /task/evaluate_public.py /app/solution/pop_val.parquet", "description": "Check eligibility semantics and score a popularity baseline" }, "result": { "timestamp": "2026-10-03T08:14:30.618Z", "content": "inel 2524 hist in catalog 2524 inel subset hist 0 histpos cat 1910 inel - histpos 614\ninel - (hist|likes|dislikes) 0\n{\n \"model\": \"/app/solution/pop_val.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:14:30.622Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app/solution", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" }, "changes": [ { "field": "workingDirectory", "from": "/app" } ] } }, { "kind": "context", "timestamp": "2026-10-03T08:14:30.622Z", "data": { "type": "total_tokens_reminder", "text": "14969331 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:14:30.624Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 5, "role": "agent", "timestamp": "2026-10-03T08:15:12.426Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:15:12.426Z", "text": "So ineligible items are anything the user has already interacted with, meaning training targets are organic positives on catalog items with zero prior interaction before the cutoff. I'm planning out a single script with separate validation and test stages, starting with preprocessing that filters training listens to target users and maps catalog items to indices.\n\nFor test, I'll append validation events too, then build a sorted event table per user by timestamp, tagging each event by type (weak/partial listen, positive listen, strong listen, like, dislike) and recency bucket. The user tower will learn per-type and per-recency scalar weights, pool the weighted item embeddings with normalization, and concatenate with log-count features to keep the design simple but expressive.\n\nNow I need to define targets carefully: an item only counts as a true positive target if the user's first-ever interaction with it falls within the prediction window and after the cutoff, since items already seen before cutoff shouldn't count as new engagement. I'm computing first interaction timestamps per user-item pair across all event types, then filtering organic positive events in each 7-day window where that first timestamp is at or after the cutoff, and setting up a series of rolling cutoffs spaced a week apart leading up to the training boundary for building multiple training/validation snapshots.\n\nFor the test stage I'll add a cutoff at the end of the training period so targets become the validation-window events, with history built from training data, plus history for the final embedding extending through validation. Per-user history for each cutoff takes the most recent 256 events before that cutoff, using searchsorted on sorted per-user timestamps to slice efficiently and building padded batch tensors in numpy. I also want to include dislikes in the history sequence so the model can learn to weight negative interactions differently.\n\nSince disliked items in the test preference state are already excluded as interacted, that state is mostly redundant with history, so I'll skip using it except where validation events already cover it. For the full softmax over the item catalog, I need to mask out each user's full history (not just the last 256) by gathering their historical item indices and setting corresponding logits to negative infinity using scatter indexing. For loss weighting, I'm planning to upweight likes relative to strong listens, treating all cutoff-user pairs as training positives.\n\nRather than deduping, I'll structure examples as (user, cutoff, pos_item, weight) tuples with batch size 1024, recomputing the user tower per example despite the redundancy since it's computationally manageable. Estimating roughly 150k positive examples per cutoff across 6 cutoffs gives about 1M examples per epoch, and the compute cost per batch—gathering history embeddings and running the softmax over 30k items—comes out to roughly 0.1-0.2 seconds per batch, which seems feasible.\n\nWith about 1000 batches per epoch taking roughly 3 minutes, 2-3 epochs is realistic if I set torch to use 8 threads. For the item tower, I'm combining an embedding layer with log-popularity features through a small MLP, and I'm folding in a bias trick—appending a constant 1 to the user vector and a learned bias dimension to the item vector—so the dot product stays exact while still letting popularity be modeled, giving a final dimension of 64+1. Time to start writing the actual code.\n\nWith roughly 50 minutes remaining, I need to keep moving efficiently.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T08:15:12.431Z", "text": "Eligibility means items never interacted with before the cutoff, and popularity alone scores 0.0107. Now I'll build the full two-tower pipeline: multi-cutoff history/target construction, item tower, history-based user tower, and full-softmax training with history masking.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:16:13.478Z", "callId": "toolu_017joaNqRLTe5vCHAWZGxFyx", "name": "Write", "input": { "file_path": "/app/solution/twotower.py", "content": "#!/usr/bin/env python3\n\"\"\"Two-tower discovery ranker for the \"Made for You\" shelf (CPU PyTorch).\n\nUsage:\n python twotower.py --stage val # fit on train_*, write validation.parquet\n python twotower.py --stage test # refit on train_*+val_*, write submission.parquet\n\nModel: score(u, i) = with\n * item tower: item-ID embedding + log-popularity features -> MLP -> d dims\n (last dim is a learned item bias, matched by a constant 1 in the user vec)\n * user tower: weighted pooling over the user's last N interaction events\n (event-type x recency-bucket learned weights over item embeddings),\n plus history-count features -> MLP -> d dims.\nTraining objective: full-softmax cross-entropy over the 30k catalog with the\nuser's already-interacted items masked out (they are never eligible), on\norganic \"discovery\" positives (organic likes weighted higher than organic\n>=80% listens) in 7-day windows following multiple historical cutoffs.\nNo user-item matrix is ever built; everything is per-example minibatch SGD.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nT_END = 24786800\nWEEK = 604800\nGAP = 1800\nN_ITEMS = 30000\nHIST_LEN = 200\n\n# event types\nLISTEN_SKIP, LISTEN_POS, LISTEN_STRONG_ORG, LIKE, DISLIKE, UNLIKE = range(6)\nN_TYPES = 6\nREC_EDGES = np.array([3600, 6 * 3600, 86400, 3 * 86400, 7 * 86400, 14 * 86400, 30 * 86400, 60 * 86400, 90 * 86400])\nN_REC = len(REC_EDGES) + 1\n\n\ndef log(*a):\n print(f\"[{time.strftime('%H:%M:%S')}]\", *a, flush=True)\n\n\ndef load_events(stage: str, uid_map: dict, item_map: dict):\n \"\"\"Return event table (uid_idx, ts, item_idx, type) restricted to target users & catalog.