{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-base", "attemptId": "fable51-high-2-1-288-base-r02-20261003T075109Z", "taskChecksum": "3046320f37568ab575f9020b1cb3239469860cc563473b0ea1e509aadf662de6", "source": { "format": "claude-code-session", "sha256": "10dee2f4b1278f9dd2004478c915bbada713c7501364903fdac60c3853adea8e", "records": 162, "bookkeepingRecords": { "queue-operation": 4, "atis-latch": 10, "last-prompt": 9 }, "usageRecords": [ { "type": "cost-state", "sessionId": "aac421c7-2f2b-4e82-89ea-d97c71ee0035", "totalCostUSD": 2.534486499999999, "totalAPIDuration": 318648, "totalAPIDurationWithoutRetries": 318591, "totalToolDuration": 919597, "totalLinesAdded": 0, "totalLinesRemoved": 0, "totalDuration": 1239419, "startTime": 1791015195125, "modelUsage": { "claude-fable-5-1": { "inputTokens": 708, "outputTokens": 28431, "thinkingTokens": 9412, "cacheReadInputTokens": 1080866, "cacheCreationInputTokens": 41782, "webSearchRequests": 0, "costUSD": 2.534486499999999 } }, "hasUnknownModelCost": false } ] }, "sessionId": "aac421c7-2f2b-4e82-89ea-d97c71ee0035", "agentVersion": "2.1.288", "model": "claude-fable-5-1", "steps": [ { "id": 1, "role": "user", "timestamp": "2026-10-03T08:13:15.538Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T08:13:15.538Z", "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": "2026-10-03T08:13:15.537Z", "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": "2026-10-03T08:13:15.537Z", "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": "2026-10-03T08:13:15.537Z", "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": "2026-10-03T08:13:15.537Z", "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": "2026-10-03T08:13:15.537Z", "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": "2026-10-03T08:13:15.537Z", "data": { "type": "auto_mode", "autoModeConsentFlow": false, "bashFirst": true, "bashFirstSteer": "strict", "steerOnly": true, "bypass": true } }, { "kind": "context", "timestamp": "2026-10-03T08:13:15.537Z", "data": { "type": "total_tokens_reminder", "text": "15000000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:15.553Z", "data": { "type": "session_context", "context": {} } }, { "kind": "context", "timestamp": "2026-10-03T08:13:15.554Z", "data": { "type": "date", "date": "2026-10-03" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:15.555Z", "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": "2026-10-03T08:13:15.556Z", "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": "2026-10-03T08:13:18.291Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:13:18.291Z", "text": "I should start by exploring the data and environment, keeping in mind I have about 60 minutes and need to work efficiently.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:13:18.294Z", "text": "I'll start by inspecting the data, the evaluator, and the environment, then build a two-tower model quickly and iterate." }, { "kind": "tool", "timestamp": "2026-10-03T08:13:18.638Z", "callId": "toolu_019mXdQykhmr7FUW29vDBWfo", "name": "Bash", "input": { "command": "cd /task && ls -la data/ && cat requirements.txt | head -50 && nproc && free -g && python -c \"import torch; print(torch.__version__, torch.get_num_threads())\"", "description": "List data files and check environment" }, "result": { "timestamp": "2026-10-03T08:13:21.643Z", "content": "total 347392\ndr-xr-xr-x 1 root root 4096 Oct 3 07:56 .\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 07:56 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 07:56 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 14\nSwap: 0 0 0\n2.8.0+cpu 4\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:21.645Z", "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": "2026-10-03T08:13:21.648Z", "data": { "type": "total_tokens_reminder", "text": "14980358 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:21.651Z", "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": "2026-10-03T08:13:23.745Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T08:13:23.745Z", "callId": "toolu_01AbARqN7NcgpkxSM6XAEiEh", "name": "Bash", "input": { "command": "cd /task && cat evaluate_public.py && echo ---- && cat ranking_evaluation.py && ls /app /app/solution 2>/dev/null", "description": "Read the public evaluator and helper module" }, "result": { "timestamp": "2026-10-03T08:13:23.774Z", "content": "#!/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----\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\n/app:\nsolution\n\n/app/solution:\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:23.777Z", "data": { "type": "total_tokens_reminder", "text": "14978797 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:23.779Z", "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": "2026-10-03T08:13:30.549Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T08:13:30.549Z", "callId": "toolu_01CSQ63EdGn3aQP8BPHSL1To", "name": "Bash", "input": { "command": "cd /task/data && python - <<'EOF'\nimport pandas as pd, numpy as np, time\nt=time.time()\nfor f in [\"train_listens\",\"train_likes\",\"train_dislikes\",\"train_unlikes\",\"train_undislikes\",\"val_listens\",\"val_likes\",\"candidate_catalog\",\"target_users\",\"validation_preference_state\",\"test_preference_state\"]:\n df=pd.read_parquet(f+\".parquet\")\n print(f, df.shape, list(df.columns), df.dtypes.to_dict() if f in(\"train_listens\",\"candidate_catalog\",\"validation_preference_state\") else \"\")\n print(df.head(3).to_string())\nprint(time.time()-t)\ntl=pd.read_parquet(\"train_listens.parquet\")\nprint(\"ts range\", tl.timestamp.min(), tl.timestamp.max(), \"users\", tl.uid.nunique(), \"items\", tl.item_id.nunique())\nprint(tl.played_ratio_pct.describe())\nprint(\"organic frac\", tl.is_organic.mean())\nvl=pd.read_parquet(\"val_listens.parquet\"); print(\"val ts\", vl.timestamp.min(), vl.timestamp.max())\nfor name in [\"validation_eligible_candidates\",\"test_eligible_candidates\"]:\n z=np.load(name+\".npz\"); print(name, {k:z[k].shape for k in z.files})\n m=np.unpackbits(z[\"packed_eligible\"],axis=1,count=len(z[\"item_ids\"]),bitorder=\"little\")\n print(\" elig per user\", m.sum(1).min(), m.sum(1).mean(), m.sum(1).max())\ntu=pd.read_parquet(\"target_users.parquet\"); print(\"targets\", len(tu))\ncat=pd.read_parquet(\"candidate_catalog.parquet\"); print(cat.head())\nEOF", "description": "Inspect schemas, sizes, and eligibility statistics" }, "result": { "timestamp": "2026-10-03T08:13:35.622Z", "content": "train_listens (43484159, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n 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\ntrain_likes (814831, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] \n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\ntrain_dislikes (101075, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] \n uid timestamp item_id is_organic\n0 100 3087560 9170134 1\n1 100 3936560 8661238 1\n2 100 20432825 4927727 1\ntrain_unlikes (292665, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] \n uid timestamp item_id is_organic\n0 100 3087555 5038504 1\n1 100 3087555 9170134 1\n2 100 4012270 7833676 0\ntrain_undislikes (19960, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] \n uid timestamp item_id is_organic\n0 100 7895900 9170134 1\n1 100 7895900 5038504 1\n2 1400 8317355 6175319 1\nval_listens (1501697, 6) ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] \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\nval_likes (37795, 4) ['uid', 'timestamp', 'item_id', 'is_organic'] \n uid timestamp item_id is_organic\n0 100 25249855 3526521 1\n1 300 25046245 4867327 1\n2 300 25046970 2674136 1\ncandidate_catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\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\ntarget_users (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] \n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\nvalidation_preference_state (469124, 3) ['uid', 'item_id', 'state'] {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\ntest_preference_state (484776, 3) ['uid', 'item_id', 'state'] \n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n0.8048305511474609\nts range 0 24786795 users 9238 items 845395\ncount 4.348416e+07\nmean 6.465021e+01\nstd 4.412183e+01\nmin 0.000000e+00\n25% 7.000000e+00\n50% 1.000000e+02\n75% 1.000000e+02\nmax 1.590000e+02\nName: played_ratio_pct, dtype: float64\norganic frac 0.5169151828370419\nval ts 24788600 25393395\nvalidation_eligible_candidates {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}\n elig per user 24657 29067.306291390727 30000\ntest_eligible_candidates {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}\n elig per user 24552 29041.3178807947 30000\ntargets 8456\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:35.625Z", "data": { "type": "total_tokens_reminder", "text": "14972691 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:35.627Z", "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": "2026-10-03T08:13:50.118Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:13:50.118Z", "text": "I'm looking at the scale here: 43M listens across 9238 users and 845k items, with a 30k-item catalog and timestamps spanning roughly 287 days in seconds. I'm trying to figure out what makes candidates eligible, suspecting that items a user already interacted with get excluded, averaging around 900 exclusions per user.\n\nI want to check the validation grading scheme too—organic likes scored at 3, organic listens at 1 among eligible candidates—and compare against a simple popularity baseline. With about 55 minutes left, I'm sketching a two-tower model: an item tower combining embeddings with popularity and organic-ratio features, and a user tower built from time-decayed, weighted pooling of history and liked items, both feeding into small MLPs, trained with sampled negatives.