{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-two-tower-case-1", "attemptId": "fable51-high-2-1-288-case1-r04-20261003T075109Z-queued", "taskChecksum": "8404d794ce8e18d154cd225cfe6b12bf6b4ee02aaefba1d315aa215c1175a013", "source": { "format": "claude-code-session", "sha256": "ef1dba88094dc0fb174d771b7a3f2c40d63112d9ab8d2003b4aa21f64a33d178", "records": 313, "bookkeepingRecords": { "queue-operation": 4, "atis-latch": 19, "last-prompt": 19 }, "usageRecords": [ { "type": "cost-state", "sessionId": "47c3e013-b2c2-46e8-8ac3-4197c05494a2", "totalCostUSD": 5.04072025, "totalAPIDuration": 451611, "totalAPIDurationWithoutRetries": 451529, "totalToolDuration": 1819388, "totalLinesAdded": 0, "totalLinesRemoved": 0, "totalDuration": 2272167, "startTime": 1791018220357, "modelUsage": { "claude-fable-5-1": { "inputTokens": 1604, "outputTokens": 39297, "thinkingTokens": 14756, "cacheReadInputTokens": 4582761, "cacheCreationInputTokens": 95707, "webSearchRequests": 0, "costUSD": 5.04072025 } }, "hasUnknownModelCost": false } ] }, "sessionId": "47c3e013-b2c2-46e8-8ac3-4197c05494a2", "agentVersion": "2.1.288", "model": "claude-fable-5-1", "steps": [ { "id": 1, "role": "user", "timestamp": "2026-10-03T09:03:40.726Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:03:40.726Z", "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": "2026-10-03T09:03:40.725Z", "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": "2026-10-03T09:03:40.725Z", "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": "2026-10-03T09:03:40.725Z", "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": "2026-10-03T09:03:40.725Z", "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": "2026-10-03T09:03:40.725Z", "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": "2026-10-03T09:03:40.725Z", "data": { "type": "auto_mode", "autoModeConsentFlow": false, "bashFirst": true, "bashFirstSteer": "strict", "steerOnly": true, "bypass": true } }, { "kind": "context", "timestamp": "2026-10-03T09:03:40.725Z", "data": { "type": "total_tokens_reminder", "text": "15000000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:40.742Z", "data": { "type": "session_context", "context": {} } }, { "kind": "context", "timestamp": "2026-10-03T09:03:40.742Z", "data": { "type": "date", "date": "2026-10-03" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:40.744Z", "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": "2026-10-03T09:03:40.745Z", "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": "2026-10-03T09:03:42.473Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:03:42.473Z", "text": "I'll start by exploring the task directory to get a sense of the current model setup.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:03:42.575Z", "text": "I'll start by inspecting the production trainer, model primitives, evaluator, and data so I can form a diagnosis before changing anything." }, { "kind": "tool", "timestamp": "2026-10-03T09:03:43.726Z", "callId": "toolu_01CjmmZLYc9a1xbasMWuNGkc", "name": "Bash", "input": { "command": "ls -la /task /task/current_model /task/data /app /app/solution 2>/dev/null; nproc; free -g; python -c \"import torch, numpy, pandas; print(torch.__version__, numpy.__version__, pandas.__version__)\"", "description": "List task files and check environment" }, "result": { "timestamp": "2026-10-03T09:03:47.754Z", "content": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Oct 3 08:59 .\ndrwxr-xr-x 1 root root 4096 Oct 3 09:02 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 3 08:59 solution\n\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 3 08:59 .\ndrwxr-xr-x 3 agent agent 4096 Oct 3 08:59 ..\n\n/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Oct 3 08:59 .\ndrwxr-xr-x 1 root root 4096 Oct 3 09:02 ..\n-r--r--r-- 1 root root 0 Oct 3 08:57 .keep\ndr-xr-xr-x 1 root root 4096 Oct 3 08:57 current_model\ndr-xr-xr-x 1 root root 4096 Oct 3 08:58 data\n-r--r--r-- 1 root root 5774 Oct 3 08:57 evaluate_public.py\n-r--r--r-- 1 root root 8265 Oct 3 08:57 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Oct 3 08:57 requirements.txt\n\n/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root 4096 Oct 3 08:57 .\ndr-xr-xr-x 1 root root 4096 Oct 3 08:59 ..\n-r--r--r-- 1 root root 80058 Oct 3 08:57 train_current_model.py\n-r--r--r-- 1 root root 15985 Oct 3 08:57 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Oct 3 08:58 .\ndr-xr-xr-x 1 root root 4096 Oct 3 08:59 ..\n-r--r--r-- 1 root root 349352 Oct 3 08:57 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Oct 3 08:57 target_users.parquet\n-r--r--r-- 1 root root 7736341 Oct 3 08:57 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Oct 3 08:58 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Oct 3 08:57 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Oct 3 08:57 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Oct 3 08:57 train_listens.parquet\n-r--r--r-- 1 root root 155988 Oct 3 08:57 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Oct 3 08:57 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Oct 3 08:57 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Oct 3 08:57 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Oct 3 08:57 val_listens.parquet\n-r--r--r-- 1 root root 7845 Oct 3 08:57 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Oct 3 08:57 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Oct 3 08:57 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Oct 3 08:58 validation_preference_state.parquet\n8\n total used free shared buff/cache available\nMem: 15 1 0 0 13 14\nSwap: 0 0 0\n2.8.0+cpu 2.2.6 2.3.3", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:47.756Z", "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": "2026-10-03T09:03:47.759Z", "data": { "type": "total_tokens_reminder", "text": "14979818 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:47.762Z", "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": "2026-10-03T09:03:49.712Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:03:49.712Z", "callId": "toolu_0159BFYf2UhSbcbrGWhtVTKi", "name": "Bash", "input": { "command": "cat /task/current_model/two_tower_model.py; echo ======; cat /task/evaluate_public.py; echo ======; cat /task/ranking_evaluation.py", "description": "Read model primitives and evaluator" }, "result": { "timestamp": "2026-10-03T09:03:49.743Z", "content": "\nOutput too large (29.3KB). Full output saved to: /logs/agent/sessions/projects/-app/47c3e013-b2c2-46e8-8ac3-4197c05494a2/tool-results/b6kyfntm8.txt\n\nPreview (first 2KB):\n\"\"\"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...\n", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:49.746Z", "data": { "type": "total_tokens_reminder", "text": "14978271 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:49.748Z", "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": "2026-10-03T09:03:51.202Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:03:51.202Z", "callId": "toolu_01Mp3QaAVgyWc1rPM3kQhsTB", "name": "Read", "input": { "file_path": "/task/current_model/two_tower_model.py" }, "result": { "timestamp": "2026-10-03T09:03:51.214Z", "content": "1\t\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\n2\tfrom __future__ import annotations\n3\t\n4\timport sys\n5\tfrom dataclasses import dataclass\n6\tfrom pathlib import Path\n7\tfrom typing import Iterator\n8\t\n9\timport numpy as np\n10\timport pandas as pd\n11\timport pyarrow.parquet as pq\n12\timport torch\n13\tfrom torch import nn\n14\t\n15\t\n16\tDAY = 86_400\n17\tTRAIN_END = 24_786_800\n18\tVALIDATION_END = 25_393_400\n19\tLISTEN_POSITIVE_THRESHOLD = 50\n20\tPROFILE_LOOKBACK_DAYS = 120\n21\tLIKE_ITEMS = 48\n22\tDISLIKE_ITEMS = 24\n23\tTOP_N = 100\n24\t\n25\t\n26\t@dataclass(frozen=True)\n27\tclass TowerConfig:\n28\t sequence_length: int = 100\n29\t embedding_dim: int = 192\n30\t hidden_dim: int = 192\n31\t user_hidden_1: int = 512\n32\t user_hidden_2: int = 256\n33\t item_hidden_1: int = 512\n34\t item_hidden_2: int = 256\n35\t dropout: float = 0.05\n36\t epochs: int = 1\n37\t learning_rate: float = 1.0e-3\n38\t weight_decay: float = 1.0e-5\n39\t gradient_clip: float = 2.0\n40\t uniform_negatives: int = 24\n41\t inbatch_negatives: int = 8\n42\t inbatch_correction_power: float = 0.5\n43\t inbatch_correction_min: float = 0.25\n44\t inbatch_correction_max: float = 4.0\n45\t seed: int = 29\n46\t\n47\t\n48\t@dataclass(frozen=True)\n49\tclass ListenEvents:\n50\t timestamps: np.ndarray\n51\t items: np.ndarray\n52\t organic: np.ndarray\n53\t completion_pct: np.ndarray\n54\t\n55\t\n56\t@dataclass(frozen=True)\n57\tclass ExplicitEvents:\n58\t timestamps: np.ndarray\n59\t items: np.ndarray\n60\t channel: np.ndarray\n61\t active: np.ndarray\n62\t organic_like: np.ndarray\n63\t\n64\t\n65\tdef log(message: str) -> None:\n66\t print(message, file=sys.stderr, flush=True)\n67\t\n68\t\n69\tdef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n70\t \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n71\t positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n72\t organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n73\t popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n74\t features = np.column_stack(\n75\t [\n76\t np.log1p(positives),\n77\t np.log1p(organic),\n78\t organic / np.maximum(positives, 1.0),\n79\t 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n80\t ]\n81\t )\n82\t means = features.mean(axis=0, keepdims=True)\n83\t scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n84\t standardized = ((features - means) / scales).astype(np.float32)\n85\t if not np.isfinite(standardized).all():\n86\t raise RuntimeError(\"catalog features contain non-finite values\")\n87\t return standardized\n88\t\n89\t\n90\tdef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n91\t if len(parts) == 1:\n92\t return parts[0]\n93\t timestamps = np.concatenate([part.timestamps for part in parts])\n94\t items = np.concatenate([part.items for part in parts])\n95\t organic = np.concatenate([part.organic for part in parts])\n96\t completion = np.concatenate([part.completion_pct for part in parts])\n97\t order = np.argsort(timestamps, kind=\"stable\")\n98\t return ListenEvents(\n99\t timestamps=timestamps[order],\n100\t items=items[order],\n101\t organic=organic[order],\n102\t completion_pct=completion[order],\n103\t )\n104\t\n105\t\n106\tdef iter_listen_users(\n107\t path: Path, item_index: pd.Index\n108\t) -> Iterator[tuple[int, ListenEvents]]:\n109\t \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n110\t parquet = pq.ParquetFile(path)\n111\t pending_uid: int | None = None\n112\t pending: list[ListenEvents] = []\n113\t previous_uid = -1\n114\t for batch in parquet.iter_batches(\n115\t batch_size=750_000,\n116\t columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n117\t ):\n118\t frame = batch.to_pandas()\n119\t mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n120\t keep = mapped >= 0\n121\t if not np.any(keep):\n122\t continue\n123\t uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n124\t timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n125\t items = mapped[keep].astype(np.int32, copy=False)\n126\t organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n127\t completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n128\t if np.any(uids[1:] < uids[:-1]):\n129\t raise RuntimeError(f\"{path.name} is not sorted by uid\")\n130\t boundaries = np.r_[\n131\t 0,\n132\t np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n133\t len(uids),\n134\t ]\n135\t for start, end in zip(boundaries[:-1], boundaries[1:]):\n136\t uid = int(uids[start])\n137\t part = ListenEvents(\n138\t timestamps[start:end].copy(),\n139\t items[start:end].copy(),\n140\t organic[start:end].copy(),\n141\t completion[start:end].copy(),\n142\t )\n143\t if pending_uid is None:\n144\t pending_uid = uid\n145\t elif uid != pending_uid:\n146\t if pending_uid < previous_uid:\n147\t raise RuntimeError(f\"{path.name} user order regressed\")\n148\t yield pending_uid, _combine_listen_chunks(pending)\n149\t previous_uid = pending_uid\n150\t pending_uid = uid\n151\t pending = []\n152\t pending.append(part)\n153\t if pending_uid is not None:\n154\t yield pending_uid, _combine_listen_chunks(pending)\n155\t\n156\t\n157\tdef merge_listen_streams(\n158\t paths: list[Path], item_index: pd.Index\n159\t) -> Iterator[tuple[int, ListenEvents]]:\n160\t \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n161\t streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n162\t heads: list[tuple[int, ListenEvents] | None] = [\n163\t next(stream, None) for stream in streams\n164\t ]\n165\t while any(head is not None for head in heads):\n166\t uid = min(head[0] for head in heads if head is not None)\n167\t parts: list[ListenEvents] = []\n168\t for position, head in enumerate(heads):\n169\t if head is not None and head[0] == uid:\n170\t parts.append(head[1])\n171\t heads[position] = next(streams[position], None)\n172\t yield uid, _combine_listen_chunks(parts)\n173\t\n174\t\n175\tdef load_explicit_events(\n176\t data: Path,\n177\t prefixes: tuple[str, ...],\n178\t item_index: pd.Index,\n179\t) -> dict[int, ExplicitEvents]:\n180\t \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n181\t specifications = (\n182\t (\"likes\", 0, 1, 0),\n183\t (\"unlikes\", 0, 0, 1),\n184\t (\"dislikes\", 1, 1, 0),\n185\t (\"undislikes\", 1, 0, 1),\n186\t )\n187\t parts: list[pd.DataFrame] = []\n188\t for family, channel, active, tie_priority in specifications:\n189\t for prefix in prefixes:\n190\t frame = pd.read_parquet(\n191\t data / f\"{prefix}_{family}.parquet\",\n192\t columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n193\t )\n194\t mapped = item_index.get_indexer(\n195\t frame.item_id.to_numpy(dtype=np.int64, copy=False)\n196\t )\n197\t keep = mapped >= 0\n198\t if not np.any(keep):\n199\t continue\n200\t retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n201\t retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n202\t retained[\"channel\"] = np.int8(channel)\n203\t retained[\"active\"] = np.int8(active)\n204\t retained[\"tie_priority\"] = np.int8(tie_priority)\n205\t retained[\"organic_like\"] = np.int8(\n206\t family == \"likes\"\n207\t ) * retained.is_organic.astype(np.int8)\n208\t parts.append(retained.drop(columns=\"is_organic\"))\n209\t if not parts:\n210\t return {}\n211\t events = pd.concat(parts, ignore_index=True)\n212\t events = events.sort_values(\n213\t [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n214\t ).reset_index(drop=True)\n215\t result: dict[int, ExplicitEvents] = {}\n216\t for uid, rows in events.groupby(\"uid\", sort=False):\n217\t result[int(uid)] = ExplicitEvents(\n218\t timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n219\t items=rows.item.to_numpy(dtype=np.int32, copy=True),\n220\t channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n221\t active=rows.active.to_numpy(dtype=np.int8, copy=True),\n222\t organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n223\t )\n224\t log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n225\t return result\n226\t\n227\t\n228\tdef empty_explicit_events() -> ExplicitEvents:\n229\t return ExplicitEvents(\n230\t timestamps=np.empty(0, dtype=np.int64),\n231\t items=np.empty(0, dtype=np.int32),\n232\t channel=np.empty(0, dtype=np.int8),\n233\t active=np.empty(0, dtype=np.int8),\n234\t organic_like=np.empty(0, dtype=np.int8),\n235\t )\n236\t\n237\t\n238\tdef pack_active_items(\n239\t active: dict[int, int], width: int, padding_item: int\n240\t) -> np.ndarray:\n241\t packed = np.full(width, padding_item, dtype=np.uint16)\n242\t if not active:\n243\t return packed\n244\t ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n245\t packed[: len(ordered)] = np.fromiter(\n246\t (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n247\t )\n248\t return packed\n249\t\n250\t\n251\tdef load_inference_state(\n252\t path: Path,\n253\t item_index: pd.Index,\n254\t uids: np.ndarray,\n255\t n_items: int,\n256\t) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n257\t \"\"\"Pack the task-provided active explicit state.\"\"\"\n258\t state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n259\t mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n260\t keep = mapped >= 0\n261\t state = state.loc[keep, [\"uid\", \"state\"]].copy()\n262\t state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n263\t user_rows = pd.Index(uids)\n264\t liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n265\t disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n266\t like_counts = np.zeros(len(uids), dtype=np.int32)\n267\t dislike_counts = np.zeros(len(uids), dtype=np.int32)\n268\t for value, width, destination, counts in (\n269\t (\"liked\", LIKE_ITEMS, liked, like_counts),\n270\t (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n271\t ):\n272\t selected = state[state.state == value]\n273\t for uid, rows in selected.groupby(\"uid\", sort=False):\n274\t row = int(user_rows.get_indexer([int(uid)])[0])\n275\t if row < 0:\n276\t continue\n277\t items = np.sort(rows.item.unique())\n278\t counts[row] = len(items)\n279\t retained = items[:width]\n280\t destination[row, : len(retained)] = retained.astype(np.uint16)\n281\t return liked, disliked, like_counts, dislike_counts\n282\t\n283\t\n284\tclass SequentialTwoTower(nn.Module):\n285\t \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n286\t\n287\t def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n288\t super().