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Two-tower retriever fable-5-1-high-04

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

This recording includes 16 readable reasoning summaries, shown in full below.

Agent
fable-5.1 · Claude Code · high effort
Recorded conversation
24:23 · 25 steps
Private reward
0.021305 · Pass
Attempt cost
$2.34
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

Task instructions

+00:00
Task instructions

Build a latency-efficient PyTorch two-tower neural-network ranker

Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search.

The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

For each event family there is a train_*.parquet and val_*.parquet file:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, timestamp, item_id, is_organic

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

  1. Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
  2. After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

  • candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
  • target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
  • validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
  • test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
  • validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

  • grade 3: the user organically likes the track;
  • grade 1: the user organically listens to at least 80% of the track;
  • grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Public tools

/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that defines, trains, and applies both towers, along with a concise /app/solution/README.md, in /app/solution/. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • The environment includes CPU PyTorch 2.8.0. No accelerator is available.
  • You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
Harness context environment
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Harness context model
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    "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."
}
Harness context deferred tools delta
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Harness context agent listing delta
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    "- 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)",
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Harness context skill listing
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  "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 <n> 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",
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Harness context auto mode
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Harness context total tokens reminder
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  "text": "<total_tokens>15000000 tokens left</total_tokens>"
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Harness context session context
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  "context": {}
}
Harness context date
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  "type": "date",
  "date": "2026-10-03"
}
Harness context remote session change
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  "url": null,
  "commit": "Co-Authored-By: Claude Fable 5.1 <[email protected]>",
  "pr": "🤖 Generated with [Claude Code](https://claude.com/claude-code)",
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Harness context prompt snapshot
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  "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 `/<skill-name>`, 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: <short-kebab-case-slug>\ndescription: <one-line summary, used to decide relevance during recall>\nmetadata:\n  type: user | feedback | project | reference\n---\n\n<the fact; for feedback/project, follow with **Why:** and **How to apply:** lines. Link related memories with [[their-name]].>\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 `<system-reminder>` 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.",
    "<total_tokens>15000000 tokens left</total_tokens>"
  ],
  "reminderFold": false,
  "echoWireToolInputs": false,
  "contextRendering": "announced"
}
02

Inspect task data, requirements, and evaluator

+00:02
Recorded reasoning 22 words

I should start exploring the environment first, keeping in mind I have about 60 minutes total, so I need to work efficiently.

Model text

I'll start by inspecting the data and tooling, then build a two-tower model with a history-based user tower.

Tool callBashInspect task data, requirements, and evaluator Returned

Tool call · Input

cd /task && ls -la data/ && cat requirements.txt && nproc && free -g && python -c "import torch; print(torch.__version__, torch.get_num_threads())" && sed -n 1,200p evaluate_public.py

Tool response

total 347392
dr-xr-xr-x 1 root root      4096 Oct  3 07:56 .
dr-xr-xr-x 1 root root      4096 Oct  3 08:08 ..
-r--r--r-- 1 root root    349352 Oct  3 07:55 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Oct  3 07:55 target_users.parquet
-r--r--r-- 1 root root   7736341 Oct  3 07:55 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Oct  3 07:56 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Oct  3 07:55 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Oct  3 07:55 train_likes.parquet
-r--r--r-- 1 root root 319183255 Oct  3 07:55 train_listens.parquet
-r--r--r-- 1 root root    155988 Oct  3 07:55 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Oct  3 07:55 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Oct  3 07:55 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Oct  3 07:55 val_likes.parquet
-r--r--r-- 1 root root   9947609 Oct  3 07:55 val_listens.parquet
-r--r--r-- 1 root root      7845 Oct  3 07:55 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Oct  3 07:55 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Oct  3 07:55 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Oct  3 07:56 validation_preference_state.parquet
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
8
               total        used        free      shared  buff/cache   available
Mem:              15           1           0           0          13          14
Swap:              0           0           0
2.8.0+cpu 4
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()
Shell cwd was reset to /app
Harness context prompt snapshot
{
  "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 `/<skill-name>`, 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: <short-kebab-case-slug>\ndescription: <one-line summary, used to decide relevance during recall>\nmetadata:\n  type: user | feedback | project | reference\n---\n\n<the fact; for feedback/project, follow with **Why:** and **How to apply:** lines. Link related memories with [[their-name]].>\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 `<system-reminder>` 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.",
    "<total_tokens>15000000 tokens left</total_tokens>"
  ],
  "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 <system-reminder> 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 <system-reminder> 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: \"<name>\", 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: \"<name>\", 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 `<<autonomous-loop-dynamic>>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<<autonomous-loop>>` 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 `<<autonomous-loop-dynamic>>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<<autonomous-loop>>` 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 `<<autonomous-loop-dynamic>>` instead (the dynamic-pacing variant, not the CronCreate-mode `<<autonomous-loop>>`). 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 (`/<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 `<command-name>` 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 (`/<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 `<command-name>` 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 <system-reminder> 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 <functions> 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 <function>{\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}}</function> line inside the <functions> 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 <system-reminder> 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 <functions> 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 <function>{\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}}</function> line inside the <functions> 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:<tool_name>\" 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 <task-notification> 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 <task-notification> 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"
}
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14980344 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
03

Profile datasets, eligibility masks, and evaluation helpers

+00:15
Tool callBashProfile datasets, eligibility masks, and evaluation helpers Returned

Tool call · Input

cd /task && python - <<'EOF'
import pandas as pd, numpy as np
for f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_likes','train_dislikes','val_listens']:
    d=pd.read_parquet(f'data/{f}.parquet'); print(f, d.shape); print(d.head(3)); print(d.dtypes.to_dict())
tl=pd.read_parquet('data/train_listens.parquet')
print('train_listens', tl.shape, tl.uid.nunique(), tl.item_id.nunique(), tl.timestamp.min(), tl.timestamp.max())
print(tl.describe())
tu=pd.read_parquet('data/target_users.parquet')
print('target users in listens', tu.uid.isin(tl.uid).mean())
z=np.load('data/validation_eligible_candidates.npz'); print({k:z[k].shape for k in z})
m=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little'); print('elig per user', m.sum(1).mean(), m.sum(1).min(), m.sum(1).max())
z2=np.load('data/test_eligible_candidates.npz'); m2=np.unpackbits(z2['packed_eligible'],axis=1,count=len(z2['item_ids']),bitorder='little'); print('test elig', m2.sum(1).mean(), (z2['item_ids']==z['item_ids']).all(), (z2['uids']==z['uids']).all())
cat=pd.read_parquet('data/candidate_catalog.parquet'); print('cat in listens items', np.isin(cat.item_id, tl.item_id.unique()).mean())
vl=pd.read_parquet('data/val_listens.parquet'); print('val window', vl.timestamp.min(), vl.timestamp.max())
EOF
ls /app; ls /app/solution 2>/dev/null; grep -n "def \|class " /task/ranking_evaluation.py | head -40

Tool response

candidate_catalog (30000, 4)
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
{'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}
target_users (8456, 3)
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359
{'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}
validation_preference_state (469124, 3)
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
test_preference_state (484776, 3)
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
train_likes (814831, 4)
   uid  timestamp  item_id  is_organic
0  100      44755   732449           1
1  100    1155860  6568592           0
2  100    1259125  5411243           1
{'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
train_dislikes (101075, 4)
   uid  timestamp  item_id  is_organic
0  100    3087560  9170134           1
1  100    3936560  8661238           1
2  100   20432825  4927727           1
{'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
val_listens (1501697, 6)
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100   24943430  9321269           0               100                   195
1  100   24943605  5452713           0               100                   175
2  100   24943765  6448777           0               100                   160
{'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
train_listens (43484159, 6) 9238 845395 0 24786795
                uid     timestamp  ...  played_ratio_pct  track_length_seconds
count  4.348416e+07  4.348416e+07  ...      4.348416e+07          4.348416e+07
mean   4.985911e+05  1.369709e+07  ...      6.465021e+01          2.072443e+02
std    2.870021e+05  7.122591e+06  ...      4.412183e+01          8.447807e+01
min    1.000000e+02  0.000000e+00  ...      0.000000e+00          5.000000e+00
25%    2.497000e+05  7.741985e+06  ...      7.000000e+00          1.650000e+02
50%    4.938000e+05  1.424461e+07  ...      1.000000e+02          2.000000e+02
75%    7.439000e+05  2.001016e+07  ...      1.000000e+02          2.350000e+02
max    1.000000e+06  2.478680e+07  ...      1.590000e+02          2.495000e+03

[8 rows x 6 columns]
target users in listens 1.0
{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
elig per user 29067.306291390727 24657 30000
test elig 29041.3178807947 True True
cat in listens items 1.0
val window 24788600 25393395
solution
39:class RankingValidationError(ValueError):
43:class CandidateSet:
52:    def __init__(self, path: Path):
67:    def is_eligible(self, uid: int, item_id: int) -> bool:
88:def read_ranking(path: Path) -> pd.DataFrame:
101:def validate_ranking(
167:def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
183:def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
207:def score_rankings(
Shell cwd was reset to /app
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14975826 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
04

Preprocess logs to compact arrays and inspect eligibility semantics

+00:44
Recorded reasoning 321 words

With 43M listens across 9238 users and 845k items but a 30k catalog, I'm realizing eligibility (~29k of 30k per user) likely excludes items the user already engaged with, meaning this is a discovery task — recommending unheard items. I'm starting to plan preprocessing: loading train listens and filtering down to catalog items.