\n\nFor training targets, I'll slice the data at multiple time cutoffs (e.g. weekly), using everything before the cutoff as user history and organic positives (likes or ≥80% listens) after the cutoff as targets restricted to catalog items and excluded from prior history to mirror the discovery setup. Given the scale (43M rows) I need vectorized preprocessing on CPU, so before building this out I want to quickly verify eligibility semantics, measure the popularity baseline, and check what fraction of validation targets were already seen in a user's train history.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:13:56.277Z", "callId": "toolu_01FaFx4Sk3dUCntnQryiunGL", "name": "Bash", "input": { "command": "cd /task/data && python - <<'EOF'\nimport pandas as pd, numpy as np, time\ntl=pd.read_parquet(\"train_listens.parquet\")\nz=np.load(\"validation_eligible_candidates.npz\")\nuids=z[\"uids\"]; items=z[\"item_ids\"]\nm=np.unpackbits(z[\"packed_eligible\"],axis=1,count=len(items),bitorder=\"little\").astype(bool)\ncat=set(items.tolist())\nitem_col={int(i):c for c,i in enumerate(items)}\n# check a few users: ineligible items vs train history\nfor r in [0,1,2]:\n u=uids[r]; h=tl[tl.uid==u]\n hist=set(h.item_id.tolist())\n inel=set(items[~m[r]].tolist())\n print(u,\"inel\",len(inel),\"hist∩cat\",len(hist&cat),\"inel⊂hist\",len(inel&hist),\"hist∩cat-inel\",len((hist&cat)-inel))\n # what's in hist&cat but eligible? maybe low-played\n hp=h[h.item_id.isin(items[m[r]])]\n print(\" eligible-but-in-history played ratio\", hp.played_ratio_pct.describe()[[\"mean\",\"50%\"]].to_dict(), \"org\", hp.is_organic.mean(), \"n\", len(hp))\n hn=h[h.item_id.isin(list(inel))]\n print(\" inel played ratio\", hn.played_ratio_pct.describe()[[\"mean\",\"50%\"]].to_dict(), \"n\", len(hn))\n# val labels\nvl=pd.read_parquet(\"val_listens.parquet\"); vk=pd.read_parquet(\"val_likes.parquet\")\ntg=set(uids.tolist())\ns=vl[(vl.uid.isin(tg))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][[\"uid\",\"item_id\"]].drop_duplicates()\nk=vk[(vk.uid.isin(tg))&(vk.is_organic==1)][[\"uid\",\"item_id\"]].drop_duplicates()\nurow={int(u):r for r,u in enumerate(uids)}\ndef elig(df):\n ok=[]\n for u,i in df.itertuples(index=False,name=None):\n c=item_col.get(int(i)); ok.append(c is not None and m[urow[int(u)],c])\n return df[np.array(ok)]\nse=elig(s); ke=elig(k)\nprint(\"strong listens\", len(s), \"eligible\", len(se), \"likes\", len(k), \"eligible\", len(ke))\nprint(\"users with labels\", len(set(se.uid)|set(ke.uid)))\nprint(\"per-user relevant median\", pd.concat([se,ke]).drop_duplicates().groupby(\"uid\").size().describe())\nEOF", "description": "Check eligibility semantics and validation label counts" }, "result": { "timestamp": "2026-10-03T08:13:59.117Z", "content": "100 inel 682 hist∩cat 682 inel⊂hist 682 hist∩cat-inel 0\n eligible-but-in-history played ratio {'mean': nan, '50%': nan} org nan n 0\n inel played ratio {'mean': 80.74118529632408, '50%': 100.0} n 2666\n200 inel 168 hist∩cat 168 inel⊂hist 168 hist∩cat-inel 0\n eligible-but-in-history played ratio {'mean': nan, '50%': nan} org nan n 0\n inel played ratio {'mean': 91.45421245421245, '50%': 100.0} n 273\n300 inel 69 hist∩cat 69 inel⊂hist 69 hist∩cat-inel 0\n eligible-but-in-history played ratio {'mean': nan, '50%': nan} org nan n 0\n inel played ratio {'mean': 60.1, '50%': 100.0} n 120\nstrong listens 255608 eligible 35597 likes 17195 eligible 4417\nusers with labels 4263\nper-user relevant median count 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n25% 2.000000\n50% 4.000000\n75% 10.000000\nmax 442.000000\ndtype: float64\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:13:59.120Z", "data": { "type": "total_tokens_reminder", "text": "14968982 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:13:59.122Z", "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": "2026-10-03T08:14:33.874Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:14:33.874Z", "text": "Eligible items are catalog entries the user hasn't listened to at all, so this is pure discovery -- predicting new items they'll listen to ≥80% or like within 7 days. I'm starting the preprocessing pipeline that loads the 43M training listens and builds an item vocabulary, prioritizing the 30k catalog plus other items meeting a minimum listen-count threshold.\n\nI'm designing multiple training cutoffs spaced a week apart, where each cutoff defines a history window and a 7-day target window for organic strong listens or likes on items the user hadn't touched before that cutoff, mimicking real eligibility. For user history, I'm building compact per-user representations using recency-weighted bags of recent positive listens and separate channels for liked/disliked items, fed through an EmbeddingBag for efficiency. For the model, I'm sketching an item tower with embeddings plus a small MLP, aiming for dot-product scoring with a softmax temperature for retrieval.\n\nI'm settling on sampled softmax with in-batch negatives (with logQ correction) plus uniform random negatives from the catalog, since in-batch negatives naturally skew popular and mixing both should help. Estimating training volume, roughly 240k positive pairs across six cutoffs for target users, which is small enough to train many epochs quickly on CPU, and I could expand to all users if needed. I'm also weighing whether to include weaker, non-organic positive signals at lower weight, but deciding to keep it strictly organic for now and only revisit if time allows, while noting the hidden test setup mirrors this with a gap after the validation window.\n\nFor inference, the user tower needs history before the correct cutoff depending on split, and I'm now mapping out how to efficiently extract each user's recent events using sorted per-user arrays and searchsorted for fast lookup, which should scale fine across all users and cutoffs. I'm also working out the weighting scheme for history events based on listen strength and organic status.\n\nNow I'm thinking through the user tower design more concretely: separate bags for recent positive listens (with decay and organic bonus), liked items, and dislikes, each capped at different sizes, feeding into a user MLP. For the item vocabulary, I'm weighing whether building it from train+val listens introduces any leakage concern for the ranking task.\n\nTo stay strict, I decide to build the vocabulary from train only, letting unseen val items map to UNK, sizing the catalog around 180k items with 64-dim embeddings for roughly 11.5M params. I'll also UNK long-tail history items given poor embedding quality at this scale, then move to writing a preprocessing script that caches per-user event arrays for both train-only and train+val views, given the data comfortably fits in memory, before planning out the remaining time for iteration and final refit.\n\nFor the event schema, I'm structuring arrays sorted by user and timestamp with fields for user index, timestamp, item index, event kind (listen/like/dislike/unlike/undislike), organic flag, and play percentage. I decide to keep only listens with substantial play duration as positive signal, dropping skips for now despite their potential as a negative signal, to save memory and simplify the first pass. For the final refit I'll concatenate validation events in, and for any given cutoff I'll define targets as events falling in the following week-long window.\n\nEligible targets require organic listens with high play ratio or organic likes, restricted to catalog items the user hasn't seen before the cutoff -- but since eligibility depends on *any* prior listen regardless of play ratio, I need a separate \"seen\" structure covering all listens including skips. I'll compute the first-listen timestamp per user-item pair via a groupby minimum, then at scoring time check whether that first-listen timestamp precedes the cutoff to determine if a candidate should be excluded.\n\nFor labeling, I'll assign higher grades to likes versus strong listens, and consider weighting the loss to emphasize likes more heavily in training. Time to start writing prep.py.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:14:48.610Z", "callId": "toolu_01RTGP2WduZ13LnxQdjYbz6D", "name": "Bash", "input": { "command": "mkdir -p /app/solution && cat > /app/solution/prep.py <<'EOF'\n\"\"\"Preprocess Yambda logs into compact per-user event arrays for the two-tower model.\n\nOutputs /app/solution/cache/prep.npz with:\n - item vocab (catalog items first, then frequent non-catalog items, then UNK)\n - sorted event arrays for train only and for train+val\n - first-listen timestamps for (user, catalog item) pairs (used to mimic eligibility)\n\"\"\"\nimport numpy as np, pandas as pd, time, os\nD = \"/task/data/\"; OUT = \"/app/solution/cache/\"; os.makedirs(OUT, exist_ok=True)\nt0 = time.time()\ncat = pd.read_parquet(D + \"candidate_catalog.parquet\")\ncat_items = cat.item_id.to_numpy().astype(np.int64) # ordered by popularity\ntargets = pd.read_parquet(D + \"target_users.parquet\").uid.to_numpy().astype(np.int64)\n\ndef load(split):\n l = pd.read_parquet(D + f\"{split}_listens.parquet\")\n l = l[l.played_ratio_pct >= 50] # keep positive listens only (skips dropped)\n l[\"kind\"] = 0\n fr = [l[[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"kind\"]]]\n for k, name in [(1, \"likes\"), (2, \"dislikes\"), (3, \"unlikes\"), (4, \"undislikes\")]:\n x = pd.read_parquet(D + f\"{split}_{name}.parquet\")\n x[\"played_ratio_pct\"] = 100; x[\"kind\"] = k\n fr.append(x[[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"kind\"]])\n return pd.concat(fr, ignore_index=True)\n\ntr = load(\"train\"); print(\"train events\", len(tr), time.time() - t0)\nva = load(\"val\"); print(\"val events\", len(va), time.time() - t0)\n\n# ---- vocab from TRAIN only (no val leakage into validation-stage features)\ncnt = tr[tr.kind == 0].item_id.value_counts()\ncat_set = set(cat_items.tolist())\nother = cnt[~cnt.index.isin(cat_set)]\nMAX_OTHER = 150_000\nother = other[other >= 5].index.to_numpy()[:MAX_OTHER]\nvocab = np.concatenate([cat_items, other.astype(np.int64)])\nV = len(vocab) + 1 # last index = UNK\nitem2idx = pd.Series(np.arange(len(vocab)), index=vocab)\nprint(\"vocab\", V, time.time() - t0)\n\n# ---- users: all users seen in train (target users first)\nall_uids = np.unique(np.concatenate([targets, tr.uid.unique().astype(np.int64), va.uid.unique().astype(np.int64)]))\nrest = np.setdiff1d(all_uids, targets)\nuids = np.concatenate([targets, rest])\nuid2idx = pd.Series(np.arange(len(uids)), index=uids)\n\ndef encode(df):\n df = df.sort_values([\"uid\", \"timestamp\"], kind=\"stable\")\n u = uid2idx.reindex(df.uid.to_numpy()).to_numpy().astype(np.int32)\n it = item2idx.reindex(df.item_id.to_numpy()).fillna(V - 1).to_numpy().astype(np.int32)\n return dict(u=u, ts=df.timestamp.to_numpy().astype(np.int64), it=it,\n org=df.is_organic.to_numpy().astype(np.uint8),\n pr=np.minimum(df.played_ratio_pct.to_numpy(), 255).astype(np.uint8),\n kind=df.kind.to_numpy().astype(np.uint8))\n\nev_tr = encode(tr); print(\"encoded train\", time.time() - t0)\nev_all = encode(pd.concat([tr, va], ignore_index=True)); print(\"encoded all\", time.time() - t0)\n\n# ---- first listen timestamp per (user, catalog item), including skips (eligibility = never listened)\ndef first_listen(split_list):\n fr = []\n for s in split_list:\n l = pd.read_parquet(D + f\"{s}_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\"])\n l = l[l.item_id.isin(cat_set)]\n fr.append(l)\n l = pd.concat(fr).groupby([\"uid\", \"item_id\"]).timestamp.min().reset_index()\n return dict(u=uid2idx.reindex(l.uid.to_numpy()).to_numpy().astype(np.int32),\n it=item2idx.reindex(l.item_id.to_numpy()).to_numpy().astype(np.int32),\n ts=l.timestamp.to_numpy().astype(np.int64))\nfl_all = first_listen([\"train\", \"val\"]) # train-only view = filter ts < train cutoff\nprint(\"first listens\", len(fl_all[\"u\"]), time.time() - t0)\n\nnp.savez(OUT + \"prep.npz\", vocab=vocab, uids=uids, n_targets=len(targets),\n **{\"tr_\" + k: v for k, v in ev_tr.items()}, **{\"all_\" + k: v for k, v in ev_all.items()},\n **{\"fl_\" + k: v for k, v in fl_all.items()})\nprint(\"done\", time.time() - t0)\nEOF\ncd /app/solution && python prep.py", "description": "Write and run preprocessing script", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:15:12.096Z", "content": "/app/solution/prep.py:66: RuntimeWarning: invalid value encountered in cast\n return dict(u=uid2idx.reindex(l.uid.to_numpy()).to_numpy().astype(np.int32),\ntrain events 28729286 1.8328659534454346\nval events 1016269 1.8796720504760742\nvocab 180001 2.7846450805664062\nencoded train 7.81818151473999\nencoded all 13.37500524520874\nfirst listens 8193532 21.20224690437317\ndone 21.869072437286377", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:15:12.101Z", "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": "2026-10-03T08:15:12.101Z", "data": { "type": "total_tokens_reminder", "text": "14964232 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:15:12.103Z", "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": "2026-10-03T08:15:16.749Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:15:16.749Z", "text": "I noticed an invalid cast issue — some uids in listens aren't in uid2idx since users with only skips wouldn't be included, causing NaN to become garbage ints. I'll fix this by filtering out rows where the uid isn't in the index, then rerun while I continue writing the model script in parallel.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:15:17.163Z", "callId": "toolu_01LhBSF9XbF7hyQv2kJAX7DU", "name": "Bash", "input": { "command": "python - <<'EOF'\nimport re\ns=open(\"prep.py\").read()\ns=s.replace(\"\"\" l = pd.concat(fr).groupby([\"uid\", \"item_id\"]).timestamp.min().reset_index()\n return dict(\"\"\",\"\"\" l = pd.concat(fr).groupby([\"uid\", \"item_id\"]).timestamp.min().reset_index()\n l = l[l.uid.isin(uid2idx.index)]\n return dict(\"\"\")\nopen(\"prep.py\",\"w\").write(s)\nEOF\npython prep.py 2>&1 | tail -2", "description": "Fix uid filter in first-listen table and rerun prep", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:15:48.004Z", "content": "first listens 8193320 20.061071634292603\ndone 29.154523134231567", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:15:48.007Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T08:15:48.007Z", "data": { "type": "total_tokens_reminder", "text": "14963550 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:15:48.009Z", "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": "2026-10-03T08:16:20.847Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:16:20.847Z", "text": "I'm planning the modeling script: loading the preprocessed data, building a per-user CSR-style index over sorted events, and designing a function that assembles weighted event bags per user up to a cutoff timestamp, giving more weight to recent and organic listens.\n\nFor training, I want to construct user-item examples within a window after each cutoff, restricted to a catalog size, excluding items already seen, and weighting likes more heavily than plain listens, while deduplicating user-item pairs per cutoff.\n\nNow I'm designing the two-tower model itself: item embeddings feeding into a small residual MLP tower producing normalized vectors, with popularity features folded in, and a user tower built from EmbeddingBag over recent interaction bags with normalized per-sample weights, scored via cosine similarity scaled by temperature so ranking stays MIPS-compatible.\n\nI'm also deciding to share the item embedding table between the user and item towers since the catalog is small enough that this helps data efficiency. For training, I'm weighing in-batch negatives with logQ correction against uniformly sampled catalog negatives, or just computing a full softmax over the entire 30k-item catalog since it's small enough to be tractable.\n\nI'll implement both full softmax and sampled softmax behind a flag, defaulting to whichever validates better, and note the tradeoff in the README. For validation I'll set cutoff at train end, build user history bags, score via the embedding dot product masking ineligible items, write top-100 to parquet, and run the public evaluator, plus an internal NDCG computation in-script for faster iteration. Then I'll test by refitting on all events with cutoffs shifted toward validation.\n\nFor training cutoffs, I'm using a 7-day window with cutoffs spaced back from train end (possibly denser, every 3.5 days, for overlapping windows and more examples). For the final refit I need to recompute the validation window boundaries and test cutoff with the 30-minute gap, then set refit cutoffs similarly but with history including validation events.\n\nHistory-cut features should be computed across all 9238 users, not just targets, to get more data. Per-user scalar features like log1p listens, organic fraction, and likes count will help modestly, but I think item-side recency features matter most for NDCG — I want to add item features like log total positive listens before cutoff and log listens in the last 7 days prior to cutoff, computed as legitimate offline features at each cutoff.\n\nI'll also add a last-7-day organic ratio per item, computed cheaply via vectorized bincounts from the event arrays, so trending items can be ranked properly. Time to write the actual script now.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:16:20.850Z", "text": "Preprocessing is done (180k-item vocab, ~29M positive events). Now writing the two-tower model script with multi-cutoff training examples, bag-of-history user tower, and full-catalog softmax." }, { "kind": "tool", "timestamp": "2026-10-03T08:17:21.221Z", "callId": "toolu_01MfQS3oEaKHUN6G67raBVDo", "name": "Bash", "input": { "command": "cat > /app/solution/two_tower.py <<'EOF'\n\"\"\"Two-tower retrieval model for the \"Made for You\" shelf (CPU PyTorch).\n\nUser tower : history-derived (no user-ID lookup). Weighted bags of recent positive\n listens, active likes, and dislikes (shared item embedding table) +\n scalar activity features -> MLP -> L2-normalised d-dim vector.\nItem tower : item-ID embedding + cutoff-time popularity features -> MLP ->\n L2-normalised d-dim vector (precomputable, MIPS-ready).\nScore : dot(user_vec, item_vec).\nObjective : temperature-scaled softmax over the 30k retrieval catalog\n (sampled softmax with in-batch + uniform negatives is the\n scale-out replacement; see --neg_mode).\nTraining examples are built from rolling 7-day windows inside the observed\nhistory, mimicking the discovery target: organic likes / organic >=80%\nlistens of catalog items the user has never listened to before the cutoff.\n\"\"\"\nimport argparse, json, os, time, math\nimport numpy as np, pandas as pd, torch, torch.nn as nn, torch.nn.functional as F\n\nD = \"/task/data/\"; OUT = \"/app/solution/\"; CACHE = OUT + \"cache/\"\nDAY = 86400; WIN = 7 * DAY\nTRAIN_END = 24786800\nVAL_START = TRAIN_END + 1800; VAL_END = VAL_START + WIN\nTEST_START = VAL_END + 1800\nN_CAT = 30000\ntorch.set_num_threads(8)\n\np = argparse.ArgumentParser()\np.add_argument(\"--stage\", default=\"val\", choices=[\"val\", \"test\"])\np.add_argument(\"--dim\", type=int, default=64)\np.add_argument(\"--hidden\", type=int, default=256)\np.add_argument(\"--epochs\", type=int, default=6)\np.add_argument(\"--bs\", type=int, default=1024)\np.add_argument(\"--lr\", type=float, default=2e-3)\np.add_argument(\"--wd\", type=float, default=1e-6)\np.add_argument(\"--n_cut\", type=int, default=8, help=\"number of rolling training cutoffs\")\np.add_argument(\"--cut_step\", type=float, default=0.5, help=\"cutoff stride in windows\")\np.add_argument(\"--recent\", type=int, default=300)\np.add_argument(\"--halflife\", type=float, default=14.0, help=\"days, recency decay for listen bag\")\np.add_argument(\"--like_w\", type=float, default=2.0, help=\"loss weight for like targets\")\np.add_argument(\"--temp\", type=float, default=0.07)\np.add_argument(\"--neg_mode\", default=\"full\", choices=[\"full\", \"sampled\"])\np.add_argument(\"--n_neg\", type=int, default=1024)\np.add_argument(\"--seed\", type=int, default=0)\np.add_argument(\"--tag\", default=\"run\")\nargs = p.parse_args()\ntorch.manual_seed(args.seed); np.random.seed(args.seed)\nt0 = time.time()\ndef log(*a): print(f\"[{time.time()-t0:7.1f}s]\", *a, flush=True)\n\n# ------------------------------------------------------------------ data\nP = np.load(CACHE + \"prep.npz\")\nvocab = P[\"vocab\"]; V = len(vocab) + 1; UNK = V - 1\nuids = P[\"uids\"]; NU = len(uids); n_targets = int(P[\"n_targets\"])\npref = \"all_\" if args.stage == \"test\" else \"tr_\"\nev = {k: P[pref + k] for k in [\"u\", \"ts\", \"it\", \"org\", \"pr\", \"kind\"]}\nfl = {k: P[\"fl_\" + k] for k in [\"u\", \"it\", \"ts\"]}\nhist_end = TEST_START if args.stage == \"test\" else TRAIN_END # inference cutoff\nif args.stage == \"val\":\n m = fl[\"ts\"] < TRAIN_END; fl = {k: v[m] for k, v in fl.items()}\nu_off = np.searchsorted(ev[\"u\"], np.arange(NU + 1))\nlog(\"events\", len(ev[\"u\"]), \"users\", NU, \"vocab\", V)\n# first-listen lookup keyed by u*N_CAT+it\nfl_key = fl[\"u\"].astype(np.int64) * N_CAT + fl[\"it\"]\nfl_order = np.argsort(fl_key); fl_key = fl_key[fl_order]; fl_ts = fl[\"ts\"][fl_order]\n\ncat_df = pd.read_parquet(D + \"candidate_catalog.parquet\")\n\ndef item_feats(cutoff):\n \"\"\"Cutoff-time item popularity features for the catalog (offline-computable).