__init__()\n289\t n_items, n_item_features = item_features.shape\n290\t dim = config.embedding_dim\n291\t self.n_items = n_items\n292\t self.context_embedding = nn.Embedding(\n293\t n_items + 1, dim, padding_idx=n_items\n294\t )\n295\t self.event_projection = nn.Sequential(\n296\t nn.Linear(dim + 3, config.hidden_dim),\n297\t nn.GELU(),\n298\t )\n299\t self.sequence_encoder = nn.GRU(\n300\t input_size=config.hidden_dim,\n301\t hidden_size=dim,\n302\t num_layers=1,\n303\t batch_first=True,\n304\t )\n305\t self.user_tower = nn.Sequential(\n306\t nn.Linear(dim * 3 + 6, config.user_hidden_1),\n307\t nn.GELU(),\n308\t nn.Dropout(config.dropout),\n309\t nn.Linear(config.user_hidden_1, config.user_hidden_2),\n310\t nn.GELU(),\n311\t nn.Dropout(config.dropout),\n312\t nn.Linear(config.user_hidden_2, dim),\n313\t )\n314\t self.candidate_embedding = nn.Embedding(n_items, dim)\n315\t self.register_buffer(\n316\t \"item_features\", torch.from_numpy(item_features), persistent=True\n317\t )\n318\t self.item_tower = nn.Sequential(\n319\t nn.Linear(dim + n_item_features, config.item_hidden_1),\n320\t nn.GELU(),\n321\t nn.Dropout(config.dropout),\n322\t nn.Linear(config.item_hidden_1, config.item_hidden_2),\n323\t nn.GELU(),\n324\t nn.Dropout(config.dropout),\n325\t nn.Linear(config.item_hidden_2, dim),\n326\t )\n327\t nn.init.normal_(self.context_embedding.weight, std=0.02)\n328\t nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n329\t with torch.no_grad():\n330\t self.context_embedding.weight[n_items].zero_()\n331\t\n332\t def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n333\t mask = items.ne(self.n_items)\n334\t vectors = self.context_embedding(items)\n335\t return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n336\t dim=1, keepdim=True\n337\t ).clamp_min(1)\n338\t\n339\t def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n340\t inputs = torch.cat(\n341\t [self.candidate_embedding(items), self.item_features[items]], dim=1\n342\t )\n343\t return self.item_tower(inputs)\n344\t\n345\t\n346\tdef invalid_negative_mask(\n347\t negatives: torch.Tensor,\n348\t targets: torch.Tensor,\n349\t context_items: torch.Tensor,\n350\t liked_items: torch.Tensor,\n351\t) -> torch.Tensor:\n352\t \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n353\t invalid = negatives.eq(targets[:, None])\n354\t invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n355\t invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n356\t for column in range(1, negatives.shape[1]):\n357\t invalid[:, column] |= negatives[:, column, None].eq(\n358\t negatives[:, :column]\n359\t ).any(dim=1)\n360\t return invalid\n361\t\n362\t\n363\tdef draw_filtered_mixed_negatives(\n364\t targets: torch.Tensor,\n365\t context_items: torch.Tensor,\n366\t liked_items: torch.Tensor,\n367\t n_items: int,\n368\t uniform_count: int,\n369\t inbatch_count: int,\n370\t item_correction: torch.Tensor,\n371\t generator: torch.Generator,\n372\t device: torch.device,\n373\t) -> tuple[torch.Tensor, torch.Tensor]:\n374\t \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n375\t batch_size = len(targets)\n376\t uniform = torch.randint(\n377\t n_items,\n378\t (batch_size, uniform_count),\n379\t generator=generator,\n380\t device=\"cpu\",\n381\t )\n382\t inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n383\t corrected_count = 0\n384\t if inbatch_count:\n385\t unique_targets = torch.unique(targets.detach().cpu())\n386\t corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n387\t if corrected_count:\n388\t priorities = torch.rand(\n389\t (batch_size, len(unique_targets)), generator=generator\n390\t )\n391\t priorities.masked_fill_(\n392\t unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n393\t -1.0,\n394\t )\n395\t columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n396\t inbatch = unique_targets[columns]\n397\t if corrected_count < inbatch_count:\n398\t filler = torch.randint(\n399\t n_items,\n400\t (batch_size, inbatch_count - corrected_count),\n401\t generator=generator,\n402\t device=\"cpu\",\n403\t )\n404\t inbatch = torch.cat([inbatch, filler], dim=1)\n405\t\n406\t negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n407\t correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n408\t if corrected_count:\n409\t correction_cpu[\n410\t :, uniform_count : uniform_count + corrected_count\n411\t ] = item_correction[inbatch[:, :corrected_count]]\n412\t negatives = negatives_cpu.to(device)\n413\t correction = correction_cpu.to(device)\n414\t for _ in range(32):\n415\t invalid = invalid_negative_mask(\n416\t negatives, targets, context_items, liked_items\n417\t )\n418\t if not bool(invalid.any()):\n419\t return negatives, correction\n420\t replacements = torch.randint(\n421\t n_items,\n422\t (int(invalid.sum().detach().cpu()),),\n423\t generator=generator,\n424\t device=\"cpu\",\n425\t ).to(device)\n426\t negatives[invalid] = replacements\n427\t correction[invalid] = 1.0\n428\t invalid = invalid_negative_mask(\n429\t negatives, targets, context_items, liked_items\n430\t )\n431\t if bool(invalid.any()):\n432\t raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n433\t return negatives, correction\n434\t", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:51.218Z", "data": { "type": "total_tokens_reminder", "text": "14977175 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:51.220Z", "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": "2026-10-03T09:03:53.612Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:03:53.612Z", "text": "The training script is large, around 80KB, so I'll need to read it in chunks.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:03:53.994Z", "callId": "toolu_01RTZYNqqe7fVLQWiZwaZV9U", "name": "Bash", "input": { "command": "cd /task/current_model && grep -n \"^def \\|^class \\|^ def \\|argparse\\|add_argument\" train_current_model.py | head -150; wc -l train_current_model.py", "description": "Outline the training script's functions and CLI args" }, "result": { "timestamp": "2026-10-03T09:03:54.022Z", "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\")\n2164 train_current_model.py\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:54.025Z", "data": { "type": "total_tokens_reminder", "text": "14969550 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:54.027Z", "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": "2026-10-03T09:03:56.007Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:03:56.007Z", "callId": "toolu_01CL1sMt1TgNUdj8kGaENzsh", "name": "Read", "input": { "file_path": "/task/current_model/train_current_model.py", "offset": 1, "limit": 340 }, "result": { "timestamp": "2026-10-03T09:03:56.017Z", "content": "1\t#!/usr/bin/env python3\n2\t\"\"\"Train the Yambda windowed two-tower baseline.\n3\t\n4\tEvery catalog listen with at least 50 percent completion is a sequence token.\n5\tWithin each selected chronological window, every adjacent target contributes a\n6\tloss. The fast default retains each user's most recent 128 training targets and\n7\t16 held-out validation targets; passing zero for either cap restores the\n8\tuncapped behavior for that side of the split. A 100-step truncated-BPTT block\n9\tcomputes all of its timestep losses and carries the detached GRU hidden state\n10\tinto the next block.\n11\t\n12\tThe model is a pure two-tower retriever: no user-ID embedding, no output\n13\tnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n14\tand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n15\tselected adjacent target retains a nonzero weight. Only files under the supplied\n16\tpublic directory are read.\n17\t\"\"\"\n18\tfrom __future__ import annotations\n19\t\n20\timport argparse\n21\timport json\n22\timport random\n23\timport sys\n24\timport time\n25\tfrom dataclasses import asdict, dataclass\n26\tfrom pathlib import Path\n27\t\n28\timport numpy as np\n29\timport pandas as pd\n30\timport torch\n31\tfrom torch import nn\n32\tfrom torch.nn import functional as F\n33\t\n34\timport two_tower_model as base\n35\t\n36\t\n37\tCACHE_VERSION = 2\n38\t\n39\t\n40\t@dataclass(frozen=True)\n41\tclass Config:\n42\t bptt_steps: int = 100\n43\t user_batch_size: int = 64\n44\t embedding_dim: int = 192\n45\t hidden_dim: int = 192\n46\t user_hidden_1: int = 512\n47\t user_hidden_2: int = 256\n48\t item_hidden_1: int = 512\n49\t item_hidden_2: int = 256\n50\t dropout: float = 0.05\n51\t epochs: int = 1\n52\t learning_rate: float = 1.0e-3\n53\t weight_decay: float = 1.0e-5\n54\t gradient_clip: float = 2.0\n55\t loss_microbatch_size: int = 65_536\n56\t uniform_negatives: int = 24\n57\t inbatch_negatives: int = 8\n58\t inbatch_correction_power: float = 0.5\n59\t inbatch_correction_min: float = 0.25\n60\t inbatch_correction_max: float = 4.0\n61\t negative_filter_scope: str = \"full-training-history\"\n62\t target_weighting: str = \"organic-strong\"\n63\t ordinary_target_weight: float = 0.25\n64\t organic_strong_target_weight: float = 1.0\n65\t organic_like_target_weight: float = 3.0\n66\t discovery_horizon_days: int = 7\n67\t validation_fraction: float = 0.10\n68\t max_train_targets_per_user: int = 128\n69\t max_validation_targets_per_user: int = 16\n70\t early_stopping_patience: int = 1\n71\t early_stopping_min_delta: float = 1.0e-4\n72\t seed: int = 29\n73\t\n74\t\n75\t@dataclass(frozen=True)\n76\tclass CandidateSet:\n77\t uids: np.ndarray\n78\t item_ids: np.ndarray\n79\t packed: np.ndarray\n80\t\n81\t @classmethod\n82\t def read(cls, path: Path) -> \"CandidateSet\":\n83\t with np.load(path, allow_pickle=False) as artifact:\n84\t result = cls(\n85\t uids=artifact[\"uids\"].astype(np.int64, copy=True),\n86\t item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n87\t packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n88\t )\n89\t expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n90\t if result.packed.shape != expected:\n91\t raise ValueError(\n92\t f\"invalid candidate artifact shape {result.packed.shape}; \"\n93\t f\"expected {expected}\"\n94\t )\n95\t return result\n96\t\n97\t\n98\t@dataclass(frozen=True)\n99\tclass SequenceData:\n100\t item_ids: np.ndarray\n101\t item_features: np.ndarray\n102\t user_ids: np.ndarray\n103\t offsets: np.ndarray\n104\t sequence_timestamps: np.ndarray\n105\t sequence_items: np.ndarray\n106\t event_features: np.ndarray\n107\t activity_features: np.ndarray\n108\t state_ids: np.ndarray\n109\t liked_states: np.ndarray\n110\t disliked_states: np.ndarray\n111\t state_counts: np.ndarray\n112\t inference_user_rows: np.ndarray\n113\t inference_liked_items: np.ndarray\n114\t inference_disliked_items: np.ndarray\n115\t inference_user_features: np.ndarray\n116\t\n117\t @property\n118\t def lengths(self) -> np.ndarray:\n119\t return np.diff(self.offsets)\n120\t\n121\t @property\n122\t def target_count(self) -> int:\n123\t return int(np.maximum(self.lengths - 1, 0).sum())\n124\t\n125\t\n126\t@dataclass(frozen=True)\n127\tclass TargetPartition:\n128\t \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n129\t\n130\t window_starts: np.ndarray\n131\t training_counts: np.ndarray\n132\t validation_counts: np.ndarray\n133\t history_training_counts: np.ndarray\n134\t\n135\t @property\n136\t def selected_count(self) -> int:\n137\t return int(self.training_counts.sum() + self.validation_counts.sum())\n138\t\n139\t\n140\tdef log(message: str) -> None:\n141\t print(message, file=sys.stderr, flush=True)\n142\t\n143\t\n144\tdef seed_everything(seed: int) -> None:\n145\t random.seed(seed)\n146\t np.random.seed(seed)\n147\t torch.manual_seed(seed)\n148\t\n149\t\n150\tdef model_config(config: Config) -> base.TowerConfig:\n151\t \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n152\t return base.TowerConfig(\n153\t sequence_length=config.bptt_steps,\n154\t embedding_dim=config.embedding_dim,\n155\t hidden_dim=config.hidden_dim,\n156\t user_hidden_1=config.user_hidden_1,\n157\t user_hidden_2=config.user_hidden_2,\n158\t item_hidden_1=config.item_hidden_1,\n159\t item_hidden_2=config.item_hidden_2,\n160\t dropout=config.dropout,\n161\t epochs=config.epochs,\n162\t learning_rate=config.learning_rate,\n163\t weight_decay=config.weight_decay,\n164\t gradient_clip=config.gradient_clip,\n165\t uniform_negatives=config.uniform_negatives,\n166\t inbatch_negatives=config.inbatch_negatives,\n167\t inbatch_correction_power=config.inbatch_correction_power,\n168\t inbatch_correction_min=config.inbatch_correction_min,\n169\t inbatch_correction_max=config.inbatch_correction_max,\n170\t seed=config.seed,\n171\t )\n172\t\n173\t\n174\tdef append_state_snapshot(\n175\t active_likes: dict[int, int],\n176\t active_dislikes: dict[int, int],\n177\t n_items: int,\n178\t liked_states: list[np.ndarray],\n179\t disliked_states: list[np.ndarray],\n180\t state_counts: list[tuple[int, int]],\n181\t) -> int:\n182\t liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n183\t disliked_states.append(\n184\t base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n185\t )\n186\t state_counts.append((len(active_likes), len(active_dislikes)))\n187\t return len(liked_states) - 1\n188\t\n189\t\n190\tdef causal_activity_features(\n191\t timestamps: np.ndarray,\n192\t items: np.ndarray,\n193\t organic: np.ndarray,\n194\t completion: np.ndarray,\n195\t) -> np.ndarray:\n196\t \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n197\t length = len(items)\n198\t result = np.zeros((length, 4), dtype=np.float16)\n199\t if not length:\n200\t return result\n201\t left_rows = np.searchsorted(\n202\t timestamps,\n203\t timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n204\t side=\"left\",\n205\t )\n206\t rows = np.arange(length, dtype=np.int64)\n207\t counts = rows - left_rows + 1\n208\t organic_sum = np.cumsum(organic.astype(np.float64))\n209\t completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n210\t before_organic = np.where(\n211\t left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n212\t )\n213\t before_completion = np.where(\n214\t left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n215\t )\n216\t\n217\t unique_counts = np.empty(length, dtype=np.int32)\n218\t frequencies: dict[int, int] = {}\n219\t left = 0\n220\t for right, raw_item in enumerate(items):\n221\t item = int(raw_item)\n222\t frequencies[item] = frequencies.get(item, 0) + 1\n223\t required_left = int(left_rows[right])\n224\t while left < required_left:\n225\t old_item = int(items[left])\n226\t remaining = frequencies[old_item] - 1\n227\t if remaining:\n228\t frequencies[old_item] = remaining\n229\t else:\n230\t del frequencies[old_item]\n231\t left += 1\n232\t unique_counts[right] = len(frequencies)\n233\t\n234\t result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n235\t result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n236\t result[:, 2] = (\n237\t (organic_sum - before_organic) / np.maximum(counts, 1)\n238\t ).astype(np.float16)\n239\t result[:, 3] = (\n240\t (completion_sum - before_completion)\n241\t / np.maximum(counts, 1)\n242\t / 100.0\n243\t ).astype(np.float16)\n244\t return result\n245\t\n246\t\n247\tdef build_sequence_parts(\n248\t listens: base.ListenEvents,\n249\t explicit: base.ExplicitEvents,\n250\t n_items: int,\n251\t liked_states: list[np.ndarray],\n252\t disliked_states: list[np.ndarray],\n253\t state_counts: list[tuple[int, int]],\n254\t) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n255\t qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n256\t timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n257\t items = listens.items[qualifying].astype(np.uint16, copy=False)\n258\t organic = listens.organic[qualifying].astype(np.float32, copy=False)\n259\t completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n260\t if not len(items):\n261\t return None\n262\t\n263\t event_features = np.zeros((len(items), 3), dtype=np.float16)\n264\t event_features[:, 0] = organic.astype(np.float16)\n265\t event_features[:, 1] = (\n266\t np.minimum(completion, 100.0) / 100.0\n267\t ).astype(np.float16)\n268\t if len(items) > 1:\n269\t gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n270\t event_features[1:, 2] = np.exp(\n271\t -gaps.astype(np.float64) / (60.0 * base.DAY)\n272\t ).astype(np.float16)\n273\t\n274\t activity = causal_activity_features(\n275\t timestamps, items, organic, completion\n276\t )\n277\t states = np.zeros(len(items), dtype=np.uint32)\n278\t active_likes: dict[int, int] = {}\n279\t active_dislikes: dict[int, int] = {}\n280\t explicit_position = 0\n281\t current_state = 0\n282\t for row, timestamp in enumerate(timestamps):\n283\t changed = False\n284\t while (\n285\t explicit_position < len(explicit.timestamps)\n286\t and int(explicit.timestamps[explicit_position]) < int(timestamp)\n287\t ):\n288\t item = int(explicit.items[explicit_position])\n289\t event_timestamp = int(explicit.timestamps[explicit_position])\n290\t destination = (\n291\t active_likes\n292\t if int(explicit.channel[explicit_position]) == 0\n293\t else active_dislikes\n294\t )\n295\t if int(explicit.active[explicit_position]) == 1:\n296\t destination[item] = event_timestamp\n297\t else:\n298\t destination.pop(item, None)\n299\t explicit_position += 1\n300\t changed = True\n301\t if changed:\n302\t current_state = append_state_snapshot(\n303\t active_likes,\n304\t active_dislikes,\n305\t n_items,\n306\t liked_states,\n307\t disliked_states,\n308\t state_counts,\n309\t )\n310\t states[row] = np.uint32(current_state)\n311\t return timestamps, items.copy(), event_features, activity, states\n312\t\n313\t\n314\tdef final_user_features(\n315\t timestamps: np.ndarray,\n316\t items: np.ndarray,\n317\t event_features: np.ndarray,\n318\t history_end: int,\n319\t like_count: int,\n320\t dislike_count: int,\n321\t) -> np.ndarray:\n322\t start = int(\n323\t np.searchsorted(\n324\t timestamps,\n325\t history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n326\t side=\"left\",\n327\t )\n328\t )\n329\t result = np.zeros(6, dtype=np.float32)\n330\t recent_items = items[start:]\n331\t if len(recent_items):\n332\t result[0] = np.log1p(len(recent_items)) / 8.0\n333\t result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n334\t result[2] = float(np.mean(event_features[start:, 0]))\n335\t result[3] = float(np.mean(event_features[start:, 1]))\n336\t result[4] = np.log1p(like_count) / 6.0\n337\t result[5] = np.log1p(dislike_count) / 4.0\n338\t return result\n339\t\n340\t", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:56.021Z", "data": { "type": "total_tokens_reminder", "text": "14968076 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:56.023Z", "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": "2026-10-03T09:03:57.877Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:03:57.877Z", "callId": "toolu_01TitCLYQkHU5u7UeUGLMmnV", "name": "Read", "input": { "file_path": "/task/current_model/train_current_model.py", "offset": 341, "limit": 530 }, "result": { "timestamp": "2026-10-03T09:03:57.884Z", "content": "341\tdef prepare_data(\n342\t public: Path, stage: str, config: Config\n343\t) -> tuple[SequenceData, CandidateSet]:\n344\t data = public / \"data\"\n345\t catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n346\t \"popularity_rank\"\n347\t )\n348\t item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n349\t item_index = pd.Index(item_ids)\n350\t n_items = len(item_ids)\n351\t if n_items >= np.iinfo(np.uint16).max:\n352\t raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n353\t\n354\t if stage == \"validation\":\n355\t prefixes = (\"train\",)\n356\t history_end = base.TRAIN_END\n357\t candidate_name = \"validation_eligible_candidates.npz\"\n358\t state_name = \"validation_preference_state.parquet\"\n359\t else:\n360\t prefixes = (\"train\", \"val\")\n361\t history_end = base.VALIDATION_END\n362\t candidate_name = \"test_eligible_candidates.npz\"\n363\t state_name = \"test_preference_state.parquet\"\n364\t eligibility = CandidateSet.read(data / candidate_name)\n365\t if not np.array_equal(eligibility.item_ids, item_ids):\n366\t raise RuntimeError(\"catalog and candidate artifact item order differ\")\n367\t\n368\t explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n369\t inference_likes, inference_dislikes, like_counts, dislike_counts = (\n370\t base.load_inference_state(\n371\t data / state_name,\n372\t item_index,\n373\t eligibility.uids,\n374\t n_items,\n375\t )\n376\t )\n377\t inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n378\t inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n379\t inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n380\t\n381\t # Snapshot zero is the shared empty explicit state.