\"\"\"\n prefixes = [\"train\"] if stage == \"val\" else [\"train\", \"val\"]\n frames = []\n for p in prefixes:\n li = pd.read_parquet(DATA / f\"{p}_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\n li = li[li.uid.isin(uid_map)]\n li = li[li.item_id.isin(item_map)]\n typ = np.where(li.played_ratio_pct.values < 50, LISTEN_SKIP, LISTEN_POS)\n typ = np.where((li.played_ratio_pct.values >= 80) & (li.is_organic.values == 1), LISTEN_STRONG_ORG, typ)\n frames.append(pd.DataFrame({\"uid\": li.uid.values, \"ts\": li.timestamp.values, \"item_id\": li.item_id.values,\n \"typ\": typ.astype(np.int8), \"org\": li.is_organic.values.astype(np.int8)}))\n for fam, t in [(\"likes\", LIKE), (\"dislikes\", DISLIKE), (\"unlikes\", UNLIKE)]:\n e = pd.read_parquet(DATA / f\"{p}_{fam}.parquet\")\n e = e[e.uid.isin(uid_map) & e.item_id.isin(item_map)]\n frames.append(pd.DataFrame({\"uid\": e.uid.values, \"ts\": e.timestamp.values, \"item_id\": e.item_id.values,\n \"typ\": np.full(len(e), t, np.int8), \"org\": e.is_organic.values.astype(np.int8)}))\n ev = pd.concat(frames, ignore_index=True)\n ev[\"u\"] = ev.uid.map(uid_map).astype(np.int32)\n ev[\"i\"] = ev.item_id.map(item_map).astype(np.int32)\n ev = ev.sort_values([\"u\", \"ts\"], kind=\"stable\").reset_index(drop=True)\n return ev\n\n\ndef build_targets(ev: pd.DataFrame, cutoffs: list[int]):\n \"\"\"Examples (u, cutoff, item, weight): organic like (w=3) or organic >=80% listen (w=1) on an\n item the user never interacted with before the cutoff, within [cutoff, cutoff+WEEK).\"\"\"\n first_ts = ev.groupby([\"u\", \"i\"]).ts.min()\n pos = ev[(ev.typ == LIKE) & (ev.org == 1) | (ev.typ == LISTEN_STRONG_ORG)]\n out = []\n for c in cutoffs:\n w = pos[(pos.ts >= c) & (pos.ts < c + WEEK)]\n ft = first_ts.reindex(pd.MultiIndex.from_arrays([w.u.values, w.i.values])).values\n w = w[ft >= c]\n g = w.groupby([\"u\", \"i\"]).typ.agg(lambda s: 3.0 if (s == LIKE).any() else 1.0).reset_index()\n out.append(np.stack([g.u.values, np.full(len(g), c), g.i.values, g.typ.values], 1))\n log(f\"cutoff {c}: {len(g)} examples, {g.u.nunique()} users, likes={int((g.typ==3).sum())}\")\n return np.concatenate(out).astype(np.float64)\n\n\nclass History:\n \"\"\"Per-user sorted event arrays with fast 'history before cutoff' slicing.\"\"\"\n\n def __init__(self, ev: pd.DataFrame, n_users: int):\n self.ts = ev.ts.values.astype(np.int64)\n self.item = ev.i.values.astype(np.int64)\n self.typ = ev.typ.values.astype(np.int64)\n counts = np.bincount(ev.u.values, minlength=n_users)\n self.start = np.concatenate([[0], np.cumsum(counts)])\n\n def batch(self, users, cutoffs, hist_len=HIST_LEN, full_mask=True):\n B = len(users)\n items = np.zeros((B, hist_len), np.int64)\n typ = np.zeros((B, hist_len), np.int64)\n rec = np.zeros((B, hist_len), np.int64)\n valid = np.zeros((B, hist_len), bool)\n ncount = np.zeros((B, 3), np.float32)\n mask_rows, mask_cols = [], []\n for b, (u, c) in enumerate(zip(users, cutoffs)):\n s, e = self.start[u], self.start[u + 1]\n e2 = s + np.searchsorted(self.ts[s:e], c, side=\"left\")\n if full_mask and e2 > s:\n it = self.item[s:e2]\n mask_cols.append(it)\n mask_rows.append(np.full(len(it), b, np.int64))\n lo = max(s, e2 - hist_len)\n n = e2 - lo\n if n > 0:\n items[b, :n] = self.item[lo:e2]\n typ[b, :n] = self.typ[lo:e2]\n age = c - self.ts[lo:e2]\n rec[b, :n] = np.searchsorted(REC_EDGES, age, side=\"right\")\n valid[b, :n] = True\n ncount[b] = [np.log1p(e2 - s), np.log1p(n), np.log1p((self.typ[s:e2] == LIKE).sum())]\n mask = None\n if full_mask and mask_rows:\n mask = (np.concatenate(mask_rows), np.concatenate(mask_cols))\n return (torch.from_numpy(items), torch.from_numpy(typ), torch.from_numpy(rec),\n torch.from_numpy(valid), torch.from_numpy(ncount), mask)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, n_items, item_feats, d=64, emb_dim=64):\n super().__init__()\n self.emb = nn.Embedding(n_items, emb_dim)\n nn.init.normal_(self.emb.weight, std=0.05)\n self.register_buffer(\"feats\", item_feats)\n self.mlp = nn.Sequential(nn.Linear(emb_dim + item_feats.shape[1], 128), nn.GELU(), nn.Linear(128, d))\n self.bias = nn.Embedding(n_items, 1)\n nn.init.zeros_(self.bias.weight)\n\n def forward(self, idx=None):\n if idx is None:\n idx = torch.arange(self.emb.num_embeddings)\n x = torch.cat([self.emb(idx), self.feats[idx]], -1)\n v = self.mlp(x)\n return torch.cat([v, self.bias(idx)], -1) # d+1 dims, last = bias\n\n\nclass UserTower(nn.Module):\n def __init__(self, item_emb: nn.Embedding, d=64, emb_dim=64):\n super().__init__()\n self.item_emb = item_emb # shared base item embedding (the item tower itself remains separate)\n self.type_rec_w = nn.Parameter(torch.zeros(N_TYPES, N_REC))\n with torch.no_grad():\n init = torch.tensor([0.0, 1.0, 1.5, 2.0, -1.0, -0.5]).unsqueeze(1).repeat(1, N_REC)\n init = init * torch.linspace(1.0, 0.4, N_REC).unsqueeze(0)\n self.type_rec_w.copy_(init)\n self.mlp = nn.Sequential(nn.Linear(2 * emb_dim + 3, 256), nn.GELU(), nn.Linear(256, d))\n\n def forward(self, items, typ, rec, valid, ncount):\n e = self.item_emb(items) # B,N,E\n w = self.type_rec_w[typ, rec] * valid.float() # B,N\n pooled = (w.unsqueeze(-1) * e).sum(1) / (w.abs().sum(1, keepdim=True) + 1.0)\n # recent-only mean (last 20 events, positive types)\n recent = valid.clone()\n recent[:, :-20] = False\n wr = (recent & (typ != LISTEN_SKIP) & (typ != DISLIKE)).float()\n pooled_r = (wr.unsqueeze(-1) * e).sum(1) / (wr.sum(1, keepdim=True) + 1.0)\n h = torch.cat([pooled, pooled_r, ncount], -1)\n v = self.mlp(h)\n return torch.cat([v, torch.ones(v.shape[0], 1)], -1) # constant 1 matches item bias\n\n\nclass TwoTower(nn.Module):\n def __init__(self, item_feats, d=64, emb_dim=64):\n super().