\"\"\"\n m = (ev[\"kind\"] == 0) & (ev[\"ts\"] < cutoff) & (ev[\"it\"] < N_CAT)\n it = ev[\"it\"][m]; ts = ev[\"ts\"][m]; org = ev[\"org\"][m]\n tot = np.bincount(it, minlength=N_CAT).astype(np.float32)\n rec = np.bincount(it[ts >= cutoff - WIN], minlength=N_CAT).astype(np.float32)\n rec2 = np.bincount(it[ts >= cutoff - 2 * WIN], minlength=N_CAT).astype(np.float32)\n orgc = np.bincount(it, weights=org, minlength=N_CAT).astype(np.float32)\n f = np.stack([np.log1p(tot), np.log1p(rec), np.log1p(rec2), orgc / np.maximum(tot, 1),\n np.log1p(rec) - np.log1p(rec2 - rec)], 1)\n return torch.tensor(f)\n\ndef build_bags(cutoff, users):\n \"\"\"History bags for `users` using only events strictly before `cutoff`.\"\"\"\n R = args.recent; lam = math.log(2) / (args.halflife * DAY)\n rec_idx, rec_w, rec_off = [], [], [0]\n like_idx, like_off = [], [0]\n dis_idx, dis_off = [], [0]\n scal = np.zeros((len(users), 4), np.float32)\n for n, u in enumerate(users):\n a, b = u_off[u], u_off[u + 1]\n b = a + np.searchsorted(ev[\"ts\"][a:b], cutoff)\n kind = ev[\"kind\"][a:b]; it = ev[\"it\"][a:b]; ts = ev[\"ts\"][a:b]; org = ev[\"org\"][a:b]\n li = kind == 0\n n_l = int(li.sum())\n lit = it[li][-R:]; lts = ts[li][-R:]; lorg = org[li][-R:]\n w = np.exp(-lam * (cutoff - lts)) * (1.0 + 0.5 * lorg)\n w = w / max(w.sum(), 1e-6)\n rec_idx.append(lit); rec_w.append(w.astype(np.float32)); rec_off.append(rec_off[-1] + len(lit))\n # active likes: likes minus later unlikes (approx: keep like if no unlike after it)\n lk = it[kind == 1]; ul = set(it[kind == 3].tolist())\n lk = lk[-200:] if not ul else np.array([x for x in lk if x not in ul], dtype=np.int32)[-200:]\n like_idx.append(lk); like_off.append(like_off[-1] + len(lk))\n ds = it[kind == 2][-100:]\n dis_idx.append(ds); dis_off.append(dis_off[-1] + len(ds))\n rec30 = lts >= cutoff - 30 * DAY\n scal[n] = [np.log1p(n_l), float(lorg.mean()) if len(lorg) else 0.5, np.log1p(len(lk)), np.log1p(rec30.sum())]\n def pack(idx, off, w=None):\n idx = np.concatenate(idx) if idx else np.zeros(0, np.int32)\n d = dict(idx=torch.tensor(idx, dtype=torch.long), off=torch.tensor(off[:-1], dtype=torch.long))\n d[\"w\"] = torch.tensor(np.concatenate(w)) if w is not None else None\n return d\n return dict(rec=pack(rec_idx, rec_off, rec_w), like=pack(like_idx, like_off), dis=pack(dis_idx, dis_off),\n scal=torch.tensor(scal))\n\ndef build_targets(cutoff):\n \"\"\"Discovery targets in [cutoff, cutoff+WIN): organic like (grade 3) / organic >=80% listen (grade 1)\n on catalog items never listened to before cutoff.\"\"\"\n m = (ev[\"ts\"] >= cutoff) & (ev[\"ts\"] < cutoff + WIN) & (ev[\"it\"] < N_CAT) & (ev[\"org\"] == 1)\n m &= ((ev[\"kind\"] == 0) & (ev[\"pr\"] >= 80)) | (ev[\"kind\"] == 1)\n u = ev[\"u\"][m]; it = ev[\"it\"][m]; g = np.where(ev[\"kind\"][m] == 1, 3, 1)\n key = u.astype(np.int64) * N_CAT + it\n # drop pairs seen before cutoff\n pos = np.searchsorted(fl_key, key); pos = np.minimum(pos, len(fl_key) - 1)\n seen = (fl_key[pos] == key) & (fl_ts[pos] < cutoff)\n u, it, g, key = u[~seen], it[~seen], g[~seen], key[~seen]\n df = pd.DataFrame(dict(key=key, u=u, it=it, g=g)).groupby(\"key\").agg(u=(\"u\", \"first\"), it=(\"it\", \"first\"), g=(\"g\", \"max\"))\n return df.u.to_numpy(), df.it.to_numpy(), df.g.to_numpy()\n\n# ------------------------------------------------------------------ model\nclass UserTower(nn.Module):\n def __init__(self, emb, d, h):\n super().__init__(); self.emb = emb\n self.mlp = nn.Sequential(nn.Linear(3 * d + 4, h), nn.GELU(), nn.Linear(h, d))\n self.skip = nn.Linear(3 * d + 4, d)\n def forward(self, bags):\n E = self.emb.weight\n rec = F.embedding_bag(bags[\"rec\"][\"idx\"], E, bags[\"rec\"][\"off\"], mode=\"sum\", per_sample_weights=bags[\"rec\"][\"w\"])\n like = F.embedding_bag(bags[\"like\"][\"idx\"], E, bags[\"like\"][\"off\"], mode=\"mean\")\n dis = F.embedding_bag(bags[\"dis\"][\"idx\"], E, bags[\"dis\"][\"off\"], mode=\"mean\")\n x = torch.cat([rec, like, dis, bags[\"scal\"]], 1)\n return F.normalize(self.mlp(x) + self.skip(x), dim=-1)\n\nclass ItemTower(nn.Module):\n def __init__(self, emb, d, h, nf):\n super().__init__(); self.emb = emb\n self.mlp = nn.Sequential(nn.Linear(d + nf, h), nn.GELU(), nn.Linear(h, d))\n self.skip = nn.Linear(nf, d)\n def forward(self, items, feats):\n e = self.emb(items); x = torch.cat([e, feats], 1)\n return F.normalize(e + self.mlp(x) + self.skip(feats), dim=-1)\n\nclass TwoTower(nn.Module):\n def __init__(self, V, d, h, nf):\n super().__init__()\n self.emb = nn.Embedding(V, d); nn.init.normal_(self.emb.weight, std=0.05)\n self.user = UserTower(self.emb, d, h); self.item = ItemTower(self.emb, d, h, nf)\n\ndef slice_bags(bags, sel):\n \"\"\"Select rows `sel` (LongTensor) from packed bags.\"\"\"\n out = {\"scal\": bags[\"scal\"][sel]}\n for k in [\"rec\", \"like\", \"dis\"]:\n b = bags[k]; off = b[\"off\"]; n = len(off)\n ends = torch.cat([off[1:], torch.tensor([len(b[\"idx\"])])])\n s, e = off[sel], ends[sel]; lens = e - s\n ar = torch.cat([torch.arange(int(a), int(c)) for a, c in zip(s.tolist(), e.tolist())]) if len(sel) else torch.zeros(0, dtype=torch.long)\n new_off = torch.cat([torch.zeros(1, dtype=torch.long), torch.cumsum(lens, 0)[:-1]])\n out[k] = dict(idx=b[\"idx\"][ar], off=new_off, w=(b[\"w\"][ar] if b[\"w\"] is not None else None))\n return out\n\n# ------------------------------------------------------------------ build training set\ncutoffs = [hist_end - int((k + 1) * args.cut_step * WIN) for k in range(args.n_cut)]\ncutoffs = [c for c in cutoffs if c + WIN <= hist_end]\nall_users = np.arange(NU)\ntrain_sets = []\nfor c in cutoffs:\n tu, ti, tg = build_targets(c)\n active = np.unique(tu)\n bags = build_bags(c, active)\n umap = -np.ones(NU, np.int64); umap[active] = np.arange(len(active))\n train_sets.append(dict(cut=c, bags=bags, feats=item_feats(c), u=torch.tensor(umap[tu]), it=torch.tensor(ti, dtype=torch.long),\n w=torch.tensor(np.where(tg == 3, args.like_w, 1.0), dtype=torch.float32)))\n log(f\"cutoff {c}: users {len(active)} targets {len(tu)} likes {(tg==3).sum()}\")\nn_total = sum(len(s[\"u\"]) for s in train_sets); log(\"total training pairs\", n_total)\n\n# ------------------------------------------------------------------ inference setup\ntgt_users = np.arange(n_targets)\ninf_bags = build_bags(hist_end, tgt_users); inf_feats = item_feats(hist_end)\nelig_name = \"test_eligible_candidates.npz\" if args.stage == \"test\" else \"validation_eligible_candidates.npz\"\nZ = np.load(D + elig_name)\nassert (Z[\"uids\"] == uids[:n_targets]).all() and (Z[\"item_ids\"] == vocab[:N_CAT]).all()\nelig = np.unpackbits(Z[\"packed_eligible\"], axis=1, count=N_CAT, bitorder=\"little\").astype(bool)\nelig_t = torch.tensor(elig)\n\n# public-window labels for quick internal NDCG (val stage only)\ntruth = None\nif args.stage == \"val\":\n tgt_set = set(uids[:n_targets].tolist()); item_col = {int(i): c for c, i in enumerate(vocab[:N_CAT])}; urow = {int(u): r for r, u in enumerate(uids[:n_targets])}\n vl = pd.read_parquet(D + \"val_listens.parquet\"); vk = pd.read_parquet(D + \"val_likes.parquet\")\n s = vl[(vl.uid.isin(tgt_set)) & (vl.is_organic == 1) & (vl.played_ratio_pct >= 80)][[\"uid\", \"item_id\"]].drop_duplicates()\n k = vk[(vk.uid.isin(tgt_set)) & (vk.is_organic == 1)][[\"uid\", \"item_id\"]].drop_duplicates()\n truth = {}\n for df, g in [(s, 1), (k, 3)]:\n for u, i in df.itertuples(index=False, name=None):\n c = item_col.get(int(i)); r = urow[int(u)]\n if c is not None and elig[r, c]: truth.setdefault(r, {})[c] = g\n log(\"truth users\", len(truth))\n\ndef ndcg10(top, truth):\n disc = 1.0 / np.log2(np.arange(2, 12)); res = []\n for r, gold in truth.items():\n gains = np.array([gold.get(int(i), 0) for i in top[r, :10]], float)\n ideal = np.array(sorted(gold.values(), reverse=True)[:10], float)\n res.append(((2 ** gains - 1) * disc[:len(gains)]).sum() / ((2 ** ideal - 1) * disc[:len(ideal)]).sum())\n return float(np.mean(res))\n\n@torch.no_grad()\ndef rank_all(model):\n model.eval()\n item_vec = model.item(torch.arange(N_CAT), inf_feats) # offline index\n tops = []\n for s in range(0, n_targets, 1024):\n sel = torch.arange(s, min(s + 1024, n_targets))\n uv = model.user(slice_bags(inf_bags, sel)) # request-time user vector\n sc = uv @ item_vec.T # MIPS\n sc[~elig_t[sel]] = -1e9\n tops.append(torch.topk(sc, 100, dim=1).indices.numpy())\n model.train()\n return np.concatenate(tops), item_vec\n\n# ------------------------------------------------------------------ train\nnf = inf_feats.shape[1]\nmodel = TwoTower(V, args.dim, args.hidden, nf)\nopt = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)\ncat_items = torch.arange(N_CAT)\nbest = (-1, None)\nsteps_per_epoch = math.ceil(n_total / args.bs)\nsched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=args.lr, total_steps=args.epochs * steps_per_epoch, pct_start=0.1)\nfor ep in range(args.epochs):\n # shuffle (set, index) pairs\n order = np.concatenate([np.stack([np.full(len(s[\"u\"]), i), np.arange(len(s[\"u\"]))], 1) for i, s in enumerate(train_sets)])\n np.random.shuffle(order)\n tot_loss = 0; nb = 0\n for b in range(0, len(order), args.bs):\n chunk = order[b:b + args.bs]\n loss = 0; cnt = 0\n for si in np.unique(chunk[:, 0]):\n S = train_sets[si]; ix = torch.tensor(chunk[chunk[:, 0] == si, 1])\n uv = model.user(slice_bags(S[\"bags\"], S[\"u\"][ix]))\n pos = S[\"it\"][ix]; w = S[\"w\"][ix]\n if args.neg_mode == \"full\":\n iv = model.item(cat_items, S[\"feats\"])\n logits = uv @ iv.T / args.temp\n l = F.cross_entropy(logits, pos, reduction=\"none\")\n else:\n neg = torch.randint(0, N_CAT, (args.n_neg,))\n cand = torch.cat([pos, neg]); iv = model.item(cand, S[\"feats\"][cand])\n logits = uv @ iv.T / args.temp\n logits[:, :len(pos)] = logits[:, :len(pos)].masked_fill(pos[None, :] == pos[:, None] & ~torch.eye(len(pos), dtype=torch.bool), -1e9)\n l = F.cross_entropy(logits, torch.arange(len(pos)), reduction=\"none\")\n loss = loss + (l * w).sum(); cnt += w.sum()\n loss = loss / cnt\n opt.zero_grad(); loss.backward(); opt.step(); sched.step()\n tot_loss += loss.item(); nb += 1\n top, item_vec = rank_all(model)\n msg = f\"epoch {ep} loss {tot_loss/nb:.4f}\"\n if truth is not None:\n sc = ndcg10(top, truth); msg += f\" val ndcg@10 {sc:.4f}\"\n if sc > best[0]: best = (sc, top.copy())\n else:\n best = (0, top.copy())\n log(msg)\n\ntop = best[1]\nlog(\"best val ndcg\", best[0])\n# ------------------------------------------------------------------ write ranking\nrows = np.repeat(uids[:n_targets], 100); items = vocab[top.reshape(-1)]; ranks = np.tile(np.arange(1, 101), n_targets)\nout = pd.DataFrame(dict(uid=rows.astype(np.int64), item_id=items.astype(np.int64), rank=ranks.astype(np.int64)))\nname = \"submission.parquet\" if args.stage == \"test\" else f\"validation_{args.tag}.parquet\"\nout.to_parquet(OUT + name, index=False)\njson.dump(dict(args=vars(args), best_val=best[0]), open(OUT + f\"cache/{args.tag}_{args.stage}.json\", \"w\"))\nlog(\"wrote\", OUT + name)\nEOF\ncd /app/solution && timeout 1500 python two_tower.py --epochs 1 --n_cut 2 --tag smoke 2>&1 | tail -20", "description": "Write two-tower training script and run a smoke test", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:17:51.369Z", "content": "[ 0.4s] events 28729286 users 9978 vocab 180001\n[ 1.7s] cutoff 24182000: users 4795 targets 45238 likes 8639\n[ 1.7s] total training pairs 45238\n[ 3.4s] truth users 4263\n[ 25.8s] epoch 0 loss 9.5886 val ndcg@10 0.0295\n[ 25.8s] best val ndcg 0.029477161949743494\n[ 25.9s] wrote /app/solution/validation_smoke.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:17:51.372Z", "data": { "type": "total_tokens_reminder", "text": "14953812 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:17:51.374Z", "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": "2026-10-03T08:18:00.598Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:18:00.598Z", "text": "I notice that with cut_step 0.5 and n_cut 2 only one valid cutoff actually gets kept, but building bags for 4795 users still runs in under 2 seconds, which is fast. Training also looks efficient at roughly 0.45s per batch, so scaling to 8 cutoffs across ~45k pairs should total around 360k training pairs.\n\nAt that pace, 6 epochs would take about 16 minutes, which eats too much of my remaining ~40 minute budget. I'll scale back to n_cut=6, cut_step=1, and 4 epochs instead — roughly 8-9 minutes — and launch that run in the background while I quickly verify the evaluator against my internal NDCG on the smoke file. I'm also considering running a second config concurrently with reduced thread counts (4 each instead of 8) so two parameter variants can train in parallel without much net slowdown.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:18:00.600Z", "text": "Smoke test works end to end (one epoch, one cutoff, NDCG 0.029). Now launching two real training configs in parallel and checking the public evaluator on the smoke file." }, { "kind": "tool", "timestamp": "2026-10-03T08:18:01.599Z", "callId": "toolu_01PuQb6MYuzvAREvLCHathtR", "name": "Bash", "input": { "command": "sed -i 's/torch.set_num_threads(8)/torch.set_num_threads(int(os.environ.get(\"NT\",\"8\")))/' two_tower.py && \\\nNT=4 nohup python two_tower.py --n_cut 6 --cut_step 1 --epochs 4 --tag A > cache/A.log 2>&1 &\ncd /app/solution && NT=4 nohup python two_tower.py --n_cut 6 --cut_step 1 --epochs 4 --temp 0.05 --like_w 3 --halflife 7 --tag B > cache/B.log 2>&1 &\nsleep 1; cd /app/solution && python /task/evaluate_public.py validation_smoke.parquet", "description": "Launch two training runs in background and verify the evaluator on the smoke output", "timeout": 120000 }, "result": { "timestamp": "2026-10-03T08:18:06.112Z", "content": "{\n \"model\": \"validation_smoke.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.029477,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:18:06.115Z", "data": { "type": "total_tokens_reminder", "text": "14952696 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:18:06.117Z", "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": "2026-10-03T08:18:09.703Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:18:09.703Z", "text": "Internal NDCG lines up with the evaluator's result exactly. While waiting, I'll set up a popularity baseline for calibration, ranking eligible items by recent-week popularity using item_feats, keeping thread usage low.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:18:09.704Z", "text": "The internal NDCG matches the public evaluator exactly. While the two runs train, I'll compute a recent-popularity baseline for calibration." }, { "kind": "tool", "timestamp": "2026-10-03T08:18:13.602Z", "callId": "toolu_01SCrokWox4xZh7Fs23DsC2B", "name": "Bash", "input": { "command": "NT=1 python - <<'EOF'\nimport numpy as np, pandas as pd\nP=np.load(\"cache/prep.npz\"); vocab=P[\"vocab\"]; uids=P[\"uids\"]; nt=int(P[\"n_targets\"])\nev={k:P[\"tr_\"+k] for k in [\"ts\",\"it\",\"kind\",\"org\",\"pr\"]}\nT=24786800; W=604800\nm=(ev[\"kind\"]==0)&(ev[\"it\"]<30000)&(ev[\"ts\"]>=T-W)&(ev[\"org\"]==1)\npop=np.bincount(ev[\"it\"][m],minlength=30000).astype(float)\nZ=np.load(\"/task/data/validation_eligible_candidates.npz\")\nel=np.unpackbits(Z[\"packed_eligible\"],axis=1,count=30000,bitorder=\"little\").astype(bool)\nsc=np.where(el,pop[None,:],-1); top=np.argsort(-sc,axis=1)[:,:100]\nout=pd.DataFrame(dict(uid=np.repeat(uids[:nt],100).astype(np.int64),item_id=vocab[top.reshape(-1)].astype(np.int64),rank=np.tile(np.arange(1,101),nt).astype(np.int64)))\nout.to_parquet(\"validation_pop.parquet\",index=False)\nEOF\npython /task/evaluate_public.py validation_pop.parquet; tail -3 cache/A.log cache/B.log", "description": "Compute recent-popularity baseline and check run progress", "timeout": 180000 }, "result": { "timestamp": "2026-10-03T08:18:20.937Z", "content": "Exit code 1\n/bin/bash: line 26: 8393 Killed NT=1 python - <<'EOF'\nimport numpy as np, pandas as pd\nP=np.load(\"cache/prep.npz\"); vocab=P[\"vocab\"]; uids=P[\"uids\"]; nt=int(P[\"n_targets\"])\nev={k:P[\"tr_\"+k] for k in [\"ts\",\"it\",\"kind\",\"org\",\"pr\"]}\nT=24786800; W=604800\nm=(ev[\"kind\"]==0)&(ev[\"it\"]<30000)&(ev[\"ts\"]>=T-W)&(ev[\"org\"]==1)\npop=np.bincount(ev[\"it\"][m],minlength=30000).astype(float)\nZ=np.load(\"/task/data/validation_eligible_candidates.npz\")\nel=np.unpackbits(Z[\"packed_eligible\"],axis=1,count=30000,bitorder=\"little\").astype(bool)\nsc=np.where(el,pop[None,:],-1); top=np.argsort(-sc,axis=1)[:,:100]\nout=pd.DataFrame(dict(uid=np.repeat(uids[:nt],100).astype(np.int64),item_id=vocab[top.reshape(-1)].astype(np.int64),rank=np.tile(np.arange(1,101),nt).astype(np.int64)))\nout.to_parquet(\"validation_pop.parquet\",index=False)\nEOF\n\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'validation_pop.parquet'\ntail: option used in invalid context -- 3", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T08:18:21.045Z", "data": { "type": "total_tokens_reminder", "text": "14951887 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:18:21.078Z", "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": "2026-10-03T08:18:27.260Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:18:27.260Z", "text": "Got OOM killed with memory at 15GB. I'm estimating the memory footprint of each training run—events array plus logits should only be a few GB each, so I'm trying to figure out where the extra memory is going, possibly in how the baseline script handles array operations.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:18:27.298Z", "callId": "toolu_01AxNNCG2jF1cVWfjDi2EDz5", "name": "Bash", "input": { "command": "cd /app/solution; free -g | head -2; ps aux | grep two_tower | grep -v grep | awk '{print $2, $4\"%\", $6/1e6\"GB\"}'; for f in A B; do echo == $f; tail -n 3 cache/$f.log; done", "description": "Check memory and training run status" }, "result": { "timestamp": "2026-10-03T08:18:27.444Z", "content": " total used free shared buff/cache available\nMem: 15 7 1 0 5 7\n8322 0.0% 0.001816GB\n8323 0.0% 0.002GB\n8326 22.5% 3.61179GB\n8327 21.9% 3.50426GB\n== A\n[ 6.4s] cutoff 21158000: users 4261 targets 37167 likes 7092\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263\n== B\n[ 6.4s] cutoff 21158000: users 4261 targets 37167 likes 7092\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:18:27.453Z", "data": { "type": "total_tokens_reminder", "text": "14950352 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:18:27.455Z", "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": "2026-10-03T08:18:31.243Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:18:31.243Z", "text": "The processes are still using 3.6GB each and the baseline got killed from memory pressure, so I need to recompute it more efficiently, using a chunked approach instead of loading everything at once.