\n382\t liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n383\t disliked_states = [\n384\t np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n385\t ]\n386\t state_counts: list[tuple[int, int]] = [(0, 0)]\n387\t user_ids: list[int] = []\n388\t offsets = [0]\n389\t timestamp_parts: list[np.ndarray] = []\n390\t item_parts: list[np.ndarray] = []\n391\t event_parts: list[np.ndarray] = []\n392\t activity_parts: list[np.ndarray] = []\n393\t state_parts: list[np.ndarray] = []\n394\t\n395\t listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n396\t started = time.monotonic()\n397\t for seen_users, (uid, listens) in enumerate(\n398\t base.merge_listen_streams(listen_paths, item_index), start=1\n399\t ):\n400\t parts = build_sequence_parts(\n401\t listens,\n402\t explicit_by_user.get(uid, base.empty_explicit_events()),\n403\t n_items,\n404\t liked_states,\n405\t disliked_states,\n406\t state_counts,\n407\t )\n408\t if parts is None:\n409\t continue\n410\t sequence_row = len(user_ids)\n411\t user_ids.append(uid)\n412\t timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n413\t item_parts.append(parts[1])\n414\t event_parts.append(parts[2])\n415\t activity_parts.append(parts[3])\n416\t state_parts.append(parts[4])\n417\t offsets.append(offsets[-1] + len(parts[1]))\n418\t inference_row = inference_rows.get(uid)\n419\t if inference_row is not None:\n420\t inference_sequence_rows[inference_row] = sequence_row\n421\t inference_user_features[inference_row] = final_user_features(\n422\t parts[0],\n423\t parts[1],\n424\t parts[2],\n425\t history_end,\n426\t int(like_counts[inference_row]),\n427\t int(dislike_counts[inference_row]),\n428\t )\n429\t if seen_users % 1_000 == 0:\n430\t log(\n431\t f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n432\t f\"states={len(liked_states):,} \"\n433\t f\"elapsed={time.monotonic() - started:.1f}s\"\n434\t )\n435\t\n436\t missing = np.flatnonzero(inference_sequence_rows < 0)\n437\t if len(missing):\n438\t inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n439\t inference_user_features[missing, 5] = (\n440\t np.log1p(dislike_counts[missing]) / 4.0\n441\t )\n442\t log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n443\t\n444\t prepared = SequenceData(\n445\t item_ids=item_ids,\n446\t item_features=base.build_item_features(catalog),\n447\t user_ids=np.asarray(user_ids, dtype=np.int64),\n448\t offsets=np.asarray(offsets, dtype=np.int64),\n449\t sequence_timestamps=np.concatenate(timestamp_parts),\n450\t sequence_items=np.concatenate(item_parts),\n451\t event_features=np.concatenate(event_parts),\n452\t activity_features=np.concatenate(activity_parts),\n453\t state_ids=np.concatenate(state_parts),\n454\t liked_states=np.stack(liked_states),\n455\t disliked_states=np.stack(disliked_states),\n456\t state_counts=np.asarray(state_counts, dtype=np.int32),\n457\t inference_user_rows=inference_sequence_rows,\n458\t inference_liked_items=inference_likes,\n459\t inference_disliked_items=inference_dislikes,\n460\t inference_user_features=inference_user_features,\n461\t )\n462\t log(\n463\t f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n464\t f\"tokens={len(prepared.sequence_items):,} \"\n465\t f\"next_item_targets={prepared.target_count:,} \"\n466\t f\"states={len(prepared.liked_states):,}\"\n467\t )\n468\t return prepared, eligibility\n469\t\n470\t\n471\tdef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n472\t return {\n473\t \"cache_version\": CACHE_VERSION,\n474\t \"stage\": stage,\n475\t \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n476\t \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n477\t \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n478\t }\n479\t\n480\t\n481\tdef save_cache(\n482\t path: Path, prepared: SequenceData, stage: str, config: Config\n483\t) -> None:\n484\t path.parent.mkdir(parents=True, exist_ok=True)\n485\t np.savez(\n486\t path,\n487\t metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n488\t **prepared.__dict__,\n489\t )\n490\t log(f\"saved public-derived full-sequence cache: {path}\")\n491\t\n492\t\n493\tdef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n494\t with np.load(path, allow_pickle=False) as artifact:\n495\t actual = json.loads(str(artifact[\"metadata\"].item()))\n496\t expected = cache_metadata(stage, config)\n497\t if actual != expected:\n498\t raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n499\t values = {\n500\t field: artifact[field].copy()\n501\t for field in SequenceData.__dataclass_fields__\n502\t }\n503\t prepared = SequenceData(**values)\n504\t log(\n505\t f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n506\t f\"targets={prepared.target_count:,}\"\n507\t )\n508\t return prepared\n509\t\n510\t\n511\tdef load_or_prepare(\n512\t public: Path,\n513\t stage: str,\n514\t config: Config,\n515\t cache: Path | None,\n516\t) -> tuple[SequenceData, CandidateSet]:\n517\t candidate_name = (\n518\t \"validation_eligible_candidates.npz\"\n519\t if stage == \"validation\"\n520\t else \"test_eligible_candidates.npz\"\n521\t )\n522\t eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n523\t if cache is not None and cache.is_file():\n524\t prepared = load_cache(cache, stage, config)\n525\t if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n526\t raise RuntimeError(\"cached catalog differs from candidate artifact\")\n527\t return prepared, eligibility\n528\t prepared, built_eligibility = prepare_data(public, stage, config)\n529\t if cache is not None:\n530\t save_cache(cache, prepared, stage, config)\n531\t return prepared, built_eligibility\n532\t\n533\t\n534\tdef build_item_correction(\n535\t prepared: SequenceData,\n536\t config: Config,\n537\t target_starts: np.ndarray,\n538\t target_counts: np.ndarray,\n539\t) -> torch.Tensor:\n540\t \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n541\t counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n542\t for user_row, count in enumerate(target_counts):\n543\t if not count:\n544\t continue\n545\t offset = int(prepared.offsets[user_row])\n546\t target_start = int(target_starts[user_row])\n547\t counts += np.bincount(\n548\t prepared.sequence_items[\n549\t offset\n550\t + target_start\n551\t + 1 : offset\n552\t + target_start\n553\t + int(count)\n554\t + 1\n555\t ].astype(np.int64),\n556\t minlength=len(prepared.item_ids),\n557\t )\n558\t total = float(counts.sum())\n559\t probabilities = (counts + 1.0) / (total + len(counts))\n560\t raw = np.power(\n561\t (1.0 / len(counts)) / probabilities,\n562\t config.inbatch_correction_power,\n563\t )\n564\t normalization = float(np.sum(counts * raw) / max(total, 1.0))\n565\t correction = np.clip(\n566\t raw / max(normalization, 1.0e-12),\n567\t config.inbatch_correction_min,\n568\t config.inbatch_correction_max,\n569\t ).astype(np.float32)\n570\t mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n571\t log(\n572\t f\"inbatch correction: min={correction.min():.3f} \"\n573\t f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n574\t )\n575\t return torch.from_numpy(correction)\n576\t\n577\t\n578\tdef build_known_positive_filter(\n579\t prepared: SequenceData, training_counts: np.ndarray\n580\t) -> np.ndarray:\n581\t \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n582\t width = (len(prepared.item_ids) + 7) // 8\n583\t packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n584\t retained = 0\n585\t for user_row, target_count in enumerate(training_counts):\n586\t if not target_count:\n587\t continue\n588\t offset = int(prepared.offsets[user_row])\n589\t # T adjacent targets consume sequence positions 0 through T. The\n590\t # chronological BCE suffix starts at T + 1 and is intentionally absent.\n591\t items = np.unique(\n592\t prepared.sequence_items[\n593\t offset : offset + int(target_count) + 1\n594\t ].astype(np.int64)\n595\t )\n596\t byte_columns = items >> 3\n597\t bit_values = np.left_shift(\n598\t np.uint8(1), (items & 7).astype(np.uint8)\n599\t )\n600\t np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n601\t retained += len(items)\n602\t log(\n603\t f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n604\t f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n605\t )\n606\t return packed\n607\t\n608\t\n609\tdef invalid_full_history_negatives(\n610\t negatives: np.ndarray,\n611\t targets: np.ndarray,\n612\t user_rows: np.ndarray,\n613\t known_positive_packed: np.ndarray,\n614\t liked_items: np.ndarray,\n615\t) -> np.ndarray:\n616\t \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617\t byte_columns = negatives >> 3\n618\t bit_columns = negatives & 7\n619\t known = known_positive_packed[user_rows[:, None], byte_columns]\n620\t invalid = ((known >> bit_columns) & 1).astype(bool)\n621\t invalid |= negatives == targets[:, None]\n622\t invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623\t for column in range(1, negatives.shape[1]):\n624\t invalid[:, column] |= (\n625\t negatives[:, column, None] == negatives[:, :column]\n626\t ).any(axis=1)\n627\t return invalid\n628\t\n629\t\n630\tdef draw_full_history_filtered_negatives(\n631\t targets: torch.Tensor,\n632\t user_rows: np.ndarray,\n633\t liked_items: torch.Tensor,\n634\t known_positive_packed: np.ndarray,\n635\t n_items: int,\n636\t config: Config,\n637\t item_correction: torch.Tensor,\n638\t generator: torch.Generator,\n639\t device: torch.device,\n640\t) -> tuple[torch.Tensor, torch.Tensor]:\n641\t \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n642\t targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n643\t liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n644\t batch_size = len(targets_cpu)\n645\t uniform = torch.randint(\n646\t n_items,\n647\t (batch_size, config.uniform_negatives),\n648\t generator=generator,\n649\t device=\"cpu\",\n650\t )\n651\t inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n652\t corrected_count = 0\n653\t if config.inbatch_negatives:\n654\t unique_targets = torch.unique(targets.detach().cpu())\n655\t corrected_count = min(\n656\t config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n657\t )\n658\t if corrected_count:\n659\t priorities = torch.rand(\n660\t (batch_size, len(unique_targets)), generator=generator\n661\t )\n662\t priorities.masked_fill_(\n663\t unique_targets[None, :].eq(\n664\t torch.from_numpy(targets_cpu)[:, None]\n665\t ),\n666\t -1.0,\n667\t )\n668\t columns = torch.topk(\n669\t priorities, k=corrected_count, dim=1\n670\t ).indices\n671\t inbatch = unique_targets[columns]\n672\t if corrected_count < config.inbatch_negatives:\n673\t filler = torch.randint(\n674\t n_items,\n675\t (batch_size, config.inbatch_negatives - corrected_count),\n676\t generator=generator,\n677\t device=\"cpu\",\n678\t )\n679\t inbatch = torch.cat([inbatch, filler], dim=1)\n680\t\n681\t negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682\t correction = np.ones_like(negatives, dtype=np.float32)\n683\t if corrected_count:\n684\t correction[\n685\t :, config.uniform_negatives : config.uniform_negatives + corrected_count\n686\t ] = item_correction[inbatch[:, :corrected_count]].numpy()\n687\t for _ in range(32):\n688\t invalid = invalid_full_history_negatives(\n689\t negatives,\n690\t targets_cpu,\n691\t user_rows,\n692\t known_positive_packed,\n693\t liked_cpu,\n694\t )\n695\t if not np.any(invalid):\n696\t return (\n697\t torch.from_numpy(negatives).to(device),\n698\t torch.from_numpy(correction).to(device),\n699\t )\n700\t replacements = torch.randint(\n701\t n_items,\n702\t (int(invalid.sum()),),\n703\t generator=generator,\n704\t device=\"cpu\",\n705\t ).numpy()\n706\t negatives[invalid] = replacements\n707\t # Rejected in-batch items become ordinary uniform draws.\n708\t correction[invalid] = 1.0\n709\t invalid = invalid_full_history_negatives(\n710\t negatives,\n711\t targets_cpu,\n712\t user_rows,\n713\t known_positive_packed,\n714\t liked_cpu,\n715\t )\n716\t if np.any(invalid):\n717\t raise RuntimeError(\"full-history negative resampling failed\")\n718\t return (\n719\t torch.from_numpy(negatives).to(device),\n720\t torch.from_numpy(correction).to(device),\n721\t )\n722\t\n723\t\n724\tdef draw_training_negatives(\n725\t positive_items: torch.Tensor,\n726\t known_history: torch.Tensor,\n727\t liked_items: torch.Tensor,\n728\t user_rows: np.ndarray,\n729\t known_positive_packed: np.ndarray | None,\n730\t prepared: SequenceData,\n731\t config: Config,\n732\t item_correction: torch.Tensor,\n733\t generator: torch.Generator,\n734\t device: torch.device,\n735\t) -> tuple[torch.Tensor, torch.Tensor]:\n736\t if config.negative_filter_scope == \"full-training-history\":\n737\t if known_positive_packed is None:\n738\t raise RuntimeError(\"full-history negative filter was not built\")\n739\t return draw_full_history_filtered_negatives(\n740\t positive_items,\n741\t user_rows,\n742\t liked_items,\n743\t known_positive_packed,\n744\t len(prepared.item_ids),\n745\t config,\n746\t item_correction,\n747\t generator,\n748\t device,\n749\t )\n750\t return base.draw_filtered_mixed_negatives(\n751\t positive_items,\n752\t known_history,\n753\t liked_items,\n754\t len(prepared.item_ids),\n755\t config.uniform_negatives,\n756\t config.inbatch_negatives,\n757\t item_correction,\n758\t generator,\n759\t device,\n760\t )\n761\t\n762\t\n763\tdef event_sequence(\n764\t model: base.SequentialTwoTower,\n765\t items: torch.Tensor,\n766\t features: torch.Tensor,\n767\t hidden: torch.Tensor,\n768\t) -> tuple[torch.Tensor, torch.Tensor]:\n769\t inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n770\t projected = model.event_projection(inputs)\n771\t return model.sequence_encoder(projected, hidden)\n772\t\n773\t\n774\tdef pool_explicit_states(\n775\t model: base.SequentialTwoTower,\n776\t prepared: SequenceData,\n777\t state_ids: np.ndarray,\n778\t device: torch.device,\n779\t) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n780\t unique_states, inverse = np.unique(state_ids, return_inverse=True)\n781\t liked_items = torch.from_numpy(\n782\t prepared.liked_states[unique_states].astype(np.int64)\n783\t ).to(device)\n784\t disliked_items = torch.from_numpy(\n785\t prepared.disliked_states[unique_states].astype(np.int64)\n786\t ).to(device)\n787\t liked_vectors = model._mean_context_channel(liked_items)\n788\t disliked_vectors = model._mean_context_channel(disliked_items)\n789\t inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n790\t counts = prepared.state_counts[state_ids]\n791\t return (\n792\t liked_vectors[inverse_rows],\n793\t disliked_vectors[inverse_rows],\n794\t torch.from_numpy(counts.astype(np.float32)).to(device),\n795\t )\n796\t\n797\t\n798\tdef user_queries(\n799\t model: base.SequentialTwoTower,\n800\t sequence_vectors: torch.Tensor,\n801\t liked_vectors: torch.Tensor,\n802\t disliked_vectors: torch.Tensor,\n803\t user_features: torch.Tensor,\n804\t) -> torch.Tensor:\n805\t return model.user_tower(\n806\t torch.cat(\n807\t [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n808\t dim=1,\n809\t )\n810\t )\n811\t\n812\t\n813\tdef candidate_vectors(\n814\t model: base.SequentialTwoTower,\n815\t positives: torch.Tensor,\n816\t negatives: torch.Tensor,\n817\t) -> tuple[torch.Tensor, torch.Tensor]:\n818\t all_items = torch.cat([positives, negatives.reshape(-1)])\n819\t unique_items, inverse = torch.unique(all_items, return_inverse=True)\n820\t encoded = model.encode_items(unique_items)\n821\t positive_vectors = encoded[inverse[: len(positives)]]\n822\t negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823\t len(positives), negatives.shape[1], -1\n824\t )\n825\t return positive_vectors, negative_vectors\n826\t\n827\t\n828\tdef history_windows(\n829\t sequence: np.ndarray,\n830\t start: int,\n831\t count: int,\n832\t width: int,\n833\t padding_item: int,\n834\t) -> np.ndarray:\n835\t base_start = max(0, start - width + 1)\n836\t segment = sequence[base_start : start + count]\n837\t padded = np.concatenate(\n838\t [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n839\t )\n840\t windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n841\t offset = start - base_start\n842\t return windows[offset : offset + count].copy()\n843\t\n844\t\n845\tdef corrected_negative_loss(\n846\t logits: torch.Tensor,\n847\t correction: torch.Tensor,\n848\t config: Config,\n849\t) -> torch.Tensor:\n850\t raw = F.softplus(logits)\n851\t total = config.uniform_negatives + config.inbatch_negatives\n852\t result = torch.zeros(len(logits), device=logits.device)\n853\t if config.uniform_negatives:\n854\t result += config.uniform_negatives * raw[\n855\t :, : config.uniform_negatives\n856\t ].mean(dim=1)\n857\t if