__init__()\n self.item_tower = ItemTower(N_ITEMS, item_feats, d, emb_dim)\n self.user_tower = UserTower(self.item_tower.emb, d, emb_dim)\n\n\ndef train(model, hist, examples, epochs, bs, lr, seed=0):\n opt = torch.optim.Adam(model.parameters(), lr=lr)\n rng = np.random.default_rng(seed)\n n = len(examples)\n steps = epochs * ((n + bs - 1) // bs)\n sched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=lr, total_steps=steps, pct_start=0.1)\n step = 0\n for ep in range(epochs):\n perm = rng.permutation(n)\n tot, cnt, t0 = 0.0, 0, time.time()\n model.train()\n for b0 in range(0, n, bs):\n ex = examples[perm[b0:b0 + bs]]\n users = ex[:, 0].astype(np.int64)\n cuts = ex[:, 1].astype(np.int64)\n pos = torch.from_numpy(ex[:, 2].astype(np.int64))\n w = torch.from_numpy(ex[:, 3].astype(np.float32))\n items, typ, rec, valid, ncount, mask = hist.batch(users, cuts)\n uv = model.user_tower(items, typ, rec, valid, ncount)\n iv = model.item_tower()\n logits = uv @ iv.T\n if mask is not None:\n logits[torch.from_numpy(mask[0]), torch.from_numpy(mask[1])] = -1e4\n loss = (F.cross_entropy(logits, pos, reduction=\"none\") * w).sum() / w.sum()\n opt.zero_grad()\n loss.backward()\n opt.step()\n sched.step()\n step += 1\n tot += loss.item() * len(ex)\n cnt += len(ex)\n if (b0 // bs) % 100 == 0:\n log(f\"ep {ep} step {b0//bs} loss {tot/cnt:.4f} ({time.time()-t0:.0f}s)\")\n log(f\"epoch {ep} done: loss {tot/cnt:.4f} in {time.time()-t0:.0f}s\")\n\n\n@torch.no_grad()\ndef rank(model, hist, uids, uid_map, elig_path, cutoff, out_path):\n model.eval()\n z = np.load(elig_path)\n assert (z[\"uids\"] == uids).all()\n elig = np.unpackbits(z[\"packed_eligible\"], axis=1, count=N_ITEMS, bitorder=\"little\").astype(bool)\n iv = model.item_tower() # offline item index\n item_ids = z[\"item_ids\"]\n rows = []\n for b0 in range(0, len(uids), 512):\n us = np.arange(b0, min(b0 + 512, len(uids)))\n items, typ, rec, valid, ncount, _ = hist.batch(us, np.full(len(us), cutoff), full_mask=False)\n uv = model.user_tower(items, typ, rec, valid, ncount) # request-time user embedding\n scores = (uv @ iv.T).numpy() # maximum inner product search\n scores[~elig[us]] = -np.inf\n top = np.argsort(-scores, axis=1)[:, :100]\n rows.append(pd.DataFrame({\"uid\": np.repeat(uids[us], 100), \"item_id\": item_ids[top.ravel()],\n \"rank\": np.tile(np.arange(1, 101), len(us))}))\n sub = pd.concat(rows, ignore_index=True)\n sub.to_parquet(out_path, index=False)\n log(f\"wrote {out_path} ({len(sub)} rows)\")\n # save embeddings too\n np.save(OUT / f\"item_vectors_{out_path.stem}.npy\", iv.numpy())\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument(\"--stage\", choices=[\"val\", \"test\"], required=True)\n ap.add_argument(\"--epochs\", type=int, default=3)\n ap.add_argument(\"--bs\", type=int, default=1024)\n ap.add_argument(\"--lr\", type=float, default=3e-3)\n ap.add_argument(\"--n-cutoffs\", type=int, default=6)\n ap.add_argument(\"--d\", type=int, default=64)\n ap.add_argument(\"--seed\", type=int, default=0)\n args = ap.parse_args()\n torch.manual_seed(args.seed)\n torch.set_num_threads(8)\n\n cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n item_map = {int(i): k for k, i in enumerate(cat.item_id.values)}\n elig_path = DATA / (\"validation_eligible_candidates.npz\" if args.stage == \"val\" else \"test_eligible_candidates.npz\")\n uids = np.load(elig_path)[\"uids\"]\n uid_map = {int(u): k for k, u in enumerate(uids)}\n item_feats = torch.tensor(np.stack([np.log1p(cat.positive_listens.values), np.log1p(cat.organic_positive_listens.values),\n np.log1p(cat.popularity_rank.values)], 1), dtype=torch.float32)\n item_feats = (item_feats - item_feats.mean(0)) / item_feats.std(0)\n\n log(\"loading events\")\n ev = load_events(args.stage, uid_map, item_map)\n log(f\"events: {len(ev)}\")\n hist = History(ev, len(uids))\n\n if args.stage == \"val\":\n cutoffs = [T_END - k * WEEK for k in range(1, args.n_cutoffs + 1)]\n rank_cutoff = T_END + 1\n else:\n # validation window as a training target (history = train), plus earlier cutoffs\n cutoffs = [T_END + GAP] + [T_END - k * WEEK for k in range(1, args.n_cutoffs)]\n rank_cutoff = int(ev.ts.max()) + 1\n examples = build_targets(ev, cutoffs)\n log(f\"total examples {len(examples)}\")\n\n model = TwoTower(item_feats, d=args.d)\n train(model, hist, examples, args.epochs, args.bs, args.lr, args.seed)\n torch.save(model.state_dict(), OUT / f\"model_{args.stage}.pt\")\n out = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n rank(model, hist, uids, uid_map, elig_path, rank_cutoff, out)\n\n\nif __name__ == \"__main__\":\n main()\n" }, "result": { "timestamp": "2026-10-03T08:16:13.502Z", "content": "File created successfully at: /app/solution/twotower.py (file state is current in your context — no need to Read it back)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:16:13.510Z", "data": { "type": "total_tokens_reminder", "text": "14960953 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:16:13.514Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 6, "role": "agent", "timestamp": "2026-10-03T08:16:23.131Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:16:23.131Z", "text": "I'm checking the time windows line up correctly between validation and test cutoffs, confirming the val timestamps stay within the train boundary and the test window properly follows the gap. I'm also noting a potential performance concern with the groupby aggregation in build_targets possibly being slow with a lambda function.\n\nI should vectorize that groupby instead — computing a boolean mask for the LIKE type then taking a max aggregation would be much faster than the lambda approach. I also want to get a safe baseline submission out quickly (popularity-based) for test eligibility, while running a small quick epoch in parallel just to catch bugs early.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:16:23.133Z", "text": "Let me fix the slow groupby lambda, write a safety popularity submission for the test stage, and do a quick smoke run." }, { "kind": "tool", "timestamp": "2026-10-03T08:16:28.250Z", "callId": "toolu_01P6LJS5Vxy3htbcUMpp1zQz", "name": "Bash", "input": { "command": "python - <<'EOF'\nimport re\ns=open('twotower.py').read()\ns=s.replace(''' g = w.groupby([\"u\", \"i\"]).typ.agg(lambda s: 3.0 if (s == LIKE).any() else 1.0).reset_index()\n out.append(np.stack([g.u.values, np.full(len(g), c), g.i.values, g.typ.values], 1))''',\n''' g = pd.DataFrame({\"u\": w.u.values, \"i\": w.i.values, \"typ\": np.where(w.typ.values == LIKE, 3.0, 1.0)})\n g = g.groupby([\"u\", \"i\"]).typ.max().reset_index()\n out.append(np.stack([g.u.values, np.full(len(g), c), g.i.values, g.typ.values], 1))''')\nopen('twotower.py','w').write(s)\nEOF\npython -c \"\nimport numpy as np, pandas as pd\nz=np.load('/task/data/test_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)\nscore=-np.arange(30000)[None,:].repeat(len(m),0).astype(float); score[~m]=-1e9\ntop=np.argsort(-score,axis=1)[:,:100]\npd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':z['item_ids'][top.ravel()],'rank':np.tile(np.arange(1,101),len(m))}).to_parquet('submission.parquet',index=False)\nprint('safety submission written')\n\"\ntimeout 600 python twotower.py --stage val --epochs 1 --n-cutoffs 1 2>&1 | tail -30", "description": "Patch target builder, write safety submission, run smoke test", "timeout": 660000 }, "result": { "timestamp": "2026-10-03T08:17:22.465Z", "content": "safety submission written\n[08:16:39] loading events\n[08:16:52] events: 31668320\n[08:16:56] cutoff 24182000: 41534 