{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-case-1", "attemptId": "fable51-high-2-1-288-case1-r05-20261003T075109Z-queued", "taskChecksum": "8404d794ce8e18d154cd225cfe6b12bf6b4ee02aaefba1d315aa215c1175a013", "source": { "format": "claude-code-session", "sha256": "dfb0ab83fc93b3b92764e6883384df279458cbc09a080cabdd8724a31bc611b8", "records": 284, "bookkeepingRecords": { "queue-operation": 12, "atis-latch": 16, "last-prompt": 16 }, "usageRecords": [ { "type": "cost-state", "sessionId": "2c3ab22b-45d7-4732-94f9-353c8285f8fd", "totalCostUSD": 4.204671500000001, "totalAPIDuration": 579315, "totalAPIDurationWithoutRetries": 579234, "totalToolDuration": 1031206, "totalLinesAdded": 0, "totalLinesRemoved": 0, "totalDuration": 1616833, "startTime": 1791018656270, "modelUsage": { "claude-fable-5-1": { "inputTokens": 1290, "outputTokens": 35934, "thinkingTokens": 11029, "cacheReadInputTokens": 3229726, "cacheCreationInputTokens": 79382, "webSearchRequests": 0, "costUSD": 4.204671500000001 } }, "hasUnknownModelCost": false } ] }, "sessionId": "2c3ab22b-45d7-4732-94f9-353c8285f8fd", "agentVersion": "2.1.288", "model": "claude-fable-5-1", "steps": [ { "id": 1, "role": "user", "timestamp": "2026-10-03T09:10:56.705Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:10:56.705Z", "text": "# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n ranking script. It can reproduce the public validation ranking and produce a\n hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n `/task/data/test_preference_state.parquet`: active explicit preference state\n at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n the deadline.\n" }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" } } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "model", "identity": { "modelId": "claude-fable-5-1", "marketingName": "Fable 5.1", "knowledgeCutoff": "June 2026" }, "text": "You are powered by the model named Fable 5.1. The exact model ID is claude-fable-5-1. Assistant knowledge cutoff is June 2026." } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "deferred_tools_delta", "addedNames": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "addedLines": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "removedNames": [], "wireHiddenNames": [], "readdedNames": [], "pendingMcpServers": [], "needsAuthMcpServers": [], "failedMcpServers": [] } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "agent_listing_delta", "addedTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "addedLines": [ "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)", "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)", "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)" ], "builtInTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "removedTypes": [], "isInitial": true, "showConcurrencyNote": true } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "skill_listing", "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. Pass --max-findings to report up to n findings, or --max-findings all for every finding. The choice stays until you pass --max-findings default.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- plugin-authoring: Make a mod: a live pane, band, status line, toast or hook inside Claude Code (terminal or desktop Code tab), written as a plugin of function hooks that hot-reloads in this session. Load before writing or debugging a hooks module.\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch", "skillCount": 14, "isInitial": true, "names": [ "dataviz", "update-config", "keybindings-help", "code-review", "simplify", "fewer-permission-prompts", "loop", "schedule", "claude-api", "workflow-authoring", "run", "plugin-authoring", "init", "security-review" ] } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "auto_mode", "autoModeConsentFlow": false, "bashFirst": true, "bashFirstSteer": "strict", "steerOnly": true, "bypass": true } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.705Z", "data": { "type": "total_tokens_reminder", "text": "15000000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.722Z", "data": { "type": "session_context", "context": {} } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.722Z", "data": { "type": "date", "date": "2026-10-03" } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.723Z", "data": { "type": "remote_session_change", "url": null, "commit": "Co-Authored-By: Claude Fable 5.1 ", "pr": "🤖 Generated with [Claude Code](https://claude.com/claude-code)", "sendUserFileHint": false, "managedCommit": false, "managedPr": false } }, { "kind": "context", "timestamp": "2026-10-03T09:10:56.724Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "reminderFold": false, "echoWireToolInputs": false, "contextRendering": "announced" } } ] }, { "id": 2, "role": "agent", "timestamp": "2026-10-03T09:11:00.868Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:11:00.868Z", "text": "I should start by exploring the environment first, keeping in mind I have 60 minutes to work efficiently.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:11:00.985Z", "text": "I'll start by inspecting the production trainer, model primitives, and data layout, then reproduce the baseline backtest." }, { "kind": "tool", "timestamp": "2026-10-03T09:11:02.783Z", "callId": "toolu_01AJVneNhxVQsT125hfVvTap", "name": "Bash", "input": { "command": "ls -la /task /task/current_model /task/data /app /app/solution 2>/dev/null; nproc; free -g; cat /task/requirements.txt; wc -l /task/current_model/*.py /task/evaluate_public.py", "description": "List task files and environment resources" }, "result": { "timestamp": "2026-10-03T09:11:02.859Z", "content": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Oct 3 09:06 .\ndrwxr-xr-x 1 root root 4096 Oct 3 09:09 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:06 solution\n\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:06 .\ndrwxr-xr-x 3 agent agent 4096 Oct 3 09:06 ..\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Oct 3 09:06 .\ndrwxr-xr-x 1 root root 4096 Oct 3 09:09 ..\n-r--r--r-- 1 root root 0 Oct 3 09:04 .keep\ndr-xr-xr-x 1 root root 4096 Oct 3 09:04 current_model\ndr-xr-xr-x 1 root root 4096 Oct 3 09:05 data\n-r--r--r-- 1 root root 5774 Oct 3 09:04 evaluate_public.py\n-r--r--r-- 1 root root 8265 Oct 3 09:04 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Oct 3 09:04 requirements.txt\n\n/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root 4096 Oct 3 09:04 .\ndr-xr-xr-x 1 root root 4096 Oct 3 09:06 ..\n-r--r--r-- 1 root root 80058 Oct 3 09:04 train_current_model.py\n-r--r--r-- 1 root root 15985 Oct 3 09:04 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Oct 3 09:05 .\ndr-xr-xr-x 1 root root 4096 Oct 3 09:06 ..\n-r--r--r-- 1 root root 349352 Oct 3 09:04 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Oct 3 09:04 target_users.parquet\n-r--r--r-- 1 root root 7736341 Oct 3 09:04 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Oct 3 09:05 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Oct 3 09:04 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Oct 3 09:04 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Oct 3 09:04 train_listens.parquet\n-r--r--r-- 1 root root 155988 Oct 3 09:04 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Oct 3 09:04 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Oct 3 09:04 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Oct 3 09:04 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Oct 3 09:04 val_listens.parquet\n-r--r--r-- 1 root root 7845 Oct 3 09:04 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Oct 3 09:04 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Oct 3 09:04 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Oct 3 09:05 validation_preference_state.parquet\n8\n total used free shared buff/cache available\nMem: 15 1 0 0 13 14\nSwap: 0 0 0\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n 2164 /task/current_model/train_current_model.py\n 433 /task/current_model/two_tower_model.py\n 157 /task/evaluate_public.py\n 2754 total", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:02.861Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "tools": [ { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "schema": { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "description": { "description": "A short (3-5 word) description of the task", "type": "string" }, "prompt": { "description": "The task for the agent to perform", "type": "string" }, "subagent_type": { "description": "The type of specialized agent to use for this task", "type": "string" }, "model": { "description": "Optional model override for this agent. Takes precedence over the agent definition's model frontmatter and the configured default subagent model. If omitted, uses the agent definition's model, else the default (inherits from the parent unless a default subagent model is configured). Ignored for subagent_type: \"fork\" — forks always inherit the parent model.", "type": "string", "enum": [ "sonnet", "opus", "haiku", "fable" ] }, "run_in_background": { "description": "Agents run in the background by default; you will be notified when one completes. Set to false only when your very next action depends on this agent's result and nothing else could usefully happen while it runs — otherwise leave it in the background so the user can hand you other work.", "type": "boolean" }, "isolation": { "description": "Isolation mode. \"worktree\" creates a temporary git worktree so the agent works on an isolated copy of the repo. \"remote\" launches the agent in a remote cloud environment (always runs in background; availability is gated).", "type": "string", "enum": [ "worktree", "remote" ] } }, "required": [ "description", "prompt" ], "additionalProperties": false } } }, { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "schema": { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "command": { "description": "The command to execute", "type": "string" }, "timeout": { "description": "Optional timeout in milliseconds (max 600000 for a foreground command)", "type": "number" }, "description": { "description": "Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"", "type": "string" }, "run_in_background": { "description": "Set to true to run this command in the background. With it, `timeout` limits how long the command may run in the background before it is stopped (default 1800000 ms, max 7200000 ms).", "type": "boolean" }, "dangerouslyDisableSandbox": { "description": "Set this to true to dangerously override sandbox mode and run commands without sandboxing.", "type": "boolean" } }, "required": [ "command" ], "additionalProperties": false } } }, { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "schema": { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to modify", "type": "string" }, "old_string": { "description": "The text to replace", "type": "string" }, "new_string": { "description": "The text to replace it with (must be different from old_string)", "type": "string" }, "replace_all": { "description": "Replace all occurrences of old_string (default false)", "default": false, "type": "boolean" } }, "required": [ "file_path", "old_string", "new_string" ], "additionalProperties": false } } }, { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "schema": { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "channel": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 }, "q": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 } }, "additionalProperties": false } } }, { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "schema": { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to read", "type": "string" }, "offset": { "description": "The line number to start reading from. Only provide if the file is too large to read at once", "type": "integer", "minimum": 0, "maximum": 9007199254740991 }, "limit": { "description": "The number of lines to read. Only provide if the file is too large to read at once.", "type": "integer", "exclusiveMinimum": 0, "maximum": 9007199254740991 }, "pages": { "description": "Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.", "type": "string" } }, "required": [ "file_path" ], "additionalProperties": false } } }, { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "schema": { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "level": { "description": "Effort level the review ran at", "type": "string", "enum": [ "low", "medium", "high", "xhigh", "max" ] }, "findings": { "description": "Verified findings, most-severe first; empty if none survived", "maxItems": 32, "type": "array", "items": { "type": "object", "properties": { "file": { "description": "Repo-relative path of the file the finding is in", "type": "string" }, "line": { "description": "1-indexed line the finding anchors to", "type": "integer", "minimum": -9007199254740991, "maximum": 9007199254740991 }, "summary": { "description": "One-sentence statement of the defect", "type": "string" }, "short_summary": { "description": "Compressed label for compact UI (≤60 chars): the claim alone, no rationale or consequence clause", "type": "string", "maxLength": 60 }, "failure_scenario": { "description": "Concrete inputs/state → wrong output/crash", "type": "string" }, "category": { "description": "Short kebab-case slug of the finding type, e.g. \"correctness\", \"simplification\", \"efficiency\", \"test-coverage\"", "type": "string", "maxLength": 40 }, "verdict": { "description": "Set when a verify pass ran; absent on inline-only reviews", "type": "string", "enum": [ "CONFIRMED", "PLAUSIBLE" ] }, "outcome": { "description": "Set ONLY when re-reporting after applying fixes: what happened to this finding", "type": "string", "enum": [ "fixed", "skipped", "no_change_needed" ] } }, "required": [ "file", "summary", "failure_scenario" ], "additionalProperties": false } } }, "required": [ "findings" ], "additionalProperties": false } } }, { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "schema": { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "delaySeconds": { "description": "Seconds from now to wake up. Clamped to [60, 3600] by the runtime. Required unless `stop` is true.", "type": "number" }, "reason": { "description": "One short sentence explaining the chosen delay. Goes to telemetry and is shown to the user. Be specific. Required unless `stop` is true.", "type": "string" }, "prompt": { "description": "The /loop input to fire on wake-up. Pass the same /loop input verbatim each turn so the next firing re-enters the skill and continues the loop. For autonomous /loop (no user prompt), pass the literal sentinel `<>` instead (the dynamic-pacing variant, not the CronCreate-mode `<>`). Required unless `stop` is true.", "type": "string" }, "stop": { "description": "Set to true to end the dynamic loop immediately instead of scheduling another wakeup. When true, all other fields are ignored and no further wakeups fire.", "type": "boolean" }, "noop": { "description": "true = nothing changed (you checked and there is nothing to report). false = something happened worth keeping (edited a file, posted a message, advanced state, surfaced a finding). Consecutive noop:true ticks are collapsed in the user's terminal view and tracked as a streak. Required unless `stop` is true.", "type": "boolean" } }, "additionalProperties": false } } }, { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "schema": { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "skill": { "description": "The name of a skill from the available-skills list. Do not guess names.", "type": "string" }, "args": { "description": "Optional arguments for the skill", "type": "string" } }, "required": [ "skill" ], "additionalProperties": false } } }, { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "schema": { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "query": { "description": "Query to find deferred tools. Use \"select:\" for direct selection, or keywords to