config.inbatch_negatives:\n858\t selected = slice(config.uniform_negatives, total)\n859\t weights = correction[:, selected]\n860\t result += config.inbatch_negatives * (\n861\t (raw[:, selected] * weights).sum(dim=1)\n862\t / weights.sum(dim=1).clamp_min(1.0e-6)\n863\t )\n864\t return result / total\n865\t\n866\t\n867\tdef retained_target_window_starts(\n868\t total_targets: np.ndarray,\n869\t validation_counts: np.ndarray,\n870\t training_counts: np.ndarray,", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:57.890Z", "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": "2026-10-03T09:03:57.890Z", "data": { "type": "total_tokens_reminder", "text": "14962686 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:57.892Z", "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": "2026-10-03T09:03:59.759Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:03:59.759Z", "callId": "toolu_013bDhHaaJMKL2dSy3NRas6g", "name": "Read", "input": { "file_path": "/task/current_model/train_current_model.py", "offset": 867, "limit": 500 }, "result": { "timestamp": "2026-10-03T09:03:59.766Z", "content": "867\tdef retained_target_window_starts(\n868\t total_targets: np.ndarray,\n869\t validation_counts: np.ndarray,\n870\t training_counts: np.ndarray,\n871\t) -> np.ndarray:\n872\t \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n873\t training_region_ends = total_targets - validation_counts\n874\t window_starts = training_region_ends - training_counts\n875\t if (\n876\t np.any(window_starts < 0)\n877\t or np.any(training_counts < 0)\n878\t or np.any(validation_counts < 0)\n879\t ):\n880\t raise RuntimeError(\"chronological target partition produced negative counts\")\n881\t if not np.array_equal(\n882\t window_starts + training_counts + validation_counts, total_targets\n883\t ):\n884\t raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n885\t return window_starts\n886\t\n887\t\n888\tdef chronological_target_partition(\n889\t prepared: SequenceData,\n890\t validation_fraction: float,\n891\t max_train_targets_per_user: int,\n892\t max_validation_targets_per_user: int,\n893\t) -> TargetPartition:\n894\t \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n895\t totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n896\t validation = np.floor(totals * validation_fraction).astype(np.int64)\n897\t validation[(totals >= 2) & (validation == 0)] = 1\n898\t if max_validation_targets_per_user:\n899\t validation = np.minimum(validation, max_validation_targets_per_user)\n900\t\n901\t history_training = totals - validation\n902\t training = history_training.copy()\n903\t if max_train_targets_per_user:\n904\t training = np.minimum(training, max_train_targets_per_user)\n905\t window_starts = retained_target_window_starts(totals, validation, training)\n906\t return TargetPartition(\n907\t window_starts=window_starts,\n908\t training_counts=training,\n909\t validation_counts=validation,\n910\t history_training_counts=history_training,\n911\t )\n912\t\n913\t\n914\tdef load_organic_like_outcomes(\n915\t public: Path, stage: str, item_ids: np.ndarray\n916\t) -> dict[int, dict[int, np.ndarray]]:\n917\t \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n918\t prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n919\t frames = [\n920\t pd.read_parquet(\n921\t public / \"data\" / f\"{prefix}_likes.parquet\",\n922\t columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n923\t )\n924\t for prefix in prefixes\n925\t ]\n926\t likes = pd.concat(frames, ignore_index=True)\n927\t likes = likes[likes.is_organic == 1].copy()\n928\t mapped = pd.Index(item_ids).get_indexer(\n929\t likes.item_id.to_numpy(dtype=np.int64)\n930\t )\n931\t likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n932\t likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n933\t likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n934\t nested: dict[int, dict[int, list[int]]] = {}\n935\t for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n936\t index=False, name=None\n937\t ):\n938\t nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n939\t int(raw_timestamp)\n940\t )\n941\t result = {\n942\t uid: {\n943\t item: np.asarray(timestamps, dtype=np.int64)\n944\t for item, timestamps in items.items()\n945\t }\n946\t for uid, items in nested.items()\n947\t }\n948\t log(\n949\t f\"loaded organic-like outcomes: rows={len(likes):,} \"\n950\t f\"users={len(result):,}\"\n951\t )\n952\t return result\n953\t\n954\t\n955\tdef build_target_weights(\n956\t public: Path,\n957\t stage: str,\n958\t prepared: SequenceData,\n959\t training_counts: np.ndarray,\n960\t config: Config,\n961\t) -> np.ndarray:\n962\t \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n963\t if config.target_weighting == \"uniform\":\n964\t weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n965\t else:\n966\t weights = np.full(\n967\t len(prepared.sequence_items),\n968\t config.ordinary_target_weight,\n969\t dtype=np.float32,\n970\t )\n971\t organic_strong = (\n972\t (prepared.event_features[:, 0] > 0.5)\n973\t & (prepared.event_features[:, 1] >= 0.8)\n974\t )\n975\t weights[organic_strong] = config.organic_strong_target_weight\n976\t if config.target_weighting == \"discovery-graded\":\n977\t like_outcomes = load_organic_like_outcomes(\n978\t public, stage, prepared.item_ids\n979\t )\n980\t history_end = (\n981\t base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n982\t )\n983\t horizon = config.discovery_horizon_days * base.DAY\n984\t grade_three = 0\n985\t for user_row, uid in enumerate(prepared.user_ids):\n986\t start = int(prepared.offsets[user_row])\n987\t end = int(prepared.offsets[user_row + 1])\n988\t if end - start < 2:\n989\t continue\n990\t last_training_target = start + int(training_counts[user_row])\n991\t first_validation_target = last_training_target + 1\n992\t split_timestamp = (\n993\t int(prepared.sequence_timestamps[first_validation_target])\n994\t if first_validation_target < end\n995\t else history_end\n996\t )\n997\t user_likes = like_outcomes.get(int(uid), {})\n998\t seen = {int(prepared.sequence_items[start])}\n999\t for position in range(start + 1, end):\n1000\t item = int(prepared.sequence_items[position])\n1001\t novel = item not in seen\n1002\t seen.add(item)\n1003\t if not novel:\n1004\t # Repeats remain training examples, but they cannot be\n1005\t # eligible discovery outcomes at the corresponding\n1006\t # cutoff and therefore keep only the ordinary weight.\n1007\t weights[position] = config.ordinary_target_weight\n1008\t continue\n1009\t timestamps = user_likes.get(item)\n1010\t if timestamps is None:\n1011\t continue\n1012\t timestamp = int(prepared.sequence_timestamps[position])\n1013\t upper = min(\n1014\t timestamp + horizon,\n1015\t split_timestamp\n1016\t if position <= last_training_target\n1017\t else history_end,\n1018\t )\n1019\t outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n1020\t if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n1021\t weights[position] = config.organic_like_target_weight\n1022\t grade_three += 1\n1023\t log(\n1024\t f\"grade-3 first-encounter targets={grade_three:,} \"\n1025\t f\"horizon_days={config.discovery_horizon_days}\"\n1026\t )\n1027\t log(\n1028\t f\"target weighting={config.target_weighting} \"\n1029\t f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n1030\t f\"max={weights.max():.3f}\"\n1031\t )\n1032\t return weights\n1033\t\n1034\t\n1035\tdef build_dense_block(\n1036\t prepared: SequenceData,\n1037\t target_weights: np.ndarray,\n1038\t users: np.ndarray,\n1039\t window_starts: np.ndarray,\n1040\t process_counts: np.ndarray,\n1041\t score_starts: np.ndarray,\n1042\t start: int,\n1043\t steps: int,\n1044\t config: Config,\n1045\t) -> tuple[np.ndarray, ...]:\n1046\t \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n1047\t batch_size = len(users)\n1048\t padding_item = len(prepared.item_ids)\n1049\t input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n1050\t event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n1051\t activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n1052\t state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n1053\t targets = np.zeros((batch_size, steps), dtype=np.uint16)\n1054\t weights = np.ones((batch_size, steps), dtype=np.float32)\n1055\t scored = np.zeros((batch_size, steps), dtype=bool)\n1056\t histories = np.full(\n1057\t (batch_size, steps, config.bptt_steps),\n1058\t padding_item,\n1059\t dtype=np.uint16,\n1060\t )\n1061\t processed = np.zeros(batch_size, dtype=np.int32)\n1062\t\n1063\t lengths = prepared.lengths\n1064\t for row, user_row in enumerate(users):\n1065\t count = min(steps, max(int(process_counts[row]) - start, 0))\n1066\t if not count:\n1067\t continue\n1068\t offset = int(prepared.offsets[user_row])\n1069\t window_start = int(window_starts[row])\n1070\t absolute_start = window_start + start\n1071\t positions = slice(\n1072\t offset + absolute_start,\n1073\t offset + absolute_start + count,\n1074\t )\n1075\t target_positions = slice(\n1076\t offset + absolute_start + 1,\n1077\t offset + absolute_start + count + 1,\n1078\t )\n1079\t input_items[row, :count] = prepared.sequence_items[positions]\n1080\t event_features[row, :count] = prepared.event_features[positions]\n1081\t activity_features[row, :count] = prepared.activity_features[positions]\n1082\t state_ids[row, :count] = prepared.state_ids[positions]\n1083\t targets[row, :count] = prepared.sequence_items[target_positions]\n1084\t weights[row, :count] = target_weights[target_positions]\n1085\t score_from = min(count, max(int(score_starts[row]) - start, 0))\n1086\t scored[row, score_from:count] = True\n1087\t processed[row] = count\n1088\t user_sequence = prepared.sequence_items[\n1089\t offset : offset + int(lengths[user_row])\n1090\t ]\n1091\t histories[row, :count] = history_windows(\n1092\t user_sequence,\n1093\t absolute_start,\n1094\t count,\n1095\t config.bptt_steps,\n1096\t padding_item,\n1097\t )\n1098\t return (\n1099\t input_items,\n1100\t event_features,\n1101\t activity_features,\n1102\t state_ids,\n1103\t targets,\n1104\t scored,\n1105\t histories,\n1106\t processed,\n1107\t weights,\n1108\t )\n1109\t\n1110\t\n1111\tdef score_target_microbatches(\n1112\t model: base.SequentialTwoTower,\n1113\t prepared: SequenceData,\n1114\t config: Config,\n1115\t sequence_vectors: torch.Tensor,\n1116\t flat_states: np.ndarray,\n1117\t flat_activity: np.ndarray,\n1118\t positive_items: torch.Tensor,\n1119\t negatives: torch.Tensor,\n1120\t negative_weights: torch.Tensor,\n1121\t sample_weights: np.ndarray,\n1122\t device: torch.device,\n1123\t backward: bool,\n1124\t) -> tuple[dict[str, float], torch.Tensor | None]:\n1125\t \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n1126\t target_count = len(positive_items)\n1127\t total_weight = float(sample_weights.sum())\n1128\t if total_weight <= 0:\n1129\t raise RuntimeError(\"target block has no positive sample weight\")\n1130\t sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n1131\t statistics = {\n1132\t \"loss_sum\": 0.0,\n1133\t \"positive_sum\": 0.0,\n1134\t \"negative_sum\": 0.0,\n1135\t \"pair_correct\": 0.0,\n1136\t \"weight_sum\": 0.0,\n1137\t }\n1138\t for begin in range(0, target_count, config.loss_microbatch_size):\n1139\t end = min(begin + config.loss_microbatch_size, target_count)\n1140\t selected_states = flat_states[begin:end]\n1141\t if backward:\n1142\t sequence_input = (\n1143\t sequence_vectors[begin:end].detach().requires_grad_(True)\n1144\t )\n1145\t else:\n1146\t sequence_input = sequence_vectors[begin:end]\n1147\t liked_vectors, disliked_vectors, _ = pool_explicit_states(\n1148\t model, prepared, selected_states, device\n1149\t )\n1150\t counts = prepared.state_counts[selected_states]\n1151\t numeric = np.zeros((end - begin, 6), dtype=np.float32)\n1152\t numeric[:, :4] = flat_activity[begin:end]\n1153\t numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n1154\t numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n1155\t query_vectors = user_queries(\n1156\t model,\n1157\t sequence_input,\n1158\t liked_vectors,\n1159\t disliked_vectors,\n1160\t torch.from_numpy(numeric).to(device),\n1161\t )\n1162\t positive_vectors, negative_vectors = candidate_vectors(\n1163\t model,\n1164\t positive_items[begin:end],\n1165\t negatives[begin:end],\n1166\t )\n1167\t positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n1168\t negative_logits = torch.einsum(\n1169\t \"bd,bkd->bk\", query_vectors, negative_vectors\n1170\t )\n1171\t positive_loss = F.softplus(-positive_logits)\n1172\t negative_loss = corrected_negative_loss(\n1173\t negative_logits, negative_weights[begin:end], config\n1174\t )\n1175\t selected_weights = torch.from_numpy(\n1176\t sample_weights[begin:end].astype(np.float32, copy=False)\n1177\t ).to(device)\n1178\t loss_sum = (\n1179\t selected_weights * (positive_loss + negative_loss) / 2.0\n1180\t ).sum()\n1181\t if not bool(torch.isfinite(loss_sum)):\n1182\t raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1183\t if backward:\n1184\t (loss_sum / total_weight).backward()\n1185\t if sequence_input.grad is None:\n1186\t raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n1187\t sequence_gradient[begin:end] = sequence_input.grad\n1188\t statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n1189\t statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n1190\t statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1191\t statistics[\"pair_correct\"] += float(\n1192\t (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1193\t )\n1194\t statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n1195\t return statistics, sequence_gradient\n1196\t\n1197\t\n1198\tdef release_accelerator_cache(device: torch.device) -> None:\n1199\t if device.type == \"mps\":\n1200\t torch.mps.empty_cache()\n1201\t\n1202\t\n1203\tdef run_training_epoch(\n1204\t model: base.SequentialTwoTower,\n1205\t optimizer: torch.optim.Optimizer,\n1206\t prepared: SequenceData,\n1207\t config: Config,\n1208\t target_weights: np.ndarray,\n1209\t window_starts: np.ndarray,\n1210\t target_counts: np.ndarray,\n1211\t item_correction: torch.Tensor,\n1212\t known_positive_packed: np.ndarray | None,\n1213\t generator: torch.Generator,\n1214\t device: torch.device,\n1215\t epoch: int,\n1216\t) -> dict[str, float]:\n1217\t model.train()\n1218\t train_users = np.flatnonzero(target_counts > 0)\n1219\t sorted_users = train_users[\n1220\t np.argsort(target_counts[train_users], kind=\"stable\")\n1221\t ]\n1222\t groups = [\n1223\t sorted_users[start : start + config.user_batch_size]\n1224\t for start in range(0, len(sorted_users), config.user_batch_size)\n1225\t ]\n1226\t group_order = torch.randperm(len(groups), generator=generator).numpy()\n1227\t negative_count = config.uniform_negatives + config.inbatch_negatives\n1228\t total_loss = 0.0\n1229\t total_targets = 0\n1230\t total_weight = 0.0\n1231\t total_positive = 0.0\n1232\t total_negative = 0.0\n1233\t total_pair_correct = 0.0\n1234\t batches = 0\n1235\t started = time.monotonic()\n1236\t\n1237\t for group_number in group_order:\n1238\t users = groups[int(group_number)]\n1239\t group_counts = target_counts[users]\n1240\t hidden = torch.zeros(\n1241\t 1, len(users), config.embedding_dim, device=device\n1242\t )\n1243\t max_targets = int(group_counts.max())\n1244\t for start in range(0, max_targets, config.bptt_steps):\n1245\t steps = min(config.bptt_steps, max_targets - start)\n1246\t block = build_dense_block(\n1247\t prepared,\n1248\t target_weights,\n1249\t users,\n1250\t window_starts[users],\n1251\t group_counts,\n1252\t np.zeros(len(users), dtype=np.int64),\n1253\t start,\n1254\t steps,\n1255\t config,\n1256\t )\n1257\t sequence_outputs, _ = event_sequence(\n1258\t model,\n1259\t torch.from_numpy(block[0].astype(np.int64)).to(device),\n1260\t torch.from_numpy(block[1].astype(np.float32)).to(device),\n1261\t hidden,\n1262\t )\n1263\t hidden = torch.stack(\n1264\t [\n1265\t sequence_outputs[row, count - 1]\n1266\t if count\n1267\t else hidden[0, row]\n1268\t for row, count in enumerate(block[7])\n1269\t ]\n1270\t ).unsqueeze(0).detach()\n1271\t flat_scored = block[5].reshape(-1)\n1272\t if not np.any(flat_scored):\n1273\t continue\n1274\t mask = torch.from_numpy(flat_scored).to(device)\n1275\t sequence_vectors = sequence_outputs.reshape(\n1276\t -1, config.embedding_dim\n1277\t )[mask]\n1278\t flat_states = block[3].reshape(-1)[flat_scored]\n1279\t positive_items = torch.from_numpy(\n1280\t block[4].reshape(-1)[flat_scored].astype(np.int64)\n1281\t ).to(device)\n1282\t known_history = torch.from_numpy(\n1283\t block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1284\t np.int64\n1285\t )\n1286\t ).to(device)\n1287\t liked_items = torch.from_numpy(\n1288\t prepared.liked_states[flat_states].astype(np.int64)\n1289\t ).to(device)\n1290\t flat_user_rows = np.broadcast_to(\n1291\t users[:, None], block[5].shape\n1292\t ).reshape(-1)[flat_scored]\n1293\t negatives, negative_weights = draw_training_negatives(\n1294\t positive_items,\n1295\t known_history,\n1296\t liked_items,\n1297\t flat_user_rows,\n1298\t known_positive_packed,\n1299\t prepared,\n1300\t config,\n1301\t item_correction,\n1302\t generator,\n1303\t device,\n1304\t )\n1305\t optimizer.zero_grad(set_to_none=True)\n1306\t statistics, sequence_gradient = score_target_microbatches(\n1307\t model,\n1308\t prepared,\n1309\t config,\n1310\t sequence_vectors,\n1311\t flat_states,\n1312\t block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n1313\t positive_items,\n1314\t negatives,\n1315\t negative_weights,\n1316\t block[8].reshape(-1)[flat_scored],\n1317\t device,\n1318\t backward=True,\n1319\t )\n1320\t if sequence_gradient is None:\n1321\t raise RuntimeError(\"training block omitted its sequence gradient\")\n1322\t sequence_vectors.backward(sequence_gradient)\n1323\t nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n1324\t optimizer.step()\n1325\t\n1326\t count = len(positive_items)\n1327\t total_loss += statistics[\"loss_sum\"]\n1328\t total_targets += count\n1329\t total_weight += statistics[\"weight_sum\"]\n1330\t total_positive += statistics[\"positive_sum\"]\n1331\t total_negative += statistics[\"negative_sum\"]\n1332\t