examples, 4280 users, likes=5328\n[08:16:56] total examples 41534\n[08:16:59] ep 0 step 0 loss 10.2676 (0s)\n[08:17:15] epoch 0 done: loss 9.6208 in 16s\n[08:17:21] wrote /app/solution/validation.parquet (845600 rows)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:17:22.471Z", "data": { "type": "edited_text_file", "filename": "/app/solution/twotower.py", "snippet": "" } }, { "kind": "context", "timestamp": "2026-10-03T08:17:22.471Z", "data": { "type": "total_tokens_reminder", "text": "14959862 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:17:22.473Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 7, "role": "agent", "timestamp": "2026-10-03T08:17:31.969Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:17:31.969Z", "text": "That's fast, about 0.4 seconds per step, with only 41k examples per cutoff, fewer than expected. With 12 cutoffs that's roughly 500k examples per epoch, around 500 steps, so about 3.5 minutes per epoch. I'll evaluate the smoke test first, then run a fuller training with 12 cutoffs and 3 epochs, roughly 10 minutes, leaving about 48 minutes remaining out of the time budget.\n\nI'll plan a validation run (10 min), evaluate, then a test run (10 min), keeping a buffer. Going with n-cutoffs 10, epochs 3 in the background, and evaluating the smoke test now.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:17:31.975Z", "callId": "toolu_01Atu5mfDLAAnQPtD36fK11e", "name": "Bash", "input": { "command": "python /task/evaluate_public.py validation.parquet; nohup python twotower.py --stage val --epochs 3 --n-cutoffs 10 > val_run.log 2>&1 &\necho started", "description": "Score smoke test and launch full validation training" }, "result": { "timestamp": "2026-10-03T08:17:35.306Z", "content": "{\n \"model\": \"validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030763,\n \"quality_users\": 4263\n }\n}\nstarted", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:17:35.311Z", "data": { "type": "total_tokens_reminder", "text": "14959201 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:17:35.313Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-10-03T08:17:40.860Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:17:40.860Z", "text": "The smoke run looks promising, 0.0308 against popularity's 0.0107. I'll let the full run continue and start drafting the README in the meantime, checking back on log progress shortly.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:17:40.862Z", "text": "The one-epoch smoke model already triples the popularity baseline (0.0308 vs 0.0107). The full validation run is training in the background. Let me monitor it." }, { "kind": "tool", "timestamp": "2026-10-03T08:17:40.865Z", "callId": "toolu_01StLwehsKn6qz5NTp6jLjHz", "name": "Bash", "input": { "command": "sleep 90; tail -5 val_run.log", "description": "Check validation training progress" }, "result": { "timestamp": "2026-10-03T08:19:10.893Z", "content": "[08:17:56] cutoff 19343600: 31915 examples, 3589 users, likes=5019\n[08:17:56] cutoff 18738800: 32259 examples, 3428 users, likes=4196\n[08:17:56] total examples 366314\n[08:17:59] ep 0 step 0 loss 10.2580 (0s)\n[08:18:38] ep 0 step 100 loss 9.7166 (39s)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:19:10.896Z", "data": { "type": "total_tokens_reminder", "text": "14958899 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:19:10.898Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-10-03T08:19:17.587Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:19:17.587Z", "text": "Training should take about 7 minutes, finishing around 08:25, so I'll use that time to draft the README skeleton. After checking the validation score, I'll kick off the test refit which takes roughly 8 minutes due to the extra cutoff — still comfortably within the 09:08 deadline.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:19:17.591Z", "callId": "toolu_01DuY2qNFTZZdiMzf5VhRzM5", "name": "Bash", "input": { "command": "sleep 240; tail -4 val_run.log", "description": "Wait and check training progress", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:23:17.649Z", "content": "[08:22:11] ep 1 step 300 loss 8.8530 (114s)\n[08:22:33] epoch 1 done: loss 8.8353 in 136s\n[08:22:33] ep 2 step 0 loss 8.5587 (0s)\n[08:23:11] ep 2 step 100 loss 8.4501 (38s)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:23:17.652Z", "data": { "type": "total_tokens_reminder", "text": "14958491 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:23:17.655Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-10-03T08:23:21.656Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T08:23:21.656Z", "callId": "toolu_01RV59AG1uwjJ84Zkh5Hva24", "name": "Bash", "input": { "command": "sleep 110; tail -2 val_run.log; ls -la validation.parquet && python /task/evaluate_public.py validation.parquet", "description": "Wait for run to finish and score validation ranking", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:25:15.042Z", "content": "[08:24:49] epoch 2 done: loss 8.4436 in 136s\n[08:24:55] wrote /app/solution/validation.parquet (845600 rows)\n-rw-r--r-- 1 agent agent 1626119 Oct 3 08:24 validation.parquet\n{\n \"model\": \"validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.025151,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:25:15.045Z", "data": { "type": "total_tokens_reminder", "text": "14958197 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:25:15.047Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-10-03T08:25:31.949Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:25:31.949Z", "text": "Interesting that the full 3-epoch, 10-cutoff run scored lower (0.0252) than the quick smoke test (0.0308) — possibly overfitting, since training loss fell to 8.44 while there are 30k items, and the model may be memorizing item embeddings with too few examples, or the older cutoffs introduce distribution shift since popularity patterns differ across time windows.\n\nWith about 43 minutes left and roughly 25 minutes for experimentation, I'm going to try two quick variants in parallel: one using only recent cutoffs with fewer epochs and some weight decay, and another keeping all 10 cutoffs but just one epoch to test if extra epochs are the overfitting culprit. I'll add threading and tagging options so I can run both with limited cores (4 threads each) and compare results, estimating each should finish in a few minutes.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T08:25:31.953Z", "text": "The full run scored lower (0.0252) than the smoke run (0.0308), suggesting overfitting or stale older windows. I'll add thread/weight-decay/tag options and run two quick ablations in parallel.