search.", "type": "string" }, "max_results": { "description": "Maximum number of results to return (default: 5)", "default": 5, "type": "number" } }, "required": [ "query", "max_results" ], "additionalProperties": false } } }, { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "schema": { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "script": { "description": "Self-contained workflow script. Must begin with `export const meta = { name, description, phases }` (pure literal, no computed values) followed by the script body using agent()/parallel()/pipeline()/phase().", "type": "string", "maxLength": 524288 }, "name": { "description": "Name of a predefined workflow (built-in or from .claude/workflows/). Resolves to a self-contained script.", "type": "string" }, "description": { "description": "Ignored — set the workflow description in the script's `meta` block.", "type": "string" }, "title": { "description": "Ignored — set the workflow title in the script's `meta` block.", "type": "string" }, "args": { "description": "Optional input value exposed to the script as the global `args`, verbatim. Pass arrays/objects as actual JSON values, NOT as a JSON-encoded string — a stringified list breaks `args.filter`/`args.map` in the script. Use for parameterized named workflows (e.g. a research question)." }, "scriptPath": { "description": "Path to a workflow script file on disk. Every Workflow invocation persists its script under the session directory and returns the path in the tool result. To iterate, edit that file with Write/Edit and re-invoke Workflow with the same `scriptPath` instead of re-sending the full script. Takes precedence over `script` and `name`.", "type": "string" }, "resumeFromRunId": { "description": "Run ID of a prior Workflow invocation to resume from. Completed agent() calls with unchanged (prompt, opts) return their cached results instantly; only edited or new calls re-run. Same-session only. Stop the prior run first (TaskStop) before resuming.", "type": "string", "pattern": "^wf_[a-z0-9-]{6,}$" } }, "additionalProperties": false } } }, { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "schema": { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to write (must be absolute, not relative)", "type": "string" }, "content": { "description": "The content to write to the file", "type": "string" } }, "required": [ "file_path", "content" ], "additionalProperties": false } } } ], "cliPrefix": "You are a Claude agent, built on Anthropic's Claude Agent SDK.", "reminderFold": false, "systemTurns": true, "toolChangeHeader": true, "inlineTools": false, "keptReminders": true, "echoWireToolInputs": false, "contextRendering": "announced" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:02.863Z", "data": { "type": "total_tokens_reminder", "text": "14979817 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:02.866Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 3, "role": "agent", "timestamp": "2026-10-03T09:11:07.269Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:11:07.269Z", "callId": "toolu_01Bvbsyfq1AZHvLNz8xumDkY", "name": "Bash", "input": { "command": "cat /task/current_model/two_tower_model.py; cat /task/evaluate_public.py", "description": "Read model primitives and public evaluator" }, "result": { "timestamp": "2026-10-03T09:11:07.290Z", "content": "\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n sequence_length: int = 100\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n timestamps: np.ndarray\n items: np.ndarray\n organic: np.ndarray\n completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n timestamps: np.ndarray\n items: np.ndarray\n channel: np.ndarray\n active: np.ndarray\n organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n features = np.column_stack(\n [\n np.log1p(positives),\n np.log1p(organic),\n organic / np.maximum(positives, 1.0),\n 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n ]\n )\n means = features.mean(axis=0, keepdims=True)\n scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n standardized = ((features - means) / scales).astype(np.float32)\n if not np.isfinite(standardized).all():\n raise RuntimeError(\"catalog features contain non-finite values\")\n return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n if len(parts) == 1:\n return parts[0]\n timestamps = np.concatenate([part.timestamps for part in parts])\n items = np.concatenate([part.items for part in parts])\n organic = np.concatenate([part.organic for part in parts])\n completion = np.concatenate([part.completion_pct for part in parts])\n order = np.argsort(timestamps, kind=\"stable\")\n return ListenEvents(\n timestamps=timestamps[order],\n items=items[order],\n organic=organic[order],\n completion_pct=completion[order],\n )\n\n\ndef iter_listen_users(\n path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n parquet = pq.ParquetFile(path)\n pending_uid: int | None = None\n pending: list[ListenEvents] = []\n previous_uid = -1\n for batch in parquet.iter_batches(\n batch_size=750_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n ):\n frame = batch.to_pandas()\n mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n if not np.any(keep):\n continue\n uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n items = mapped[keep].astype(np.int32, copy=False)\n organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n if np.any(uids[1:] < uids[:-1]):\n raise RuntimeError(f\"{path.name} is not sorted by uid\")\n boundaries = np.r_[\n 0,\n np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n len(uids),\n ]\n for start, end in zip(boundaries[:-1], boundaries[1:]):\n uid = int(uids[start])\n part = ListenEvents(\n timestamps[start:end].copy(),\n items[start:end].copy(),\n organic[start:end].copy(),\n completion[start:end].copy(),\n )\n if pending_uid is None:\n pending_uid = uid\n elif uid != pending_uid:\n if pending_uid < previous_uid:\n raise RuntimeError(f\"{path.name} user order regressed\")\n yield pending_uid, _combine_listen_chunks(pending)\n previous_uid = pending_uid\n pending_uid = uid\n pending = []\n pending.append(part)\n if pending_uid is not None:\n yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n heads: list[tuple[int, ListenEvents] | None] = [\n next(stream, None) for stream in streams\n ]\n while any(head is not None for head in heads):\n uid = min(head[0] for head in heads if head is not None)\n parts: list[ListenEvents] = []\n for position, head in enumerate(heads):\n if head is not None and head[0] == uid:\n parts.append(head[1])\n heads[position] = next(streams[position], None)\n yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n data: Path,\n prefixes: tuple[str, ...],\n item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n specifications = (\n (\"likes\", 0, 1, 0),\n (\"unlikes\", 0, 0, 1),\n (\"dislikes\", 1, 1, 0),\n (\"undislikes\", 1, 0, 1),\n )\n parts: list[pd.DataFrame] = []\n for family, channel, active, tie_priority in specifications:\n for prefix in prefixes:\n frame = pd.read_parquet(\n data / f\"{prefix}_{family}.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n )\n mapped = item_index.get_indexer(\n frame.item_id.to_numpy(dtype=np.int64, copy=False)\n )\n keep = mapped >= 0\n if not np.any(keep):\n continue\n retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n retained[\"channel\"] = np.int8(channel)\n retained[\"active\"] = np.int8(active)\n retained[\"tie_priority\"] = np.int8(tie_priority)\n retained[\"organic_like\"] = np.int8(\n family == \"likes\"\n ) * retained.is_organic.astype(np.int8)\n parts.append(retained.drop(columns=\"is_organic\"))\n if not parts:\n return {}\n events = pd.concat(parts, ignore_index=True)\n events = events.sort_values(\n [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n ).reset_index(drop=True)\n result: dict[int, ExplicitEvents] = {}\n for uid, rows in events.groupby(\"uid\", sort=False):\n result[int(uid)] = ExplicitEvents(\n timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n items=rows.item.to_numpy(dtype=np.int32, copy=True),\n channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n active=rows.active.to_numpy(dtype=np.int8, copy=True),\n organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n )\n log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n return ExplicitEvents(\n timestamps=np.empty(0, dtype=np.int64),\n items=np.empty(0, dtype=np.int32),\n channel=np.empty(0, dtype=np.int8),\n active=np.empty(0, dtype=np.int8),\n organic_like=np.empty(0, dtype=np.int8),\n )\n\n\ndef pack_active_items(\n active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n packed = np.full(width, padding_item, dtype=np.uint16)\n if not active:\n return packed\n ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n packed[: len(ordered)] = np.fromiter(\n (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n )\n return packed\n\n\ndef load_inference_state(\n path: Path,\n item_index: pd.Index,\n uids: np.ndarray,\n n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"Pack the task-provided active explicit state.\"\"\"\n state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n keep = mapped >= 0\n state = state.loc[keep, [\"uid\", \"state\"]].copy()\n state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n user_rows = pd.Index(uids)\n liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n like_counts = np.zeros(len(uids), dtype=np.int32)\n dislike_counts = np.zeros(len(uids), dtype=np.int32)\n for value, width, destination, counts in (\n (\"liked\", LIKE_ITEMS, liked, like_counts),\n (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n ):\n selected = state[state.state == value]\n for uid, rows in selected.groupby(\"uid\", sort=False):\n row = int(user_rows.get_indexer([int(uid)])[0])\n if row < 0:\n continue\n items = np.sort(rows.item.unique())\n counts[row] = len(items)\n retained = items[:width]\n destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:07.294Z", "data": { "type": "total_tokens_reminder", "text": "14977519 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:07.296Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 4, "role": "agent", "timestamp": "2026-10-03T09:11:12.229Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:11:12.229Z", "text": "The training script is 2164 lines, so I should kick off the baseline run in the background right away and read through the script in parallel, focusing on the training objective and negative sampling logic while it runs.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:11:12.231Z", "text": "The trainer is long, so I'll kick off the baseline backtest in the background now and read the script while it runs." }, { "kind": "tool", "timestamp": "2026-10-03T09:11:14.742Z", "callId": "toolu_01MxC3Jp5B3DyJuheySwr4Ud", "name": "Bash", "input": { "command": "cp /task/current_model/*.py /app/solution/ && cd /app/solution && mkdir -p baseline && (time python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/baseline/production_validation.parquet) > /app/solution/baseline/run.log 2>&1; python /task/evaluate_public.py /app/solution/baseline/production_validation.parquet >> /app/solution/baseline/run.log 2>&1", "description": "Run production baseline backtest in background", "timeout": 3600000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T09:11:14.761Z", "content": "Command running in background with ID: b7kvy5zrh. Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/b7kvy5zrh.output. You will be notified when it completes. To check interim output, use Read on that file path.\nSession cwd remains /app; directory changes made by the backgrounded command do not apply to subsequent commands.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:14.765Z", "data": { "type": "total_tokens_reminder", "text": "14968370 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:14.768Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 5, "role": "agent", "timestamp": "2026-10-03T09:11:18.320Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:11:18.320Z", "callId": "toolu_01Ru23MboWHmemrD3FxnWHkH", "name": "Bash", "input": { "command": "cd /task/current_model && grep -n \"^def \\|^class \\|^ def \\|argparse\\|add_argument\" train_current_model.py | head -150", "description": "Outline functions and CLI arguments of the trainer" }, "result": { "timestamp": "2026-10-03T09:11:18.348Z", "content": "20:import argparse\n41:class Config:\n76:class CandidateSet:\n82: def read(cls, path: Path) -> \"CandidateSet\":\n99:class SequenceData:\n118: def lengths(self) -> np.ndarray:\n122: def target_count(self) -> int:\n127:class TargetPartition:\n136: def selected_count(self) -> int:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n609:def invalid_full_history_negatives(\n630:def draw_full_history_filtered_negatives(\n724:def draw_training_negatives(\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n828:def history_windows(\n845:def corrected_negative_loss(\n867:def retained_target_window_starts(\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1364:def evaluate_validation_bce(\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1923: parser = argparse.ArgumentParser(description=__doc__)\n1924: parser.add_argument(\n1927: parser.add_argument(\"--output\", type=Path, required=True)\n1928: parser.add_argument(\"--public-dir\", type=Path, required=True)\n1929: parser.add_argument(\"--prepared-cache\", type=Path)\n1930: parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n1931: parser.add_argument(\"--threads\", type=int, default=8)\n1932: parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n1933: parser.add_argument(\n1936: parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n1937: parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n1938: parser.add_argument(\n1943: parser.add_argument(\n1948: parser.add_argument(\n1953: parser.add_argument(\n1958: parser.add_argument(\n1963: parser.add_argument(\n1968: parser.add_argument(\n1973: parser.add_argument(\n1978: parser.add_argument(\n1984: parser.add_argument(\n1990: parser.add_argument(\n1995: parser.add_argument(\n2000: parser.add_argument(\"--checkpoint-dir\", type=Path)\n2001: parser.add_argument(\"--resume-checkpoint\", type=Path)\n2002: parser.add_argument(\"--seed\", type=int, default=Config.seed)\n2003: parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n2004: parser.add_argument(\"--prepare-only\", action=\"store_true\")\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:18.351Z", "data": { "type": "total_tokens_reminder", "text": "14968066 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:18.353Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 6, "role": "agent", "timestamp": "2026-10-03T09:11:22.347Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:11:22.347Z", "callId": "toolu_01Y9kiH8eA9MN45HsSF9Djka", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 1,175p train_current_model.py", "description": "Read trainer header, Config, and data classes" }, "result": { "timestamp": "2026-10-03T09:11:22.374Z", "content": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:22.378Z", "data": { "type": "total_tokens_reminder", "text": "14966603 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:22.381Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 7, "role": "agent", "timestamp": "2026-10-03T09:11:27.125Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:11:27.125Z", "callId": "toolu_01XLM5gLczea1iv22QWbu1yP", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 534,870p train_current_model.py", "description": "Read negative sampling and loss code" }, "result": { "timestamp": "2026-10-03T09:11:27.154Z", "content": "def build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:27.157Z", "data": { "type": "total_tokens_reminder", "text": "14964259 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:27.159Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-10-03T09:11:33.263Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:11:33.263Z", "text": "I want to keep reading through the training epoch logic, score_target_microbatches, train_model, and prepare_data, so I'll pull in larger chunks of the file at once.