total_pair_correct += statistics[\"pair_correct\"]\n1333\t batches += 1\n1334\t if batches % 25 == 0:\n1335\t release_accelerator_cache(device)\n1336\t if batches % 250 == 0:\n1337\t batch_bce = statistics[\"loss_sum\"] / max(\n1338\t statistics[\"weight_sum\"], 1.0e-12\n1339\t )\n1340\t running_bce = total_loss / max(total_weight, 1.0e-12)\n1341\t log(\n1342\t f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n1343\t f\"targets={total_targets:,} \"\n1344\t f\"batch_bce={batch_bce:.6f} \"\n1345\t f\"running_bce={running_bce:.6f} \"\n1346\t f\"elapsed={time.monotonic() - started:.1f}s\"\n1347\t )\n1348\t\n1349\t return {\n1350\t \"epoch\": float(epoch),\n1351\t \"loss\": total_loss / max(total_weight, 1.0e-12),\n1352\t \"positive_logit\": total_positive / max(total_targets, 1),\n1353\t \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1354\t \"pair_accuracy\": total_pair_correct\n1355\t / max(total_targets * negative_count, 1),\n1356\t \"targets\": float(total_targets),\n1357\t \"target_weight_sum\": total_weight,\n1358\t \"batches\": float(batches),\n1359\t \"seconds\": time.monotonic() - started,\n1360\t }\n1361\t\n1362\t\n1363\t@torch.no_grad()\n1364\tdef evaluate_validation_bce(\n1365\t model: base.SequentialTwoTower,\n1366\t prepared: SequenceData,", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:03:59.772Z", "data": { "type": "total_tokens_reminder", "text": "14953986 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:03:59.774Z", "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": "2026-10-03T09:04:01.363Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:04:01.363Z", "callId": "toolu_01XpvceTAVQrQgqukLoHAsKw", "name": "Read", "input": { "file_path": "/task/current_model/train_current_model.py", "offset": 1364, "limit": 800 }, "result": { "timestamp": "2026-10-03T09:04:01.478Z", "content": "1364\tdef evaluate_validation_bce(\n1365\t model: base.SequentialTwoTower,\n1366\t prepared: SequenceData,\n1367\t config: Config,\n1368\t target_weights: np.ndarray,\n1369\t window_starts: np.ndarray,\n1370\t training_counts: np.ndarray,\n1371\t validation_counts: np.ndarray,\n1372\t item_correction: torch.Tensor,\n1373\t known_positive_packed: np.ndarray | None,\n1374\t device: torch.device,\n1375\t) -> dict[str, float]:\n1376\t \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n1377\t model.eval()\n1378\t totals = training_counts + validation_counts\n1379\t users_with_validation = np.flatnonzero(validation_counts > 0)\n1380\t ordered = users_with_validation[\n1381\t np.argsort(totals[users_with_validation], kind=\"stable\")\n1382\t ]\n1383\t groups = [\n1384\t ordered[start : start + config.user_batch_size]\n1385\t for start in range(0, len(ordered), config.user_batch_size)\n1386\t ]\n1387\t generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n1388\t total_loss = 0.0\n1389\t total_targets = 0\n1390\t total_weight = 0.0\n1391\t started = time.monotonic()\n1392\t\n1393\t for group_number, users in enumerate(groups, start=1):\n1394\t group_totals = totals[users]\n1395\t group_starts = training_counts[users]\n1396\t hidden = torch.zeros(\n1397\t 1, len(users), config.embedding_dim, device=device\n1398\t )\n1399\t max_targets = int(group_totals.max())\n1400\t for start in range(0, max_targets, config.bptt_steps):\n1401\t steps = min(config.bptt_steps, max_targets - start)\n1402\t block = build_dense_block(\n1403\t prepared,\n1404\t target_weights,\n1405\t users,\n1406\t window_starts[users],\n1407\t group_totals,\n1408\t group_starts,\n1409\t start,\n1410\t steps,\n1411\t config,\n1412\t )\n1413\t sequence_outputs, _ = event_sequence(\n1414\t model,\n1415\t torch.from_numpy(block[0].astype(np.int64)).to(device),\n1416\t torch.from_numpy(block[1].astype(np.float32)).to(device),\n1417\t hidden,\n1418\t )\n1419\t hidden = torch.stack(\n1420\t [\n1421\t sequence_outputs[row, count - 1]\n1422\t if count\n1423\t else hidden[0, row]\n1424\t for row, count in enumerate(block[7])\n1425\t ]\n1426\t ).unsqueeze(0)\n1427\t flat_scored = block[5].reshape(-1)\n1428\t if not np.any(flat_scored):\n1429\t continue\n1430\t mask = torch.from_numpy(flat_scored).to(device)\n1431\t sequence_vectors = sequence_outputs.reshape(\n1432\t -1, config.embedding_dim\n1433\t )[mask]\n1434\t flat_states = block[3].reshape(-1)[flat_scored]\n1435\t positive_items = torch.from_numpy(\n1436\t block[4].reshape(-1)[flat_scored].astype(np.int64)\n1437\t ).to(device)\n1438\t known_history = torch.from_numpy(\n1439\t block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1440\t np.int64\n1441\t )\n1442\t ).to(device)\n1443\t liked_items = torch.from_numpy(\n1444\t prepared.liked_states[flat_states].astype(np.int64)\n1445\t ).to(device)\n1446\t flat_user_rows = np.broadcast_to(\n1447\t users[:, None], block[5].shape\n1448\t ).reshape(-1)[flat_scored]\n1449\t negatives, negative_weights = draw_training_negatives(\n1450\t positive_items,\n1451\t known_history,\n1452\t liked_items,\n1453\t flat_user_rows,\n1454\t known_positive_packed,\n1455\t prepared,\n1456\t config,\n1457\t item_correction,\n1458\t generator,\n1459\t device,\n1460\t )\n1461\t statistics, _ = score_target_microbatches(\n1462\t model,\n1463\t prepared,\n1464\t config,\n1465\t sequence_vectors,\n1466\t flat_states,\n1467\t block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n1468\t positive_items,\n1469\t negatives,\n1470\t negative_weights,\n1471\t block[8].reshape(-1)[flat_scored],\n1472\t device,\n1473\t backward=False,\n1474\t )\n1475\t total_loss += statistics[\"loss_sum\"]\n1476\t total_targets += len(positive_items)\n1477\t total_weight += statistics[\"weight_sum\"]\n1478\t if group_number % 25 == 0:\n1479\t release_accelerator_cache(device)\n1480\t return {\n1481\t \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482\t \"validation_targets\": float(total_targets),\n1483\t \"validation_target_weight_sum\": total_weight,\n1484\t \"validation_seconds\": time.monotonic() - started,\n1485\t }\n1486\t\n1487\t\n1488\tdef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1489\t return {\n1490\t name: value.detach().cpu().clone()\n1491\t for name, value in model.state_dict().items()\n1492\t }\n1493\t\n1494\t\n1495\tdef save_epoch_checkpoint(\n1496\t checkpoint_dir: Path,\n1497\t epoch: int,\n1498\t model_state: dict[str, torch.Tensor],\n1499\t config: Config,\n1500\t diagnostics: dict[str, float],\n1501\t optimizer: torch.optim.Optimizer,\n1502\t generator: torch.Generator,\n1503\t) -> Path:\n1504\t checkpoint_dir.mkdir(parents=True, exist_ok=True)\n1505\t path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n1506\t torch.save(\n1507\t {\n1508\t \"epoch\": epoch,\n1509\t \"config\": asdict(config),\n1510\t \"diagnostics\": diagnostics,\n1511\t \"model_state\": model_state,\n1512\t \"optimizer_state\": optimizer.state_dict(),\n1513\t \"generator_state\": generator.get_state(),\n1514\t },\n1515\t path,\n1516\t )\n1517\t log(f\"saved epoch checkpoint: {path}\")\n1518\t return path\n1519\t\n1520\t\n1521\tdef train_model(\n1522\t public: Path,\n1523\t stage: str,\n1524\t prepared: SequenceData,\n1525\t config: Config,\n1526\t device: torch.device,\n1527\t checkpoint_dir: Path | None,\n1528\t resume_checkpoint: Path | None,\n1529\t) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n1530\t partition = chronological_target_partition(\n1531\t prepared,\n1532\t config.validation_fraction,\n1533\t config.max_train_targets_per_user,\n1534\t config.max_validation_targets_per_user,\n1535\t )\n1536\t training_counts = partition.training_counts\n1537\t validation_counts = partition.validation_counts\n1538\t log(\n1539\t f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n1540\t f\"validation={int(validation_counts.sum()):,} \"\n1541\t f\"selected={partition.selected_count:,} \"\n1542\t f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n1543\t f\"all={prepared.target_count:,}\"\n1544\t )\n1545\t model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n1546\t device\n1547\t )\n1548\t optimizer = torch.optim.AdamW(\n1549\t model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n1550\t )\n1551\t generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n1552\t item_correction = build_item_correction(\n1553\t prepared,\n1554\t config,\n1555\t partition.window_starts,\n1556\t training_counts,\n1557\t )\n1558\t target_weights = build_target_weights(\n1559\t public,\n1560\t stage,\n1561\t prepared,\n1562\t partition.history_training_counts,\n1563\t config,\n1564\t )\n1565\t known_positive_packed = (\n1566\t build_known_positive_filter(prepared, partition.history_training_counts)\n1567\t if config.negative_filter_scope == \"full-training-history\"\n1568\t else None\n1569\t )\n1570\t history: list[dict[str, float]] = []\n1571\t best_loss = float(\"inf\")\n1572\t best_epoch = 0\n1573\t best_state: dict[str, torch.Tensor] | None = None\n1574\t epochs_without_improvement = 0\n1575\t start_epoch = 0\n1576\t\n1577\t if resume_checkpoint is not None:\n1578\t payload = torch.load(\n1579\t resume_checkpoint, map_location=device, weights_only=False\n1580\t )\n1581\t stored_config = payload.get(\"config\", {})\n1582\t architecture_fields = (\n1583\t \"bptt_steps\",\n1584\t \"embedding_dim\",\n1585\t \"hidden_dim\",\n1586\t \"user_hidden_1\",\n1587\t \"user_hidden_2\",\n1588\t \"item_hidden_1\",\n1589\t \"item_hidden_2\",\n1590\t )\n1591\t mismatches = {\n1592\t name: (stored_config.get(name), getattr(config, name))\n1593\t for name in architecture_fields\n1594\t if stored_config.get(name) != getattr(config, name)\n1595\t }\n1596\t if mismatches:\n1597\t raise RuntimeError(\n1598\t f\"resume checkpoint architecture differs: {mismatches}\"\n1599\t )\n1600\t stored_filter_scope = stored_config.get(\n1601\t \"negative_filter_scope\", \"last-100\"\n1602\t )\n1603\t stored_target_weighting = stored_config.get(\n1604\t \"target_weighting\", \"uniform\"\n1605\t )\n1606\t stored_weight_values = (\n1607\t stored_config.get(\"ordinary_target_weight\", 1.0),\n1608\t stored_config.get(\"organic_strong_target_weight\", 1.0),\n1609\t stored_config.get(\"organic_like_target_weight\", 3.0),\n1610\t stored_config.get(\"discovery_horizon_days\", 7),\n1611\t )\n1612\t current_weight_values = (\n1613\t config.ordinary_target_weight,\n1614\t config.organic_strong_target_weight,\n1615\t config.organic_like_target_weight,\n1616\t config.discovery_horizon_days,\n1617\t )\n1618\t stored_window_values = (\n1619\t stored_config.get(\"validation_fraction\", 0.10),\n1620\t stored_config.get(\"max_train_targets_per_user\", 0),\n1621\t stored_config.get(\"max_validation_targets_per_user\", 0),\n1622\t )\n1623\t current_window_values = (\n1624\t config.validation_fraction,\n1625\t config.max_train_targets_per_user,\n1626\t config.max_validation_targets_per_user,\n1627\t )\n1628\t objective_changed = (\n1629\t stored_filter_scope != config.negative_filter_scope\n1630\t or stored_target_weighting != config.target_weighting\n1631\t or stored_weight_values != current_weight_values\n1632\t or stored_window_values != current_window_values\n1633\t )\n1634\t model.load_state_dict(payload[\"model_state\"])\n1635\t if \"optimizer_state\" in payload and not objective_changed:\n1636\t optimizer.load_state_dict(payload[\"optimizer_state\"])\n1637\t for group in optimizer.param_groups:\n1638\t group[\"lr\"] = config.learning_rate\n1639\t group[\"weight_decay\"] = config.weight_decay\n1640\t log(f\"restored optimizer state from {resume_checkpoint}\")\n1641\t else:\n1642\t log(\n1643\t f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n1644\t )\n1645\t if \"generator_state\" in payload and not objective_changed:\n1646\t generator.set_state(payload[\"generator_state\"])\n1647\t start_epoch = int(payload[\"epoch\"])\n1648\t if start_epoch >= config.epochs:\n1649\t raise RuntimeError(\n1650\t f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n1651\t )\n1652\t prior = {\n1653\t str(name): float(value)\n1654\t for name, value in payload.get(\"diagnostics\", {}).items()\n1655\t }\n1656\t if objective_changed:\n1657\t prior.pop(\"validation_loss\", None)\n1658\t log(\n1659\t \"BCE objective changed: filter \"\n1660\t f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1661\t \"target weighting \"\n1662\t f\"{stored_target_weighting}->{config.target_weighting}, \"\n1663\t f\"target window {stored_window_values}->{current_window_values}; \"\n1664\t \"recomputing the resume checkpoint's validation BCE\"\n1665\t )\n1666\t if \"validation_loss\" not in prior:\n1667\t prior.update(\n1668\t evaluate_validation_bce(\n1669\t model,\n1670\t prepared,\n1671\t config,\n1672\t target_weights,\n1673\t partition.window_starts,\n1674\t training_counts,\n1675\t validation_counts,\n1676\t item_correction,\n1677\t known_positive_packed,\n1678\t device,\n1679\t )\n1680\t )\n1681\t history.append(prior)\n1682\t best_loss = float(prior[\"validation_loss\"])\n1683\t best_epoch = start_epoch\n1684\t best_state = snapshot_model(model)\n1685\t log(\n1686\t f\"resumed validation-BCE training: epoch={start_epoch} \"\n1687\t f\"loss={best_loss:.6f}\"\n1688\t )\n1689\t\n1690\t for epoch in range(start_epoch + 1, config.epochs + 1):\n1691\t diagnostics = run_training_epoch(\n1692\t model,\n1693\t optimizer,\n1694\t prepared,\n1695\t config,\n1696\t target_weights,\n1697\t partition.window_starts,\n1698\t training_counts,\n1699\t item_correction,\n1700\t known_positive_packed,\n1701\t generator,\n1702\t device,\n1703\t epoch,\n1704\t )\n1705\t validation = evaluate_validation_bce(\n1706\t model,\n1707\t prepared,\n1708\t config,\n1709\t target_weights,\n1710\t partition.window_starts,\n1711\t training_counts,\n1712\t validation_counts,\n1713\t item_correction,\n1714\t known_positive_packed,\n1715\t device,\n1716\t )\n1717\t diagnostics.update(validation)\n1718\t history.append(diagnostics)\n1719\t current_state = snapshot_model(model)\n1720\t if checkpoint_dir is not None:\n1721\t save_epoch_checkpoint(\n1722\t checkpoint_dir,\n1723\t epoch,\n1724\t current_state,\n1725\t config,\n1726\t diagnostics,\n1727\t optimizer,\n1728\t generator,\n1729\t )\n1730\t improved = (\n1731\t diagnostics[\"validation_loss\"]\n1732\t < best_loss - config.early_stopping_min_delta\n1733\t )\n1734\t if improved:\n1735\t best_loss = diagnostics[\"validation_loss\"]\n1736\t best_epoch = epoch\n1737\t best_state = current_state\n1738\t epochs_without_improvement = 0\n1739\t if checkpoint_dir is not None:\n1740\t torch.save(\n1741\t {\n1742\t \"epoch\": epoch,\n1743\t \"config\": asdict(config),\n1744\t \"diagnostics\": diagnostics,\n1745\t \"model_state\": current_state,\n1746\t \"optimizer_state\": optimizer.state_dict(),\n1747\t \"generator_state\": generator.get_state(),\n1748\t },\n1749\t checkpoint_dir / \"best.pt\",\n1750\t )\n1751\t else:\n1752\t epochs_without_improvement += 1\n1753\t log(\n1754\t f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755\t f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1756\t f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n1757\t f\"train_targets={int(diagnostics['targets']):,} \"\n1758\t f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1759\t f\"train_seconds={diagnostics['seconds']:.1f} \"\n1760\t f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1761\t )\n1762\t release_accelerator_cache(device)\n1763\t if epochs_without_improvement >= config.early_stopping_patience:\n1764\t log(\n1765\t f\"early stopping after epoch={epoch}; \"\n1766\t f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1767\t )\n1768\t break\n1769\t\n1770\t if best_state is None:\n1771\t raise RuntimeError(\"training did not produce a valid checkpoint\")\n1772\t model.load_state_dict(best_state)\n1773\t model.to(device).eval()\n1774\t log(\n1775\t f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1776\t f\"loss={best_loss:.6f}\"\n1777\t )\n1778\t return model, history\n1779\t\n1780\t\n1781\t@torch.inference_mode()\n1782\tdef inference_queries(\n1783\t model: base.SequentialTwoTower,\n1784\t prepared: SequenceData,\n1785\t config: Config,\n1786\t device: torch.device,\n1787\t) -> torch.Tensor:\n1788\t sequence_rows = prepared.inference_user_rows\n1789\t lengths = np.where(\n1790\t sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n1791\t )\n1792\t ordered = np.argsort(lengths, kind=\"stable\")\n1793\t result = torch.zeros(\n1794\t len(sequence_rows), config.embedding_dim, dtype=torch.float32\n1795\t )\n1796\t for group_start in range(0, len(ordered), config.user_batch_size):\n1797\t output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798\t rows = sequence_rows[output_rows]\n1799\t group_lengths = lengths[output_rows]\n1800\t hidden = torch.zeros(\n1801\t 1, len(output_rows), config.embedding_dim, device=device\n1802\t )\n1803\t for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n1804\t steps = min(\n1805\t config.bptt_steps,\n1806\t int(group_lengths.max(initial=0)) - start,\n1807\t )\n1808\t items = np.full(\n1809\t (len(output_rows), steps),\n1810\t len(prepared.item_ids),\n1811\t dtype=np.uint16,\n1812\t )\n1813\t features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814\t counts = np.zeros(len(output_rows), dtype=np.int32)\n1815\t for local_row, sequence_row in enumerate(rows):\n1816\t if sequence_row < 0:\n1817\t continue\n1818\t count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n1819\t if not count:\n1820\t continue\n1821\t offset = int(prepared.offsets[sequence_row])\n1822\t selected = slice(offset + start, offset + start + count)\n1823\t items[local_row, :count] = prepared.sequence_items[selected]\n1824\t features[local_row, :count] = prepared.event_features[selected]\n1825\t counts[local_row] = count\n1826\t outputs, _ = event_sequence(\n1827\t model,\n1828\t torch.from_numpy(items.astype(np.int64)).to(device),\n1829\t torch.from_numpy(features.astype(np.float32)).to(device),\n1830\t hidden,\n1831\t )\n1832\t next_rows: list[torch.Tensor] = []\n1833\t for local_row, count in enumerate(counts):\n1834\t next_rows.append(\n1835\t outputs[local_row, count - 1]\n1836\t if count\n1837\t else hidden[0, local_row]\n1838\t )\n1839\t hidden = torch.stack(next_rows).unsqueeze(0)\n1840\t\n1841\t liked_items = torch.from_numpy(\n1842\t