Given the goal is predicting organic likes or high-completion listens on never-interacted items within 7 days, a two-tower model seems like the right approach — a user tower pooling recent listening history and an item tower with embeddings plus popularity features, trained with sampled softmax similar to YouTube's recommendation architecture. With a tight 60-minute budget and about 3 minutes already spent, I need to move quickly: load the listens data, mark positives using a play-ratio threshold, and map item IDs into the catalog vocabulary, deciding how to handle items outside the catalog.

For training, I'll simulate the evaluation setup by taking multiple time cutoffs per user, using history before the cutoff and first-time positive interactions in the following 7-day window as targets — repeating this across a few windows to augment the roughly 9,238 users into a larger set of training examples.

For the model, I'm leaning toward a two-tower setup: a user tower that pools recency/popularity/organic-weighted history embeddings through an MLP, and an item tower combining item embeddings with popularity features, scored via dot product. I'll train with sampled softmax using in-batch plus random negatives, considering a log-Q correction for popularity bias and a learnable item bias term appended as an extra dimension to the vectors.

Given the time constraints, I need to stay pragmatic and sketch a concrete plan: first a prep script to load and filter the listens/likes/dislikes data into a compact sorted npz, then a training script to build cutoff-based examples, train the model, validate rankings, and produce the final submission — while making sure to mask out already-interacted items per the eligibility rules.

Tool callBashPreprocess logs to compact arrays and inspect eligibility semantics Returned

Tool call · Input

mkdir -p /app/solution && cd /app/solution && cat > prep.py <<'EOF'
"""Preprocess raw Yambda logs into compact numpy arrays restricted to the 30k catalog."""
import numpy as np, pandas as pd, time
D='/task/data/'
t0=time.time()
cat=pd.read_parquet(D+'candidate_catalog.parquet')
items=cat.item_id.values.astype(np.int64)            # index = popularity rank order
item2idx=pd.Series(np.arange(len(items)),index=items)
users=pd.read_parquet(D+'target_users.parquet').uid.values.astype(np.int64)
def load(fam, split):
    cols=['uid','timestamp','item_id','is_organic']+(['played_ratio_pct'] if fam=='listens' else [])
    d=pd.read_parquet(f'{D}{split}_{fam}.parquet',columns=cols)
    d=d[d.item_id.isin(item2idx.index)]
    d['iid']=item2idx.loc[d.item_id.values].values.astype(np.int32)
    d=d.sort_values(['uid','timestamp'],kind='stable')
    out={'uid':d.uid.values.astype(np.int64),'ts':d.timestamp.values.astype(np.int64),'iid':d.iid.values,'org':d.is_organic.values.astype(np.int8)}
    if fam=='listens': out['pr']=d.played_ratio_pct.values.astype(np.int16)
    return out
for split in ['train','val']:
    for fam in ['listens','likes','dislikes','unlikes','undislikes']:
        o=load(fam,split); np.savez(f'/app/solution/{split}_{fam}.npz',**o); print(split,fam,len(o['uid']),round(time.time()-t0,1))
np.save('/app/solution/items.npy',items); np.save('/app/solution/users.npy',users)
EOF
python prep.py && python - <<'EOF'
import numpy as np, pandas as pd
D='/task/data/'
z=np.load('/app/solution/train_listens.npz'); print('catalog share rows',len(z['uid']))
# what does eligibility exclude?
e=np.load(D+'validation_eligible_candidates.npz'); m=np.unpackbits(e['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)
items=np.load('/app/solution/items.npy'); uids=e['uids']
r=0; u=uids[r]
sel=z['uid']==u; hist=set(z['iid'][sel]); pos=set(z['iid'][sel & (z['pr']>=50)])
inel=set(np.where(~m[r])[0]); print('user',u,'inelig',len(inel),'hist all',len(hist),'pos',len(pos),'inel∩hist',len(inel&hist),'inel∩pos',len(inel&pos), 'inel-hist',len(inel-hist))
lk=np.load('/app/solution/train_likes.npz'); likes=set(lk['iid'][lk['uid']==u]); dk=np.load('/app/solution/train_dislikes.npz'); dis=set(dk['iid'][dk['uid']==u])
print('inel-hist-likes-dis',len(inel-hist-likes-dis),'likes not in hist',len(likes-hist), 'likes in inel', len(likes&inel))
ps=pd.read_parquet(D+'validation_preference_state.parquet'); print(ps.state.value_counts()); pu=ps[ps.uid==u]; print('pref state user', len(pu))
# val label stats
vl=np.load('/app/solution/val_listens.npz'); vk=np.load('/app/solution/val_likes.npz')
tu=set(np.load('/app/solution/users.npy'))
s=(vl['org']==1)&(vl['pr']>=80); print('val strong organic rows',s.sum(),'users',len(set(vl['uid'][s])&tu)); print('val organic likes',(vk['org']==1).sum(), 'users', len(set(vk['uid'][vk['org']==1])&tu))
print('frac organic in val listens', vl['org'].mean(), 'frac organic in train', z['org'].mean())
EOF

Tool response

train listens 31249613 6.2
train likes 554824 6.3
train dislikes 65321 6.3
train unlikes 93346 6.3
train undislikes 6779 6.3
val listens 1011771 6.5
val likes 21055 6.5
val dislikes 2012 6.5
val unlikes 2988 6.5
val undislikes 179 6.5
catalog share rows 31249613
user 100 inelig 682 hist all 682 pos 600 inel∩hist 682 inel∩pos 600 inel-hist 0
inel-hist-likes-dis 0 likes not in hist 0 likes in inel 13
state
liked       418435
disliked     50689
Name: count, dtype: int64
pref state user 15
val strong organic rows 291434 users 6027
val organic likes 13059 users 2073
frac organic in val listens 0.5143486025988094 frac organic in train 0.4998293578867681
Harness context environment
{
  "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"
    }
  ]
}
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14971087 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
05

Write and verify a popularity fallback submission

+02:12
Recorded reasoning 921 words

Since eligible targets are first-time items, I'm planning the training script: using catalog-only listens with positives defined as plays above 50%, generating multiple cutoff points spaced a week apart before the training end timestamp, and for each cutoff splitting events into history (before cutoff) and organic targets (after).

For targets, I want strong listens weighted normally and likes weighted higher since they're a stronger taste signal, while keeping only first-time items relative to each cutoff. I'm working out the pandas logic to efficiently compute per-user-item first interaction timestamps so I can filter target rows to genuinely novel items at each cutoff.

For history features, instead of building a global sparse user-item matrix (which risks violating the no-global-matrix constraint), I'll represent each user's history as a padded sequence of their last ~200 distinct positive items with recency/type-based weights — a standard sequence input. I'm also deciding to share the item embedding table between the user and item towers, which should help given the limited data and moderate catalog size.

For per-item history weighting, I'll compute scalar features like log listen count, recency, liked flag, and organic fraction, then run a small MLP on them to produce attention-style weights before pooling into a weighted mean embedding.

For the loss, I'm going with sampled softmax: one target per user-context per epoch, negatives drawn from in-batch targets plus uniform catalog samples, with log-Q correction to debias frequent in-batch negatives, skipping history-masking for now to save time.

Actually, with 512 batch size and only 30k items, a full softmax over the entire catalog is computationally trivial — item tower forward pass and the logit matmul both run in well under a billion flops — so I should just do full softmax with multi-hot weighted cross-entropy instead of sampling, avoiding any sampling bias entirely.

History items should be masked out of the softmax denominator since they aren't real candidates — I can scatter -inf into the masked positions using the padded history indices. With 9238 users and 8 weekly cutoffs giving ~74k contexts, batches of 256 yield ~290 steps per epoch, likely finishing in well under a minute given the cheap per-step cost, and I'll restrict to users with at least one target in the window.

I'm also considering denser cutoffs (every 2 days with 7-day overlapping windows) to get more contexts, maybe 150k instead, but I'll start with the weekly×8 setup and benchmark before expanding. For validation/test, the context cutoff moves forward (train end, then val end), with item scores computed over eligible items and top-100 ranked; the item tower combines item embeddings with log popularity and a bias term alongside a constant user-side bias dimension.