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:11:33.268Z", "callId": "toolu_017m5QW2fimJYVYdmsx8WRmh", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 1035,1365p train_current_model.py", "description": "Read dense block builder, microbatch scoring, and training epoch" }, "result": { "timestamp": "2026-10-03T09:11:33.298Z", "content": "def build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_microbatches(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n target_count = len(positive_items)\n total_weight = float(sample_weights.sum())\n if total_weight <= 0:\n raise RuntimeError(\"target block has no positive sample weight\")\n sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n statistics = {\n \"loss_sum\": 0.0,\n \"positive_sum\": 0.0,\n \"negative_sum\": 0.0,\n \"pair_correct\": 0.0,\n \"weight_sum\": 0.0,\n }\n for begin in range(0, target_count, config.loss_microbatch_size):\n end = min(begin + config.loss_microbatch_size, target_count)\n selected_states = flat_states[begin:end]\n if backward:\n sequence_input = (\n sequence_vectors[begin:end].detach().requires_grad_(True)\n )\n else:\n sequence_input = sequence_vectors[begin:end]\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, selected_states, device\n )\n counts = prepared.state_counts[selected_states]\n numeric = np.zeros((end - begin, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity[begin:end]\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_input,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item training produced non-finite loss\")\n if backward:\n (loss_sum / total_weight).backward()\n if sequence_input.grad is None:\n raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n sequence_gradient[begin:end] = sequence_input.grad\n statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:33.301Z", "data": { "type": "total_tokens_reminder", "text": "14959578 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:33.303Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-10-03T09:11:51.426Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:11:51.426Z", "text": "Now I'm diagnosing the loss setup: 24 uniform negatives plus 8 in-batch negatives with propensity-corrected weighting, using a BCE/softplus loss that weights positives and averages negatives. I'm noting that uniform sampling over a 30k catalog mostly yields trivially easy negatives from unpopular items, so the model likely just learns popularity rather than meaningful ranking signal.\n\nI'm considering whether a popularity-proportional or log-uniform hard-negative sampling scheme, or switching to sampled softmax with logQ correction, would better align training negatives with the platform's actual candidate pool size and improve discrimination among plausible items, since the task specifically asks me to focus on how negatives are selected and used.\n\nGiven the 60-minute budget, I should check on the baseline run's progress and quickly scan the rest of the pipeline (training loop, validation, candidate ranking, and data prep) along with the candidate set sizes before committing to a specific change.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:11:51.428Z", "text": "Next I need: the baseline run's progress, the remaining trainer sections (train_model, validation BCE, ranking, main), and the candidate set sizes. All independent, so I'll fetch them together." }, { "kind": "tool", "timestamp": "2026-10-03T09:11:51.430Z", "callId": "toolu_01VvPvtLyVt58FWp5Rh94qY7", "name": "Bash", "input": { "command": "cat /app/solution/baseline/run.log | tail -20", "description": "Check baseline run progress" }, "result": { "timestamp": "2026-10-03T09:11:51.446Z", "content": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.7s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.6s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.7s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.1s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=24.8s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.4s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:51.450Z", "data": { "type": "total_tokens_reminder", "text": "14954003 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:51.452Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-10-03T09:11:55.077Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:11:55.077Z", "callId": "toolu_01Rji4JUfbuM3kKD1FzUaFy7", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 1521,1790p train_current_model.py", "description": "Read train_model orchestration" }, "result": { "timestamp": "2026-10-03T09:11:55.097Z", "content": "def train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:11:55.101Z", "data": { "type": "total_tokens_reminder", "text": "14953642 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:55.102Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-10-03T09:12:00.681Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:12:00.681Z", "text": "I should pull up the main argument parser section, the evaluate_validation_bce function, rank_candidates, and the candidate set size stats all together to get a fuller picture.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:12:00.685Z", "callId": "toolu_01HMTmapNW8vJLvd3SS6Jjnh", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 1861,1925p train_current_model.py && sed -n 2004,2164p train_current_model.py", "description": "Read ranking and CLI main sections" }, "result": { "timestamp": "2026-10-03T09:12:00.711Z", "content": "def rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:12:00.714Z", "data": { "type": "total_tokens_reminder", "text": "14950068 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:12:00.715Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-10-03T09:12:05.806Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:12:05.806Z", "text": "I should check baseline progress and data stats like candidate set sizes and the popularity distribution of validation relevant items versus the catalog, since these are independent checks.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:12:11.334Z", "callId": "toolu_01AvMa4bG3aSCQqCWZ8yCp2V", "name": "Bash", "input": { "command": "tail -5 /app/solution/baseline/run.log; cd /task/data && python - <<'EOF'\nimport numpy as np, pandas as pd\nc=np.load('validation_eligible_candidates.npz'); \npacked=c['packed_eligible']; n=len(c['item_ids'])\ncnt=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\nprint('users',len(c['uids']),'items',n,'eligible per user: mean',cnt.mean(),'min',cnt.min(),'max',cnt.max())\ncat=pd.read_parquet('candidate_catalog.parquet'); print(cat.head()); print(cat.columns.tolist())\ntu=pd.read_parquet('target_users.parquet'); print('targets',len(tu))\nvl=pd.read_parquet('val_listens.parquet'); vk=pd.read_parquet('val_likes.parquet')\ntargets=set(tu.uid)\nstrong=vl[vl.uid.isin(targets)&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=vk[vk.uid.isin(targets)&(vk.is_organic==1)][['uid','item_id']].drop_duplicates()\nrank=pd.Series(cat.popularity_rank.values,index=cat.item_id.values)\nfor name,df in [('strong',strong),('likes',likes)]:\n r=rank.reindex(df.item_id).dropna()\n print(name,len(df),'in catalog',len(r),'rank quantiles',np.quantile(r,[.1,.25,.5,.75,.9]))\nEOF", "description": "Inspect candidate set sizes and popularity of relevant items" }, "result": { "timestamp": "2026-10-03T09:12:13.790Z", "content": "prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.1s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=44.8s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\nusers 8456 items 30000 eligible per user: mean 29067.306291390727 min 24657 max 30000\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\n['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntargets 8456\nstrong 255608 in catalog 170791 rank quantiles [ 207. 996. 4079. 11258. 20059.]\nlikes 17195 in catalog 8528 rank quantiles [ 442. 1648.75 5554. 13506.5 21728.7 ]\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:12:13.794Z", "data": { "type": "total_tokens_reminder", "text": "14945975 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:12:13.796Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-10-03T09:12:35.994Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:12:35.994Z", "text": "The candidate sets are nearly the full 30k catalog, so this is essentially full-catalog retrieval, and the relevant items skew heavily popular—median rank 4079 with a quarter in the top 1000. That means popularity bias actually helps here, since training with uniform negatives tends to push the model toward scoring popular items higher, which aligns well with this metric.\n\nWith one epoch and roughly 1.05M training targets drawn from each user's last 128 interactions, training time will likely be the real constraint, so I want to see how long the baseline takes before iterating further. On the negative-sampling side, uniform draws over 30k items mostly surface easy tail negatives with weak gradient signal, since the model mainly needs to discriminate among popular head items — suggesting popularity-weighted sampling with a logQ correction could help, and sampled softmax may outperform pointwise BCE for ranking metrics like NDCG.\n\nI'm also noting the current in-batch negative setup only uses 8 per target from a batch of users sorted by target count, with a weight-based popularity correction rather than a logit-based one. Given limited time remaining, my plan is to add a popularity-based negative sampler option and a sampled-softmax loss with logQ correction, while keeping BCE as a fallback.\n\nWith roughly 1.05M targets and batches of 64 users × 100 steps, I'm estimating around 286 batches per epoch, so each run should be fast enough to fit two or three experiments within the time budget — I'll check the training log to confirm actual speed.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:12:36.008Z", "callId": "toolu_01AcHXsvqPucmggA9CDGeUC8", "name": "Bash", "input": { "command": "sleep 60; tail -4 /app/solution/baseline/run.log; cd /task/current_model && sed -n 1364,1400p train_current_model.py && sed -n 955,1035p train_current_model.py", "description": "Check training speed and read validation BCE and target weighting code" }, "result": { "timestamp": "2026-10-03T09:13:36.068Z", "content": "chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\ndef build_target_weights(\n public: Path,\n stage: str,\n prepared: SequenceData,\n training_counts: np.ndarray,\n config: Config,\n) -> np.ndarray:\n \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n if config.target_weighting == \"uniform\":\n weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n else:\n weights = np.full(\n len(prepared.sequence_items),\n config.ordinary_target_weight,\n dtype=np.float32,\n )\n organic_strong = (\n (prepared.event_features[:, 0] > 0.5)\n & (prepared.event_features[:, 1] >= 0.8)\n )\n weights[organic_strong] = config.organic_strong_target_weight\n if config.target_weighting == \"discovery-graded\":\n like_outcomes = load_organic_like_outcomes(\n public, stage, prepared.item_ids\n )\n history_end = (\n base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n )\n horizon = config.discovery_horizon_days * base.DAY\n grade_three = 0\n for user_row, uid in enumerate(prepared.user_ids):\n start = int(prepared.offsets[user_row])\n end = int(prepared.offsets[user_row + 1])\n if end - start < 2:\n continue\n last_training_target = start + int(training_counts[user_row])\n first_validation_target = last_training_target + 1\n split_timestamp = (\n int(prepared.sequence_timestamps[first_validation_target])\n if first_validation_target < end\n else history_end\n )\n user_likes = like_outcomes.get(int(uid), {})\n seen = {int(prepared.sequence_items[start])}\n for position in range(start + 1, end):\n item = int(prepared.sequence_items[position])\n novel = item not in seen\n seen.add(item)\n if not novel:\n # Repeats remain training examples, but they cannot be\n # eligible discovery outcomes at the corresponding\n # cutoff and therefore keep only the ordinary weight.\n weights[position] = config.ordinary_target_weight\n continue\n timestamps = user_likes.get(item)\n if timestamps is None:\n continue\n timestamp = int(prepared.sequence_timestamps[position])\n upper = min(\n timestamp + horizon,\n split_timestamp\n if position <= last_training_target\n else history_end,\n )\n outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n weights[position] = config.organic_like_target_weight\n grade_three += 1\n log(\n f\"grade-3 first-encounter targets={grade_three:,} \"\n f\"horizon_days={config.discovery_horizon_days}\"\n )\n log(\n f\"target weighting={config.target_weighting} \"\n f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n f\"max={weights.max():.3f}\"\n )\n return weights\n\n\ndef build_dense_block(\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:36.080Z", "data": { "type": "total_tokens_reminder", "text": "14944230 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:36.087Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-10-03T09:14:20.040Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:14:20.040Z", "text": "While training runs in the background, I'll use the time to draft the code changes. I'm planning to add a configurable negative sampling strategy, introducing a popularity-based option alongside the existing uniform approach, which I'll implement across the training script and model files.\n\nI'm also designing a sampled-softmax loss option with a logQ correction, where popularity-sampled negatives get corrected with their log proposal probability while positives remain uncorrected, following standard candidate sampling practice. Updating the negative drawing logic...\n\nSo popularity sampling with logQ correction is the principled choice since it approximates full softmax, and I'll make that configurable while keeping in mind the tight time budget — only a couple experiments are feasible before needing roughly 15 minutes for a final refit on train+val. I'll write the sampled softmax loss code now (subtracting log q from logits, comparing positive against sampled negatives) while the baseline runs, then launch the popularity+logQ experiment as soon as it finishes.\n\nI should keep the baseline run alive for comparison rather than killing it early, since I need that score for the README. I'm also noticing that with 6400 targets times 32 negatives, the unique item draws nearly cover the entire 30k catalog per batch, so increasing negatives further wouldn't meaningfully change compute cost.\n\nNow I'm planning the code changes: adding config fields for negative sampling strategy, sampling power, loss type, and log-Q correction, plus building a negative sampling distribution function alongside the existing item correction logic.\n\nI'm designing a sampler abstraction that handles drawing negatives and computing log-proposal probabilities, so the filtered negatives function can return negatives, correction weights, and log-q values uniformly whether negatives come from a learned distribution or in-batch sampling.\n\nFor scoring with sampled-softmax, I need to correct logits by subtracting the appropriate log proposal probability per slot—using the empirical in-batch frequency for the positive and in-batch negatives, and the popularity-based log q for uniform negatives—then properly combine these into a single expected-count correction when both slot types are mixed in the union sample.\n\nThen I apply softmax cross-entropy across the positive and negative logits with the sample weighting, while still tracking raw-logit stats like positive_sum, negative_sum, and pair_correct, and I should check whether evaluate_validation_bce shares this same loss computation for consistency during early stopping.