prepared.inference_liked_items[output_rows].astype(np.int64)\n1843\t ).to(device)\n1844\t disliked_items = torch.from_numpy(\n1845\t prepared.inference_disliked_items[output_rows].astype(np.int64)\n1846\t ).to(device)\n1847\t queries = user_queries(\n1848\t model,\n1849\t hidden.squeeze(0),\n1850\t model._mean_context_channel(liked_items),\n1851\t model._mean_context_channel(disliked_items),\n1852\t torch.from_numpy(\n1853\t prepared.inference_user_features[output_rows].astype(np.float32)\n1854\t ).to(device),\n1855\t )\n1856\t result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1857\t return result\n1858\t\n1859\t\n1860\t@torch.inference_mode()\n1861\tdef rank_candidates(\n1862\t model: base.SequentialTwoTower,\n1863\t prepared: SequenceData,\n1864\t eligibility: CandidateSet,\n1865\t config: Config,\n1866\t device: torch.device,\n1867\t ranking_batch_size: int,\n1868\t) -> tuple[pd.DataFrame, dict[str, float]]:\n1869\t model.eval()\n1870\t queries = inference_queries(model, prepared, config, device)\n1871\t item_parts: list[torch.Tensor] = []\n1872\t for start in range(0, len(prepared.item_ids), 2_048):\n1873\t rows = torch.arange(\n1874\t start,\n1875\t min(start + 2_048, len(prepared.item_ids)),\n1876\t dtype=torch.long,\n1877\t device=device,\n1878\t )\n1879\t item_parts.append(model.encode_items(rows).cpu())\n1880\t items = torch.cat(item_parts)\n1881\t rows: list[tuple[int, int, int]] = []\n1882\t for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883\t end = min(start + ranking_batch_size, len(eligibility.uids))\n1884\t scores = queries[start:end] @ items.T\n1885\t eligible = np.unpackbits(\n1886\t eligibility.packed[start:end],\n1887\t axis=1,\n1888\t count=len(prepared.item_ids),\n1889\t bitorder=\"little\",\n1890\t ).astype(bool, copy=False)\n1891\t scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n1892\t selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n1893\t for local_row, columns in enumerate(selected):\n1894\t uid = int(eligibility.uids[start + local_row])\n1895\t rows.extend(\n1896\t (uid, int(prepared.item_ids[column]), rank)\n1897\t for rank, column in enumerate(columns, start=1)\n1898\t )\n1899\t if end == len(eligibility.uids) or end % 1_024 == 0:\n1900\t log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1901\t query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n1902\t item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n1903\t diagnostics = {\n1904\t \"query_norm_mean\": float(query_norm.mean()),\n1905\t \"query_norm_std\": float(query_norm.std()),\n1906\t \"item_norm_mean\": float(item_norm.mean()),\n1907\t \"item_norm_std\": float(item_norm.std()),\n1908\t }\n1909\t return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1910\t\n1911\t\n1912\tdef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913\t output.parent.mkdir(parents=True, exist_ok=True)\n1914\t if output.suffix == \".parquet\":\n1915\t frame.to_parquet(output, index=False, compression=\"zstd\")\n1916\t elif output.suffix == \".csv\":\n1917\t frame.to_csv(output, index=False)\n1918\t else:\n1919\t raise ValueError(\"--output must end in .parquet or .csv\")\n1920\t\n1921\t\n1922\tdef main() -> int:\n1923\t parser = argparse.ArgumentParser(description=__doc__)\n1924\t parser.add_argument(\n1925\t \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1926\t )\n1927\t parser.add_argument(\"--output\", type=Path, required=True)\n1928\t parser.add_argument(\"--public-dir\", type=Path, required=True)\n1929\t parser.add_argument(\"--prepared-cache\", type=Path)\n1930\t parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n1931\t parser.add_argument(\"--threads\", type=int, default=8)\n1932\t parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n1933\t parser.add_argument(\n1934\t \"--user-batch-size\", type=int, default=Config.user_batch_size\n1935\t )\n1936\t parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n1937\t parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n1938\t parser.add_argument(\n1939\t \"--loss-microbatch-size\",\n1940\t type=int,\n1941\t default=Config.loss_microbatch_size,\n1942\t )\n1943\t parser.add_argument(\n1944\t \"--negative-filter-scope\",\n1945\t choices=(\"full-training-history\", \"last-100\"),\n1946\t default=Config.negative_filter_scope,\n1947\t )\n1948\t parser.add_argument(\n1949\t \"--target-weighting\",\n1950\t choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n1951\t default=Config.target_weighting,\n1952\t )\n1953\t parser.add_argument(\n1954\t \"--ordinary-target-weight\",\n1955\t type=float,\n1956\t default=Config.ordinary_target_weight,\n1957\t )\n1958\t parser.add_argument(\n1959\t \"--organic-strong-target-weight\",\n1960\t type=float,\n1961\t default=Config.organic_strong_target_weight,\n1962\t )\n1963\t parser.add_argument(\n1964\t \"--organic-like-target-weight\",\n1965\t type=float,\n1966\t default=Config.organic_like_target_weight,\n1967\t )\n1968\t parser.add_argument(\n1969\t \"--discovery-horizon-days\",\n1970\t type=int,\n1971\t default=Config.discovery_horizon_days,\n1972\t )\n1973\t parser.add_argument(\n1974\t \"--validation-fraction\",\n1975\t type=float,\n1976\t default=Config.validation_fraction,\n1977\t )\n1978\t parser.add_argument(\n1979\t \"--max-train-targets-per-user\",\n1980\t type=int,\n1981\t default=Config.max_train_targets_per_user,\n1982\t help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1983\t )\n1984\t parser.add_argument(\n1985\t \"--max-validation-targets-per-user\",\n1986\t type=int,\n1987\t default=Config.max_validation_targets_per_user,\n1988\t help=\"retain this many final validation targets per user; 0 is uncapped\",\n1989\t )\n1990\t parser.add_argument(\n1991\t \"--early-stopping-patience\",\n1992\t type=int,\n1993\t default=Config.early_stopping_patience,\n1994\t )\n1995\t parser.add_argument(\n1996\t \"--early-stopping-min-delta\",\n1997\t type=float,\n1998\t default=Config.early_stopping_min_delta,\n1999\t )\n2000\t parser.add_argument(\"--checkpoint-dir\", type=Path)\n2001\t parser.add_argument(\"--resume-checkpoint\", type=Path)\n2002\t parser.add_argument(\"--seed\", type=int, default=Config.seed)\n2003\t parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n2004\t parser.add_argument(\"--prepare-only\", action=\"store_true\")\n2005\t args = parser.parse_args()\n2006\t\n2007\t if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n2008\t parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n2009\t if (\n2010\t args.epochs < 1\n2011\t or args.learning_rate <= 0\n2012\t or args.loss_microbatch_size < 1\n2013\t or args.early_stopping_patience < 1\n2014\t or args.early_stopping_min_delta < 0\n2015\t or args.ordinary_target_weight <= 0\n2016\t or args.organic_strong_target_weight <= 0\n2017\t or args.organic_like_target_weight <= 0\n2018\t or args.discovery_horizon_days < 1\n2019\t or args.max_train_targets_per_user < 0\n2020\t or args.max_validation_targets_per_user < 0\n2021\t ):\n2022\t parser.error(\"training counts, learning rate, and patience are invalid\")\n2023\t if not 0.0 < args.validation_fraction < 1.0:\n2024\t parser.error(\"validation fraction must be strictly between zero and one\")\n2025\t public = args.public_dir.resolve()\n2026\t if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n2027\t parser.error(f\"public task data not found under {public}\")\n2028\t config = Config(\n2029\t bptt_steps=args.bptt_steps,\n2030\t user_batch_size=args.user_batch_size,\n2031\t epochs=args.epochs,\n2032\t learning_rate=args.learning_rate,\n2033\t loss_microbatch_size=args.loss_microbatch_size,\n2034\t negative_filter_scope=args.negative_filter_scope,\n2035\t target_weighting=args.target_weighting,\n2036\t ordinary_target_weight=args.ordinary_target_weight,\n2037\t organic_strong_target_weight=args.organic_strong_target_weight,\n2038\t organic_like_target_weight=args.organic_like_target_weight,\n2039\t discovery_horizon_days=args.discovery_horizon_days,\n2040\t validation_fraction=args.validation_fraction,\n2041\t max_train_targets_per_user=args.max_train_targets_per_user,\n2042\t max_validation_targets_per_user=args.max_validation_targets_per_user,\n2043\t early_stopping_patience=args.early_stopping_patience,\n2044\t early_stopping_min_delta=args.early_stopping_min_delta,\n2045\t seed=args.seed,\n2046\t )\n2047\t torch.set_num_threads(args.threads)\n2048\t torch.set_num_interop_threads(1)\n2049\t seed_everything(config.seed)\n2050\t device = torch.device(args.device)\n2051\t if device.type == \"mps\" and not torch.backends.mps.is_available():\n2052\t parser.error(\"MPS was requested but is unavailable\")\n2053\t\n2054\t started = time.monotonic()\n2055\t prepared, eligibility = load_or_prepare(\n2056\t public,\n2057\t args.stage,\n2058\t config,\n2059\t args.prepared_cache.resolve() if args.prepared_cache else None,\n2060\t )\n2061\t target_partition = chronological_target_partition(\n2062\t prepared,\n2063\t config.validation_fraction,\n2064\t config.max_train_targets_per_user,\n2065\t config.max_validation_targets_per_user,\n2066\t )\n2067\t if args.prepare_only:\n2068\t print(\n2069\t json.dumps(\n2070\t {\n2071\t \"stage\": args.stage,\n2072\t \"sequence_users\": len(prepared.user_ids),\n2073\t \"sequence_tokens\": len(prepared.sequence_items),\n2074\t \"next_item_targets\": prepared.target_count,\n2075\t \"selected_training_targets\": int(\n2076\t target_partition.training_counts.sum()\n2077\t ),\n2078\t \"selected_validation_targets\": int(\n2079\t target_partition.validation_counts.sum()\n2080\t ),\n2081\t \"discarded_old_targets\": int(\n2082\t target_partition.window_starts.sum()\n2083\t ),\n2084\t \"target_users\": len(eligibility.uids),\n2085\t \"runtime_seconds\": round(time.monotonic() - started, 3),\n2086\t \"config\": asdict(config),\n2087\t },\n2088\t indent=2,\n2089\t )\n2090\t )\n2091\t return 0\n2092\t\n2093\t model, training_history = train_model(\n2094\t public,\n2095\t args.stage,\n2096\t prepared,\n2097\t config,\n2098\t device,\n2099\t args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n2100\t args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n2101\t )\n2102\t ranking, embedding_diagnostics = rank_candidates(\n2103\t model,\n2104\t prepared,\n2105\t eligibility,\n2106\t config,\n2107\t device,\n2108\t args.ranking_batch_size,\n2109\t )\n2110\t write_ranking(ranking, args.output.resolve())\n2111\t print(\n2112\t json.dumps(\n2113\t {\n2114\t \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n2115\t \"stage\": args.stage,\n2116\t \"output\": str(args.output.resolve()),\n2117\t \"rows\": len(ranking),\n2118\t \"users\": int(ranking.uid.nunique()),\n2119\t \"runtime_seconds\": round(time.monotonic() - started, 3),\n2120\t \"public_inputs_only\": True,\n2121\t \"architecture\": {\n2122\t \"family\": \"pure-windowed-sequence-two-tower\",\n2123\t \"sequence_training\": (\n2124\t \"every-adjacent-target-within-recent-chronological-window\"\n2125\t ),\n2126\t \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2127\t \"bptt_hidden_state\": \"carried-and-detached\",\n2128\t \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n2129\t \"loss_execution\": \"gradient-accumulated-microbatches\",\n2130\t \"user_id_embedding\": False,\n2131\t \"user_mlp\": \"582-512-256-192\",\n2132\t \"item_mlp\": \"196-512-256-192\",\n2133\t \"normalization\": \"none\",\n2134\t \"score\": \"raw-dot-product\",\n2135\t \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n2136\t \"negative_sampling\": (\n2137\t \"24-uniform-plus-8-propensity-corrected-inbatch\"\n2138\t ),\n2139\t \"false_negative_filter\": config.negative_filter_scope,\n2140\t \"target_weighting\": config.target_weighting,\n2141\t },\n2142\t \"config\": asdict(config),\n2143\t \"sequence_tokens\": len(prepared.sequence_items),\n2144\t \"all_next_item_targets\": prepared.target_count,\n2145\t \"selected_training_targets\": int(\n2146\t target_partition.training_counts.sum()\n2147\t ),\n2148\t \"selected_validation_targets\": int(\n2149\t target_partition.validation_counts.sum()\n2150\t ),\n2151\t \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n2152\t \"training_history\": training_history,\n2153\t \"embedding_diagnostics\": embedding_diagnostics,\n2154\t \"torch_version\": torch.__version__,\n2155\t \"device\": str(device),\n2156\t },\n2157\t indent=2,\n2158\t )\n2159\t )\n2160\t return 0\n2161\t\n2162\t\n2163\tif __name__ == \"__main__\":", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:04:01.483Z", "data": { "type": "total_tokens_reminder", "text": "14945490 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:04:01.485Z", "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": "2026-10-03T09:04:06.309Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:04:06.309Z", "text": "I've finished reading the script, so now I'll kick off the baseline run in the background using the prepared cache to skip prep time on later runs, given I've used maybe 3-4 of my 60 minutes. While that trains, I'll check out the evaluator and data stats in parallel.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:04:06.312Z", "text": "I've read through the full trainer. Now I'll kick off the production baseline backtest in the background with a prepared cache, while also reviewing the evaluator and checking data statistics.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:04:07.414Z", "callId": "toolu_01SjkSuJ9AAVizA6QroZ3dh9", "name": "Bash", "input": { "command": "mkdir -p /app/solution/cache /app/solution/runs && cd /app/solution && cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && cp /task/current_model/train_current_model.py /app/solution/runs/baseline_train.py && nohup python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache/validation.npz --output /app/solution/production_validation.parquet > /app/solution/runs/baseline.json 2> /app/solution/runs/baseline.log &\necho started", "description": "Copy production code to solution dir and start baseline validation run in background" }, "result": { "timestamp": "2026-10-03T09:04:07.478Z", "content": "started", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:04:07.482Z", "data": { "type": "total_tokens_reminder", "text": "14931919 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:04:07.485Z", "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": "2026-10-03T09:04:09.874Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:04:09.874Z", "callId": "toolu_01MBcixjcekqwiCUaTzMYQZr", "name": "Bash", "input": { "command": "cat /task/evaluate_public.py; echo =====; sed -n 1,80p /task/ranking_evaluation.py", "description": "Read the public evaluator" }, "result": { "timestamp": "2026-10-03T09:04:09.898Z", "content": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n=====\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:04:09.902Z", "data": { "type": "total_tokens_reminder", "text": "14931774 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:04:09.904Z", "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": "2026-10-03T09:04:24.670Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:04:24.670Z", "text": "While the baseline runs, I'm examining the data stats to diagnose the negative sampling setup: uniform negatives drawn from the 30k catalog are mostly unpopular items since the catalog skews heavy-tailed, so the model likely just learns a popularity prior, while the in-batch negatives use propensity correction with power 0.5 clipped between 0.25 and 4.\n\nI'm also considering whether that correction actually gives an unbiased BCE estimate relative to uniform negatives, though that may not even be the right objective for ranking. More importantly, validation candidates exclude all prior train encounters, meaning the task is really about ranking never-before-seen items by whether the user will organically like or substantially listen to them in the next week — so popularity-based negative sampling could be introducing a mismatch between training signal and this cold-candidate ranking goal.\n\nI should check how uniform negative sampling over a long-tailed catalog might just teach the model \"popular beats unpopular\" without real discriminative signal, so sampling negatives proportional to popularity^0.75 (like word2vec) could help. I want to look at candidate set sizes per user, the split between organic and recommended training targets with their respective weights, and compute a popularity-baseline NDCG to see how much of the signal is just popularity. I also need to keep an eye on time budget — about 6 of 60 minutes used — so I should write a quick analysis script to check candidate sizes, popularity baseline score, and where relevant items fall in the popularity ranking before running anything expensive.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:04:30.697Z", "callId": "toolu_017rzQCf5RvJjEwQD7aP48T2", "name": "Bash", "input": { "command": "cd /app/solution && cat > runs/analyze.py <<'EOF'\nimport numpy as np, pandas as pd, sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, score_rankings\nsys.path.insert(0,'/task'); import evaluate_public as ep\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nprint(cat.head()); print(cat.columns.tolist()); print(cat.describe())\ntu=pd.read_parquet(D+'target_users.parquet'); print('target users',len(tu))\nel=CandidateSet(D+'validation_eligible_candidates.npz')\nmask=np.unpackbits(el.packed,axis=1,count=len(el.item_ids),bitorder='little').astype(bool)\nprint('eligible per user: mean',mask.sum(1).mean(),'min',mask.sum(1).min())\ntargets=set(tu.uid.astype(int))\ngraded=ep.validation_truth(el,targets)\nprint('users with relevant',len(graded))\ngrades=[g for d in graded.values() for g in d.values()]\nprint('relevant items total',len(grades),'grade3',sum(g==3 for g in grades))\n# popularity rank of relevant items\ncol=el.item_column\npr=cat.set_index('item_id').popularity_rank\nranks=np.array([pr[i] for d in graded.values() for i in d])\nprint('relevant item popularity rank quantiles',np.percentile(ranks,[10,25,50,75,90]))\n# popularity baseline\norder=cat.sort_values('popularity_rank').item_id.to_numpy()\nranked={}\nfor r,uid in enumerate(el.uids):\n m=mask[r]; cols=np.flatnonzero(m) # columns are in popularity order already? check\n ranked[int(uid)]=[int(el.item_ids[c]) for c in cols[:100]]\nprint('item_ids in popularity order?', np.array_equal(el.item_ids, order))\nprint('popularity baseline', score_rankings(ranked,graded))\n# organic popularity baseline\noorder=np.argsort(-cat.set_index('item_id').loc[el.item_ids].organic_positive_listens.to_numpy(),kind='stable')\nranked={}\nfor r,uid in enumerate(el.uids):\n m=mask[r]; cols=oorder[m[oorder]][:100]\n ranked[int(uid)]=[int(el.item_ids[c]) for c in cols]\nprint('organic popularity baseline', score_rankings(ranked,graded))\nEOF\npython runs/analyze.py 2>&1 | tail -40; echo; tail -3 runs/baseline.log", "description": "Compute data stats and popularity baselines; check baseline progress" }, "result": { "timestamp": "2026-10-03T09:04:34.669Z", "content": " 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']\n item_id ... popularity_rank\ncount 3.000000e+04 ... 