No item metadata beyond ID is available, so I'll rely purely on ID embeddings for the 30k catalog items, all of which have sufficient listen history. I'll let the model learn popularity and recency trends implicitly through the bias term and by weighting more recent cutoffs higher during training, and I'll use raw dot-product scoring with weight decay for regularization plus random dropout of 20% of history items as augmentation.

Now I'm working out the per-user history construction: for each cutoff, I take the most recent 256 distinct items a user listened to (with listen completion ≥50%), computing log-scaled counts, log-scaled recency in days, an approximate "liked" flag from like/unlike counts, and organic listening fraction, while also considering how to flag disliked items that appear in history without over-complicating the ineligible-item filtering.

Given the 31M rows across 8 cutoffs, I'm estimating groupby costs and considering filtering to positive listens only or limiting to a recency window to speed things up, possibly switching to a sort-once-then-mask-per-cutoff approach with numpy for efficiency.

For selecting the top-L items per user by recency, I'm planning a lexsort on (uid, -last_ts) and ranking within groups. For targets, I'm defining organic listens with pr>=80 within a 7-day window after cutoff, requiring first_ts(uid,iid) to fall at or after cutoff across all listens regardless of play rate to establish eligibility.

I'm now working out the cutoff schedule: with train end at 24786800 seconds and a 7-day window, I need cutoffs spaced roughly 2 days apart going back in time so the window still fits before the train end, covering about 20 cutoffs across the ~287 days of historical data.

For the final refit, I'm mapping out how the test cutoff aligns with the validation window end, accounting for a small 30-minute gap between history and target window that should be negligible for model accuracy.

Now I'm planning the actual implementation: a single twotower.py file with a CLI supporting val/test stages, designed to fit within memory for 31M rows. I'm sketching out the user tower architecture — embedding the history items, running them through an attention mechanism with masking for padding, and computing softmax weights over valid items.

I'm working through the pooling and projection layers, combining pooled history with history-length features, adding a residual connection to preserve item2vec-like geometry, then mirroring this with an item tower that combines item embeddings with popularity features and a bias term, before defining the final scoring function that concatenates user and item vectors.

For training I'm planning AdamW with a cosine schedule, around 10-20 epochs, batch size 256, and I'll build an internal NDCG@10 validation check using held-out events to pick the best epoch without leaking test information. I also want to make sure I save a quick popularity-based baseline submission early on so there's always a valid fallback file at the expected output path, even before the full model finishes training.

Recorded reasoning 20 words

I'll exclude already-listened items so targets are first-time positives, save a popularity fallback submission first, then build the two-tower trainer.

Tool callBashWrite and verify a popularity fallback submission Returned

Tool call · Input

cat > fallback.py <<'EOF'
import numpy as np, pandas as pd
D='/task/data/'
e=np.load(D+'test_eligible_candidates.npz'); m=np.unpackbits(e['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)
items=e['item_ids'].astype(np.int64); score=-np.arange(30000,dtype=np.float32)[None,:].repeat(len(e['uids']),0)
score[~m]=-1e9; top=np.argsort(-score,axis=1)[:,:100]
df=pd.DataFrame({'uid':np.repeat(e['uids'].astype(np.int64),100),'item_id':items[top].ravel(),'rank':np.tile(np.arange(1,101),len(e['uids']))})
df.to_parquet('/app/solution/submission.parquet',index=False); print(df.shape)
EOF
python fallback.py && python - <<'EOF'
import pandas as pd
tu=pd.read_parquet('/task/data/target_users.parquet'); s=pd.read_parquet('/app/solution/submission.parquet'); print(set(tu.uid)==set(s.uid), s.groupby('uid').size().unique())
EOF

Tool response

(845600, 3)
True [100]
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14965706 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
06

Write two-tower trainer and run a first validation experiment

+02:27
Recorded reasoning 10 words

Now I'm moving on to writing the main script itself.

Tool callBashWrite two-tower trainer and run a first validation experiment Returned

Tool call · Input

cat > twotower.py <<'EOF'
"""Two-tower ranker for the "Made for You" shelf.

User tower : feature-gated attention pooling over the user's recent positive-listen history
             (item embeddings + per-item behaviour features) followed by a residual MLP.
Item tower : item-id embedding + log-popularity -> residual MLP, plus a learned bias slot.
Score      : dot(user_vec, item_vec) where item_vec=[v_i, b_i] and user_vec=[u, 1].
Training   : multi-cutoff temporal windows; full softmax over the 30k catalog with graded
             (weighted) multi-hot targets, history items masked out of the softmax.
No user-item matrix is ever materialised or decomposed: every batch only touches the
B x L padded history sequences of the users in the batch.
"""
import argparse, time, json, math, os
import numpy as np, pandas as pd, torch, torch.nn as nn, torch.nn.functional as F

S = '/app/solution/'; D = '/task/data/'
N_ITEMS = 30000; DAY = 86400; WEEK = 7 * DAY
TRAIN_END = 24786800; VAL_END = 24788600 + WEEK   # 25393400
torch.set_num_threads(8)

# ----------------------------------------------------------------------------- data
def load_events(stage):
    """Concatenate train (+val for the test stage) event families."""
    splits = ['train'] if stage == 'val' else ['train', 'val']
    out = {}
    for fam in ['listens', 'likes', 'dislikes', 'unlikes']:
        parts = [np.load(f'{S}{sp}_{fam}.npz') for sp in splits]
        keys = parts[0].files
        d = {k: np.concatenate([p[k] for p in parts]) for k in keys}
        o = np.lexsort((d['ts'], d['uid']))
        out[fam] = {k: v[o] for k, v in d.items()}
    return out

def build_contexts(ev, cutoffs, users, L, with_targets=True):
    """For each cutoff build per-user padded history + per-context graded targets.

    Returns hist (N,L) int32 (-1 pad), feats (N,L,F) float32, ctx_uid (N,), ctx_cut (N,),
    targets: list of (ctx_row, item, weight) arrays.
    """
    li = ev['listens']; lk = ev['likes']; dk = ev['dislikes']; ul = ev['unlikes']
    uid_all, ts_all, iid_all, pr_all, org_all = li['uid'], li['ts'], li['iid'], li['pr'], li['org']
    key_all = uid_all * N_ITEMS + iid_all
    # first interaction (any listen / like / dislike) time per (uid,item) -> defines "seen"
    k_other = np.concatenate([lk['uid'] * N_ITEMS + lk['iid'], dk['uid'] * N_ITEMS + dk['iid']])
    t_other = np.concatenate([lk['ts'], dk['ts']])
    kk = np.concatenate([key_all, k_other]); tt = np.concatenate([ts_all, t_other])
    o = np.argsort(kk, kind='stable'); kk, tt = kk[o], tt[o]
    first_key, first_idx = np.unique(kk, return_index=True)
    first_ts = np.minimum.reduceat(tt, first_idx)
    pos = pr_all >= 50
    uidx = pd.Series(np.arange(len(users)), index=users)
    H, Fe, C_uid, C_cut, T = [], [], [], [], []
    base = 0
    for c in cutoffs:
        m = pos & (ts_all < c)
        k = key_all[m]; t = ts_all[m]; og = org_all[m]
        o = np.argsort(k, kind='stable'); k, t, og = k[o], t[o], og[o]
        gk, gi, cnt = np.unique(k, return_index=True, return_counts=True)
        last = np.maximum.reduceat(t, gi); orgs = np.add.reduceat(og.astype(np.int32), gi)
        gu = gk // N_ITEMS; gitem = gk % N_ITEMS
        # like state at cutoff: likes before c minus unlikes before c
        lm = lk['ts'] < c; lkk = lk['uid'][lm] * N_ITEMS + lk['iid'][lm]
        um = ul['ts'] < c; ulk = ul['uid'][um] * N_ITEMS + ul['iid'][um]
        liked_keys = np.setdiff1d(np.unique(lkk), np.unique(ulk))
        liked = np.isin(gk, liked_keys)
        dm = dk['ts'] < c; disl = np.isin(gk, np.unique(dk['uid'][dm] * N_ITEMS + dk['iid'][dm]))
        # keep users of interest, sort by (user, -last) and keep L most recent distinct items
        keep = np.isin(gu, users)
        gu, gitem, cnt, last, orgs, liked, disl = gu[keep], gitem[keep], cnt[keep], last[keep], orgs[keep], liked[keep], disl[keep]
        o = np.lexsort((-last, gu)); gu, gitem, cnt, last, orgs, liked, disl = gu[o], gitem[o], cnt[o], last[o], orgs[o], liked[o], disl[o]
        ustart = np.r_[0, np.flatnonzero(np.diff(gu)) + 1]
        rank = np.arange(len(gu)) - np.repeat(ustart, np.diff(np.r_[ustart, len(gu)]))
        nh = np.bincount(uidx.loc[gu].values, minlength=len(users))
        sel = rank < L
        rows = uidx.loc[gu[sel]].values; cols = rank[sel]
        h = np.full((len(users), L), -1, np.int32); h[rows, cols] = gitem[sel]
        f = np.zeros((len(users), L, 5), np.float32)
        age = (c - last[sel]) / DAY
        f[rows, cols, 0] = np.log1p(cnt[sel]); f[rows, cols, 1] = np.log1p(age)
        f[rows, cols, 2] = liked[sel]; f[rows, cols, 3] = orgs[sel] / cnt[sel]; f[rows, cols, 4] = disl[sel]
        H.append(h); Fe.append(f); C_uid.append(users); C_cut.append(np.full(len(users), c))
        if with_targets:
            w_end = c + WEEK
            tm = (ts_all >= c) & (ts_all < w_end) & (org_all == 1) & (pr_all >= 80)
            tk = key_all[tm]
            lm2 = (lk['ts'] >= c) & (lk['ts'] < w_end) & (lk['org'] == 1)
            lkk2 = lk['uid'][lm2] * N_ITEMS + lk['iid'][lm2]
            def firsttime(keys):
                keys = np.unique(keys)
                ft = first_ts[np.searchsorted(first_key, keys)]
                keys = keys[ft >= c]
                return keys[np.isin(keys // N_ITEMS, users)]
            tk = firsttime(tk); lkk2 = firsttime(lkk2)
            g = pd.DataFrame({'k': np.concatenate([tk, lkk2]), 'w': np.r_[np.ones(len(tk)), np.full(len(lkk2), 3.0)]})
            g = g.groupby('k').w.max().reset_index()
            r = base + uidx.loc[g.k.values // N_ITEMS].values
            T.append((r.astype(np.int64), (g.k.values % N_ITEMS).astype(np.int64), g.w.values.astype(np.float32)))
        base += len(users)
        print(f'  cutoff {c}: hist users {int((nh>0).sum())}, targets {0 if not with_targets else len(T[-1][0])}', flush=True)
    H = np.concatenate(H); Fe = np.concatenate(Fe); C_uid = np.concatenate(C_uid); C_cut = np.concatenate(C_cut)
    if with_targets:
        T = tuple(np.concatenate([t[i] for t in T]) for i in range(3))
    return H, Fe, C_uid, C_cut, T