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:14:20.430Z", "callId": "toolu_01NsRGhu5UKods7ES9Vr4Yk6", "name": "Bash", "input": { "command": "cd /task/current_model && sed -n 1400,1488p train_current_model.py; sed -n 1933,2000p train_current_model.py; tail -2 /app/solution/baseline/run.log", "description": "Read validation loss loop and remaining CLI args, check progress" }, "result": { "timestamp": "2026-10-03T09:14:20.509Z", "content": " for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:14:20.513Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T09:14:20.513Z", "data": { "type": "total_tokens_reminder", "text": "14939767 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:14:20.516Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-10-03T09:14:30.128Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:14:30.128Z", "text": "The baseline training seems slow — 250 batches haven't logged after 3 minutes, which is concerning given roughly 286 batches total, but I'll not waste time profiling and just move forward. I'll go ahead and start editing the training script to apply the needed modifications.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:14:30.132Z", "text": "Since relevant items skew popular while uniform negatives are mostly easy tail items, I'll add a popularity-proportional negative sampler and a sampled-softmax loss with logQ correction, keeping the existing BCE as a fallback.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:15:30.950Z", "callId": "toolu_01J8TY9HTcEoxXs1jRcoED4b", "name": "Bash", "input": { "command": "cd /app/solution && python - <<'EOF'\nimport re\np='train_current_model.py'; s=open(p).read()\n\n# 1. Config fields\ns=s.replace(''' negative_filter_scope: str = \"full-training-history\"\n''',''' negative_filter_scope: str = \"full-training-history\"\n negative_sampling: str = \"uniform\"\n negative_sampling_power: float = 0.75\n loss_type: str = \"bce\"\n logq_correction: bool = True\n''')\n\n# 2. Negative sampler: factor target counts, add sampler class\ns=s.replace('''def build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n''','''def count_training_targets(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Count how often each catalog item appears as a selected training target.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\nclass NegativeSampler:\n \"\"\"Proposal distribution for the non-in-batch negative slots.\n\n ``uniform`` reproduces the production behaviour. ``popularity`` draws\n negatives proportionally to ``count^power`` of training-target frequency, so\n the sampled negatives concentrate on the popular head where the discovery\n outcomes actually live instead of on trivially separable tail tracks. The\n sampler also exposes ``log_expected_count`` for sampled-softmax logQ\n correction: the log of the expected number of times an item is drawn across\n the mixed uniform/popularity and in-batch slots.\n \"\"\"\n\n def __init__(\n self, target_counts: np.ndarray, config: Config\n ) -> None:\n n_items = len(target_counts)\n self.n_items = n_items\n self.mode = config.negative_sampling\n total = float(target_counts.sum())\n empirical = (target_counts + 1.0) / (total + n_items)\n if config.negative_sampling == \"popularity\":\n raw = np.power(target_counts + 1.0, config.negative_sampling_power)\n probabilities = raw / raw.sum()\n self.probabilities = torch.from_numpy(probabilities.astype(np.float64))\n elif config.negative_sampling == \"uniform\":\n probabilities = np.full(n_items, 1.0 / n_items)\n self.probabilities = None\n else:\n raise ValueError(f\"unknown negative sampling {config.negative_sampling}\")\n expected = (\n config.uniform_negatives * probabilities\n + config.inbatch_negatives * empirical\n )\n self.log_expected_count = torch.from_numpy(\n np.log(np.maximum(expected, 1.0e-12)).astype(np.float32)\n )\n log(\n f\"negative sampler={config.negative_sampling} \"\n f\"power={config.negative_sampling_power} \"\n f\"max_prob={probabilities.max():.5f} \"\n f\"head1000_mass={np.sort(probabilities)[::-1][:1000].sum():.3f}\"\n )\n\n def draw(self, count: int, generator: torch.Generator) -> torch.Tensor:\n if count <= 0:\n return torch.empty(0, dtype=torch.long)\n if self.probabilities is None:\n return torch.randint(self.n_items, (count,), generator=generator)\n return torch.multinomial(\n self.probabilities, count, replacement=True, generator=generator\n )\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n''')\n\n# 3. draw_full_history_filtered_negatives: use sampler\ns=s.replace(''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n''',''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n if sampler is None:\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n else:\n uniform = sampler.draw(\n batch_size * config.uniform_negatives, generator\n ).reshape(batch_size, config.uniform_negatives)\n''')\ns=s.replace(''' replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n''',''' if sampler is None:\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n else:\n replacements = sampler.draw(int(invalid.sum()), generator).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n''')\n\n# 4. draw_training_negatives: pass sampler\ns=s.replace(''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(''',''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n sampler,\n )\n if sampler is not None and sampler.probabilities is not None:\n raise RuntimeError(\"popularity negatives require the full-history filter\")\n return base.draw_filtered_mixed_negatives(''')\n\n# 5. Loss: sampled softmax in score_target_microbatches\ns=s.replace(''' sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"''',''' sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"''')\ns=s.replace(''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n''',''' selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"sampled-softmax\":\n loss_sum = (\n selected_weights\n * sampled_softmax_loss(\n positive_logits,\n negative_logits,\n positive_items[begin:end],\n negatives[begin:end],\n sampler,\n config,\n )\n ).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n''')\ns=s.replace('''def retained_target_window_starts(''','''def sampled_softmax_loss(\n positive_logits: torch.Tensor,\n negative_logits: torch.Tensor,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sampler: \"NegativeSampler | None\",\n config: Config,\n) -> torch.Tensor:\n \"\"\"Per-target sampled-softmax cross entropy with logQ correction.\n\n Every candidate logit, including the positive, is shifted by the log of its\n expected sampled count under the mixed proposal, so the sampled objective\n approximates the full-catalog softmax rather than a popularity-debiased one.\n \"\"\"\n logits = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n if config.logq_correction and sampler is not None:\n log_q = sampler.log_expected_count.to(logits.device)\n items = torch.cat([positive_items[:, None], negatives], dim=1)\n logits = logits - log_q[items]\n return torch.logsumexp(logits, dim=1) - logits[:, 0]\n\n\ndef retained_target_window_starts(''')\n\n# 6. Thread sampler through run_training_epoch and evaluate_validation_bce\ns=s.replace(''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()''',''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n sampler: \"NegativeSampler | None\" = None,\n) -> dict[str, float]:\n model.train()''')\ns=s.replace(''' item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"''',''' item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"''')\n# calls to draw_training_negatives (two sites) and score_target_microbatches (two sites)\ns=s.replace(''' config,\n item_correction,\n generator,\n device,\n )\n''',''' config,\n item_correction,\n generator,\n device,\n sampler,\n )\n''')\ns=s.replace(''' device,\n backward=True,\n )''',''' device,\n backward=True,\n sampler=sampler,\n )''')\ns=s.replace(''' device,\n backward=False,\n )''',''' device,\n backward=False,\n sampler=sampler,\n )''')\n\n# 7. train_model: build sampler, pass to epoch/eval calls\ns=s.replace(''' known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []''',''' known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n sampler = NegativeSampler(\n count_training_targets(prepared, partition.window_starts, training_counts),\n config,\n )\n history: list[dict[str, float]] = []''')\ns=s.replace(''' item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)''',''' item_correction,\n known_positive_packed,\n device,\n sampler,\n )\n )\n history.append(prior)''')\ns=s.replace(''' known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )''',''' known_positive_packed,\n generator,\n device,\n epoch,\n sampler,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n sampler,\n )''')\n\n# 8. CLI args\ns=s.replace(''' parser.add_argument(\n \"--target-weighting\",''',''' parser.add_argument(\n \"--negative-sampling\",\n choices=(\"uniform\", \"popularity\"),\n default=Config.negative_sampling,\n help=\"proposal for the non-in-batch negative slots\",\n )\n parser.add_argument(\n \"--negative-sampling-power\",\n type=float,\n default=Config.negative_sampling_power,\n help=\"exponent applied to training-target frequency for popularity negatives\",\n )\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"sampled-softmax\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--no-logq-correction\",\n action=\"store_true\",\n help=\"disable expected-count logQ correction in the sampled softmax\",\n )\n parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n )\n parser.add_argument(\n \"--target-weighting\",''')\ns=s.replace(''' negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,''',''' negative_filter_scope=args.negative_filter_scope,\n negative_sampling=args.negative_sampling,\n negative_sampling_power=args.negative_sampling_power,\n loss_type=args.loss_type,\n logq_correction=not args.no_logq_correction,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n target_weighting=args.target_weighting,''')\ns=s.replace(''' \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''',''' \"objective\": (\n \"sample-weighted-BCE-at-every-timestep\"\n if config.loss_type == \"bce\"\n else \"sample-weighted-logQ-corrected-sampled-softmax\"\n ),\n \"negative_sampling\": (\n f\"{config.uniform_negatives}-{config.negative_sampling}\"\n f\"-plus-{config.inbatch_negatives}-inbatch\"\n ),''')\nopen(p,'w').write(s)\nprint(s.count('sampler'))\nEOF\npython -c \"import ast,sys;ast.parse(open('/app/solution/train_current_model.py').read());print('ok')\"; grep -n \"sampler\" /app/solution/train_current_model.py | wc -l; tail -2 /app/solution/baseline/run.log", "description": "Patch trainer with popularity sampler and sampled softmax" }, "result": { "timestamp": "2026-10-03T09:15:31.240Z", "content": "Traceback (most recent call last):\n File \"\", line 448, in \nPermissionError: [Errno 13] Permission denied: 'train_current_model.py'\nok\n0\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:15:31.253Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app/solution", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" }, "changes": [ { "field": "workingDirectory", "from": "/app" } ] } }, { "kind": "context", "timestamp": "2026-10-03T09:15:31.253Z", "data": { "type": "total_tokens_reminder", "text": "14931199 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:15:31.261Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-10-03T09:15:39.699Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:15:39.699Z", "text": "The copied files came through as read-only, so I need to chmod them before rerunning. I'm also noticing training is unusually slow for the baseline—only 250 batches in ~6 minutes—so I should check whether the background process is actually using all 8 threads. I'll need to redo the patch by saving the script to a file instead of using the heredoc, since that got consumed.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:15:39.703Z", "text": "The copied files kept read-only permissions. I'll fix that, re-apply the patch from a saved script, and check how far the baseline has progressed." }, { "kind": "tool", "timestamp": "2026-10-03T09:15:39.709Z", "callId": "toolu_01BeW6A1zNTq94KFJnVg5GXj", "name": "Bash", "input": { "command": "chmod u+w *.py && ls -la && ps aux | grep train_current | head -3 && tail -1 baseline/run.log", "description": "Make solution files writable and check baseline process" }, "result": { "timestamp": "2026-10-03T09:15:39.793Z", "content": "total 112\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:11 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:06 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:11 baseline\n-rw-r--r-- 1 agent agent 80058 Oct 3 09:11 train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Oct 3 09:11 two_tower_model.py\nagent 8129 0.0 0.0 4492 3212 ? Ss 09:11 0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1791018662823-rcsy4d.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \\builtin unalias -- 'unsetenv'; \\builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'cp /task/current_model/*.py /app/solution/ && cd /app/solution && mkdir -p baseline && (time python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/baseline/production_validation.parquet) > /app/solution/baseline/run.log 2>&1; python /task/evaluate_public.py /app/solution/baseline/production_validation.parquet >> /app/solution/baseline/run.log 2>&1' < /dev/null && pwd -P >| /tmp/claude-9cf4-cwd\nagent 8132 0.0 0.0 4492 1924 ? S 09:11 0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1791018662823-rcsy4d.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \\builtin unalias -- 'unsetenv'; \\builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'cp /task/current_model/*.py /app/solution/ && cd /app/solution && mkdir -p baseline && (time python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/baseline/production_validation.parquet) > /app/solution/baseline/run.log 2>&1; python /task/evaluate_public.py /app/solution/baseline/production_validation.parquet >> /app/solution/baseline/run.log 2>&1' < /dev/null && pwd -P >| /tmp/claude-9cf4-cwd\nagent 8133 449 34.2 7658052 5483972 ? Rl 09:11 19:51 python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/baseline/production_validation.parquet\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:15:39.804Z", "data": { "type": "total_tokens_reminder", "text": "14930672 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:15:39.806Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-10-03T09:15:56.448Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:15:56.448Z", "text": "The baseline is still running the already-loaded training script, so editing the file now is safe since it's compiled in memory. With about 286 batches per epoch and no progress logged after 5.5 minutes using 5.5GB of memory, I'm realizing the full epoch could take quite a while.\n\nWith only 44 minutes left, I need to apply the patch immediately and decide how to run the new experiment without starving CPU resources — running three processes on 8 cores is too slow, so I'll launch the validation experiment with the new config at 4 threads now, let it speed up once the baseline finishes, then commit to the final submission refit by around minute 30-31 based on whether it beats baseline.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:16:52.385Z", "callId": "toolu_018xFat6v52Xx9QMDsoVBANa", "name": "Bash", "input": { "command": "cat > /app/solution/patch_negatives.py <<'EOF'\np='/app/solution/train_current_model.py'; s=open(p).read()\ndef rep(a,b,count=1):\n global s\n assert s.count(a)==count, (s.count(a), a[:80])\n s=s.replace(a,b)\n\nrep(''' negative_filter_scope: str = \"full-training-history\"\n''',''' negative_filter_scope: str = \"full-training-history\"\n negative_sampling: str = \"uniform\"\n negative_sampling_power: float = 0.75\n loss_type: str = \"bce\"\n logq_correction: bool = True\n''')\n\nrep('''def build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n''','''def count_training_targets(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Count how often each catalog item appears as a selected training target.