30000.000000\nmean 4.698278e+06 ... 15000.500000\nstd 2.700274e+06 ... 8660.398374\nmin 2.060000e+02 ... 1.000000\n25% 2.382368e+06 ... 7500.750000\n50% 4.687992e+06 ... 15000.500000\n75% 7.040711e+06 ... 22500.250000\nmax 9.390294e+06 ... 30000.000000\n\n[8 rows x 4 columns]\ntarget users 8456\neligible per user: mean 29067.306291390727 min 24657\nusers with relevant 4263\nrelevant items total 37769 grade3 4417\nrelevant item popularity rank quantiles [ 428. 1733. 5622. 13374. 21680.2]\nitem_ids in popularity order? True\npopularity baseline {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\norganic popularity baseline {'quality_ndcg@10': 3.2e-05, 'quality_users': 4263}\n\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:04:34.675Z", "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": "2026-10-03T09:04:34.675Z", "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": "2026-10-03T09:04:34.675Z", "data": { "type": "total_tokens_reminder", "text": "14926638 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:04:34.678Z", "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": "2026-10-03T09:05:04.450Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:05:04.450Z", "text": "The weirdly low organic popularity baseline score is making me suspect an indexing bug, so I'm tracing through the argsort and masking logic to check whether the item order stays aligned after selecting by the mask.\n\nActually I think the real culprit is that organic_positive_listens is likely unsigned, so negating it wraps around and corrupts the ranking — that explains the bug, though it's not critical since the NDCG of ~0.011 reflects a genuinely hard discovery task where relevant items have a median popularity rank of 5622 out of 30000.\n\nThat means 75% of relevant items sit in the top 13k by popularity, so uniform negative sampling mostly pulls from unpopular tail items that are too easy to distinguish — popularity-weighted negatives would be far more informative. While the baseline prep runs, I'm thinking through how to improve negative sampling: uniform sampling from a heavy-tailed 30k catalog mostly grabs irrelevant tail items that give weak gradients, whereas the 8 in-batch negatives drawn from the 64-user batch's unique targets could be more useful.\n\nBut the correction weight scheme clips and downweights popular items further, which actually weakens the hard-negative signal from popular items, and since the BCE loss averages positives and negatives separately, each target still contributes equally. Given the CPU budget, I'm considering replacing uniform negatives with popularity-smoothed sampling (p proportional to count^α) to create harder, word2vec-style negatives, possibly without a logQ correction so the model learns to suppress popular items more aggressively, which seems reasonable since retrieval is really happening among the popular tier anyway.\n\nAnother option is switching to sampled-softmax with logQ correction, following the standard approach from Yi et al. 2019, since the task explicitly allows changing how negatives are selected and used. A third option is simply increasing the number of negatives, though I need to check how that affects the unique item lookups given the batch and target sizes.\n\nI should also check baseline training time first since that limits how many experiments I can realistically run, and design the change as a configurable option so one script supports both uniform and popularity-based sampling — where popularity sampling draws from a distribution proportional to (count+1)^α using the target counts already computed, via torch.multinomial.\n\nFor the loss, I'm weighing a sampled-softmax formulation with logQ correction: negatives get corrected by subtracting log q_k, while the positive stays uncorrected since it isn't drawn from the sampling distribution — following the standard Bengio-style sampled-softmax approach.\n\nGiven the time budget, I'll keep things pragmatic: implement popularity-smoothed negative sampling with a configurable α, add sampled-softmax with logQ correction as an option, and keep BCE as the default fallback. Then I'll run a couple of comparison experiments before refitting for the final submission.\n\nI need to figure out the baseline runtime first, since the refit on train+val has only slightly more data so it should take roughly the same time. I'll run experiments in parallel using fewer threads each to stay within the time budget, and prepare the submission-stage cache now so the final refit can skip the prep step.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:05:04.809Z", "callId": "toolu_01JvarXWyumj3m8pLZikDYz6", "name": "Bash", "input": { "command": "tail -4 runs/baseline.log; python - <<'EOF'\nimport pyarrow.parquet as pq\nfor f in ['train_listens','val_listens','train_likes','val_likes']:\n print(f, pq.ParquetFile(f'/task/data/{f}.parquet').metadata.num_rows)\nimport pandas as pd\nl=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid'])\nprint('train users',l.uid.nunique())\nEOF", "description": "Check baseline progress and dataset sizes" }, "result": { "timestamp": "2026-10-03T09:05:06.931Z", "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\nsaved public-derived full-sequence cache: /app/solution/cache/validation.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ntrain_listens 43484159\nval_listens 1501697\ntrain_likes 814831\nval_likes 37795\ntrain users 9238", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:05:06.936Z", "data": { "type": "total_tokens_reminder", "text": "14923581 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:05:06.938Z", "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": "2026-10-03T09:05:10.861Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:05:10.861Z", "text": "Prep finished in about 50 seconds and training is now running on 1.05M targets. I'll kick off the submission-stage prepare-only step in the background since it's mostly single-core and takes about a minute, then move on to writing the improved trainer.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:05:10.866Z", "text": "Data prep is fast (about 50 seconds). Training covers about 1.05M targets. I'll pre-build the submission-stage cache in the background now, and meanwhile write the improved negative-sampling code." }, { "kind": "tool", "timestamp": "2026-10-03T09:05:12.252Z", "callId": "toolu_01E4D2Uami4m7K5i7BHA54c9", "name": "Bash", "input": { "command": "nohup python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache/submission.npz --output /app/solution/runs/unused.parquet --prepare-only --threads 2 > runs/prep_submission.json 2> runs/prep_submission.log &\nsleep 45; tail -3 runs/baseline.log", "description": "Prepare submission-stage cache in background; check baseline training speed" }, "result": { "timestamp": "2026-10-03T09:05:57.312Z", "content": "inbatch 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", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:05:57.321Z", "data": { "type": "total_tokens_reminder", "text": "14923003 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:05:57.327Z", "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": 15, "role": "agent", "timestamp": "2026-10-03T09:06:59.178Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:06:59.178Z", "text": "I'm planning the code changes to add while the baseline trains, adding configuration options for negative sampling strategy (uniform vs popularity-based), loss type selection between BCE and sampled softmax, and a logQ correction flag to adjust for sampling bias.\n\nFor implementation, I'll build a negative sampler that computes item frequencies from training targets with smoothing, raising them to a power to create a popularity distribution with an associated CDF. Then I'll modify the negative sampling function to draw from this distribution via searchsorted when in popularity mode, while keeping the in-batch negative logic intact, and I'll track the log probability of each drawn negative so the logQ correction can be applied properly during loss computation.\n\nThe tricky part is approximating the in-batch sampling distribution for logQ correction — since popular items appear in nearly every batch regardless of their true frequency, the effective sampling probability flattens out compared to raw popularity, so using the full frequency distribution would overcorrect. I'll add a configurable power parameter for the in-batch logQ correction, defaulting to something like 0.5, to better approximate this flattened effect.\n\nActually, I think it's cleaner to redesign the negative sampling altogether: use a power-law popularity distribution (count+1)^0.75 for sampling negatives, and share those negatives across the whole block of targets rather than per-example, which is both cheaper computationally and gives more effective negatives per target.\n\nThis cuts negative-item encoding cost drastically—shared negatives (say 512) instead of per-row negatives (6400×32) means far fewer unique embeddings to compute, so I can afford to push negative count up (1024-2048) while still masking out known positives per user via the packed bit-filter.\n\nI'm also considering full in-batch softmax, using all 6400 targets within a block as negatives for each other—same user occupies ~100 rows so those overlapping targets must be masked as false negatives too, alongside logQ correction for target item frequency.\n\nSo the known-positive filter masks any training-target item for that user, resolving same-user collisions in-batch. For each target row, the candidate set becomes the positive plus unique batch targets plus shared sampled negatives, with logQ-corrected scores and known positives masked out except the true target, then cross-entropy loss weighted by sample weight.\n\nWhether to apply the logQ correction to the positive itself is a real design choice — doing so targets the true p(i|u) including popularity, while skipping it yields a popularity-debiased p(i|u)/q(i) ranking that might help discovery of rare items but is hard to predict without testing. I'll expose this as a tunable `logq_weight` float between 0 and 1 rather than a hard on/off switch.\n\nTime check — I should move to implementation now rather than keep deliberating. I'm sketching the config additions for negative sampling: a shared popularity-based negative pool, in-batch softmax negatives, and the logQ correction weight, while skipping temperature scaling since the dot product itself can absorb that.\n\nI'm designing a new softmax scoring function that processes blocks of rows together, sharing negatives across the block to keep memory manageable — roughly 6400 positive rows against ~7400 combined negative/positive columns, which comes out to a reasonable 190MB logits tensor per microbatch. I need to make sure liked items and known positives are properly masked out from the negative candidates.\n\nFor the actual computation, I'm building unique candidate items by combining positives with sampled negatives, encoding them once, then computing logits as query vectors against candidate vectors. I still need to work out the log-q correction terms separately for in-batch positives versus the shared sampled negatives, since they come from different sampling distributions.\n\nI'll simplify rather than modeling the exact mixture: just use correction_i = logq_weight · log(p_i), where p_i ∝ count+1, applied uniformly regardless of whether the column came from the sampler or in-batch negatives. I'll treat logq_weight as a tunable value to try, like 1.0 or 0.5, rather than deriving the exact blend.\n\nNow for masking invalid candidates: I need to exclude known positives and liked items except the actual positive column. Computing the known-positive mask by comparing packed bits against candidate indices for a full block is doable but somewhat heavy in numpy, so I'll compute it per-user (64 users) instead of per-row, then index into that smaller mask by each row's user to get the full invalid mask efficiently.\n\nComputing a dense N×48×C liked-item comparison is too expensive at ~2.3G ops, so instead I'll exploit that most likes overlap with listens already covered by the known-positive filter. For the remaining cases, I'll group rows by unique user state, then for each unique state use searchsorted on the sorted candidate array to locate liked items and scatter True into a smaller states×C mask — since there are only a few hundred unique states per block, this stays cheap. Then I'll apply log-q correction to the logits, mask invalid entries with -inf, and compute the per-row loss as logsumexp minus the positive logit.\n\nFor gradients, I'll preserve the production microbatching trick — detaching the sequence input, requiring grad, and accumulating gradients per microbatch, recomputing candidate vectors inside the loop just like production does for correctness. For validation, since the default is one epoch with patience 1, early stopping is essentially trivial, so I'll just route validation through the same deterministic softmax loss for reporting and best-state selection.\n\nTo minimize changes, I want `draw_training_negatives` and `score_target_microbatches` to both dispatch on `config.loss_objective`, with the softmax path returning shared negatives plus a dummy weights tensor. I'll add user_rows and known_positive_packed as parameters to the scoring function since both call sites already have flat_user_rows and known_positive_packed available for masking.\n\nFor the sampler, build_negative_sampler needs to return log probabilities and a cdf for α-power sampling, and since the count computation duplicates build_item_correction's loop, I should refactor that into a shared target_item_counts helper. I'll also check whether 2 epochs instead of 1 is worth the extra time, but first I need to see how long the baseline takes before deciding.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:06:59.188Z", "callId": "toolu_01UqqhYNhTYoQfcafPSqHujx", "name": "Bash", "input": { "command": "tail -2 runs/baseline.log && cat runs/prep_submission.log | tail -2", "description": "Check baseline training and submission prep progress" }, "result": { "timestamp": "2026-10-03T09:06:59.278Z", "content": "target 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\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/cache/submission.npz", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:06:59.282Z", "data": { "type": "total_tokens_reminder", "text": "14918201 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:06:59.284Z", "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": "2026-10-03T09:07:03.658Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:07:03.658Z", "text": "The baseline hasn't hit the 250-batch log mark yet after about 3 minutes, which seems slow, but working out the math shows total batches is around 286, so the log point is near the end anyway — not worth worrying about, I'll just proceed with writing the code.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:07:03.661Z", "text": "The submission cache is ready. Now I'll implement the improved objective combining popularity-sampled negatives with in-batch negatives under a sampled-softmax loss with log-Q correction, while keeping the existing production BCE path as a config option.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:07:35.862Z", "callId": "toolu_01D6fWsSTnMEHeKjHG1EqWeH", "name": "Bash", "input": { "command": "python - <<'EOF'\nimport re\nsrc = open('train_current_model.py').read()\n\n# 1. Config additions\nsrc = src.replace(''' negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"''', ''' negative_filter_scope: str = \"full-training-history\"\n loss_objective: str = \"sampled-softmax\"\n shared_negatives: int = 2048\n negative_popularity_power: float = 0.75\n inbatch_softmax_negatives: bool = True\n logq_weight: float = 1.0\n target_weighting: str = \"organic-strong\"''')\n\n# 2. helper: target item counts + sampler, inserted before build_item_correction\nhelper = '''\ndef target_item_counts(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Count how often every catalog item is 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\n@dataclass(frozen=True)\nclass NegativeSampler:\n \"\"\"Popularity-smoothed shared negative sampler with log-Q corrections.\"\"\"\n\n cdf: torch.Tensor\n log_q: torch.Tensor\n log_target_frequency: torch.Tensor\n\n def draw(self, count: int, generator: torch.Generator) -> torch.Tensor:\n uniform = torch.rand(count, generator=generator)\n return torch.searchsorted(self.cdf, uniform).clamp_(\n max=len(self.cdf) - 1\n )\n\n\ndef build_negative_sampler(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> NegativeSampler:\n \"\"\"Sample shared negatives proportional to smoothed target popularity.\n\n Uniform draws over a 30k long-tail catalog are dominated by items the\n listener would never be shown, so they carry almost no ranking signal.\n Drawing negatives proportional to ``(count + 1) ** power`` concentrates\n the loss on plausible, popular confusers while the log-Q correction keeps\n the softmax an unbiased estimate of the full next-item distribution.