# ----------------------------------------------------------------------------- model
class UserTower(nn.Module):
    def __init__(self, n_items, d, n_feat, emb=None, drop=0.2):
        super().__init__()
        self.emb = emb if emb is not None else nn.Embedding(n_items + 1, d, padding_idx=n_items)
        self.gate = nn.Sequential(nn.Linear(n_feat, 32), nn.ReLU(), nn.Linear(32, 1))
        self.fmod = nn.Linear(n_feat, d)          # feature-conditioned modulation of item embeddings
        self.mlp = nn.Sequential(nn.Linear(d + 1, 2 * d), nn.ReLU(), nn.Linear(2 * d, d))
        self.drop = drop; self.n_items = n_items
    def forward(self, hist, feats):
        mask = hist >= 0
        if self.training and self.drop > 0:
            mask = mask & (torch.rand(mask.shape) > self.drop)
        idx = torch.where(mask, hist, torch.full_like(hist, self.n_items))
        e = self.emb(idx) * (1 + torch.tanh(self.fmod(feats)))
        a = self.gate(feats).squeeze(-1).masked_fill(~mask, -1e4)
        w = torch.softmax(a, dim=1) * mask.float()
        p = (w.unsqueeze(-1) * e).sum(1)
        n = mask.float().sum(1, keepdim=True)
        u = p + self.mlp(torch.cat([p, torch.log1p(n) / 5], 1))
        return torch.cat([u, torch.ones(u.shape[0], 1)], 1)

class ItemTower(nn.Module):
    def __init__(self, n_items, d, logpop):
        super().__init__()
        self.emb = nn.Embedding(n_items, d)
        nn.init.normal_(self.emb.weight, std=0.05)
        self.register_buffer('logpop', logpop)
        self.mlp = nn.Sequential(nn.Linear(d + 1, 2 * d), nn.ReLU(), nn.Linear(2 * d, d))
        self.bias = nn.Sequential(nn.Linear(d + 1, 32), nn.ReLU(), nn.Linear(32, 1))
    def forward(self, items):
        e = self.emb(items); x = torch.cat([e, self.logpop[items].unsqueeze(1)], 1)
        v = e + self.mlp(x)
        return torch.cat([v, self.bias(x)], 1)

class TwoTower(nn.Module):
    def __init__(self, n_items, d, n_feat, logpop, share):
        super().__init__()
        self.item = ItemTower(n_items, d, logpop)
        emb = None
        if share:  # shared base table, padding row appended via separate param
            emb = SharedEmb(self.item.emb, n_items)
        self.user = UserTower(n_items, d, n_feat, emb)
    def all_items(self):
        return self.item(torch.arange(N_ITEMS))

class SharedEmb(nn.Module):
    def __init__(self, emb, n_items):
        super().__init__(); self.emb = emb; self.n_items = n_items
    def forward(self, idx):
        w = torch.cat([self.emb.weight, torch.zeros(1, self.emb.weight.shape[1])], 0)
        return F.embedding(idx, w)

# ----------------------------------------------------------------------------- train / infer
def ndcg10(ranked, graded):
    s = []
    for u, items in ranked.items():
        g = graded.get(u)
        if not g: continue
        dcg = sum((2 ** g.get(it, 0) - 1) / math.log2(r + 2) for r, it in enumerate(items[:10]))
        ideal = sorted(g.values(), reverse=True)[:10]
        idcg = sum((2 ** v - 1) / math.log2(r + 2) for r, v in enumerate(ideal))
        s.append(dcg / idcg)
    return float(np.mean(s)), len(s)

def val_truth(users, elig):
    vl = np.load(S + 'val_listens.npz'); vk = np.load(S + 'val_likes.npz')
    uset = set(users.tolist()); uidx = {u: i for i, u in enumerate(users)}
    g = {}
    m = (vl['org'] == 1) & (vl['pr'] >= 80)
    for u, i in zip(vl['uid'][m], vl['iid'][m]):
        if u in uset and elig[uidx[u], i]: g.setdefault(int(u), {})[int(i)] = 1
    m = vk['org'] == 1
    for u, i in zip(vk['uid'][m], vk['iid'][m]):
        if u in uset and elig[uidx[u], i]: g.setdefault(int(u), {})[int(i)] = 3
    return g

def rank_users(model, H, Fe, elig, bs=1024):
    model.eval(); out = np.zeros((len(H), 100), np.int64)
    with torch.no_grad():
        V = model.all_items()
        for s in range(0, len(H), bs):
            u = model.user(torch.from_numpy(H[s:s+bs]).long(), torch.from_numpy(Fe[s:s+bs]))
            sc = u @ V.T
            sc[~torch.from_numpy(elig[s:s+bs])] = -1e9
            out[s:s+bs] = torch.topk(sc, 100, dim=1).indices.numpy()
    return out