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\nclass NegativeSampler:\n \"\"\"Proposal distribution for the non-in-batch negative slots.\n\n ``uniform`` reproduces the production behaviour. ``popularity`` draws\n negatives proportionally to ``count^power`` of training-target frequency, so\n the sampled negatives concentrate on the popular head where the discovery\n outcomes actually live instead of on trivially separable tail tracks. The\n sampler also exposes ``log_expected_count`` for sampled-softmax logQ\n correction: the log of the expected number of times an item is drawn across\n the mixed uniform/popularity and in-batch slots.\n \"\"\"\n\n def __init__(self, target_counts: np.ndarray, config: Config) -> None:\n n_items = len(target_counts)\n self.n_items = n_items\n self.mode = config.negative_sampling\n total = float(target_counts.sum())\n empirical = (target_counts + 1.0) / (total + n_items)\n if config.negative_sampling == \"popularity\":\n raw = np.power(target_counts + 1.0, config.negative_sampling_power)\n probabilities = raw / raw.sum()\n self.probabilities = torch.from_numpy(probabilities.astype(np.float64))\n elif config.negative_sampling == \"uniform\":\n probabilities = np.full(n_items, 1.0 / n_items)\n self.probabilities = None\n else:\n raise ValueError(f\"unknown negative sampling {config.negative_sampling}\")\n expected = (\n config.uniform_negatives * probabilities\n + config.inbatch_negatives * empirical\n )\n self.log_expected_count = torch.from_numpy(\n np.log(np.maximum(expected, 1.0e-12)).astype(np.float32)\n )\n log(\n f\"negative sampler={config.negative_sampling} \"\n f\"power={config.negative_sampling_power} \"\n f\"max_prob={probabilities.max():.5f} \"\n f\"head1000_mass={np.sort(probabilities)[::-1][:1000].sum():.3f}\"\n )\n\n def draw(self, count: int, generator: torch.Generator) -> torch.Tensor:\n if count <= 0:\n return torch.empty(0, dtype=torch.long)\n if self.probabilities is None:\n return torch.randint(self.n_items, (count,), generator=generator)\n return torch.multinomial(\n self.probabilities, count, replacement=True, generator=generator\n )\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n''')\n\nrep(''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n''',''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n if sampler is None:\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n else:\n uniform = sampler.draw(\n batch_size * config.uniform_negatives, generator\n ).reshape(batch_size, config.uniform_negatives)\n''')\nrep(''' replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n''',''' if sampler is None:\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n else:\n replacements = sampler.draw(int(invalid.sum()), generator).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary proposal draws.\n correction[invalid] = 1.0\n''')\n\nrep(''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(''',''' item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n sampler,\n )\n if sampler is not None and sampler.probabilities is not None:\n raise RuntimeError(\"popularity negatives require the full-history filter\")\n return base.draw_filtered_mixed_negatives(''')\n\nrep(''' sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"''',''' sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n sampler: \"NegativeSampler | None\" = None,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"''')\nrep(''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n''',''' selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_type == \"sampled-softmax\":\n loss_sum = (\n selected_weights\n * sampled_softmax_loss(\n positive_logits,\n negative_logits,\n positive_items[begin:end],\n negatives[begin:end],\n sampler,\n config,\n )\n ).sum()\n else:\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n''')\nrep('''def retained_target_window_starts(''','''def sampled_softmax_loss(\n positive_logits: torch.Tensor,\n negative_logits: torch.Tensor,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n sampler: \"NegativeSampler | None\",\n config: Config,\n) -> torch.Tensor:\n \"\"\"Per-target sampled-softmax cross entropy with logQ correction.\n\n Every candidate logit, including the positive, is shifted by the log of its\n expected sampled count under the mixed proposal, so the sampled objective\n approximates the full-catalog softmax rather than a popularity-debiased one.\n \"\"\"\n logits = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n if config.logq_correction and sampler is not None:\n log_q = sampler.log_expected_count.to(logits.device)\n items = torch.cat([positive_items[:, None], negatives], dim=1)\n logits = logits - log_q[items]\n return torch.logsumexp(logits, dim=1) - logits[:, 0]\n\n\ndef retained_target_window_starts(''')\n\nrep(''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()''',''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n sampler: \"NegativeSampler | None\" = None,\n) -> dict[str, float]:\n model.train()''')\nrep(''' item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"''',''' item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n sampler: \"NegativeSampler | None\" = None,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"''')\nrep(''' config,\n item_correction,\n generator,\n device,\n )\n''',''' config,\n item_correction,\n generator,\n device,\n sampler,\n )\n''',2)\nrep(''' device,\n backward=True,\n )''',''' device,\n backward=True,\n sampler=sampler,\n )''')\nrep(''' device,\n backward=False,\n )''',''' device,\n backward=False,\n sampler=sampler,\n )''')\n\nrep(''' known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []''',''' known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n sampler = NegativeSampler(\n count_training_targets(prepared, partition.window_starts, training_counts),\n config,\n )\n history: list[dict[str, float]] = []''')\nrep(''' item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)''',''' item_correction,\n known_positive_packed,\n device,\n sampler,\n )\n )\n history.append(prior)''')\nrep(''' known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )''',''' known_positive_packed,\n generator,\n device,\n epoch,\n sampler,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n sampler,\n )''')\n\nrep(''' parser.add_argument(\n \"--target-weighting\",''',''' parser.add_argument(\n \"--negative-sampling\",\n choices=(\"uniform\", \"popularity\"),\n default=Config.negative_sampling,\n help=\"proposal for the non-in-batch negative slots\",\n )\n parser.add_argument(\n \"--negative-sampling-power\",\n type=float,\n default=Config.negative_sampling_power,\n help=\"exponent applied to training-target frequency for popularity negatives\",\n )\n parser.add_argument(\n \"--loss-type\",\n choices=(\"bce\", \"sampled-softmax\"),\n default=Config.loss_type,\n )\n parser.add_argument(\n \"--no-logq-correction\",\n action=\"store_true\",\n help=\"disable expected-count logQ correction in the sampled softmax\",\n )\n parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n )\n parser.add_argument(\n \"--target-weighting\",''')\nrep(''' negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,''',''' negative_filter_scope=args.negative_filter_scope,\n negative_sampling=args.negative_sampling,\n negative_sampling_power=args.negative_sampling_power,\n loss_type=args.loss_type,\n logq_correction=not args.no_logq_correction,\n uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n target_weighting=args.target_weighting,''')\nrep(''' \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''',''' \"objective\": (\n \"sample-weighted-BCE-at-every-timestep\"\n if config.loss_type == \"bce\"\n else \"sample-weighted-logQ-corrected-sampled-softmax\"\n ),\n \"negative_sampling\": (\n f\"{config.uniform_negatives}-{config.negative_sampling}\"\n f\"-plus-{config.inbatch_negatives}-inbatch\"\n ),''')\nopen(p,'w').write(s)\nprint(\"patched\")\nEOF\npython patch_negatives.py && python -c \"import ast;ast.parse(open('train_current_model.py').read());print('syntax ok')\" && grep -c sampler train_current_model.py && tail -1 baseline/run.log", "description": "Apply negative-sampling patch and syntax check" }, "result": { "timestamp": "2026-10-03T09:16:52.642Z", "content": "patched\nsyntax ok\n25\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:16:52.649Z", "data": { "type": "total_tokens_reminder", "text": "14922991 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:16:52.651Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-10-03T09:17:07.276Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:17:07.276Z", "text": "The baseline has finished training and is now ranking, so I'm checking the log timing before launching the next experiment. Since a full run takes about 45s of prep and errors would surface within a minute of training starting, I'll kick off the experiment in the background and monitor the log — configured with popularity-based negative sampling and sampled-softmax loss with logQ correction.\n\nI'm debating whether to increase the negative count or keep it fixed for a clean comparison, since more negatives tend to help softmax at relatively low cost, but time is the real constraint since I can only afford maybe two experiments. I'm leaning toward running two concurrently at 4 threads each: one with popularity sampling at 24 uniform-slot plus 8 in-batch negatives, and a second variant either scaling up negatives further or isolating the loss change with uniform sampling — but I want to check how long the baseline actually took to train before deciding.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:17:07.279Z", "callId": "toolu_017jrvtQSqn7XdJcowY3SVF7", "name": "Bash", "input": { "command": "grep -E \"epoch=1/1 train_loss|batches=|real|ranked users=8\" baseline/run.log | tail -5; cat baseline/run.log | grep -A3 '\"scores\"'", "description": "Check baseline timing and score" }, "result": { "timestamp": "2026-10-03T09:17:07.354Z", "content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=212.2s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=225.5 validation_seconds=17.5", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:17:07.363Z", "data": { "type": "total_tokens_reminder", "text": "14922323 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:17:07.365Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 19, "role": "agent", "timestamp": "2026-10-03T09:17:15.451Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:17:15.451Z", "text": "With only 225s per epoch, I have room to run two experiments concurrently at 4 threads each, maybe even two epochs. I'll launch E1 (popularity 0.75 with sampled-softmax, logQ correction, 24+8 negatives) and E2 (popularity 0.75) now, about 20 minutes in.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:17:15.454Z", "text": "Since the baseline trained in under 4 minutes, I have room to run two concurrent experiments at 4 threads each. I'll launch popularity-based sampled softmax negatives, one with 24+8 negatives and another with 56+8.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:17:15.464Z", "callId": "toolu_01XeT8DMjYzgCJL6MpT2xRd2", "name": "Bash", "input": { "command": "mkdir -p exp1 exp2 && (python train_current_model.py --stage validation --public-dir /task --threads 4 --negative-sampling popularity --loss-type sampled-softmax --output /app/solution/exp1/validation.parquet > exp1/run.log 2>&1; python /task/evaluate_public.py exp1/validation.parquet >> exp1/run.log 2>&1) ", "description": "Run experiment 1: popularity negatives with sampled softmax", "timeout": 1800000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T09:17:15.499Z", "content": "Command running in background with ID: b1sal1v36. Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/b1sal1v36.output. You will be notified when it completes. To check interim output, use Read on that file path.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:17:15.505Z", "data": { "type": "total_tokens_reminder", "text": "14921601 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:17:15.507Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 20, "role": "agent", "timestamp": "2026-10-03T09:17:19.214Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:17:19.214Z", "callId": "toolu_01FBYkY7NjbhcAKUoydaWjqj", "name": "Bash", "input": { "command": "(python train_current_model.py --stage validation --public-dir /task --threads 4 --negative-sampling popularity --loss-type sampled-softmax --uniform-negatives 56 --inbatch-negatives 8 --output /app/solution/exp2/validation.parquet > exp2/run.log 2>&1; python /task/evaluate_public.py exp2/validation.parquet >> exp2/run.log 2>&1)", "description": "Run experiment 2: same with 56+8 negatives", "timeout": 1800000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T09:17:19.251Z", "content": "Command running in background with ID: bxsblt0lg. Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bxsblt0lg.output. You will be notified when it completes. To check interim output, use Read on that file path.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:17:19.259Z", "data": { "type": "total_tokens_reminder", "text": "14921201 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:17:19.262Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-10-03T09:17:21.926Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:17:21.926Z", "callId": "toolu_01LotrJWwtMvPUBMFbxP9vvH", "name": "Bash", "input": { "command": "sleep 45; tail -3 baseline/run.log; echo ---; tail -3 exp1/run.log; echo ---; tail -3 exp2/run.log", "description": "Check baseline score and experiment startup" }, "result": { "timestamp": "2026-10-03T09:18:06.995Z", "content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=212.2s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=225.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n---\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=16.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=25.8s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=36.4s\n---\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=16.4s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=25.6s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=34.7s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:18:07.008Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T09:18:07.009Z", "data": { "type": "total_tokens_reminder", "text": "14920934 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:18:07.011Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-10-03T09:18:12.843Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:18:12.843Z", "text": "The ranking process seems slow because inference runs the GRU over each user's full sequence, which could involve massive token counts — that's probably the bottleneck rather than the ranking batches themselves. While that finishes, I'll start drafting the README skeleton and a small unit test.\n\nWriting sampler/loss tests...\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:18:12.844Z", "text": "While the three runs proceed, I'll add a small test for the new sampler and loss, then check progress." }, { "kind": "tool", "timestamp": "2026-10-03T09:18:21.878Z", "callId": "toolu_01E8w3HVEqpBmXT6coDRyjCY", "name": "Bash", "input": { "command": "cat > /app/solution/test_negative_sampling.py <<'EOF'\n\"\"\"Unit checks for the popularity negative sampler and sampled-softmax loss.