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:18:31.248Z", "text": "Both runs are alive (about 3.5 GB each). The baseline script was OOM-killed because it materialized a dense score matrix. Rerunning it chunked." }, { "kind": "tool", "timestamp": "2026-10-03T08:18:35.004Z", "callId": "toolu_01BzvApGQGGLxKsmrwpHYc3V", "name": "Bash", "input": { "command": "NT=1 python - <<'EOF'\nimport numpy as np, pandas as pd\nP=np.load(\"cache/prep.npz\"); vocab=P[\"vocab\"]; uids=P[\"uids\"]; nt=int(P[\"n_targets\"])\nev={k:P[\"tr_\"+k] for k in [\"ts\",\"it\",\"kind\",\"org\"]}\nT=24786800; W=604800\nm=(ev[\"kind\"]==0)&(ev[\"it\"]<30000)&(ev[\"ts\"]>=T-W)&(ev[\"org\"]==1)\npop=np.bincount(ev[\"it\"][m],minlength=30000).astype(np.float32); del ev\nZ=np.load(\"/task/data/validation_eligible_candidates.npz\")\nel=np.unpackbits(Z[\"packed_eligible\"],axis=1,count=30000,bitorder=\"little\").astype(bool)\ntops=[]\nfor s in range(0,nt,500):\n sc=np.where(el[s:s+500],pop[None,:],-1); tops.append(np.argsort(-sc,axis=1)[:,:100])\ntop=np.concatenate(tops)\nout=pd.DataFrame(dict(uid=np.repeat(uids[:nt],100).astype(np.int64),item_id=vocab[top.reshape(-1)].astype(np.int64),rank=np.tile(np.arange(1,101),nt).astype(np.int64)))\nout.to_parquet(\"validation_pop.parquet\",index=False)\nEOF\npython /task/evaluate_public.py validation_pop.parquet; for f in A B; do echo == $f; tail -n 2 cache/$f.log; done", "description": "Compute chunked popularity baseline and check training progress", "timeout": 180000 }, "result": { "timestamp": "2026-10-03T08:18:47.271Z", "content": "{\n \"model\": \"validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.028456,\n \"quality_users\": 4263\n }\n}\n== A\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263\n== B\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:18:47.322Z", "data": { "type": "total_tokens_reminder", "text": "14949330 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:18:47.344Z", "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": "2026-10-03T08:18:54.278Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:18:54.278Z", "text": "The organic popularity baseline is around 0.0285, and the smoke model after one epoch hit 0.0295 — this is a genuinely hard task sitting in the NDCG ~0.03 range. Each epoch takes roughly 4 minutes given the batch count and thread contention, so I'll wait a few minutes and check results while drafting the README skeleton in the meantime.\n\nWith about 40 minutes left, my plan is to decide on the config around the 30-minute mark, then launch the test refit (similarly timed to the validation run), while making sure I have an early valid submission ready as insurance. I'll generate a popularity-based fallback submission now using the test eligibility data and train+val events, structurally valid in case the two-tower model doesn't finish in time, then overwrite it with the real model output once ready.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:18:54.283Z", "text": "Popularity baseline scores 0.0285 NDCG@10, and even the one-epoch smoke model beat it. I'll write an insurance submission for the test stage now so a valid file exists, then wait for epoch results." }, { "kind": "tool", "timestamp": "2026-10-03T08:18:58.023Z", "callId": "toolu_01LtNaCHiRLN4Eo23gQZqdzp", "name": "Bash", "input": { "command": "NT=1 python - <<'EOF'\nimport numpy as np, pandas as pd\nP=np.load(\"cache/prep.npz\"); vocab=P[\"vocab\"]; uids=P[\"uids\"]; nt=int(P[\"n_targets\"])\nev={k:P[\"all_\"+k] for k in [\"ts\",\"it\",\"kind\",\"org\"]}\nT=25395200; W=604800\nm=(ev[\"kind\"]==0)&(ev[\"it\"]<30000)&(ev[\"ts\"]>=T-W)&(ev[\"org\"]==1)\npop=np.bincount(ev[\"it\"][m],minlength=30000).astype(np.float32); del ev\nZ=np.load(\"/task/data/test_eligible_candidates.npz\")\nassert (Z[\"uids\"]==uids[:nt]).all() and (Z[\"item_ids\"]==vocab[:30000]).all()\nel=np.unpackbits(Z[\"packed_eligible\"],axis=1,count=30000,bitorder=\"little\").astype(bool)\ntops=[]\nfor s in range(0,nt,500):\n sc=np.where(el[s:s+500],pop[None,:],-1); tops.append(np.argsort(-sc,axis=1)[:,:100])\ntop=np.concatenate(tops)\nout=pd.DataFrame(dict(uid=np.repeat(uids[:nt],100).astype(np.int64),item_id=vocab[top.reshape(-1)].astype(np.int64),rank=np.tile(np.arange(1,101),nt).astype(np.int64)))\nout.to_parquet(\"submission.parquet\",index=False); print(\"fallback written\", out.shape)\nEOF\nsleep 100; for f in A B; do echo == $f; tail -n 2 cache/$f.log; done", "description": "Write fallback test submission, then check training progress", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:20:44.889Z", "content": "fallback written (845600, 3)\n== A\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263\n== B\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:20:44.894Z", "data": { "type": "total_tokens_reminder", "text": "14948103 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:20:44.896Z", "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": "2026-10-03T08:20:47.925Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:20:47.925Z", "text": "The epoch is running slower than expected, likely due to resource contention. I'll draft the README while waiting, then check back in about two minutes.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:20:47.927Z", "callId": "toolu_01SypBE1UjNw2NFvRWMvXSk2", "name": "Bash", "input": { "command": "sleep 150; cd /app/solution; for f in A B; do echo == $f; tail -n 2 cache/$f.log; done", "description": "Wait for first epoch results", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:23:17.953Z", "content": "== A\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263\n== B\n[ 6.4s] total training pairs 249613\n[ 8.2s] truth users 4263", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:23:17.956Z", "data": { "type": "total_tokens_reminder", "text": "14947794 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:23:17.958Z", "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": "2026-10-03T08:23:25.867Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:23:25.867Z", "text": "Nine minutes with no epoch finished is way slower than the smoke test suggested it'd be — I'd expected something closer to four minutes even with thread contention. I should check CPU usage with top to see if something's actually bottlenecked or if contention is just worse than anticipated.\n\nWith 33 minutes left, I need to decide: kill B and give A all 8 threads for faster epochs, or kill both and run the test refit directly with a trimmed config and a parallel val run, risking contention again. Checking top's CPU% should tell me which path is viable.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:23:25.873Z", "callId": "toolu_01VQrFpJjzteJQ2EM6L6Wg4x", "name": "Bash", "input": { "command": "top -bn1 | head -15; cd /app/solution; ls -la cache/", "description": "Check CPU usage of the training processes" }, "result": { "timestamp": "2026-10-03T08:23:26.064Z", "content": "top - 08:23:26 up 31 min, 0 user, load average: 0.30, 1.12, 1.90\nTasks: 8 total, 1 running, 7 sleeping, 0 stopped, 0 zombie\n%Cpu(s): 0.0 us, 0.0 sy, 0.0 ni,100.0 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st \nMiB Mem : 15614.2 total, 8451.7 free, 1033.6 used, 6481.7 buff/cache \nMiB Swap: 0.0 total, 0.0 free, 0.0 used. 14580.6 avail Mem \n\n PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND\n 1 root 20 0 2592 1704 1596 S 0.0 0.0 0:00.02 sh\n 7 root 20 0 2920 1720 1620 S 0.0 0.0 0:00.00 sleep\n 8050 agent 20 0 4476 3364 3028 S 0.0 0.0 0:00.01 bash\n 8057 agent 20 0 5604140 187528 64968 S 0.0 1.2 0:04.69 claude\n 8058 agent 20 0 2924 1668 1556 S 0.0 0.0 0:00.02 tee\n 8560 agent 20 0 4492 3220 2852 S 0.0 0.0 0:00.00 bash\n 8561 agent 20 0 9084 5148 3104 R 0.0 0.0 0:00.00 top\n 8562 agent 20 0 2928 1692 1580 S 0.0 0.0 0:00.00 head\ntotal 1214516\ndrwxr-xr-x 2 agent agent 4096 Oct 3 08:18 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:19 ..\n-rw-r--r-- 1 agent agent 502 Oct 3 08:18 A.log\n-rw-r--r-- 1 agent agent 502 Oct 3 08:18 B.log\n-rw-r--r-- 1 agent agent 1243639333 Oct 3 08:15 prep.npz\n-rw-r--r-- 1 agent agent 290 Oct 3 08:17 smoke_val.json", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:23:26.067Z", "data": { "type": "total_tokens_reminder", "text": "14947229 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:23:26.068Z", "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": "2026-10-03T08:23:39.431Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:23:39.431Z", "text": "The training processes have disappeared entirely, likely killed by an OOM event. They were still alive during my ps check at 3.6GB each, so the kill must have happened afterward, possibly triggered by the second baseline run or my fallback script pushing memory usage too high.\n\nWith roughly 28 minutes used and 32 remaining, I need to be decisive: I'll kick off one val-stage training run with n_cut 5 and epochs 3, which should take about 6 minutes given the batch count and timing, followed immediately by the equivalent test refit run, for roughly 12 minutes total. I'll chain these sequentially in a single background shell command using run_in_background so they're tracked and persist without resource contention, leaving margin for wrapping up the README afterward.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T08:23:39.435Z", "text": "The two parallel training runs died silently, likely from memory contention. I'll run validation-stage fit and test-stage refit sequentially instead, using all 8 threads.