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:25:36.587Z", "callId": "toolu_011Zj2MLguwJSsXAT3UcS4df", "name": "Bash", "input": { "command": "python - <<'EOF'\ns=open('twotower.py').read()\ns=s.replace(''' ap.add_argument(\"--seed\", type=int, default=0)''',''' ap.add_argument(\"--seed\", type=int, default=0)\n ap.add_argument(\"--threads\", type=int, default=8)\n ap.add_argument(\"--wd\", type=float, default=0.0)\n ap.add_argument(\"--tag\", type=str, default=\"\")''')\ns=s.replace(\"torch.set_num_threads(8)\",\"torch.set_num_threads(args.threads)\")\ns=s.replace(\"def train(model, hist, examples, epochs, bs, lr, seed=0):\\n opt = torch.optim.Adam(model.parameters(), lr=lr)\",\n \"def train(model, hist, examples, epochs, bs, lr, seed=0, wd=0.0):\\n opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=wd)\")\ns=s.replace(\"train(model, hist, examples, args.epochs, args.bs, args.lr, args.seed)\",\"train(model, hist, examples, args.epochs, args.bs, args.lr, args.seed, args.wd)\")\ns=s.replace(''' out = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")''',\n''' out = OUT / ((\"validation\" if args.stage == \"val\" else \"submission\") + args.tag + \".parquet\")''')\ns=s.replace('torch.save(model.state_dict(), OUT / f\"model_{args.stage}.pt\")','torch.save(model.state_dict(), OUT / f\"model_{args.stage}{args.tag}.pt\")')\nopen('twotower.py','w').write(s)\nEOF\nnohup python twotower.py --stage val --epochs 1 --n-cutoffs 4 --threads 4 --tag _A > runA.log 2>&1 &\nnohup python twotower.py --stage val --epochs 1 --n-cutoffs 10 --threads 4 --wd 0.01 --tag _B > runB.log 2>&1 &\nsleep 150; tail -2 runA.log runB.log", "description": "Add options and launch two ablation runs in parallel", "timeout": 200000 }, "result": { "timestamp": "2026-10-03T08:28:06.799Z", "content": "Exit code 1\n/bin/bash: line 35: 8497 Killed nohup python twotower.py --stage val --epochs 1 --n-cutoffs 4 --threads 4 --tag _A > runA.log 2>&1\n/bin/bash: line 35: 8498 Killed nohup python twotower.py --stage val --epochs 1 --n-cutoffs 10 --threads 4 --wd 0.01 --tag _B > runB.log 2>&1\ntail: option used in invalid context -- 2", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T08:28:06.839Z", "data": { "type": "total_tokens_reminder", "text": "14956470 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:28:06.852Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-10-03T08:28:13.771Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:28:13.771Z", "text": "Running both processes in parallel on 43M rows caused an out-of-memory error exceeding 15GB, so I need to run them sequentially instead. It's 08:28 with about 40 minutes left, so I'll run A first since it's fast (~1.5 min plus 20s load), then follow with B.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:28:13.775Z", "text": "Both processes were killed by the out-of-memory killer from loading the listens twice in parallel. I'll run them sequentially with all threads." }, { "kind": "tool", "timestamp": "2026-10-03T08:28:13.786Z", "callId": "toolu_01D29RQjyALLRcccCHLVAyoS", "name": "Bash", "input": { "command": "python twotower.py --stage val --epochs 1 --n-cutoffs 4 --tag _A > runA.log 2>&1; tail -1 runA.log; python /task/evaluate_public.py validation_A.parquet", "description": "Run 4-cutoff 1-epoch ablation and score it", "timeout": 400000 }, "result": { "timestamp": "2026-10-03T08:29:47.408Z", "content": "[08:29:43] wrote /app/solution/validation_A.parquet (845600 rows)\n{\n \"model\": \"validation_A.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021782,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:29:47.411Z", "data": { "type": "total_tokens_reminder", "text": "14955951 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:29:47.413Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-10-03T08:30:14.314Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:30:14.314Z", "text": "It's odd that more cutoffs seem to hurt performance—4 cutoffs at 1 epoch gives 0.0218, 1 cutoff at 1 epoch gives 0.0308, and 10 cutoffs at 3 epochs gives 0.0252. Maybe the most recent cutoff matters most and older ones just dilute the signal, or the variance across these 4263 users is simply too high given how small the NDCG values are.\n\nThe real driver might be temporal drift: the hidden test window is one step further out, so what matters is recency of popularity—new releases dominate organic discovery in music streaming. An item bias trained over 10 weeks averages popularity across stale older weeks, whereas the 1-cutoff model learns bias only from the most recent week, which explains why it performs best. This suggests I should add item features capturing recent popularity, like listens in the last 7 days before cutoff or item age, though making these cutoff-dependent during training adds complexity.\n\nI'm planning to group training batches by cutoff so each batch uses a consistent set of time-dependent item features (7-day and 28-day popularity, item age), precomputing a small per-cutoff feature matrix for all items—cheap and matches how production item towers would compute time features offline. With about 38 minutes left, I'll implement this, validate across a few cutoffs and epochs, then run the test, falling back to the simple 1-cutoff config if it doesn't beat the current best score. Now I'm writing the code to compute these per-cutoff item features from the event log.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T08:30:14.319Z", "text": "Recent item popularity seems to matter most, while older cutoffs just dilute signal with stale biases. I'll update the item tower to be cutoff-aware, adding per-cutoff recent-popularity and item-age features trained in cutoff-homogeneous batches.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:30:41.445Z", "callId": "toolu_01REB1ZQQ99mNwCjmu3sxcv4", "name": "Bash", "input": { "command": "python - <<'EOF'\ns=open('twotower.py').read()\n# item tower takes dynamic features\ns=s.replace(''' def __init__(self, n_items, item_feats, d=64, emb_dim=64):\n super().__init__()\n self.emb = nn.Embedding(n_items, emb_dim)\n nn.init.normal_(self.emb.weight, std=0.05)\n self.register_buffer(\"feats\", item_feats)\n self.mlp = nn.Sequential(nn.Linear(emb_dim + item_feats.shape[1], 128), nn.GELU(), nn.Linear(128, d))''',\n''' def __init__(self, n_items, n_feats, d=64, emb_dim=64):\n super().__init__()\n self.emb = nn.Embedding(n_items, emb_dim)\n nn.init.normal_(self.emb.weight, std=0.05)\n self.mlp = nn.Sequential(nn.Linear(emb_dim + n_feats, 128), nn.GELU(), nn.Linear(128, d))''')\ns=s.replace(''' def forward(self, idx=None):\n if idx is None:\n idx = torch.arange(self.emb.num_embeddings)\n x = torch.cat([self.emb(idx), self.feats[idx]], -1)''',\n''' def forward(self, feats):\n \"\"\"feats: [n_items, n_feats] cutoff-specific item features (recent popularity, age, ...).