\"\"\"\nimport numpy as np\nimport torch\n\nimport train_current_model as tm\n\n\ndef test_popularity_sampler_concentrates_on_head():\n counts = np.array([1000.0, 100.0, 10.0, 0.0])\n config = tm.Config(negative_sampling=\"popularity\", negative_sampling_power=1.0)\n sampler = tm.NegativeSampler(counts, config)\n probs = sampler.probabilities.numpy()\n assert abs(probs.sum() - 1.0) < 1e-9\n assert probs[0] > probs[1] > probs[2] > probs[3] > 0\n draws = sampler.draw(20_000, torch.Generator().manual_seed(0)).numpy()\n assert draws.min() >= 0 and draws.max() < 4\n assert np.bincount(draws, minlength=4)[0] > 15_000\n\n\ndef test_uniform_sampler_matches_production_randint():\n counts = np.ones(50)\n sampler = tm.NegativeSampler(counts, tm.Config())\n assert sampler.probabilities is None\n draws = sampler.draw(1_000, torch.Generator().manual_seed(1)).numpy()\n assert draws.min() >= 0 and draws.max() < 50\n\n\ndef test_sampled_softmax_logq_correction():\n counts = np.array([10.0, 5.0, 1.0])\n config = tm.Config(\n negative_sampling=\"popularity\",\n loss_type=\"sampled-softmax\",\n uniform_negatives=2,\n inbatch_negatives=0,\n )\n sampler = tm.NegativeSampler(counts, config)\n pos = torch.tensor([1.0])\n neg = torch.tensor([[0.5, -0.5]])\n items = torch.tensor([0])\n negatives = torch.tensor([[1, 2]])\n loss = tm.sampled_softmax_loss(pos, neg, items, negatives, sampler, config)\n logits = torch.cat([pos[:, None], neg], dim=1) - sampler.log_expected_count[\n torch.cat([items[:, None], negatives], dim=1)\n ]\n expected = torch.logsumexp(logits, dim=1) - logits[:, 0]\n assert torch.allclose(loss, expected)\n uncorrected = tm.sampled_softmax_loss(\n pos, neg, items, negatives, sampler, tm.Config(loss_type=\"sampled-softmax\", logq_correction=False)\n )\n assert torch.allclose(uncorrected, torch.logsumexp(torch.cat([pos[:, None], neg], 1), 1) - pos)\n\n\nif __name__ == \"__main__\":\n test_popularity_sampler_concentrates_on_head()\n test_uniform_sampler_matches_production_randint()\n test_sampled_softmax_logq_correction()\n print(\"all negative-sampling tests passed\")\nEOF\ncd /app/solution && python test_negative_sampling.py 2>&1 | grep -v \"negative sampler=\"; tail -2 baseline/run.log; tail -1 exp1/run.log; tail -1 exp2/run.log", "description": "Run unit tests for sampler and loss, check run progress" }, "result": { "timestamp": "2026-10-03T09:18:29.365Z", "content": "all negative-sampling tests passed\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=225.5 validation_seconds=17.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=52.6s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=52.5s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:18:29.375Z", "data": { "type": "total_tokens_reminder", "text": "14919020 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:18:29.378Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-10-03T09:18:36.497Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:18:36.497Z", "text": "The baseline ranking is taking a while since it's running inference over full sequences with 20M tokens through the GRU under contention—about 24 minutes so far. I'll give it a bit more time and check back on all three in a couple minutes.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:18:36.501Z", "callId": "toolu_01Y51y2iaLSyc7iuHMoNjaaz", "name": "Bash", "input": { "command": "sleep 120; cd /app/solution; grep -E \"ranked users|scores|ndcg\" baseline/run.log | tail -3; echo ---; grep -E \"negative sampler|batches=|train_loss\" exp1/run.log | tail -3; echo ---; grep -E \"negative sampler|batches=|train_loss\" exp2/run.log | tail -3", "description": "Check all three runs after two minutes" }, "result": { "timestamp": "2026-10-03T09:20:36.639Z", "content": "Exit code 143\nCommand timed out after 2m 0s", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:18:53.692Z", "data": { "type": "queued_command", "prompt": "\nb1sal1v36\ntoolu_01XeT8DMjYzgCJL6MpT2xRd2\n/tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/b1sal1v36.output\nfailed\nBackground command \"Run experiment 1: popularity negatives with sampled softmax\" failed with exit code 1\n", "source_uuid": "589c9ddd-7836-4621-94da-089e3e486d10", "delivery_id": "6c7d4bcc-3977-4570-8547-80949cf8605f", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:18:53.692Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:18:53.754Z", "data": { "type": "queued_command", "prompt": "\nb7kvy5zrh\ntoolu_01MxC3Jp5B3DyJuheySwr4Ud\n/tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/b7kvy5zrh.output\nfailed\nBackground command \"Run production baseline backtest in background\" failed with exit code 1\n", "source_uuid": "1e55f05d-e26e-4342-86d5-a20f53bb84ae", "delivery_id": "eb26bee7-cdb6-4bf6-bbbd-042429486c9a", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:18:53.754Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:20:36.691Z", "data": { "type": "total_tokens_reminder", "text": "14918515 tokens left" } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-10-03T09:20:39.566Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:20:39.566Z", "callId": "toolu_011aUMKxrA7exPC1ChHBn5LZ", "name": "Bash", "input": { "command": "cd /app/solution; tail -15 exp1/run.log; echo ----; tail -12 baseline/run.log; free -g | head -2", "description": "Inspect failures of experiment 1 and baseline" }, "result": { "timestamp": "2026-10-03T09:20:39.630Z", "content": " return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'exp1/validation.parquet'\n----\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/baseline/production_validation.parquet'\n total used free shared buff/cache available\nMem: 15 5 3 0 6 10", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:20:39.633Z", "data": { "type": "total_tokens_reminder", "text": "14917674 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:20:39.637Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 25, "role": "agent", "timestamp": "2026-10-03T09:20:43.747Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:20:43.747Z", "callId": "toolu_013HJ6S4V23Mom4e9ksDstAV", "name": "Bash", "input": { "command": "cd /app/solution; grep -n -B2 -A12 \"Traceback\" baseline/run.log | head -40; echo ----; grep -n -B2 -A12 \"Traceback\" exp1/run.log | head -40; echo; dmesg 2>/dev/null | tail -3; tail -2 exp2/run.log", "description": "Find the actual trainer errors in baseline and experiment 1 logs" }, "result": { "timestamp": "2026-10-03T09:20:43.810Z", "content": "23-user\t39m43.236s\n24-sys\t1m44.330s\n25:Traceback (most recent call last):\n26- File \"/task/evaluate_public.py\", line 157, in \n27- main()\n28- File \"/task/evaluate_public.py\", line 152, in main\n29- ranked = load_submission(path, eligibility, targets)\n30- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n31- File \"/task/evaluate_public.py\", line 113, in load_submission\n32- frame = validate_ranking(read_ranking(path), targets)\n33- ^^^^^^^^^^^^^^^^^^\n34- File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n35- return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n36- ^^^^^^^^^^^^^^^^^^^^^\n37- File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n----\n10-prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=81.3s\n11-target users without qualifying catalog sequence=7\n12:Traceback (most recent call last):\n13- File \"/task/evaluate_public.py\", line 157, in \n14- main()\n15- File \"/task/evaluate_public.py\", line 152, in main\n16- ranked = load_submission(path, eligibility, targets)\n17- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n18- File \"/task/evaluate_public.py\", line 113, in load_submission\n19- frame = validate_ranking(read_ranking(path), targets)\n20- ^^^^^^^^^^^^^^^^^^\n21- File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n22- return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n23- ^^^^^^^^^^^^^^^^^^^^^\n24- File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nnegative sampler=popularity power=0.75 max_prob=0.00102 head1000_mass=0.178", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:20:43.814Z", "data": { "type": "total_tokens_reminder", "text": "14916625 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:20:43.816Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-10-03T09:21:02.446Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:21:02.446Z", "text": "Both the baseline and exp1 died silently with no traceback, which points to an OOM-kill: with 15GB total memory and three processes each using ~5.5GB, the kernel likely SIGKILLed them. I want to check the baseline log's timing line to confirm, but it seems plausible the OOM killer took out both runs while exp2 behaved differently.\n\nWith exp2 still running, I'm now in a tighter spot — about 32 minutes left. My priorities: keep a guaranteed valid submission, get a validation score from exp2's new method, and plan the final submission refit (prep, training, ranking) which could take ~12 minutes total, so I need to start it by around minute 38-44. Since two concurrent processes at ~5.5GB each fits within memory (unlike three), I'm considering starting the final refit with the new config now, running it alongside exp2, so I don't lose time waiting on validation results.\n\nHowever, I realize I have no baseline score to compare against since that run died during ranking — I'd need to rerun it (~10 min), and memory only allows two processes at once. My plan is to start the submission refit with the new config immediately, then once exp2 finishes, queue a baseline validation run to get the before-score for comparison; if the new method underperforms, I'll pivot to refitting baseline for submission, which is tight but still doable within the time left.\n\nI'm launching the refit now with 4 threads, keeping an eye on the ranking phase's GRU inference since it took over 5 minutes under contention last time. The negative sampler stats for exp2 look mild — top-1000 items capturing 17.8% of mass versus 3.3% uniform, nothing alarming. Proceeding with the launch.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:21:02.453Z", "text": "Both the baseline and experiment 1 were likely killed by the OOM killer (three trainers at ~5.5 GB each exceed the 15 GB limit), while experiment 2 survived and is training. I'll limit to two processes going forward, and to protect the deadline I'm starting the final hidden-test refit with the new objective now, alongside experiment 2.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:21:02.957Z", "callId": "toolu_01A89twXpSt6vkGvSfxkiyYb", "name": "Bash", "input": { "command": "mkdir -p final && (time python train_current_model.py --stage submission --public-dir /task --threads 4 --negative-sampling popularity --loss-type sampled-softmax --uniform-negatives 56 --inbatch-negatives 8 --output /app/solution/final/submission_candidate.parquet) > final/run.log 2>&1", "description": "Run final hidden-test refit with the new negative-sampling objective", "timeout": 2400000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T09:21:02.975Z", "content": "Command running in background with ID: bo0lkvgfj. Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bo0lkvgfj.output. You will be notified when it completes. To check interim output, use Read on that file path.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:21:02.980Z", "data": { "type": "total_tokens_reminder", "text": "14914246 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:21:02.984Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-10-03T09:21:08.370Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:21:08.370Z", "callId": "toolu_01LVLowANJUwYhSrXKsTvDn4", "name": "Bash", "input": { "command": "cd /app/solution; sleep 50; grep -E \"batches=|train_loss|ranked\" exp2/run.log | tail -3; echo ---; tail -1 final/run.log; free -g | sed -n 2p; sed -n 1782,1860p /task/current_model/train_current_model.py | grep -n \"for \\|range\" ", "description": "Check experiment 2 progress, final refit startup, and memory" }, "result": { "timestamp": "2026-10-03T09:21:58.419Z", "content": "---\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=46.2s\nMem: 15 6 2 0 6 8\n15: for group_start in range(0, len(ordered), config.user_batch_size):\n22: for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n34: for local_row, sequence_row in enumerate(rows):\n52: for local_row, count in enumerate(counts):", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:21:58.423Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T09:21:58.423Z", "data": { "type": "total_tokens_reminder", "text": "14913922 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:21:58.425Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-10-03T09:22:04.038Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:22:04.038Z", "text": "Exp2's log still hasn't hit \"batches=250\" — it's been training about 5 minutes with 4 threads under contention. I should pull the full tail to check its progress.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:22:04.042Z", "callId": "toolu_01QUj9sYVJ2MM9cUF7QcRVxp", "name": "Bash", "input": { "command": "cd /app/solution; tail -2 exp2/run.log; echo ---; tail -1 final/run.log; ps aux | grep \"train_current\" | grep -v grep | awk '{print $3, $4, $6/1e6 \"GB\"}'", "description": "Check experiment 2 and final refit status" }, "result": { "timestamp": "2026-10-03T09:22:04.085Z", "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nnegative sampler=popularity power=0.75 max_prob=0.00102 head1000_mass=0.178\n---\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\n0.0 0.0 0.0032GB\n0.0 0.0 0.00172GB\n202 29.6 4.73557GB\n0.0 0.0 0.003268GB\n0.0 0.0 0.00204GB\n102 11.6 1.85984GB", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:22:04.089Z", "data": { "type": "total_tokens_reminder", "text": "14913478 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:22:04.091Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 29, "role": "agent", "timestamp": "2026-10-03T09:22:11.832Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:22:11.832Z", "text": "Exp2 is running slower than expected at only 202% CPU with 4 threads under contention and double the negatives, so it hasn't hit 250 batches in about 6 minutes despite the baseline doing so in 212 seconds. With roughly 31 minutes elapsed total, I'll wait a bit longer and use the time to draft the README while it finishes the epoch.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:22:11.838Z", "callId": "toolu_017XqATuS7xrnPag7oxuMfsj", "name": "Bash", "input": { "command": "cd /app/solution; sleep 90; grep -E \"batches=|train_loss|ranked|restored\" exp2/run.log | tail -3; echo ---; grep -E \"sampler|batches=|train_loss\" final/run.log | tail -2", "description": "Wait and check training progress of both runs" }, "result": { "timestamp": "2026-10-03T09:23:41.966Z", "content": " ranked = load_submission(path, eligibility, targets)\n---\nnegative sampler=popularity power=0.75 max_prob=0.00101 head1000_mass=0.178", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:22:21.281Z", "data": { "type": "queued_command", "prompt": "\nbxsblt0lg\ntoolu_01FBYkY7NjbhcAKUoydaWjqj\n/tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bxsblt0lg.output\nfailed\nBackground command \"Run experiment 2: same with 56+8 negatives\" failed with exit code 1\n", "source_uuid": "8eff3139-9882-4f5c-a2ca-421ed6b7e886", "delivery_id": "c7894abd-624c-42c2-8065-4a3125908c0a", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:22:21.281Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:23:42.002Z", "data": { "type": "total_tokens_reminder", "text": "14912914 tokens left" } } ] }, { "id": 30, "role": "agent", "timestamp": "2026-10-03T09:23:47.587Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:23:47.587Z", "callId": "toolu_01F7xLR96KkQPJCA19xnf8Vj", "name": "Bash", "input": { "command": "cd /app/solution; grep -v \"^ \\|^Traceback\\|File \\\"/opt\" exp2/run.log | tail -8; cat /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bxsblt0lg.output | tail -5; dmesg 2>&1 | tail -2; cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/memory.peak 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null", "description": "Diagnose experiment 2 death and check cgroup memory limits" }, "result": { "timestamp": "2026-10-03T09:23:47.665Z", "content": "target users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nnegative sampler=popularity power=0.75 max_prob=0.00102 head1000_mass=0.178\nFileNotFoundError: [Errno 2] No such file or directory: 'exp2/validation.parquet'\n/bin/bash: line 1: 8334 Killed python train_current_model.py --stage validation --public-dir /task --threads 4 --negative-sampling popularity --loss-type sampled-softmax --uniform-negatives 56 --inbatch-negatives 8 --output /app/solution/exp2/validation.parquet > exp2/run.log 2>&1\n\n[exited with code 1]\ndmesg: read kernel buffer failed: Operation not permitted\n8589934592\n8589934592\nlow 0\nhigh 0\nmax 1868\noom 6\noom_kill 3\noom_group_kill 0\nsock_throttled 0", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:23:47.670Z", "data": { "type": "total_tokens_reminder", "text": "14912230 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:23:47.674Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-10-03T09:24:09.101Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:24:09.101Z", "text": "I realize the actual cgroup limit is 8GB, not 15GB, so only one trainer process can run at a time given ~5GB usage each — that explains the three OOM kills so far. The final refit is now running alone and peaked at 8.59GB, right at the ceiling, with about 26 minutes left to see if it survives.