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:23:39.438Z", "callId": "toolu_011Nqim2kFErnxy7WkRdWVbv", "name": "Bash", "input": { "command": "(NT=8 python two_tower.py --stage val --n_cut 5 --cut_step 1 --epochs 3 --tag C > cache/C_val.log 2>&1; NT=8 python two_tower.py --stage test --n_cut 5 --cut_step 1 --epochs 3 --tag C > cache/C_test.log 2>&1)", "description": "Run validation fit then test refit sequentially in background", "timeout": 1800000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T08:23:39.460Z", "content": "Command running in background with ID: bruuv8pn9. Output is being written to: /tmp/claude-1000/-app/aac421c7-2f2b-4e82-89ea-d97c71ee0035/tasks/bruuv8pn9.output. You will be notified when it completes. To check interim output, use Read on that file path.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:23:39.463Z", "data": { "type": "total_tokens_reminder", "text": "14945434 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:23:39.465Z", "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": "2026-10-03T08:23:41.265Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T08:23:41.265Z", "callId": "toolu_01PeTmzV21An839YiiJNsgBf", "name": "Bash", "input": { "command": "sleep 120; cd /app/solution; tail -n 3 cache/C_val.log; free -g | head -2", "description": "Check validation run progress after two minutes", "timeout": 200000 }, "result": { "timestamp": "2026-10-03T08:25:41.309Z", "content": "[ 9.1s] cutoff 21762800: users 4425 targets 40336 likes 7028\n[ 9.1s] total training pairs 212446\n[ 10.8s] truth users 4263\n total used free shared buff/cache available\nMem: 15 4 3 0 6 10", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:25:41.315Z", "data": { "type": "total_tokens_reminder", "text": "14945168 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:25:41.319Z", "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": "2026-10-03T08:25:42.503Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T08:25:42.503Z", "text": "While it trains, I'll draft the README so only the numbers need filling in." }, { "kind": "tool", "timestamp": "2026-10-03T08:25:57.169Z", "callId": "toolu_01KJPJHRxKWVtrNE2D9DnpX4", "name": "Bash", "input": { "command": "cat > /app/solution/README.md <<'EOF'\n# Two-tower discovery ranker for the \"Made for You\" shelf\n\n## Files\n- `prep.py` – one-off preprocessing of `/task/data` into `cache/prep.npz` (item vocabulary, per-user sorted event arrays for the train-only and train+val views, first-listen timestamps for eligibility mimicry).\n- `two_tower.py` – defines, trains and applies both towers; writes the ranking.\n- `submission.parquet` – final hidden-test ranking (100 eligible items per target user, ranks 1..100).\n- `validation_C.parquet` – the validation-stage ranking scored with `/task/evaluate_public.py`.\n\n## Model (CPU PyTorch 2.8, `two_tower.py`)\nScore = `dot(user_vec, item_vec)`, both vectors d=64 and L2-normalised, so the item\nvectors can be precomputed into a MIPS index and only one user vector is computed per request.\n\n- **Shared item embedding table**: 180,001 rows (30k catalog items first, then the 150k most\n frequent non-catalog history items, plus UNK), 64 dims.\n- **Item tower**: `item_emb + MLP([item_emb, item_feats]) + Linear(item_feats)` → L2 norm.\n `item_feats` are five cutoff-time popularity features (log total positive listens, log listens in\n the last 7 and 14 days, organic share, trend) computed offline for the cutoff.\n- **User tower** (no user-ID lookup): three `EmbeddingBag`s over the user's history before the cutoff\n – recency-weighted (14-day half-life, organic bonus) bag of the last 300 positive listens,\n mean bag of active likes (likes minus unlikes), mean bag of dislikes – concatenated with four\n activity scalars, then `MLP(196→256→64) + Linear skip` → L2 norm.\n- **Objective**: temperature-scaled (τ=0.07) softmax cross-entropy over the full 30k retrieval catalog\n (`--neg_mode sampled` switches to in-batch + uniform sampled softmax, the drop-in choice for a\n multi-million-item catalog; no user–item matrix is ever built). Like targets get loss weight 2.\n- **Training examples**: rolling 7-day windows inside the observed history (5 cutoffs, one window\n apart). For each cutoff the targets are organic likes (grade 3) and organic ≥80% listens (grade 1)\n of catalog items the user had never listened to before that cutoff, i.e. the same discovery\n definition as the evaluator. Adam, OneCycle LR 2e-3, batch 1024, 3 epochs.\n\n## Protocol\n1. Validation stage: history = `train_*` only, cutoff = training end; ranking scored on the public window.\n2. Final stage: identical configuration refit on `train_* + val_*`, history cutoff = test start,\n candidates from `test_eligible_candidates.npz`.\n\n## Results (public validation, graded NDCG@10 over 4263 users)\n| model | NDCG@10 |\n| --- | --- |\n| recent organic popularity (eligible items) | 0.0285 |\n| two-tower (this submission's config) | VAL_SCORE |\n\n## Commands\n```sh\ncd /app/solution\npython prep.py\npython two_tower.py --stage val --n_cut 5 --cut_step 1 --epochs 3 --tag C # -> validation_C.parquet\npython /task/evaluate_public.py validation_C.parquet\npython two_tower.py --stage test --n_cut 5 --cut_step 1 --epochs 3 --tag C # -> submission.parquet\n```\nEOF\nsleep 60; tail -n 2 /app/solution/cache/C_val.log", "description": "Write README draft and check progress", "timeout": 200000 }, "result": { "timestamp": "2026-10-03T08:26:57.236Z", "content": "[ 10.8s] truth users 4263\n[ 177.9s] epoch 0 loss 9.5154 val ndcg@10 0.0248", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:26:57.241Z", "data": { "type": "total_tokens_reminder", "text": "14943633 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:26:57.244Z", "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": 19, "role": "agent", "timestamp": "2026-10-03T08:27:04.904Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:27:04.904Z", "text": "Epoch 0 ran slower than expected at 167s, so three epochs plus ranking will take around 8.5 minutes, then another 9 minutes for the test run—roughly 18 minutes total, landing near 08:43-08:45. Given the container started around 07:52-08:00, the 60-minute deadline puts the cutoff near 08:52-08:56, so there's a reasonable cushion.\n\nEpoch 0's NDCG of 0.0248 is actually below the popularity baseline of 0.0285, and the loss near 9.5 is close to the uniform-random value of ln(30000)≈10.3, suggesting the model has barely started learning during this warmup phase. I want to see how epochs 1 and 2 perform before deciding whether to fall back to the popularity baseline or trust the model's output—I'd prefer to submit the trained model's ranking if it ends up competitive, but I'll hold off judgment until more epochs complete.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:27:04.908Z", "callId": "toolu_01RjiCoudu5WZzJH8poWWDPY", "name": "Bash", "input": { "command": "sleep 170; tail -n 2 /app/solution/cache/C_val.log", "description": "Wait for epoch 1 result", "timeout": 300000 }, "result": { "timestamp": "2026-10-03T08:29:54.976Z", "content": "[ 177.9s] epoch 0 loss 9.5154 val ndcg@10 0.0248\n[ 341.7s] epoch 1 loss 9.0396 val ndcg@10 0.0215", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:29:54.988Z", "data": { "type": "total_tokens_reminder", "text": "14943095 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:29:54.994Z", "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": 20, "role": "agent", "timestamp": "2026-10-03T08:30:11.004Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:30:11.004Z", "text": "I'm noticing validation NDCG is dropping as training loss falls, which suggests overfitting rather than genuine learning—oddly the quick smoke run outperformed this longer one. I'm weighing possible causes: the temperature scaling on normalized vectors producing a wide logit range, the model latching onto user-specific quirks that don't generalize, or the popularity features in the item tower not contributing as expected.\n\nA more likely culprit: item ID embeddings are memorizing which items were popular during training windows rather than adapting to current trends, and a simple popularity-at-cutoff baseline using recent listens is already beating this model (0.0285 vs 0.0215). With roughly 17-20 minutes left and a test run that would finish around 08:47, I'm weighing whether to kill this run now and instead replicate the smoke-test configuration (fewer cutoffs, one epoch) which scored above baseline, versus letting the current run finish despite its weaker trajectory.\n\nGiven the time pressure, I'll kill the background job and run a faster config instead: n_cut 3, epochs 1, same learning rate, sequentially for val then test, roughly 5 minutes total. The item tower's skip connection lets popularity features through directly, so stopping early should keep the model close to the strong popularity baseline while adding a bit of personalization — that's my best bet given the constraints.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T08:30:11.016Z", "text": "Validation NDCG is dropping as training loss falls—longer training is overfitting past windows, while the one-epoch smoke model beat the popularity baseline (0.0295 vs 0.0285). I'm killing the current job and switching to a short 1-epoch, 3-cutoff schedule for validation and test.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:30:11.023Z", "callId": "toolu_01PYXMos1AcTXe4hANXZjtC9", "name": "Bash", "input": { "command": "pkill -f two_tower.py; sleep 2; cd /app/solution && NT=8 python two_tower.py --stage val --n_cut 3 --cut_step 1 --epochs 1 --tag D 2>&1 | tail -4 && python /task/evaluate_public.py validation_D.parquet", "description": "Kill long run and train the short-schedule validation model", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:30:11.112Z", "content": "Exit code 144", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T08:30:11.107Z", "data": { "type": "queued_command", "prompt": "\nbruuv8pn9\ntoolu_011Nqim2kFErnxy7WkRdWVbv\n/tmp/claude-1000/-app/aac421c7-2f2b-4e82-89ea-d97c71ee0035/tasks/bruuv8pn9.output\nfailed\nBackground command \"Run validation fit then test refit sequentially in background\" failed with exit code 144\n", "source_uuid": "5cc5ab11-2e13-4e96-9263-485ae4bb9c43", "delivery_id": "862e0e60-1174-41b6-b9a3-ec5d2b83aab1", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T08:30:11.107Z" } }, { "kind": "context", "timestamp": "2026-10-03T08:30:11.120Z", "data": { "type": "total_tokens_reminder", "text": "14941905 tokens left" } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-10-03T08:30:14.379Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:30:14.379Z", "text": "Oh, pkill matched the string in my own command line and killed itself. I'll rerun without using pkill.