\"\"\"\n idx = torch.arange(self.emb.num_embeddings)\n x = torch.cat([self.emb(idx), feats], -1)''')\ns=s.replace(''' def __init__(self, item_feats, d=64, emb_dim=64):\n super().__init__()\n self.item_tower = ItemTower(N_ITEMS, item_feats, d, emb_dim)''',\n''' def __init__(self, n_feats, d=64, emb_dim=64):\n super().__init__()\n self.item_tower = ItemTower(N_ITEMS, n_feats, d, emb_dim)''')\n# item feature builder\ns=s.replace('''class History:''','''def item_features(ev: pd.DataFrame, cat: pd.DataFrame, cutoffs: list[int]):\n \"\"\"Cutoff-specific item features: log counts of positive listens in the 7/28 days before the\n cutoff, log item age in days (first appearance), plus static catalog popularity.\"\"\"\n pos = ev[(ev.typ == LISTEN_POS) | (ev.typ == LISTEN_STRONG_ORG)]\n first = pos.groupby(\"i\").ts.min().reindex(range(N_ITEMS)).fillna(T_END).values\n static = np.stack([np.log1p(cat.positive_listens.values), np.log1p(cat.organic_positive_listens.values),\n np.log1p(cat.popularity_rank.values)], 1)\n feats = {}\n for c in cutoffs:\n w7 = pos[(pos.ts >= c - WEEK) & (pos.ts < c)]\n w28 = pos[(pos.ts >= c - 4 * WEEK) & (pos.ts < c)]\n c7 = np.bincount(w7.i.values, minlength=N_ITEMS)\n c28 = np.bincount(w28.i.values, minlength=N_ITEMS)\n age = np.clip((c - first) / 86400.0, 0, None)\n feats[c] = np.concatenate([static, np.stack([np.log1p(c7), np.log1p(c28), np.log1p(age),\n np.log1p(c7) - np.log1p(c28 / 4.0)], 1)], 1).astype(np.float32)\n allf = np.concatenate(list(feats.values()))\n mu, sd = allf.mean(0), allf.std(0) + 1e-6\n return {c: torch.from_numpy((f - mu) / sd) for c, f in feats.items()}\n\n\nclass History:''')\n# training: cutoff-homogeneous batches\ns=s.replace('''def train(model, hist, examples, epochs, bs, lr, seed=0, wd=0.0):\n opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=wd)\n rng = np.random.default_rng(seed)\n n = len(examples)\n steps = epochs * ((n + bs - 1) // bs)\n sched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=lr, total_steps=steps, pct_start=0.1)\n step = 0\n for ep in range(epochs):\n perm = rng.permutation(n)\n tot, cnt, t0 = 0.0, 0, time.time()\n model.train()\n for b0 in range(0, n, bs):\n ex = examples[perm[b0:b0 + bs]]''',\n'''def make_batches(examples, bs, rng):\n \"\"\"Shuffle within each cutoff, chunk, then shuffle batch order (batches share one cutoff).\"\"\"\n batches = []\n for c in np.unique(examples[:, 1]):\n idx = np.where(examples[:, 1] == c)[0]\n rng.shuffle(idx)\n batches += [idx[k:k + bs] for k in range(0, len(idx), bs)]\n rng.shuffle(batches)\n return batches\n\n\ndef train(model, hist, examples, feats, epochs, bs, lr, seed=0, wd=0.0):\n opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=wd)\n rng = np.random.default_rng(seed)\n n = len(examples)\n steps = epochs * len(make_batches(examples, bs, rng))\n sched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=lr, total_steps=steps, pct_start=0.1)\n step = 0\n for ep in range(epochs):\n tot, cnt, t0 = 0.0, 0, time.time()\n model.train()\n for bi, idx in enumerate(make_batches(examples, bs, rng)):\n b0 = bi * bs\n ex = examples[idx]''')\ns=s.replace(''' iv = model.item_tower()\n logits = uv @ iv.T''',''' iv = model.item_tower(feats[int(cuts[0])])\n logits = uv @ iv.T''')\ns=s.replace('''def rank(model, hist, uids, uid_map, elig_path, cutoff, out_path):''','''def rank(model, hist, uids, uid_map, elig_path, cutoff, out_path, feats):''')\ns=s.replace(''' iv = model.item_tower() # offline item index''',''' iv = model.item_tower(feats) # offline item index''')\ns=s.replace(''' item_feats = torch.tensor(np.stack([np.log1p(cat.positive_listens.values), np.log1p(cat.organic_positive_listens.values),\n np.log1p(cat.popularity_rank.values)], 1), dtype=torch.float32)\n item_feats = (item_feats - item_feats.mean(0)) / item_feats.std(0)\n''','')\ns=s.replace(''' examples = build_targets(ev, cutoffs)\n log(f\"total examples {len(examples)}\")\n\n model = TwoTower(item_feats, d=args.d)\n train(model, hist, examples, args.epochs, args.bs, args.lr, args.seed, args.wd)''',\n''' examples = build_targets(ev, cutoffs)\n log(f\"total examples {len(examples)}\")\n feats = item_features(ev, cat, cutoffs + [rank_cutoff])\n\n model = TwoTower(feats[rank_cutoff].shape[1], d=args.d)\n train(model, hist, examples, feats, args.epochs, args.bs, args.lr, args.seed, args.wd)''')\ns=s.replace(''' rank(model, hist, uids, uid_map, elig_path, rank_cutoff, out)''',''' rank(model, hist, uids, uid_map, elig_path, rank_cutoff, out, feats[rank_cutoff])''')\nopen('twotower.py','w').write(s)\nEOF\npython twotower.py --stage val --epochs 2 --n-cutoffs 6 --tag _C > runC.log 2>&1; tail -3 runC.log; python /task/evaluate_public.py validation_C.parquet", "description": "Add cutoff-aware item features and run validation experiment", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:34:08.930Z", "content": "[08:33:47] ep 1 step 200 loss 8.8940 (76s)\n[08:33:58] epoch 1 done: loss 8.8923 in 86s\n[08:34:05] wrote /app/solution/validation_C.parquet (845600 rows)\n{\n \"model\": \"validation_C.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030877,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:34:08.933Z", "data": { "type": "total_tokens_reminder", "text": "14951606 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:34:08.934Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-10-03T08:34:18.971Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:34:18.971Z", "text": "The 0.0309 result with 6 cutoffs and 2 epochs matches the smoke run with time-aware features now. At 08:34 with a ~09:08 deadline, I have about 34 minutes left and each test refit takes roughly 3.5 minutes, so there's room for maybe 1-2 more experiments. I'm weighing whether to lower the learning rate or increase weight decay to reduce item embedding overfit, possibly with a smaller embedding size.\n\nI'll run experiment D with 1 epoch, 6 cutoffs, and wd=0.1 using decoupled AdamW to shrink embeddings further, then pick the best config and immediately run the test, since each validation run takes about 3 minutes including loading time. Let me kick off run D now.