\n\nSince nothing can run concurrently, I'll let the final refit finish uninterrupted rather than risk another collision — it should complete around minute 46-48, leaving a tight window to squeeze in a validation run before time's up. There's no cheaper way to get validation evidence for the new method, so the priority is getting the submission out and drafting the README with the diagnosis while monitoring the refit's memory and progress.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:24:09.107Z", "text": "Found the real constraint: the cgroup caps memory at 8 GB (not the 15 GB free showed), which is why all three earlier parallel runs got OOM-killed. I'm now running the final refit alone and will monitor it while drafting the README.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:24:09.109Z", "callId": "toolu_01VFvwvwu5FFNxvvUBq4VqT6", "name": "Bash", "input": { "command": "cd /app/solution; cat /sys/fs/cgroup/memory.current; grep -E \"batches=|train_loss|ranked|restored\" final/run.log | tail -3; ps aux | grep \"train_current\" | grep -v grep | awk '{print $3, $6/1e6 \"GB\"}'", "description": "Check final refit progress and memory usage" }, "result": { "timestamp": "2026-10-03T09:24:09.155Z", "content": "4795310080\n0.0 0.003268GB\n0.0 0.00204GB\n206 4.59942GB", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:24:09.159Z", "data": { "type": "total_tokens_reminder", "text": "14910737 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:24:09.160Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-10-03T09:24:15.395Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:24:15.395Z", "text": "The final refit is still training and hasn't hit 250 batches yet, though memory and CPU usage look fine. While I wait, I'll draft the README with placeholders for the scores to fill in later, using a heredoc to write it out.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:24:33.640Z", "callId": "toolu_01WTqSQje8Tdbgh2Lk5X8pqw", "name": "Bash", "input": { "command": "cd /app/solution; cat > README.md <<'EOF'\n# Made for You two-tower: negative-sampling improvement\n\n## Diagnosis\n\nThe production trainer scores every adjacent next-item target with a pointwise,\nsample-weighted BCE against 24 **uniform** catalog negatives plus 8 in-batch\nnegatives whose popularity is only down-weighted (propensity weights clipped to\n[0.25, 4]).\n\nTwo public-data facts make that choice weak for the discovery objective:\n\n- The platform candidate sets are almost the whole 30,000-track catalog\n (mean 29,067 eligible items per user), so ranking is full-catalog retrieval.\n- Relevant validation outcomes concentrate in the popular head: the median\n popularity rank of an organic 80%-listen is about 4,100 and a quarter of them\n sit inside the top 1,000 tracks.\n\nA uniform draw from 30,000 items therefore almost never produces a plausible\ncompetitor; 24 negatives per target are mostly tail tracks the model separates\ntrivially after a few hundred batches, so gradient signal about *which popular\ntrack* a listener wants is weak. The in-batch slots do hit the head, but under\npointwise BCE each is an independent binary decision with an ad hoc clipped\nweight rather than a calibrated correction, so the head/tail trade-off is tuned\nby the clipping constants instead of by the data.\n\n## Change (training objective only; serving contract unchanged)\n\nImplemented in `train_current_model.py` (new `NegativeSampler`,\n`count_training_targets`, `sampled_softmax_loss`; threaded through\n`draw_training_negatives`, `score_target_microbatches`, `run_training_epoch`,\n`evaluate_validation_bce`, `train_model`):\n\n1. **Popularity-proportional negatives** (`--negative-sampling popularity`,\n `--negative-sampling-power 0.75`): the non-in-batch negative slots are drawn\n with probability proportional to `(training-target count + 1)^0.75`, so\n negatives concentrate on the head where the real competition is. The\n full-training-history false-negative filter is kept; rejected draws are\n re-drawn from the same proposal.\n2. **Sampled softmax with logQ correction** (`--loss-type sampled-softmax`):\n each target's positive and its negatives are scored jointly with a\n cross-entropy whose logits are shifted by the log *expected sampled count*\n of each item under the mixed proposal\n (`uniform_negatives * q_pop(i) + inbatch_negatives * p_target(i)`).\n This makes the sampled objective approximate the full-catalog softmax\n rather than a popularity-debiased one, replacing the clipped BCE weights.\n3. **More negatives per target** (`--uniform-negatives 56 --inbatch-negatives 8`),\n cheap because the item tower already encodes almost every catalog item per\n block; only the dot-product block grows.\n\nThe model, towers, data protocol, chronological split, early stopping, and\nindexed dot-product ranking are untouched; `--loss-type bce\n--negative-sampling uniform` reproduces the production trainer exactly.\n`test_negative_sampling.py` covers the sampler and the loss.\n\n## Public backtest\n\nRESULTS_PLACEHOLDER\n\n## Operational note\n\nThe sandbox cgroup allows 8 GB; one trainer peaks near 5 GB, so runs must be\nsequential. Concurrent runs in this attempt were OOM-killed.\n\n## Submission command\n\n```sh\npython /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --negative-sampling popularity --loss-type sampled-softmax \\\n --uniform-negatives 56 --inbatch-negatives 8 \\\n --output /app/solution/submission.parquet\n```\nEOF\nsleep 45; grep -E \"batches=|train_loss|ranked|restored\" final/run.log | tail -3", "description": "Write README draft and check final refit progress" }, "result": { "timestamp": "2026-10-03T09:25:18.684Z", "content": "(Bash completed with no output)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:25:18.688Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T09:25:18.688Z", "data": { "type": "total_tokens_reminder", "text": "14909145 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:25:18.690Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-10-03T09:25:30.801Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:25:30.801Z", "text": "The final refit has been running about 6 minutes without logging 250 batches yet, which is worrying given the baseline took 212s at double the thread count—I've used roughly 38 of 60 minutes. I want to check CPU usage growth and the log tail to confirm it's actually progressing, since I don't have py-spy available to inspect it directly.\n\nBut more importantly, I realize I currently have zero valid submissions, so if this run dies I'm left with nothing. I should immediately generate a cheap fallback using a popularity-based ranking computed with numpy alone, writing it to submission.parquet now so a valid file always exists, to be overwritten later if the real model finishes in time.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:25:30.804Z", "text": "Since the final refit is still training without logging progress yet, I'll first create a cheap popularity-only fallback submission to ensure a valid artifact exists, then check on training progress.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:25:35.217Z", "callId": "toolu_01HAXr48udo73GT4DZHSyrWS", "name": "Bash", "input": { "command": "cd /app/solution; python - <<'EOF'\nimport numpy as np, pandas as pd\nc=np.load('/task/data/test_eligible_candidates.npz')\nuids=c['uids']; items=c['item_ids']; n=len(items)\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nrank=pd.Series(cat.popularity_rank.values,index=cat.item_id.values).reindex(items).to_numpy(dtype=np.float64)\nrows=[]\nfor r in range(len(uids)):\n elig=np.unpackbits(c['packed_eligible'][r],count=n,bitorder='little').astype(bool)\n score=np.where(elig,-rank,-np.inf)\n top=np.argsort(-score,kind='stable')[:100]\n rows.append(np.column_stack([np.full(100,uids[r]),items[top],np.arange(1,101)]))\ndf=pd.DataFrame(np.concatenate(rows),columns=['uid','item_id','rank']).astype('int64')\ntu=pd.read_parquet('/task/data/target_users.parquet')\nassert set(df.uid)==set(tu.uid) and len(df)==100*len(tu)\ndf.to_parquet('submission.parquet',index=False)\nprint('fallback written',df.shape)\nEOF\nls -la submission.parquet; grep -E \"batches=|train_loss|ranked|restored\" final/run.log | tail -3; ps aux | grep \"stage submission\" | grep -v grep | awk '{print \"cpu%\",$3,\"cputime\",$10,\"rss\",$6/1e6\"GB\"}'", "description": "Write popularity fallback submission and check refit progress" }, "result": { "timestamp": "2026-10-03T09:27:35.318Z", "content": "Command did not complete within its 120s timeout and was moved to the background (ID: bgseuhwj1). Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bgseuhwj1.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.\nSession cwd remains /app/solution; directory changes made by the backgrounded command do not apply to subsequent commands.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:27:35.324Z", "data": { "type": "total_tokens_reminder", "text": "14908000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:27:35.327Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 34, "role": "agent", "timestamp": "2026-10-03T09:27:42.324Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:27:42.324Z", "callId": "toolu_015aWwJ7uD4KErtFk2kz8aiB", "name": "Bash", "input": { "command": "cd /app/solution; ls -la submission.parquet 2>&1; grep -E \"batches=|train_loss|ranked|restored\" final/run.log | tail -3; ps aux | grep -E \"stage submission|python -\" | grep -v grep | awk '{print \"cpu%\",$3,\"cputime\",$10,\"rss\",$6/1e6\"GB\", $12, $13}'; cat /sys/fs/cgroup/memory.current", "description": "Check fallback file, refit progress, and process states" }, "result": { "timestamp": "2026-10-03T09:27:42.376Z", "content": "ls: cannot access 'submission.parquet': No such file or directory\nepoch=1/1 batches=250 targets=994,228 batch_bce=3.524802 running_bce=3.996880 elapsed=289.1s\ncpu% 0.0 cputime 0:00 rss 0.003268GB -c source\ncpu% 0.0 cputime 0:00 rss 0.00204GB -c source\ncpu% 240 cputime 16:02 rss 5.68734GB train_current_model.py --stage\ncpu% 100 cputime 2:07 rss 0.161076GB - \n6046887936", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:27:42.381Z", "data": { "type": "total_tokens_reminder", "text": "14907559 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:27:42.383Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 35, "role": "agent", "timestamp": "2026-10-03T09:27:53.998Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:27:53.998Z", "text": "The 250-batch log at 289s elapsed makes sense given the earlier prep and filtering contention took extra time, so training is essentially complete now. Next up is the validation BCE calculation, which should take about 30 seconds.\n\nThe fallback ranking script is running much slower than expected—likely due to the argsort or unpacking operations per row rather than the simple loop overhead—but it's chugging along at full CPU with modest memory use, so I'll let it finish. With about 41 minutes used so far, I just need to wait for the final refit and fallback script to complete, then validate and copy the output to the submission file.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:27:54.003Z", "callId": "toolu_01HcL4gaJLvyYKg6UDTPk27i", "name": "Bash", "input": { "command": "cd /app/solution; sleep 75; ls -la submission.parquet 2>&1 | tail -1; grep -E \"train_loss|ranked users|restored\" final/run.log | tail -3", "description": "Wait for fallback and refit progress" }, "result": { "timestamp": "2026-10-03T09:29:09.041Z", "content": "ls: cannot access 'submission.parquet': No such file or directory\nepoch=1/1 train_loss=3.97979 validation_loss=3.77456 pair_accuracy=0.632 train_targets=1,067,606 validation_targets=131,997 train_seconds=311.5 validation_seconds=27.9\nrestored best validation-BCE checkpoint: epoch=1 loss=3.774557", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:29:09.045Z", "data": { "type": "total_tokens_reminder", "text": "14906818 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:29:09.048Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 36, "role": "agent", "timestamp": "2026-10-03T09:29:15.218Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:29:15.218Z", "text": "The final refit has moved into ranking while the fallback script keeps crawling along slowly — I'll check both again shortly. In the meantime I can get ahead by writing the validation script for the submission file, covering schema and eligibility checks, so it's ready to run as soon as the file shows up.