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument('--stage', default='val'); ap.add_argument('--d', type=int, default=64)
    ap.add_argument('--L', type=int, default=200); ap.add_argument('--ncut', type=int, default=12)
    ap.add_argument('--step_days', type=float, default=2); ap.add_argument('--epochs', type=int, default=12)
    ap.add_argument('--bs', type=int, default=256); ap.add_argument('--lr', type=float, default=2e-3)
    ap.add_argument('--wd', type=float, default=1e-6); ap.add_argument('--share', type=int, default=1)
    ap.add_argument('--drop', type=float, default=0.2); ap.add_argument('--recency_w', type=float, default=0.0)
    ap.add_argument('--eval_every', type=int, default=2); ap.add_argument('--out', default='validation.parquet')
    ap.add_argument('--seed', type=int, default=0)
    a = ap.parse_args(); torch.manual_seed(a.seed); np.random.seed(a.seed)
    t0 = time.time()
    users = np.load(S + 'users.npy'); items = np.load(S + 'items.npy')
    cat = pd.read_parquet(D + 'candidate_catalog.parquet')
    logpop = torch.tensor(np.log1p(cat.positive_listens.values.astype(np.float32))); logpop = (logpop - logpop.mean()) / logpop.std()
    end = TRAIN_END if a.stage == 'val' else VAL_END
    ev = load_events(a.stage)
    cutoffs = [int(end - WEEK - k * a.step_days * DAY) for k in range(a.ncut)]
    print('building contexts', flush=True)
    H, Fe, C_uid, C_cut, T = build_contexts(ev, cutoffs, users, a.L)
    Hq, Fq, _, _, _ = build_contexts(ev, [end], users, a.L, with_targets=False)
    elig_file = 'validation_eligible_candidates.npz' if a.stage == 'val' else 'test_eligible_candidates.npz'
    e = np.load(D + elig_file); assert (e['uids'] == users).all() and (e['item_ids'] == items).all()
    elig = np.unpackbits(e['packed_eligible'], axis=1, count=N_ITEMS, bitorder='little').astype(bool)
    graded = val_truth(users, elig) if a.stage == 'val' else None
    # contexts with at least one target
    tr, ti, tw = T
    ctx_ids = np.unique(tr); print(f'contexts {len(ctx_ids)}, target pairs {len(tr)}, prep {time.time()-t0:.0f}s', flush=True)
    order = np.argsort(tr, kind='stable'); tr, ti, tw = tr[order], ti[order], tw[order]
    starts = np.searchsorted(tr, ctx_ids); ends = np.searchsorted(tr, ctx_ids, side='right')
    # recency weighting of cutoffs (newer windows matter more)
    cut_age = (end - C_cut) / WEEK
    model = TwoTower(N_ITEMS, a.d, Fe.shape[2], logpop, bool(a.share))
    opt = torch.optim.AdamW(model.parameters(), lr=a.lr, weight_decay=a.wd)
    nb = math.ceil(len(ctx_ids) / a.bs); sched = torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=a.lr, total_steps=a.epochs * nb, pct_start=0.15)
    best = (-1, None)
    for ep in range(a.epochs):
        model.train(); perm = np.random.permutation(len(ctx_ids)); tot = 0; te = time.time()
        for b in range(nb):
            sel = perm[b * a.bs:(b + 1) * a.bs]; rows = ctx_ids[sel]
            h = torch.from_numpy(H[rows]).long(); f = torch.from_numpy(Fe[rows])
            # multi-hot graded targets
            lens = ends[sel] - starts[sel]
            bi = np.repeat(np.arange(len(sel)), lens)
            pi = np.concatenate([np.arange(s_, e_) for s_, e_ in zip(starts[sel], ends[sel])])
            Y = torch.zeros(len(sel), N_ITEMS); Y[bi, ti[pi]] = torch.from_numpy(tw[pi])
            u = model.user(h, f); V = model.all_items()
            logits = u @ V.T
            hm = torch.zeros(len(sel), N_ITEMS, dtype=torch.bool)
            hv = h >= 0; hm[torch.arange(len(sel)).unsqueeze(1).expand_as(h)[hv], h[hv]] = True
            logits = logits.masked_fill(hm, -1e4)
            logp = torch.log_softmax(logits, 1)
            cw = torch.from_numpy(np.exp(-a.recency_w * cut_age[rows]).astype(np.float32))
            loss = -((Y * logp).sum(1) / Y.sum(1) * cw).sum() / cw.sum()
            opt.zero_grad(); loss.backward(); opt.step(); sched.step(); tot += loss.item()
        msg = f'ep {ep+1} loss {tot/nb:.4f} ({time.time()-te:.0f}s)'
        if a.stage == 'val' and ((ep + 1) % a.eval_every == 0 or ep + 1 == a.epochs):
            top = rank_users(model, Hq, Fq, elig)
            sc, n = ndcg10({int(u): top[i].tolist() for i, u in enumerate(users)}, graded)
            msg += f' val ndcg@10 {sc:.4f} (n={n})'
            if sc > best[0]: best = (sc, {k: v.clone() for k, v in model.state_dict().items()})
        print(msg, flush=True)
    if a.stage == 'val' and best[1] is not None:
        model.load_state_dict(best[1]); print(f'best val ndcg@10 {best[0]:.4f}')
    top = rank_users(model, Hq, Fq, elig)
    df = pd.DataFrame({'uid': np.repeat(users, 100), 'item_id': items[top].ravel(), 'rank': np.tile(np.arange(1, 101), len(users))})
    df.to_parquet(S + a.out, index=False); torch.save(model.state_dict(), S + f'model_{a.stage}.pt')
    print(f'wrote {a.out}; total {time.time()-t0:.0f}s')

if __name__ == '__main__':
    main()
EOF
timeout 1500 python twotower.py --stage val --ncut 8 --epochs 6 --eval_every 2 2>&1 | tail -30

Tool response

Command did not complete within its 120s timeout and was moved to the background (ID: bjt56ed6e). Output is being written to: /tmp/claude-1000/-app/c1e2970a-793b-420c-b6f6-9930e2c35ef3/tasks/bjt56ed6e.output. You will be notified when it completes. If it is still running after 30m in the background, it will be stopped and you will be notified. To check interim output, use Read on that file path.
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14958343 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
07

Check training progress

+05:30
Tool callBashCheck training progress Returned

Tool call · Input

sleep 45; cat /tmp/claude-1000/-app/c1e2970a-793b-420c-b6f6-9930e2c35ef3/tasks/bjt56ed6e.output

Tool response

building contexts
  cutoff 24182000: hist users 8345, targets 41544
  cutoff 24009200: hist users 8311, targets 41121
  cutoff 23836400: hist users 8276, targets 40233
  cutoff 23663600: hist users 8229, targets 40446
  cutoff 23490800: hist users 8206, targets 39867
  cutoff 23318000: hist users 8164, targets 40084
  cutoff 23145200: hist users 8122, targets 40709
  cutoff 22972400: hist users 8085, targets 40042
  cutoff 24786800: hist users 8449, targets 0
contexts 33756, target pairs 324046, prep 40s
ep 1 loss 9.6725 (15s)
ep 2 loss 8.9426 (16s) val ndcg@10 0.0256 (n=4263)
ep 3 loss 8.5269 (14s)
ep 4 loss 8.1804 (16s) val ndcg@10 0.0239 (n=4263)
ep 5 loss 7.9468 (14s)
ep 6 loss 7.8408 (17s) val ndcg@10 0.0230 (n=4263)
best val ndcg@10 0.0256
wrote validation.parquet; total 138s

[exited with code 0]
Harness context queued command
{
  "type": "queued_command",
  "prompt": "<task-notification>\n<task-id>bjt56ed6e</task-id>\n<tool-use-id>toolu_01WWECk1prokNs7UEULDT73R</tool-use-id>\n<output-file>/tmp/claude-1000/-app/c1e2970a-793b-420c-b6f6-9930e2c35ef3/tasks/bjt56ed6e.output</output-file>\n<status>completed</status>\n<summary>Background command \"Write two-tower trainer and run a first validation experiment\" completed (exit code 0)</summary>\n</task-notification>",
  "source_uuid": "7c6ca4be-1b9b-479c-9163-5245e0e5836e",
  "delivery_id": "4bd073b9-1d5c-4962-8593-09109ca5bb6d",
  "commandMode": "task-notification",
  "origin": {
    "kind": "task-notification",
    "producer": "session-task"
  },
  "timestamp": "2026-10-03T08:18:49.739Z"
}
Harness context silent turn reminder
{
  "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."
}
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14958031 tokens left</total_tokens>"
}
08

Evaluate popularity baseline and launch regularized run with more cutoffs

+06:23
Recorded reasoning 115 words

NDCG is sitting at 0.0256 and dropping as training continues — clear overfitting, with the item-id embedding essentially memorizing 33k contexts against 30k×64 embedding parameters. I want a popularity-ranking baseline on val to see if this even beats a trivial heuristic, and I'm considering fixes like higher dropout, stronger weight decay, fewer epochs, or a lower learning rate.

Given limited time, I'll run the popularity baseline in parallel with a bigger-data, more-regularized experiment: more cutoffs for more training data, fixed AdamW weight decay (my earlier 1e-6 was essentially doing nothing — bumping to something meaningful like 0.02), stronger dropout, and checking every epoch in case an earlier checkpoint generalizes better than the one currently selected.

Model text

First run gives NDCG@10 of 0.0256 at epoch 2, then degrades, so the model overfits quickly. I'll check the popularity baseline for reference and rerun with more cutoffs and stronger regularization.