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:34:18.973Z", "text": "Cutoff-aware item features recovered the score with more data (0.0309 on 6 cutoffs, 2 epochs). One more regularization ablation, then the refit." }, { "kind": "tool", "timestamp": "2026-10-03T08:34:20.251Z", "callId": "toolu_01L64gNU5XV5o1Y3dMt8i5md", "name": "Bash", "input": { "command": "python twotower.py --stage val --epochs 2 --n-cutoffs 6 --wd 0.1 --lr 2e-3 --tag _D > runD.log 2>&1; tail -1 runD.log; python /task/evaluate_public.py validation_D.parquet", "description": "Run weight-decay ablation and score it", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:37:46.396Z", "content": "[08:37:42] wrote /app/solution/validation_D.parquet (845600 rows)\n{\n \"model\": \"validation_D.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.032211,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:37:46.399Z", "data": { "type": "total_tokens_reminder", "text": "14950867 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:37:46.401Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-10-03T08:37:54.028Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:37:54.028Z", "text": "With about 30 minutes left, I should launch the test refit now with config D since parallel runs keep getting killed due to memory limits. I'll run it sequentially to secure the deliverable first, then squeeze in one more validation experiment if time allows.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:37:54.030Z", "text": "Weight decay helped (0.0322). Launching the hidden-test refit with that configuration now to secure the deliverable, then I'll use remaining time for one more ablation." }, { "kind": "tool", "timestamp": "2026-10-03T08:37:58.142Z", "callId": "toolu_014a7AtZbyQc8D7doTj7f848", "name": "Bash", "input": { "command": "cp submission.parquet submission_pop_backup.parquet; python twotower.py --stage test --epochs 2 --n-cutoffs 6 --wd 0.1 --lr 2e-3 --tag _D > test_run.log 2>&1; tail -3 test_run.log; python - <<'EOF'\nimport pandas as pd, numpy as np, sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, read_ranking\nfrom pathlib import Path\np=Path('/app/solution/submission_D.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(p),t)\ncs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nok=all(cs.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint('valid structure, all eligible:', ok, len(f))\nif ok:\n import shutil; shutil.copy(p,'/app/solution/submission.parquet'); print('promoted to submission.parquet')\nEOF", "description": "Refit on train+val for hidden test, validate, promote submission", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:41:28.828Z", "content": "[08:41:07] ep 1 step 200 loss 9.0949 (76s)\n[08:41:19] epoch 1 done: loss 9.0927 in 88s\n[08:41:25] wrote /app/solution/submission_D.parquet (845600 rows)\nvalid structure, all eligible: True 845600\npromoted to submission.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:41:28.831Z", "data": { "type": "total_tokens_reminder", "text": "14950014 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:41:28.833Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-10-03T08:41:37.577Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:41:37.577Z", "text": "It's about 08:41 with roughly 26 minutes left before the deadline, leaving room for one more validation experiment, a possible refit, and writing the README with some buffer to spare.\n\nI'll settle on trying stronger regularization with weight decay 0.3, 2 epochs, and 8 cutoffs to keep it around 4 minutes, then start drafting the README while that runs.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:41:37.578Z", "text": "Submission is secured and validated. Using remaining time for one more ablation with stronger decay and more cutoffs, while writing the README." }, { "kind": "tool", "timestamp": "2026-10-03T08:41:37.988Z", "callId": "toolu_01NTSkiaqEC94R8Jyuf8JYe9", "name": "Bash", "input": { "command": "python twotower.py --stage val --epochs 2 --n-cutoffs 8 --wd 0.3 --lr 2e-3 --tag _E > runE.log 2>&1; tail -1 runE.log; python /task/evaluate_public.py validation_E.parquet", "description": "Run stronger-decay 8-cutoff ablation and score it", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:45:55.487Z", "content": "[08:45:51] wrote /app/solution/validation_E.parquet (845600 rows)\n{\n \"model\": \"validation_E.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.031786,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:45:55.490Z", "data": { "type": "total_tokens_reminder", "text": "14949356 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:45:55.492Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-10-03T08:46:03.753Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:46:03.753Z", "text": "0.0318 beats 0.0322, so I'll keep option D. With about 22 minutes left, running another experiment isn't worth the risk to my time buffer — I'll finalize now by writing the README and cleaning up the intermediate files.\n\nCopying validation_D to validation.parquet and removing the large temp files.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:46:03.754Z", "text": "Config D remains best (0.0322). I'll finalize: promote the D validation ranking, write the README, and clean up." }, { "kind": "tool", "timestamp": "2026-10-03T08:46:21.257Z", "callId": "toolu_01Bq1rjS5tnuzv8Z6o31ZcfB", "name": "Bash", "input": { "command": "cp validation_D.parquet validation.parquet; rm -f pop_val.parquet nohup.out validation_A.parquet validation_B.parquet; ls -la; cat > README.md <<'EOF'\n# Two-tower discovery ranker (\"Made for You\" shelf)\n\nAll code is in `twotower.py` (CPU PyTorch 2.8, no accelerator). Final artifact:\n`submission.parquet` (8,456 target users x 100 ranked eligible items).\n\n## Architecture (score = dot product of two independently computed vectors)\n\n* **Item tower** (precomputable offline, stored in the MIPS index): item-ID\n embedding (64-d) concatenated with 7 item features (static catalog\n popularity, log positive-listen counts in the 7 / 28 days before the cutoff,\n log item age, 7d-vs-28d trend) -> MLP(128, GELU) -> 64-d, plus a learned\n per-item bias appended as a 65th dimension.\n* **User tower** (one forward pass at request time, from history only, no\n user-ID lookup): the user's last 200 events (listens <50%, listens >=50%,\n organic >=80% listens, likes, dislikes, unlikes) embedded through the shared\n item-embedding table and pooled with learned event-type x recency-bucket\n weights; a second pooling over the 20 most recent positive events; three\n log-count features -> MLP(256, GELU) -> 64-d, plus a constant 1 as the 65th\n dimension so the dot product includes the item bias exactly.\n* Relevance = ``; retrieval = masked maximum inner product\n over the user's cutoff-correct eligible candidate set.