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:29:15.223Z", "text": "Training finished and the refit is in the ranking phase. I'll prepare the submission validator now and poll both background jobs." }, { "kind": "tool", "timestamp": "2026-10-03T09:29:21.584Z", "callId": "toolu_01WULZ2o2WnQ8PzQmf5SUFxt", "name": "Bash", "input": { "command": "cd /app/solution; cat > check_submission.py <<'EOF'\nimport sys, numpy as np, pandas as pd\npath=sys.argv[1]\ndf=pd.read_parquet(path)\ntu=pd.read_parquet('/task/data/target_users.parquet')\nc=np.load('/task/data/test_eligible_candidates.npz')\nuids=c['uids']; items=c['item_ids']; n=len(items)\nassert set(df.columns)>= {'uid','item_id','rank'}\nfor col in ('uid','item_id','rank'):\n assert np.issubdtype(df[col].dtype, np.integer) and np.isfinite(df[col]).all(), col\nassert set(df.uid)==set(tu.uid), 'user set mismatch'\ng=df.groupby('uid')\nassert (g.size()==100).all() and (g.item_id.nunique()==100).all()\nassert (g['rank'].min()==1).all() and (g['rank'].max()==100).all() and (g['rank'].nunique()==100).all()\nrow=pd.Series(np.arange(len(uids)),index=uids); col=pd.Series(np.arange(n),index=items)\nr=row.reindex(df.uid).to_numpy(); ci=col.reindex(df.item_id).to_numpy()\nassert not np.isnan(ci).any(), 'item not in catalog'\nci=ci.astype(int)\nbits=(c['packed_eligible'][r, ci>>3] >> (ci&7).astype(np.uint8)) & 1\nassert bits.all(), f'{(~bits.astype(bool)).sum()} ineligible rows'\nprint('submission valid:', path, df.shape)\nEOF\nsleep 60; ls -la submission.parquet final/*.parquet 2>&1; grep -E \"ranked users\" final/run.log | tail -1", "description": "Write submission validator and poll the jobs" }, "result": { "timestamp": "2026-10-03T09:30:21.615Z", "content": "ls: cannot access 'submission.parquet': No such file or directory\n-rw-r--r-- 1 agent agent 1207655 Oct 3 09:29 final/submission_candidate.parquet\nranked users=8,456/8,456", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:29:17.820Z", "data": { "type": "queued_command", "prompt": "\nbo0lkvgfj\ntoolu_01A89twXpSt6vkGvSfxkiyYb\n/tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bo0lkvgfj.output\ncompleted\nBackground command \"Run final hidden-test refit with the new negative-sampling objective\" completed (exit code 0)\n", "source_uuid": "35ca24b0-1d5b-4ceb-ba37-9c7bfc6ce84b", "delivery_id": "9fef7ada-f995-408b-b7dc-11563c48b702", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:29:17.820Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:30:21.621Z", "data": { "type": "total_tokens_reminder", "text": "14905828 tokens left" } } ] }, { "id": 37, "role": "agent", "timestamp": "2026-10-03T09:30:24.919Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:30:24.919Z", "text": "The refit completed. Validating the artifact and installing it as the submission." }, { "kind": "tool", "timestamp": "2026-10-03T09:30:27.519Z", "callId": "toolu_01Qzb9ejZq3mS6zJBqsUKRjr", "name": "Bash", "input": { "command": "cd /app/solution; python check_submission.py final/submission_candidate.parquet && cp final/submission_candidate.parquet submission.parquet && ls -la submission.parquet && grep real final/run.log; pkill -f \"fallback\" ; ps aux | grep \"python -\" | grep -v grep | awk '{print $2}' | xargs -r kill 2>/dev/null; echo killed-fallback", "description": "Validate refit output, install as submission, stop the fallback job" }, "result": { "timestamp": "2026-10-03T09:30:29.478Z", "content": "Exit code 144\nsubmission valid: final/submission_candidate.parquet (845600, 3)\n-rw-r--r-- 1 agent agent 1207655 Oct 3 09:30 submission.parquet\nreal\t8m14.836s", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:30:29.482Z", "data": { "type": "total_tokens_reminder", "text": "14905158 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:30:29.484Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 38, "role": "agent", "timestamp": "2026-10-03T09:30:40.412Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:30:40.412Z", "text": "I need to verify the kill actually worked, since that fallback script could still overwrite submission.parquet if it's still running. With only about 14 minutes left, making sure that process is truly dead is critical before I consider the submission safe.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:30:41.349Z", "callId": "toolu_01FnmqWq9LE6BVkDnubWL3t9", "name": "Bash", "input": { "command": "cd /app/solution; ps aux | grep -E \"python\" | grep -v grep | awk '{print $2, $3, $12, $13, $14}'; ls -la submission.parquet; python -c \"\nimport pandas as pd; a=pd.read_parquet('submission.parquet'); b=pd.read_parquet('final/submission_candidate.parquet'); print('identical', a.equals(b))\"", "description": "Confirm no stray processes and submission matches refit output" }, "result": { "timestamp": "2026-10-03T09:30:43.021Z", "content": "8691 100 - \n-rw-r--r-- 1 agent agent 1207655 Oct 3 09:30 submission.parquet\nidentical True", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:30:29.495Z", "data": { "type": "queued_command", "prompt": "\nbgseuhwj1\ntoolu_01HAXr48udo73GT4DZHSyrWS\n/tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/bgseuhwj1.output\nfailed\nBackground command \"Write popularity fallback submission and check refit progress\" failed with exit code 144\n", "source_uuid": "286868e7-5ce7-45e7-8e5e-ae2cae23dfdf", "delivery_id": "26406037-ab51-4c15-974f-75b314afd91b", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:30:29.495Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:30:43.025Z", "data": { "type": "total_tokens_reminder", "text": "14904351 tokens left" } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-10-03T09:30:48.310Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:30:48.310Z", "text": "That PID 8691 at 100% CPU is still the fallback heredoc script running—I need to kill it explicitly so it doesn't overwrite submission.parquet.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:30:48.316Z", "callId": "toolu_01GSDutUnLtrYFBxHRTSQbcG", "name": "Bash", "input": { "command": "kill -9 8691 2>/dev/null; sleep 1; ps aux | grep python | grep -v grep | wc -l; ls -la /app/solution/submission.parquet", "description": "Kill the stray fallback writer so it cannot overwrite the submission" }, "result": { "timestamp": "2026-10-03T09:30:49.349Z", "content": "0\n-rw-r--r-- 1 agent agent 1207655 Oct 3 09:30 /app/solution/submission.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:30:49.352Z", "data": { "type": "total_tokens_reminder", "text": "14903754 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:30:49.354Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-10-03T09:30:56.549Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:30:56.549Z", "text": "With about 13 minutes left, I'll kick off the validation-stage run with the new config at 8 threads in the background, which should take roughly 8 minutes alone, while I use this time to finalize the README with what I've got so far, adding the validation score if it lands in time around minute 57.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:30:56.550Z", "text": "Submission is installed and valid. With the remaining time I'll run the new objective's public backtest alone at 8 threads to capture an after-score, and finish the README in parallel." }, { "kind": "tool", "timestamp": "2026-10-03T09:30:57.634Z", "callId": "toolu_01HvvGugJptHaJx8dPNmS37f", "name": "Bash", "input": { "command": "mkdir -p exp3 && (python train_current_model.py --stage validation --public-dir /task --threads 8 --negative-sampling popularity --loss-type sampled-softmax --uniform-negatives 56 --inbatch-negatives 8 --output /app/solution/exp3/validation.parquet > exp3/run.log 2>&1 && python /task/evaluate_public.py exp3/validation.parquet > exp3/score.json 2>&1)", "description": "Run public backtest of the new objective for the README", "timeout": 900000, "run_in_background": true }, "result": { "timestamp": "2026-10-03T09:30:57.650Z", "content": "Command running in background with ID: b8lnvnyjl. Output is being written to: /tmp/claude-1000/-app/2c3ab22b-45d7-4732-94f9-353c8285f8fd/tasks/b8lnvnyjl.output. You will be notified when it completes. To check interim output, use Read on that file path.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:30:57.653Z", "data": { "type": "total_tokens_reminder", "text": "14903214 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:30:57.655Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-10-03T09:31:11.606Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:31:11.606Z", "callId": "toolu_0133ZMCqzuw1Txgbp1RgPfud", "name": "Bash", "input": { "command": "cd /app/solution; python - <<'EOF'\ns=open('README.md').read()\nresults='''| Run | Stage | Public NDCG@10 | Notes |\n| --- | --- | --- | --- |\n| Production (24 uniform + 8 in-batch, BCE) | validation | not captured | trained to validation BCE 0.4966, pair accuracy 0.755; the process was OOM-killed during ranking while two other trainers shared the 8 GB cgroup, so no public score was recorded |\n| Popularity negatives + logQ sampled softmax (56 + 8) | validation | see `exp3/score.json` if present | run started after the final refit; if the file is absent the backtest did not finish inside the 60-minute budget |\n| Popularity negatives + logQ sampled softmax (56 + 8) | submission refit on train + val | n/a (hidden) | 8m15s wall clock at 4 threads, training loss 3.98, chronological validation loss 3.77 (softmax CE, not comparable to BCE); artifact validated for schema, user coverage, 100 distinct ranks, and test eligibility |\n\nHonest status: the diagnosis and the implementation are complete and unit\ntested, and the shipped `submission.parquet` was produced by the improved\nobjective, but the attempt did not capture a side-by-side public score because\nconcurrent runs were OOM-killed. Rerun both commands below sequentially to\nreproduce the comparison:\n\n```sh\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --output /app/solution/baseline/production_validation.parquet\npython /task/evaluate_public.py /app/solution/baseline/production_validation.parquet\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --negative-sampling popularity --loss-type sampled-softmax \\\n --uniform-negatives 56 --inbatch-negatives 8 \\\n --output /app/solution/exp3/validation.parquet\npython /task/evaluate_public.py /app/solution/exp3/validation.parquet\n```'''\nopen('README.md','w').write(s.replace('RESULTS_PLACEHOLDER',results))\nEOF\nrm -f patch_negatives.py; ls; tail -1 exp3/run.log", "description": "Fill README results section and tidy the solution directory" }, "result": { "timestamp": "2026-10-03T09:31:11.658Z", "content": "README.md\nbaseline\ncheck_submission.py\nexp1\nexp2\nexp3\nfinal\nsubmission.parquet\ntest_negative_sampling.py\ntrain_current_model.py\ntwo_tower_model.py\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:31:11.661Z", "data": { "type": "total_tokens_reminder", "text": "14902244 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:31:11.663Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-10-03T09:31:28.942Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:31:28.942Z", "text": "About 49 minutes have passed, and the exp3 run should finish around minute 57-58, so I'll check back periodically rather than doing anything else right now. I might also peek at the final-run JSON diagnostics for the README, though that's optional. I'll wait about 4 minutes before checking again.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:31:28.945Z", "callId": "toolu_01Ka1orkwVkMTvjZLPzvwiNs", "name": "Bash", "input": { "command": "cd /app/solution; sleep 110; grep -E \"batches=|train_loss|ranked users|restored\" exp3/run.log | tail -2; grep -A4 embedding_diagnostics final/run.log | head -6", "description": "Poll the backtest run and read refit embedding diagnostics" }, "result": { "timestamp": "2026-10-03T09:33:18.985Z", "content": " \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.2588462829589844,\n \"query_norm_std\": 0.44649699330329895,\n \"item_norm_mean\": 1.9051653146743774,\n \"item_norm_std\": 0.9155890941619873", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:33:18.988Z", "data": { "type": "total_tokens_reminder", "text": "14901843 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:33:18.990Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 43, "role": "agent", "timestamp": "2026-10-03T09:33:23.401Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:33:23.401Z", "callId": "toolu_015hLJvuiRPsn8VB2vbGG1qE", "name": "Bash", "input": { "command": "cd /app/solution; sleep 100; grep -E \"batches=|train_loss|ranked users|restored\" exp3/run.log | tail -2; ls exp3", "description": "Poll the backtest run again" }, "result": { "timestamp": "2026-10-03T09:35:03.446Z", "content": "run.log", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:35:03.455Z", "data": { "type": "total_tokens_reminder", "text": "14901591 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:35:03.458Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-10-03T09:35:10.436Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:35:10.436Z", "text": "Given the tight timing, I'll do one more poll in about 100 seconds and then write the final summary regardless of whether the score file has appeared, since the README already covers both outcomes.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:35:10.529Z", "callId": "toolu_017ZCtudUHKDpaw8ntBBV3sL", "name": "Bash", "input": { "command": "cd /app/solution; sleep 100; grep -E \"batches=|train_loss|ranked users|restored\" exp3/run.log | tail -2; ls -la submission.parquet; cat exp3/score.json 2>/dev/null", "description": "Final poll of the backtest and confirm submission intact" }, "result": { "timestamp": "2026-10-03T09:36:50.602Z", "content": "Exit code 1\nepoch=1/1 batches=250 targets=987,417 batch_bce=3.918087 running_bce=3.980031 elapsed=268.7s\n-rw-r--r-- 1 agent agent 1207655 Oct 3 09:30 submission.parquet", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:36:50.608Z", "data": { "type": "total_tokens_reminder", "text": "14901218 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:36:50.610Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-10-03T09:36:56.094Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:36:56.094Z", "text": "I'm running low on time with about 56 minutes used, and the backtest still needs validation and ranking which could take a few more minutes. I'll do one last short check, then wrap up with a final summary noting that exp3's score will land in exp3/score.json if it completes in time.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:36:56.104Z", "callId": "toolu_01WjnvoSTbJT1EfBiYCBYfuu", "name": "Bash", "input": { "command": "cd /app/solution; sleep 40; grep -E \"train_loss|ranked users|restored\" exp3/run.log | tail -2; cat exp3/score.json 2>/dev/null || echo \"score pending\"", "description": "Last check of the backtest status" }, "result": { "timestamp": "2026-10-03T09:37:36.173Z", "content": "epoch=1/1 train_loss=3.97024 validation_loss=3.68510 pair_accuracy=0.635 train_targets=1,052,953 validation_targets=130,103 train_seconds=285.9 validation_seconds=25.2\nrestored best validation-BCE checkpoint: epoch=1 loss=3.685104\nscore pending", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:37:36.181Z", "data": { "type": "silent_turn_reminder", "text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan." } }, { "kind": "context", "timestamp": "2026-10-03T09:37:36.182Z", "data": { "type": "total_tokens_reminder", "text": "14900814 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:37:36.184Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 46, "role": "agent", "timestamp": "2026-10-03T09:37:47.939Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:37:47.939Z", "text": "The deadline is here, so I'm stopping with the artifact in place.\n\n**Delivered.** `/app/solution/submission.parquet` is a regular file produced by the improved trainer refit on train + val. It passed my validator for schema, exact target-user coverage, 100 distinct items and ranks 1 to 100 per user, and test-cutoff eligibility. The improved implementation is at `/app/solution/train_current_model.py` with its module, a unit test, a validator script, and a README.\n\n**Diagnosis.** Candidate sets are nearly the full 30k catalog, and relevant outcomes sit in the popular head with a median popularity rank around 4,100. The production objective draws 24 uniform negatives, which are almost all trivially separable tail tracks, and treats its 8 in-batch negatives as independent BCE decisions with clipped ad hoc weights. The model gets little signal about which popular track a listener wants.\n\n**Change.** Negatives are now drawn proportionally to training-target frequency to the 0.75 power, with the full-history false-negative filter kept. The loss is a sampled softmax over the positive and its negatives, with each logit corrected by the log expected sample count under the mixed proposal, so it approximates full-catalog softmax rather than a debiased one. Negatives per target rose to 56 + 8. Production behavior remains reproducible via the default flags.\n\n**What was not verified.** The sandbox cgroup allows 8 GB, which I discovered only after three concurrent runs were OOM-killed, including the baseline during its ranking phase. As a result I have no side-by-side public NDCG. The baseline reached validation BCE 0.4966 before dying. The new objective's public backtest was still ranking when time ran out. If it finished, its score is in `exp3/score.json`. The README states this plainly and lists the two sequential commands that reproduce the comparison.\n\n**Next step for whoever picks this up.** Run the baseline and new-objective validation commands from the README one at a time, and confirm the improvement before relying on it for the launch gate." } ] } ] }