Tool callBashEvaluate popularity baseline and launch regularized run with more cutoffs Returned

Tool call · Input

python - <<'EOF' &
import numpy as np, pandas as pd
D='/task/data/'
e=np.load(D+'validation_eligible_candidates.npz'); m=np.unpackbits(e['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)
items=e['item_ids'].astype(np.int64); score=-np.arange(30000,dtype=np.float32)[None,:].repeat(len(e['uids']),0)
score[~m]=-1e9; top=np.argsort(-score,axis=1)[:,:100]
pd.DataFrame({'uid':np.repeat(e['uids'].astype(np.int64),100),'item_id':items[top].ravel(),'rank':np.tile(np.arange(1,101),len(e['uids']))}).to_parquet('/app/solution/val_pop.parquet',index=False)
EOF
wait; python /task/evaluate_public.py /app/solution/val_pop.parquet; python /task/evaluate_public.py /app/solution/validation.parquet
nohup python twotower.py --stage val --ncut 24 --step_days 1 --epochs 5 --eval_every 1 --wd 0.02 --drop 0.3 --out val_b.parquet > run_b.log 2>&1 &
echo started

Tool response

[1]+  Done                    python - <<'EOF'
import numpy as np, pandas as pd
D='/task/data/'
e=np.load(D+'validation_eligible_candidates.npz'); m=np.unpackbits(e['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)
items=e['item_ids'].astype(np.int64); score=-np.arange(30000,dtype=np.float32)[None,:].repeat(len(e['uids']),0)
score[~m]=-1e9; top=np.argsort(-score,axis=1)[:,:100]
pd.DataFrame({'uid':np.repeat(e['uids'].astype(np.int64),100),'item_id':items[top].ravel(),'rank':np.tile(np.arange(1,101),len(e['uids']))}).to_parquet('/app/solution/val_pop.parquet',index=False)
EOF

{
  "model": "/app/solution/val_pop.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.025595,
    "quality_users": 4263
  }
}
started
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14956119 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
09

Add weak non-organic targets option and launch parallel variant

+06:53
Recorded reasoning 168 words

The model score of 0.0256 is 2.4x over the popularity baseline of 0.0107, confirming the public evaluator aligns with my internal metric. While run b finishes (~7-8 min), I'm thinking about next steps: the overfitting fix is already in progress, and I should also consider whether the item tower's bias is dominating—switching to cosine similarity with temperature instead of raw dot product might generalize better.

Other ideas worth considering: weighting non-organic strong listens as weak targets (w=0.3-0.5) to add more training signal, since recommender-driven listens still correlate with taste and could help with overfitting. Ensembling scores across 2-3 seeds is also a cheap way to boost NDCG. With about 16 minutes used and 44 remaining, I need to save 12+ minutes for the final refit, so I'll launch one more experiment in parallel with run b rather than sequentially—keeping norm off to isolate the effect, I'll run variant c testing nonorg_w at 0.5 via a new flag, requiring a small code change to support the weak target weighting.

Model text

Popularity baseline scores 0.0107, so the first model already gives 2.4x. While the regularized run trains, I'll add an option for weak non-organic targets and run it in parallel.

Tool callBashAdd weak non-organic targets option and launch parallel variant Returned

Tool call · Input

python - <<'EOF'
import re
s=open('twotower.py').read()
s=s.replace("def build_contexts(ev, cutoffs, users, L, with_targets=True):","def build_contexts(ev, cutoffs, users, L, with_targets=True, nonorg_w=0.0):")
s=s.replace("""            tm = (ts_all >= c) & (ts_all < w_end) & (org_all == 1) & (pr_all >= 80)
            tk = key_all[tm]""","""            tm = (ts_all >= c) & (ts_all < w_end) & (org_all == 1) & (pr_all >= 80)
            tk = key_all[tm]
            nm = (ts_all >= c) & (ts_all < w_end) & (org_all == 0) & (pr_all >= 80)
            nk = key_all[nm]""")
s=s.replace("""            tk = firsttime(tk); lkk2 = firsttime(lkk2)
            g = pd.DataFrame({'k': np.concatenate([tk, lkk2]), 'w': np.r_[np.ones(len(tk)), np.full(len(lkk2), 3.0)]})""","""            tk = firsttime(tk); lkk2 = firsttime(lkk2); nk = firsttime(nk) if nonorg_w > 0 else nk[:0]
            g = pd.DataFrame({'k': np.concatenate([tk, lkk2, nk]), 'w': np.r_[np.ones(len(tk)), np.full(len(lkk2), 3.0), np.full(len(nk), nonorg_w)]})""")
s=s.replace("ap.add_argument('--seed', type=int, default=0)","ap.add_argument('--seed', type=int, default=0); ap.add_argument('--nonorg_w', type=float, default=0.0)")
s=s.replace("H, Fe, C_uid, C_cut, T = build_contexts(ev, cutoffs, users, a.L)","H, Fe, C_uid, C_cut, T = build_contexts(ev, cutoffs, users, a.L, nonorg_w=a.nonorg_w)")
open('twotower.py','w').write(s)
EOF
nohup python twotower.py --stage val --ncut 24 --step_days 1 --epochs 5 --eval_every 1 --wd 0.02 --drop 0.3 --nonorg_w 0.5 --out val_c.parquet > run_c.log 2>&1 &
sleep 60; tail -3 run_b.log

Tool response

  cutoff 22799600: hist users 8059, targets 39090
  cutoff 22713200: hist users 8034, targets 39464
  cutoff 22626800: hist users 8019, targets 39048
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14953984 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
10

Wait and check both training runs

+08:01
Tool callBashWait and check both training runs Error

Tool call · Input

sleep 150; tail -4 run_b.log; echo ---; tail -3 run_c.log

Tool response · Tool error

Exit code 143
Command timed out after 2m 0s
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14953788 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
11

Check both training runs

+10:04
Tool callBashCheck both training runs Returned

Tool call · Input

tail -4 /app/solution/run_b.log; echo ---; tail -3 /app/solution/run_c.log

Tool response

  cutoff 22454000: hist users 7977, targets 38177
  cutoff 22367600: hist users 7962, targets 37998
  cutoff 22281200: hist users 7949, targets 37770
  cutoff 22194800: hist users 7935, targets 37545
---
  cutoff 24786800: hist users 8449, targets 0
contexts 143357, target pairs 3339206, prep 100s
ep 1 loss 9.3802 (64s) val ndcg@10 0.0222 (n=4263)
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14953624 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
12

Check process status and log progress

+10:08
Recorded reasoning 49 words

Run_b's log seems stuck at that cutoff while run_c, despite starting later, has already reached epoch 1 — that's odd given I used flush=True, so maybe there's contention slowing run_b down or it actually crashed. I should check the full run_b log and verify the processes are still running.

Tool callBashCheck process status and log progress Returned

Tool call · Input

cd /app/solution; ps aux | grep twotower | grep -v grep | awk '{print $2,$3,$4,$10}'; wc -l run_b.log run_c.log; grep -c cutoff run_b.log; grep ep run_b.log

Tool response

8422 399 21.3 12:35
  25 run_b.log
  28 run_c.log
  53 total
24
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14953134 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
13

Check run c epoch scores

+10:16
Recorded reasoning 129 words

Only one twotower process is still running—run b seems to have died silently after 24 cutoffs, likely OOM-killed since running two processes concurrently building large arrays could have exceeded the 15GB memory limit.

Run c is alive at epoch 1 with ndcg 0.0222, a bit lower than the earlier 0.0256 but that's expected with OneCycle's high initial lr; I'll let it run a few more epochs at 64s each. With roughly 22 minutes used and 38 remaining, I need to start the final refit by around minute 45, leaving until about minute 40 for experiments—meanwhile I'll draft the README and plan the final run to train on test-stage contexts with a fixed epoch count rather than relying on validation-based early stopping, then wait for run c's next couple epochs.

Model text

Run b died silently, most likely from memory pressure with two 31M-row builds in parallel. Run c is alive at 0.0222 after epoch 1. I'll let it continue and check again.

Tool callBashCheck run c epoch scores Returned

Tool call · Input

sleep 100; grep ep /app/solution/run_c.log; dmesg 2>/dev/null | grep -i -c kill

Tool response

contexts 143357, target pairs 3339206, prep 100s
ep 1 loss 9.3802 (64s) val ndcg@10 0.0222 (n=4263)
ep 2 loss 8.3837 (65s) val ndcg@10 0.0253 (n=4263)
0
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14952490 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
14

Check run c later epochs

+12:07
Recorded reasoning 172 words

Epoch 2 lands at 0.0253, close to run a's ep2 result of 0.0256, so I'll let epochs 3-5 finish before deciding. If run c peaks around 0.026-0.028 by epoch 3-4, I'm leaning toward a final config of ncut 24 with step 1, the best epoch count found, nonorg_w 0.5, wd 0.02, and dropout 0.3.

I'm not sure nonorg_w actually helps versus run b (which died), and there's no time to redo b properly — run a without nonorg got a comparable 0.0256 at ep2. With noisy NDCG over only ~4263 evaluated users, gains might just be noise anyway, especially since item embeddings as free parameters likely drive overfitting more than this knob.

I'll wait out the remaining ~3.5 minutes for run c to finish, draft the README skeleton in the meantime, then launch the final refit around minute 30 using the best epoch count — roughly 6 minutes for 4 epochs. Running the refit now in parallel risks memory contention like what killed run b, so I'll hold off until c completes.