\n\n## Training objective\n\nFull-softmax cross-entropy over the 30,000-track catalog (items the user has\nalready interacted with before the cutoff are masked out, as they are never\neligible). Positives are \"discovery\" events in the 7 days after each cutoff on\nnever-before-interacted catalog tracks: organic likes (weight 3) and organic\n>=80% listens (weight 1). Six weekly cutoffs are used (for the hidden test, the\npublic validation window itself is the most recent training window). Batches\nare cutoff-homogeneous so the item tower sees cutoff-specific popularity/age\nfeatures. AdamW (lr 2e-3 one-cycle, weight decay 0.1), batch 1024, 2 epochs,\n~3 minutes on 8 CPU cores. No user-item matrix is built or factorized; training\nis per-example minibatch SGD and scales to millions of users/items (sampled\nsoftmax would replace the full catalog softmax at that scale).\n\n## Public validation score (history = train_*, outcomes = val_*)\n\n```\npython twotower.py --stage val --epochs 2 --n-cutoffs 6 --wd 0.1 --lr 2e-3\npython /task/evaluate_public.py /app/solution/validation.parquet\n# quality_ndcg@10 = 0.032211 (4263 scored users); popularity baseline = 0.010710\n```\n\nAblations (NDCG@10): 1 cutoff/1 epoch 0.0308; 10 cutoffs/3 epochs without\ncutoff-aware item features 0.0252; with cutoff-aware features 6 cutoffs/2 epochs\n0.0309; + weight decay 0.1 **0.0322** (selected); wd 0.3 / 8 cutoffs 0.0318.\n\n## Final submission command (refit on train_* + val_*)\n\n```\npython twotower.py --stage test --epochs 2 --n-cutoffs 6 --wd 0.1 --lr 2e-3 --tag _D\ncp submission_D.parquet submission.parquet\n```\n\nOutputs: `submission.parquet`, `model_test_D.pt` (weights),\n`item_vectors_submission_D.npy` (offline item index vectors).\nEOF\npython /task/evaluate_public.py validation.parquet | head -5; python -c \"\nimport pandas as pd; s=pd.read_parquet('submission.parquet'); print(s.shape, s.uid.nunique(), s['rank'].min(), s['rank'].max(), s.duplicated(['uid','item_id']).sum())\"", "description": "Finalize README, promote validation file, verify submission" }, "result": { "timestamp": "2026-10-03T08:46:26.310Z", "content": "total 102144\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:46 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:08 ..\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:41 item_vectors_submission_D.npy\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:24 item_vectors_validation.npy\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:29 item_vectors_validation_A.npy\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:34 item_vectors_validation_C.npy\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:37 item_vectors_validation_D.npy\n-rw-r--r-- 1 agent agent 7800128 Oct 3 08:45 item_vectors_validation_E.npy\n-rw-r--r-- 1 agent agent 8075991 Oct 3 08:41 model_test_D.pt\n-rw-r--r-- 1 agent agent 8434147 Oct 3 08:24 model_val.pt\n-rw-r--r-- 1 agent agent 8434183 Oct 3 08:29 model_val_A.pt\n-rw-r--r-- 1 agent agent 8075974 Oct 3 08:33 model_val_C.pt\n-rw-r--r-- 1 agent agent 8075974 Oct 3 08:37 model_val_D.pt\n-rw-r--r-- 1 agent agent 8075974 Oct 3 08:45 model_val_E.pt\n-rw-r--r-- 1 agent agent 549 Oct 3 08:29 runA.log\n-rw-r--r-- 1 agent agent 26 Oct 3 08:25 runB.log\n-rw-r--r-- 1 agent agent 896 Oct 3 08:34 runC.log\n-rw-r--r-- 1 agent agent 896 Oct 3 08:37 runD.log\n-rw-r--r-- 1 agent agent 1032 Oct 3 08:45 runE.log\n-rw-r--r-- 1 agent agent 1279435 Oct 3 08:41 submission.parquet\n-rw-r--r-- 1 agent agent 1279435 Oct 3 08:41 submission_D.parquet\n-rw-r--r-- 1 agent agent 731032 Oct 3 08:37 submission_pop_backup.parquet\n-rw-r--r-- 1 agent agent 896 Oct 3 08:41 test_run.log\n-rw-r--r-- 1 agent agent 15149 Oct 3 08:30 twotower.py\n-rw-r--r-- 1 agent agent 1467 Oct 3 08:24 val_run.log\n-rw-r--r-- 1 agent agent 1279850 Oct 3 08:46 validation.parquet\n-rw-r--r-- 1 agent agent 1389962 Oct 3 08:34 validation_C.parquet\n-rw-r--r-- 1 agent agent 1279850 Oct 3 08:37 validation_D.parquet\n-rw-r--r-- 1 agent agent 1280384 Oct 3 08:45 validation_E.parquet\n{\n \"model\": \"validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.032211,\n \"quality_users\": 4263\n(845600, 3) 8456 1 100 0", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:46:26.313Z", "data": { "type": "total_tokens_reminder", "text": "14947525 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:46:26.315Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-10-03T08:46:40.740Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T08:46:40.740Z", "text": "Done. The final submission is in place, validated, and documented.\n\n**What was built** (`/app/solution/twotower.py`)\n- **Item tower**: item-ID embedding plus cutoff-specific features (recent 7-day and 28-day positive-listen counts, item age, trend, static popularity) through an MLP, with a learned bias appended as an extra dimension. Item vectors are precomputed and saved as the offline index.\n- **User tower**: no user-ID lookup. It pools the user's last 200 events (partial listens, listens, organic strong listens, likes, dislikes, unlikes) with learned event-type by recency-bucket weights, adds a recent-positives pooling and count features, then an MLP. A constant 1 matches the item bias, so relevance is exactly the dot product.\n- **Objective**: full-softmax cross-entropy over the 30k catalog with already-interacted items masked out. Positives are organic likes (weight 3) and organic 80%+ listens (weight 1) on never-before-heard tracks in the week after each of six weekly cutoffs. Batches are cutoff-homogeneous so item features are time-correct. No user-item matrix is built.\n\n**Validation results** (graded NDCG@10, 4263 scored users)\n\n| Config | Score |\n| --- | --- |\n| Popularity baseline | 0.0107 |\n| 10 cutoffs, no time-aware item features | 0.0252 |\n| 6 cutoffs, time-aware features | 0.0309 |\n| Same plus weight decay 0.1 (selected) | 0.0322 |\n| Weight decay 0.3, 8 cutoffs | 0.0318 |\n\nThe key finding was that older training windows hurt until the item tower was given cutoff-specific popularity and age features. Recent popularity dominates organic discovery in this data.\n\n**Deliverables**\n- `submission.parquet`: refit on train plus validation events, 8456 users by 100 items, all ranks 1 to 100, no duplicates, every item checked against the test eligibility sets.\n- `validation.parquet`: the public validation ranking for the selected config.\n- `README.md`: architecture, objective, scores, and the exact refit command.\n- `model_test_D.pt` and `item_vectors_submission_D.npy`: fitted weights and offline item index.\n\nOne caveat: the two parallel ablation attempts were killed by the out-of-memory killer, so all runs were sequential. That limited tuning to five configurations within the time budget. Larger embedding dimension and longer history windows were not explored." } ] } ] }