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:30:14.390Z", "callId": "toolu_01VrZDoMyKtbdujxZrRAztsF", "name": "Bash", "input": { "command": "ps aux | grep -c \"[t]wo_tower\"; NT=8 python two_tower.py --stage val --n_cut 3 --cut_step 1 --epochs 1 --tag D 2>&1 | tail -3 && python /task/evaluate_public.py validation_D.parquet", "description": "Train the short-schedule validation model and score it", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:31:50.234Z", "content": "2\n[ 88.2s] epoch 0 loss 9.5138 val ndcg@10 0.0305\n[ 88.2s] best val ndcg 0.030490559125383194\n[ 88.2s] wrote /app/solution/validation_D.parquet\n{\n \"model\": \"validation_D.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.030491,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:31:50.237Z", "data": { "type": "total_tokens_reminder", "text": "14941299 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:31:50.239Z", "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": 22, "role": "agent", "timestamp": "2026-10-03T08:31:55.028Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:31:55.028Z", "text": "Validation score of 0.0305 beats popularity at 0.0285, so it's time to run the test refit in the foreground, which should take about a minute and a half, then move on to finalizing the README. I'll also double check those two stray processes I noticed to see if they're just leftover remnants from a killed background subshell.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:31:55.029Z", "text": "Validation NDCG@10 is 0.0305, above the popularity baseline. Now refitting the same configuration on train+val for the hidden-test submission." }, { "kind": "tool", "timestamp": "2026-10-03T08:31:55.031Z", "callId": "toolu_01P6PKEbb4AB1jLUnoidA2TN", "name": "Bash", "input": { "command": "NT=8 python two_tower.py --stage test --n_cut 3 --cut_step 1 --epochs 1 --tag D 2>&1 | tail -3; ps aux | grep \"[t]wo_tower\" | awk '{print $2,$12,$13,$14,$15}'", "description": "Refit on train+val and write the test submission", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T08:33:29.587Z", "content": "[ 90.2s] epoch 0 loss 9.5214\n[ 90.2s] best val ndcg 0\n[ 90.2s] wrote /app/solution/submission.parquet\n8806 -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1791015198681-6hlaeq.sh 2>/dev/null\n8863 -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1791015198681-6hlaeq.sh 2>/dev/null", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:33:29.589Z", "data": { "type": "total_tokens_reminder", "text": "14940734 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:33:29.591Z", "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": 23, "role": "agent", "timestamp": "2026-10-03T08:33:32.575Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:33:32.575Z", "text": "I should validate the submission structurally against test eligibility and update the README at the same time, since these two tasks are independent.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T08:33:33.861Z", "callId": "toolu_01Qr3FJpxos33sfSu19GtcU6", "name": "Bash", "input": { "command": "python - <<'EOF'\nimport sys; sys.path.insert(0,\"/task\")\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import CandidateSet, validate_ranking\nfrom pathlib import Path\nt=set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\nf=validate_ranking(pd.read_parquet(\"submission.parquet\"), t)\ne=CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\nok=all(e.is_eligible(u,i) for u,i in f[[\"uid\",\"item_id\"]].itertuples(index=False,name=None))\nprint(\"rows\",len(f),\"all eligible\",ok)\nEOF\nls -la /app/solution", "description": "Validate submission structure and eligibility", "timeout": 120000 }, "result": { "timestamp": "2026-10-03T08:33:36.551Z", "content": "rows 845600 all eligible True\ntotal 3708\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:31 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:09 ..\n-rw-r--r-- 1 agent agent 3044 Oct 3 08:25 README.md\ndrwxr-xr-x 2 agent agent 4096 Oct 3 08:33 cache\n-rw-r--r-- 1 agent agent 3978 Oct 3 08:15 prep.py\n-rw-r--r-- 1 agent agent 1173785 Oct 3 08:33 submission.parquet\n-rw-r--r-- 1 agent agent 15096 Oct 3 08:18 two_tower.py\n-rw-r--r-- 1 agent agent 1143006 Oct 3 08:31 validation_D.parquet\n-rw-r--r-- 1 agent agent 649247 Oct 3 08:18 validation_pop.parquet\n-rw-r--r-- 1 agent agent 780903 Oct 3 08:17 validation_smoke.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:33:36.553Z", "data": { "type": "total_tokens_reminder", "text": "14940135 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:33:36.555Z", "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": 24, "role": "agent", "timestamp": "2026-10-03T08:33:41.119Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T08:33:41.119Z", "text": "The timestamp discrepancy is just clock drift, nothing to worry about. I'll finalize the README with the updated config details, scores, tag D, and an overfitting note, then clean up by removing the stale smoke validation files while keeping pop as the baseline.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T08:33:41.120Z", "text": "The submission is structurally valid and fully eligible. Finalizing the README with the actual configuration and scores." }, { "kind": "tool", "timestamp": "2026-10-03T08:33:44.957Z", "callId": "toolu_01Tnd6578eSTMz5e8VEReVaq", "name": "Bash", "input": { "command": "rm -f validation_smoke.parquet && python - <<'EOF'\ns=open(\"README.md\").read()\ns=s.replace(\"validation_C.parquet\",\"validation_D.parquet\").replace(\"--tag C\",\"--tag D\")\ns=s.replace(\"--n_cut 5 --cut_step 1 --epochs 3\",\"--n_cut 3 --cut_step 1 --epochs 1\")\ns=s.replace(\"(5 cutoffs, one window\\n apart)\",\"(3 cutoffs, one window\\n apart)\")\ns=s.replace(\"Adam, OneCycle LR 2e-3, batch 1024, 3 epochs.\",\"Adam, OneCycle LR 2e-3, batch 1024, 1 epoch.\\n Longer schedules (5 cutoffs x 3 epochs) lowered the training loss but reduced public NDCG@10\\n (0.0248 -> 0.0215), i.e. the model overfits item identities of past windows, so the short\\n schedule that stays close to cutoff-time popularity plus personalisation was selected.\")\ns=s.replace(\"| two-tower (this submission's config) | VAL_SCORE |\",\"| two-tower, 3 cutoffs x 1 epoch (selected, `validation_D.parquet`) | **0.0305** |\\n| two-tower, 5 cutoffs x 3 epochs (best epoch) | 0.0248 |\")\nopen(\"README.md\",\"w\").write(s)\nEOF\ngrep -n \"0.0305\\|epochs 1\\|tag D\" README.md | head; ls", "description": "Update README with final configuration and scores" }, "result": { "timestamp": "2026-10-03T08:33:45.015Z", "content": "42:| two-tower, 3 cutoffs x 1 epoch (selected, `validation_D.parquet`) | **0.0305** |\n49:python two_tower.py --stage val --n_cut 3 --cut_step 1 --epochs 1 --tag D # -> validation_D.parquet\n51:python two_tower.py --stage test --n_cut 3 --cut_step 1 --epochs 1 --tag D # -> submission.parquet\nREADME.md\ncache\nprep.py\nsubmission.parquet\ntwo_tower.py\nvalidation_D.parquet\nvalidation_pop.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T08:33:45.018Z", "data": { "type": "total_tokens_reminder", "text": "14938975 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T08:33:45.020Z", "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": 25, "role": "agent", "timestamp": "2026-10-03T08:33:54.471Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T08:33:54.471Z", "text": "The deliverables are complete and validated in `/app/solution/`.\n\n**Outcome.** The final submission at `/app/solution/submission.parquet` passes the shared validator (845,600 rows, 100 ranks per target user, every item eligible under the test candidate sets). It was produced by the two-tower model refit on train plus validation history, with the test-start cutoff.\n\n**Public validation score** (graded NDCG@10 over 4263 users):\n\n| ranking | NDCG@10 |\n| --- | --- |\n| recent organic popularity baseline | 0.0285 |\n| selected two-tower (3 cutoffs, 1 epoch) | 0.0305 |\n| longer two-tower schedule (5 cutoffs, 3 epochs) | 0.0248 |\n\n**Model.** Both towers live in `two_tower.py`. The item tower maps an item-ID embedding plus five cutoff-time popularity features through an MLP to a 64-dim L2-normalised vector, which is precomputable for the MIPS index. The user tower never looks up a user ID. It pools the user's history before the cutoff into three embedding bags, recency-weighted positive listens, active likes, and dislikes, concatenates four activity scalars, and runs an MLP to a 64-dim vector. The score is the plain dot product. Training uses softmax cross-entropy over the 30k catalog with targets built from rolling 7-day windows that mimic the discovery objective (organic likes and organic 80% listens of never-heard catalog items). A sampled-softmax flag is provided as the scale-out option, and no user-item matrix is ever constructed.\n\n**What I observed.** Longer training lowered the loss but reduced validation NDCG, which suggests the model overfits item identities from past windows rather than cutoff-time trends. The short schedule was therefore selected and used for the refit. Two earlier parallel runs died silently, most likely from memory pressure, which cost time that would otherwise have gone to tuning.\n\n**Files.** `prep.py` builds the cached event arrays, `two_tower.py` trains and ranks for either stage, `README.md` records the architecture, objective, scores, and exact commands, and `validation_D.parquet` is the scored validation ranking." } ] } ] }