Tool callBashCheck run c later epochs Returned

Tool call · Input

sleep 110; grep ep /app/solution/run_c.log | tail -3

Tool response

ep 2 loss 8.3837 (65s) val ndcg@10 0.0253 (n=4263)
ep 3 loss 7.8488 (64s) val ndcg@10 0.0237 (n=4263)
ep 4 loss 7.4093 (65s) val ndcg@10 0.0220 (n=4263)
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14951758 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
15

Launch heavier-regularization validation run

+14:08
Recorded reasoning 160 words

Performance peaks again at epoch 2 then starts overfitting, with the loss dropping fast as the model memorizes user-specific targets. This suggests I need stronger regularization — higher dropout on history, a smaller embedding dimension, larger weight decay, or fewer epochs — rather than just tuning the schedule length.

With ~31 minutes left and needing to launch the final refit by minute 42, I'm weighing whether to run one more validation experiment (drop 0.5, wd 0.1, epochs 3) before committing, given memory risk from concurrent runs that caused an earlier OOM. I'll sequence things safely: run the validation experiment alone first (~5.5 min), then launch the final refit with whatever configuration wins, aiming to land around minute 42.

I'll let run c finish its last epoch (about a minute left) before it writes out its validation results, but I'll go ahead and start the new run now since run c is past its memory-heavy phase and brief contention is acceptable.

Model text

Run c peaks at epoch 2 (0.0253) and then overfits. I'll run one more validation experiment with heavier regularization and a shorter schedule, then launch the final refit.

Tool callBashLaunch heavier-regularization validation run Returned

Tool call · Input

cd /app/solution; nohup python twotower.py --stage val --ncut 24 --step_days 1 --epochs 3 --eval_every 1 --wd 0.1 --drop 0.5 --nonorg_w 0.5 --out val_d.parquet > run_d.log 2>&1 &
sleep 90; grep -E "ep |best" run_c.log | tail -2; tail -2 run_d.log

Tool response

ep 5 loss 7.2149 (70s) val ndcg@10 0.0211 (n=4263)
best val ndcg@10 0.0253
  cutoff 22540400: hist users 7997, targets 135220
  cutoff 22454000: hist users 7977, targets 134736
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14950673 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
16

Score run c publicly and check run d progress

+15:41
Tool callBashScore run c publicly and check run d progress Returned

Tool call · Input

cd /app/solution; python /task/evaluate_public.py val_c.parquet | grep ndcg; sleep 100; grep ep run_d.log | tail -3

Tool response

    "quality_ndcg@10": 0.025267,
contexts 143357, target pairs 3339206, prep 100s
ep 1 loss 9.2551 (64s) val ndcg@10 0.0248 (n=4263)
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14950414 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
17

Wait for run d to finish

+17:29
Recorded reasoning 52 words

About 34 minutes elapsed, with epoch 1 at 0.0248 under the 3-epoch schedule; I'll let epochs 2 and 3 finish over the next couple minutes, then launch the final refit around minute 37 using the best config, which should wrap up by minute 43, leaving time for the README and submission validation.

Tool callBashWait for run d to finish Error

Tool call · Input

sleep 125; grep -E "ep |best" /app/solution/run_d.log | tail -3

Tool response · Tool error

Exit code 143
Command timed out after 2m 0s
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14950060 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
18

Read run d results

+19:31
Tool callBashRead run d results Returned

Tool call · Input

grep -E "ep |best" /app/solution/run_d.log | tail -3

Tool response

ep 2 loss 8.3299 (68s) val ndcg@10 0.0247 (n=4263)
ep 3 loss 7.9810 (64s) val ndcg@10 0.0230 (n=4263)
best val ndcg@10 0.0248
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14949909 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
19

Add early-stop flag and launch final test-stage refit

+19:42
Recorded reasoning 130 words

Heavier regularization doesn't actually improve things, and runs a and c land at essentially the same noise-level performance. With about 37 minutes spent, I decide to go with run c's configuration but trim to 2 epochs, and launch the final refit on combined train+val data for the test stage.

Actually, to exactly replicate the validated behavior, I add a --stop_after argument so the OneCycle schedule still runs its full 5-epoch plan but training breaks right after epoch 2 — safer than retraining with a shortened schedule. I launch the final run with ncut 24, step_days 1, epochs 5, stop_after 2, wd 0.02, drop 0.3, nonorg_w 0.5, outputting to submission_tt.parquet, expecting about 4.5 minutes total runtime, then plan to validate and copy results to submission.parquet while writing the README in parallel.

Recorded reasoning 32 words

Neither stronger regularization nor extra epochs outperformed the epoch-2 checkpoint from run c, so I'll add a stop-after flag to reproduce that exactly and launch the final refit on train plus validation.

Tool callBashAdd early-stop flag and launch final test-stage refit Returned

Tool call · Input

python - <<'EOF'
s=open('twotower.py').read()
s=s.replace("ap.add_argument('--nonorg_w', type=float, default=0.0)","ap.add_argument('--nonorg_w', type=float, default=0.0); ap.add_argument('--stop_after', type=int, default=0)")
s=s.replace("        print(msg, flush=True)\n","        print(msg, flush=True)\n        if a.stop_after and ep + 1 >= a.stop_after: break\n")
open('twotower.py','w').write(s)
EOF
grep -n "stop_after" twotower.py | head; nohup python twotower.py --stage test --ncut 24 --step_days 1 --epochs 5 --stop_after 2 --wd 0.02 --drop 0.3 --nonorg_w 0.5 --out submission_tt.parquet > run_final.log 2>&1 &
echo launched

Tool response

203:    ap.add_argument('--seed', type=int, default=0); ap.add_argument('--nonorg_w', type=float, default=0.0); ap.add_argument('--stop_after', type=int, default=0)
256:        if a.stop_after and ep + 1 >= a.stop_after: break
launched
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14948755 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
20

Write README and check final refit progress

+20:02
Tool callBashWrite README and check final refit progress Returned

Tool call · Input

cp val_c.parquet validation.parquet && cat > README.md <<'EOF'
# Two-tower "Made for You" ranker (CPU PyTorch)

All code lives in this directory: `prep.py` (raw parquet -> compact catalog-indexed
numpy arrays), `twotower.py` (context building, model, training, ranking) and
`fallback.py` (popularity-only safety submission, superseded by the model output).

## Architecture (`twotower.py`)

Both towers output a vector of dimension d+1 = 65 and the relevance score is exactly the
dot product `user_vec . item_vec`, so item vectors are precomputed offline and served by
maximum-inner-product search; one user-tower forward pass per request.

* **Item tower**: 64-d item-id embedding + standardised log popularity -> residual MLP
  (65->128->64) giving `v_i`; a second small MLP gives a scalar bias `b_i`.
  `item_vec = [v_i, b_i]`.
* **User tower** (no user-id lookup): the user's 200 most recent distinct positive-listen
  catalog items (played >= 50%), each with 5 behaviour features
  (log count, log recency in days, active-like flag, organic fraction, dislike flag).
  Item embeddings (table shared with the item tower) are modulated by a feature-conditioned
  gate `(1 + tanh(W f))`, pooled with feature-derived attention weights (softmax over the
  history), passed through a residual MLP together with log history length to give `u`.
  `user_vec = [u, 1]`. History dropout 0.3 during training.
* Nothing user x item is ever materialised globally: a batch only touches the
  `B x 200` padded histories of its users, so the recipe scales to millions of users/items.

## Training objective

Temporal multi-window training. For 24 daily-spaced cutoffs `c` (newest = end-7d),
history = events before `c`, targets = items the user had never interacted with before `c`
that in `[c, c+7d)` received an organic like (weight 3), an organic listen >= 80%
(weight 1) or a non-organic listen >= 80% (weight 0.5). The loss is a graded listwise
softmax over the full 30k catalog (history items masked out of the softmax):
`-sum_j w_j log softmax(u . V^T)_j / sum_j w_j`, AdamW (lr 2e-3 one-cycle, wd 0.02),
batch 256. The model overfits after ~2 epochs, so training stops after epoch 2
(selected on validation NDCG).

At ranking time, the user tower is applied to the history up to the cutoff, scores are
taken against all item vectors, ineligible items (from the provided `*_eligible_candidates.npz`)
are masked and the top 100 are emitted.

## Results (public validation window, `python /task/evaluate_public.py validation.parquet`)

| ranker | NDCG@10 (4263 users) |
| --- | --- |
| popularity order (eligible) | 0.0107 |
| two-tower, 8 weekly cutoffs, 6-epoch schedule, best epoch 2 | 0.0256 |
| **two-tower, 24 daily cutoffs, weak non-organic targets, epoch 2 (selected)** | **0.0253** |
| same, wd 0.1 / dropout 0.5 / 3-epoch schedule | 0.0248 |

`validation.parquet` is the selected validation-stage ranking (history = train only).

## Commands

```sh
python prep.py                                             # once
# validation-stage model and ranking (train history -> val outcomes)
python twotower.py --stage val  --ncut 24 --step_days 1 --epochs 5 --stop_after 2 \
    --wd 0.02 --drop 0.3 --nonorg_w 0.5 --out validation.parquet
python /task/evaluate_public.py /app/solution/validation.parquet
# final submission: refit on train + val, rank against test eligibility
python twotower.py --stage test --ncut 24 --step_days 1 --epochs 5 --stop_after 2 \
    --wd 0.02 --drop 0.3 --nonorg_w 0.5 --out submission_tt.parquet
cp submission_tt.parquet submission.parquet
```
EOF
sleep 60; tail -2 run_final.log

Tool response

  cutoff 23319800: hist users 8164, targets 139038
  cutoff 23233400: hist users 8142, targets 139447
Harness context silent turn reminder
{
  "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."
}
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14947080 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
21

Write submission checker and wait for final training

+21:06
Recorded reasoning 51 words

About 41 minutes have passed, with final run prep wrapping up soon and two epochs taking roughly 2.5 minutes after that. I'll wait around 3.5 minutes total, then validate results and copy them over, and in the meantime I'm writing a submission validator script that checks ranking validity and test eligibility.

Tool callBashWrite submission checker and wait for final training Returned

Tool call · Input

cd /app/solution; cat > check_submission.py <<'EOF'
import sys; sys.path.insert(0,'/task')
import pandas as pd, numpy as np
from pathlib import Path
from ranking_evaluation import CandidateSet, read_ranking, validate_ranking
p=Path(sys.argv[1]); targets=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
f=validate_ranking(read_ranking(p), targets); el=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))
ok=all(el.is_eligible(int(u),int(i)) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))
print('rows',len(f),'users',f.uid.nunique(),'all eligible',ok)
EOF
sleep 150; grep -E "contexts|ep |wrote" run_final.log

Tool response

Command did not complete within its 120s timeout and was moved to the background (ID: bqw765nmn). Output is being written to: /tmp/claude-1000/-app/c1e2970a-793b-420c-b6f6-9930e2c35ef3/tasks/bqw765nmn.output. You will be notified when it completes. If it is still running after 30m in the background, it will be stopped and you will be notified. To check interim output, use Read on that file path.
Session cwd remains /app/solution; directory changes made by the backgrounded command do not apply to subsequent commands.
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14946477 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
22

Check final training status

+23:10
Tool callBashCheck final training status Returned

Tool call · Input

sleep 45; grep -E "contexts|ep |wrote" /app/solution/run_final.log; ls -la /app/solution/*.parquet

Tool response

building contexts
contexts 146499, target pairs 3346208, prep 98s
ep 1 loss 9.4201 (64s)
ep 2 loss 8.4364 (66s)
wrote submission_tt.parquet; total 231s
-rw-r--r-- 1 agent agent  735840 Oct  3 08:15 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent 1645427 Oct  3 08:36 /app/solution/submission_tt.parquet
-rw-r--r-- 1 agent agent 1639424 Oct  3 08:27 /app/solution/val_c.parquet
-rw-r--r-- 1 agent agent 1400556 Oct  3 08:32 /app/solution/val_d.parquet
-rw-r--r-- 1 agent agent  728599 Oct  3 08:19 /app/solution/val_pop.parquet
-rw-r--r-- 1 agent agent 1639424 Oct  3 08:33 /app/solution/validation.parquet
Harness context queued command
{
  "type": "queued_command",
  "prompt": "<task-notification>\n<task-id>bqw765nmn</task-id>\n<tool-use-id>toolu_01UDwaNaoDoJhobeJcuUTyBe</tool-use-id>\n<output-file>/tmp/claude-1000/-app/c1e2970a-793b-420c-b6f6-9930e2c35ef3/tasks/bqw765nmn.output</output-file>\n<status>completed</status>\n<summary>Background command \"Write submission checker and wait for final training\" completed (exit code 0)</summary>\n</task-notification>",
  "source_uuid": "b24e9965-73ea-4905-b992-36104f646315",
  "delivery_id": "3ce8accd-3073-4bba-b69b-a7928155d32c",
  "commandMode": "task-notification",
  "origin": {
    "kind": "task-notification",
    "producer": "session-task"
  },
  "timestamp": "2026-10-03T08:36:37.946Z"
}
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14946143 tokens left</total_tokens>"
}
23

Validate and install final submission

+23:57

Model text

The final refit finished. Validating the submission and installing it over the popularity fallback.

Tool callBashValidate and install final submission Returned

Tool call · Input

python check_submission.py submission_tt.parquet && cp submission_tt.parquet submission.parquet && python check_submission.py submission.parquet && python - <<'EOF'
import pandas as pd
s=pd.read_parquet('/app/solution/submission.parquet'); print(s.dtypes.to_dict()); print(s.head(3))
# sanity: overlap of final top-10 with validation-stage top-10 for same users
v=pd.read_parquet('/app/solution/validation.parquet')
a=s[s['rank']<=10].groupby('uid').item_id.apply(set); b=v[v['rank']<=10].groupby('uid').item_id.apply(set)
print('mean top10 overlap val-vs-test rankers', (a.combine(b, lambda x,y: len(x&y)/10)).mean())
EOF
rm -f val_pop.parquet nohup.out; ls

Tool response

rows 845600 users 8456 all eligible True
rows 845600 users 8456 all eligible True
{'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}
   uid  item_id  rank
0  100  4948220     1
1  100  5488609     2
2  100  8250570     3
mean top10 overlap val-vs-test rankers 0.3192526017029328
README.md
check_submission.py
fallback.py
items.npy
model_test.pt
model_val.pt
prep.py
run_b.log
run_c.log
run_d.log
run_final.log
submission.parquet
submission_tt.parquet
train_dislikes.npz
train_likes.npz
train_listens.npz
train_undislikes.npz
train_unlikes.npz
twotower.py
users.npy
val_c.parquet
val_d.parquet
val_dislikes.npz
val_likes.npz
val_listens.npz
val_undislikes.npz
val_unlikes.npz
validation.parquet
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14945047 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
24

Finalize README and reconfirm validation score

+24:10
Recorded reasoning 24 words

I'm about 47 minutes in now. I should add a quick note to the README mentioning check_submission.py, then wrap up with a final recap.

Tool callBashFinalize README and reconfirm validation score Returned

Tool call · Input

cat >> README.md <<'EOF'

`python check_submission.py submission.parquet` re-validates the artifact (8456 users x 100
rows, ranks 1-100, all items test-eligible). The final `submission.parquet` was produced
by the test-stage command above (train + val history, 2 epochs, 231 s on 8 CPU threads).
EOF
python /task/evaluate_public.py validation.parquet | grep ndcg

Tool response

    "quality_ndcg@10": 0.025267,
Harness context total tokens reminder
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14944374 tokens left</total_tokens>"
}
Harness context batching reminder sent
{
  "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"
}
25

Final response

+24:23

Model text

The two-tower ranker is built, validated, refit on train plus validation, and the final submission is in place.

**Deliverables in /app/solution/**

  • submission.parquet passes the checker: 8456 users, 100 ranks each, every item test-eligible.
  • twotower.py defines, trains, and applies both towers. prep.py converts the raw logs. README.md documents architecture, objective, scores, and commands.
  • validation.parquet is the validation-stage ranking scored by the public evaluator.

Model

  • Item tower: 64-d item embedding plus log popularity through a residual MLP, with a learned bias slot.
  • User tower: no user-ID lookup. It pools the user's 200 most recent distinct positive catalog listens with feature-driven attention over count, recency, like state, organic fraction, and dislike flags, then a residual MLP. The user vector carries a constant 1 so the bias folds into a plain dot product.
  • Training: 24 daily-spaced temporal cutoffs with 7-day target windows, graded listwise softmax over the full 30k catalog, history items masked out. Targets are first-time organic likes (weight 3), organic 80% listens (weight 1), and non-organic 80% listens (weight 0.5). No global user-item matrix is built.

Validation NDCG@10

rankerscore
popularity baseline0.0107
selected two-tower0.0253

All variants overfit after two epochs, so the schedule stops at epoch 2. Heavier regularization scored slightly lower at 0.0248. One parallel experiment died during preprocessing, most likely from memory pressure, so the comparison without weak non-organic targets at 24 cutoffs was not completed. The 8-cutoff run without them scored 0.0256, within noise of the selected model.

Recorded session usage
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    "totalAPIDurationWithoutRetries": 312352,
    "totalToolDuration": 1150115,
    "totalLinesAdded": 0,
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    "totalDuration": 1463530,
    "startTime": 1791015179333,
    "modelUsage": {
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        "thinkingTokens": 9833,
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About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
fable51-high-2-1-288-base-r04-20261003T075109Z
Recorded model
claude-fable-5-1
Harness version
2.1.288
Session
c1e2970a-793b-420c-b6f6-9930e2c35ef3
Source SHA-256
c24375e23905eee16a75afbf90d0e11fcf8265b541b5f40403e9c87bb4c791e7

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.