\n \"\"\"\n counts = target_item_counts(prepared, target_starts, target_counts)\n smoothed = np.power(counts + 1.0, config.negative_popularity_power)\n probabilities = smoothed / smoothed.sum()\n frequency = (counts + 1.0) / (counts.sum() + len(counts))\n cdf = np.cumsum(probabilities)\n cdf[-1] = 1.0\n top = int(np.argsort(-probabilities)[:1000].size)\n log(\n f\"negative sampler: power={config.negative_popularity_power} \"\n f\"shared={config.shared_negatives} \"\n f\"mass_top1000={float(np.sort(probabilities)[::-1][:top].sum()):.3f} \"\n f\"inbatch_softmax={config.inbatch_softmax_negatives} \"\n f\"logq_weight={config.logq_weight}\"\n )\n return NegativeSampler(\n cdf=torch.from_numpy(cdf.astype(np.float64)),\n log_q=torch.from_numpy(np.log(probabilities).astype(np.float32)),\n log_target_frequency=torch.from_numpy(\n np.log(frequency).astype(np.float32)\n ),\n )\n\n\ndef softmax_candidate_set(\n positive_items: torch.Tensor,\n sampler: NegativeSampler,\n config: Config,\n generator: torch.Generator,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build one block's shared candidate column set and its log-Q vector.\"\"\"\n parts = [torch.unique(positive_items.detach().cpu())]\n if config.shared_negatives:\n parts.append(sampler.draw(config.shared_negatives, generator))\n candidates = torch.unique(torch.cat(parts))\n if config.inbatch_softmax_negatives:\n # Both the in-batch targets and the shared draws are monotone in\n # popularity; use the empirical target frequency as the correction.\n log_q = sampler.log_target_frequency[candidates]\n else:\n log_q = sampler.log_q[candidates]\n positive_columns = torch.searchsorted(candidates, positive_items.detach().cpu())\n return candidates, config.logq_weight * log_q, positive_columns\n\n\ndef softmax_invalid_mask(\n candidates: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n flat_states: np.ndarray,\n prepared: SequenceData,\n) -> np.ndarray:\n \"\"\"Mask known positives and active likes per row of the shared columns.\"\"\"\n unique_users, user_inverse = np.unique(user_rows, return_inverse=True)\n if known_positive_packed is not None:\n bytes_ = known_positive_packed[unique_users[:, None], candidates >> 3]\n per_user = ((bytes_ >> (candidates & 7)) & 1).astype(bool)\n else:\n per_user = np.zeros((len(unique_users), len(candidates)), dtype=bool)\n invalid = per_user[user_inverse]\n unique_states, state_inverse = np.unique(flat_states, return_inverse=True)\n liked = prepared.liked_states[unique_states].astype(np.int64)\n columns = np.searchsorted(candidates, liked)\n columns = np.minimum(columns, len(candidates) - 1)\n hit = candidates[columns] == liked\n per_state = np.zeros((len(unique_states), len(candidates)), dtype=bool)\n state_rows = np.broadcast_to(\n np.arange(len(unique_states))[:, None], liked.shape\n )\n per_state[state_rows[hit], columns[hit]] = True\n invalid |= per_state[state_inverse]\n return invalid\n\n\ndef score_target_microbatches_softmax(\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 user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n sampler: NegativeSampler,\n generator: torch.Generator,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Sampled-softmax loss over shared popularity negatives and batch targets.\"\"\"\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 candidates, log_q, positive_columns = softmax_candidate_set(\n positive_items, sampler, config, generator\n )\n candidate_np = candidates.numpy()\n invalid_np = softmax_invalid_mask(\n candidate_np, user_rows, known_positive_packed, flat_states, prepared\n )\n rows = np.arange(target_count)\n invalid_np[rows, positive_columns.numpy()] = False\n candidates_dev = candidates.to(device)\n log_q_dev = log_q.to(device)\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 negative_columns = len(candidates) - 1\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 candidate_vectors_ = model.encode_items(candidates_dev)\n logits = query_vectors @ candidate_vectors_.T - log_q_dev[None, :]\n invalid = torch.from_numpy(invalid_np[begin:end]).to(device)\n logits = logits.masked_fill(invalid, float(\"-inf\"))\n columns = positive_columns[begin:end].to(device)\n positive_logits = logits.gather(1, columns[:, None]).squeeze(1)\n loss = torch.logsumexp(logits, dim=1) - positive_logits\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"sampled-softmax 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 with torch.no_grad():\n detached = logits.detach()\n finite = torch.isfinite(detached)\n negative_total = detached.masked_fill(~finite, 0.0).sum() - (\n positive_logits.detach().sum()\n )\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_total.cpu())\n better = (positive_logits.detach()[:, None] > detached) & finite\n statistics[\"pair_correct\"] += float(better.sum().cpu())\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n statistics[\"negative_columns\"] = float(negative_columns)\n return statistics, sequence_gradient\n\n'''\nsrc = src.replace('\\ndef build_item_correction(', helper + '\\ndef build_item_correction(')\nopen('train_current_model.py','w').write(src)\nprint(\"ok\")\nEOF", "description": "Insert sampler and sampled-softmax scoring helpers into trainer" }, "result": { "timestamp": "2026-10-03T09:07:36.053Z", "content": "Exit code 1\nTraceback (most recent call last):\n File \"\", line 244, in \nPermissionError: [Errno 13] Permission denied: 'train_current_model.py'", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:07:36.057Z", "data": { "type": "total_tokens_reminder", "text": "14913718 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:07:36.060Z", "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": "2026-10-03T09:07:38.090Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:07:38.090Z", "callId": "toolu_01KYRFa4haVcDxAE6MztvJYC", "name": "Bash", "input": { "command": "ls -la && chmod u+w train_current_model.py two_tower_model.py && ls -la train_current_model.py", "description": "Fix read-only permissions on copied files" }, "result": { "timestamp": "2026-10-03T09:07:38.151Z", "content": "total 116\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:04 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:59 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:06 cache\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:05 runs\n-r--r--r-- 1 agent agent 80058 Oct 3 09:04 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Oct 3 09:04 two_tower_model.py\n-rw-r--r-- 1 agent agent 80058 Oct 3 09:04 train_current_model.py", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:07:38.154Z", "data": { "type": "total_tokens_reminder", "text": "14913500 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:07:38.156Z", "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": "2026-10-03T09:07:41.333Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:07:41.333Z", "text": "The baseline run already has train_current_model.py loaded into memory, so editing the file won't affect that running process — that's fine. I'll re-run the patch script, saving it to a file so I can reuse it.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:08:11.177Z", "callId": "toolu_01U9SHAYu6aC1xRsT5Aue3WU", "name": "Bash", "input": { "command": "cat > runs/patch1.py <<'PYEOF'\nsrc = open('train_current_model.py').read()\nsrc = src.replace(''' negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"''', ''' negative_filter_scope: str = \"full-training-history\"\n loss_objective: str = \"sampled-softmax\"\n shared_negatives: int = 2048\n negative_popularity_power: float = 0.75\n inbatch_softmax_negatives: bool = True\n logq_weight: float = 1.0\n target_weighting: str = \"organic-strong\"''')\nhelper = open('runs/helper.py').read()\nassert '\\ndef build_item_correction(' in src\nsrc = src.replace('\\ndef build_item_correction(', helper + '\\ndef build_item_correction(')\nopen('train_current_model.py','w').write(src)\nprint(\"ok\")\nPYEOF\ncat > runs/helper.py <<'PYEOF'\n\ndef target_item_counts(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Count how often every catalog item is 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\n@dataclass(frozen=True)\nclass NegativeSampler:\n \"\"\"Popularity-smoothed shared negative sampler with log-Q corrections.\"\"\"\n\n cdf: torch.Tensor\n log_q: torch.Tensor\n log_target_frequency: torch.Tensor\n\n def draw(self, count: int, generator: torch.Generator) -> torch.Tensor:\n uniform = torch.rand(count, generator=generator, dtype=torch.float64)\n return torch.searchsorted(self.cdf, uniform).clamp_(\n max=len(self.cdf) - 1\n )\n\n\ndef build_negative_sampler(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> NegativeSampler:\n \"\"\"Sample shared negatives proportional to smoothed target popularity.\n\n Uniform draws over a 30k long-tail catalog are dominated by items the\n listener would never be shown, so they carry almost no ranking signal.\n Drawing negatives proportional to ``(count + 1) ** power`` concentrates\n the loss on plausible, popular confusers while the log-Q correction keeps\n the softmax an estimate of the full next-item distribution.\n \"\"\"\n counts = target_item_counts(prepared, target_starts, target_counts)\n smoothed = np.power(counts + 1.0, config.negative_popularity_power)\n probabilities = smoothed / smoothed.sum()\n frequency = (counts + 1.0) / (counts.sum() + len(counts))\n cdf = np.cumsum(probabilities)\n cdf[-1] = 1.0\n log(\n f\"negative sampler: power={config.negative_popularity_power} \"\n f\"shared={config.shared_negatives} \"\n f\"mass_top1000={float(np.sort(probabilities)[::-1][:1000].sum()):.3f} \"\n f\"inbatch_softmax={config.inbatch_softmax_negatives} \"\n f\"logq_weight={config.logq_weight}\"\n )\n return NegativeSampler(\n cdf=torch.from_numpy(cdf.astype(np.float64)),\n log_q=torch.from_numpy(np.log(probabilities).astype(np.float32)),\n log_target_frequency=torch.from_numpy(\n np.log(frequency).astype(np.float32)\n ),\n )\n\n\ndef softmax_candidate_set(\n positive_items: torch.Tensor,\n sampler: NegativeSampler,\n config: Config,\n generator: torch.Generator,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build one block's shared candidate column set and its log-Q vector.\"\"\"\n positives_cpu = positive_items.detach().cpu()\n parts = []\n if config.inbatch_softmax_negatives:\n parts.append(torch.unique(positives_cpu))\n if config.shared_negatives:\n parts.append(sampler.draw(config.shared_negatives, generator))\n if not config.inbatch_softmax_negatives:\n # Every positive must still be a column so each row can be scored.\n parts.append(torch.unique(positives_cpu))\n candidates = torch.unique(torch.cat(parts))\n if config.inbatch_softmax_negatives:\n # Both the in-batch targets and the shared draws are monotone in\n # popularity; use the empirical target frequency as the correction.\n log_q = sampler.log_target_frequency[candidates]\n else:\n log_q = sampler.log_q[candidates]\n positive_columns = torch.searchsorted(candidates, positives_cpu)\n return candidates, config.logq_weight * log_q, positive_columns\n\n\ndef softmax_invalid_mask(\n candidates: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n flat_states: np.ndarray,\n prepared: SequenceData,\n) -> np.ndarray:\n \"\"\"Mask known positives and active likes per row of the shared columns.\"\"\"\n unique_users, user_inverse = np.unique(user_rows, return_inverse=True)\n if known_positive_packed is not None:\n bytes_ = known_positive_packed[unique_users[:, None], candidates >> 3]\n per_user = ((bytes_ >> (candidates & 7).astype(np.uint8)) & 1).astype(bool)\n else:\n per_user = np.zeros((len(unique_users), len(candidates)), dtype=bool)\n invalid = per_user[user_inverse]\n unique_states, state_inverse = np.unique(flat_states, return_inverse=True)\n liked = prepared.liked_states[unique_states].astype(np.int64)\n columns = np.searchsorted(candidates, liked)\n columns = np.minimum(columns, len(candidates) - 1)\n hit = candidates[columns] == liked\n per_state = np.zeros((len(unique_states), len(candidates)), dtype=bool)\n state_rows = np.broadcast_to(\n np.arange(len(unique_states))[:, None], liked.shape\n )\n per_state[state_rows[hit], columns[hit]] = True\n invalid |= per_state[state_inverse]\n return invalid\n\n\ndef score_target_microbatches_softmax(\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 user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n sampler: NegativeSampler,\n generator: torch.Generator,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Sampled-softmax loss over shared popularity negatives and batch targets.\"\"\"\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 candidates, log_q, positive_columns = softmax_candidate_set(\n positive_items, sampler, config, generator\n )\n candidate_np = candidates.numpy()\n invalid_np = softmax_invalid_mask(\n candidate_np, user_rows, known_positive_packed, flat_states, prepared\n )\n invalid_np[np.arange(target_count), positive_columns.numpy()] = False\n candidates_dev = candidates.to(device)\n log_q_dev = log_q.to(device)\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 \"negative_columns\": float(len(candidates) - 1),\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 column_vectors = model.encode_items(candidates_dev)\n logits = query_vectors @ column_vectors.T - log_q_dev[None, :]\n invalid = torch.from_numpy(invalid_np[begin:end]).to(device)\n logits = logits.masked_fill(invalid, float(\"-inf\"))\n columns = positive_columns[begin:end].to(device)\n positive_logits = logits.gather(1, columns[:, None]).squeeze(1)\n loss = torch.logsumexp(logits, dim=1) - positive_logits\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (selected_weights * loss).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"sampled-softmax 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 with torch.no_grad():\n detached = logits.detach()\n finite = torch.isfinite(detached)\n negative_total = detached.masked_fill(~finite, 0.0).sum() - (\n positive_logits.detach().sum()\n )\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_total.cpu())\n better = (positive_logits.detach()[:, None] > detached) & finite\n statistics[\"pair_correct\"] += float(better.sum().cpu())\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\nPYEOF\npython runs/patch1.py && grep -n \"loss_objective\\|def score_target_microbatches_softmax\\|def build_negative_sampler\" train_current_model.py; tail -2 runs/baseline.log", "description": "Apply the sampler and softmax-loss patch to the trainer" }, "result": { "timestamp": "2026-10-03T09:08:11.258Z", "content": "ok\n62: loss_objective: str = \"sampled-softmax\"\n580:def build_negative_sampler(\n672:def score_target_microbatches_softmax(\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": "2026-10-03T09:08:11.261Z", "data": { "type": "total_tokens_reminder", "text": "14908907 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:08:11.263Z", "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": "2026-10-03T09:08:15.937Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:08:15.937Z", "text": "The baseline is still stuck around batch 250 after seven minutes, which is eating into my time budget — I should check CPU usage with top since the prep job ran concurrently with 2 threads. Now I'm wiring the dispatch logic into run_training_epoch and evaluate_validation_bce so they route through the sampled-softmax path when that loss objective is configured.\n\nWriting patch script...\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:08:43.952Z", "callId": "toolu_01Cyyz1apadHxJ3Zw1Eu9wvX", "name": "Bash", "input": { "command": "top -bn1 | head -15 | tail -8; cat > runs/patch2.py <<'PYEOF'\nsrc = open('train_current_model.py').read()\ndef rep(old, new, count=1):\n global src\n assert src.count(old) == count, (src.count(old), old[:80])\n src = src.replace(old, new)\n\n# --- run_training_epoch: signature + dispatch\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(''' 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 )''', ''' optimizer.zero_grad(set_to_none=True)\n if config.loss_objective == \"sampled-softmax\":\n if sampler is None:\n raise RuntimeError(\"negative sampler was not built\")\n statistics, sequence_gradient = score_target_microbatches_softmax(\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 flat_user_rows,\n known_positive_packed,\n sampler,\n generator,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n else:\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, 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# negative_count for diagnostics\nrep(''' negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0''', ''' negative_count = config.uniform_negatives + config.inbatch_negatives\n total_negative_columns = 0.0\n total_loss = 0.0''')\nrep(''' total_pair_correct += statistics[\"pair_correct\"]\n batches += 1''', ''' total_pair_correct += statistics[\"pair_correct\"]\n total_negative_columns += count * statistics.get(\n \"negative_columns\", float(negative_count)\n )\n batches += 1''')\nrep(''' \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),''', ''' \"negative_logit\": total_negative / max(total_negative_columns, 1),\n \"pair_accuracy\": total_pair_correct / max(total_negative_columns, 1),''')\n\n# --- evaluate_validation_bce\nrep(''' 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.\"\"\"''', ''' 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(''' 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 )''', ''' if config.loss_objective == \"sampled-softmax\":\n if sampler is None:\n raise RuntimeError(\"negative sampler was not built\")\n statistics, _ = score_target_microbatches_softmax(\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 flat_user_rows,\n known_positive_packed,\n sampler,\n generator,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n else:\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\n# --- train_model: build sampler and pass it\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 = (\n build_negative_sampler(\n prepared, config, partition.window_starts, training_counts\n )\n if config.loss_objective == \"sampled-softmax\"\n else None\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(''', ''' known_positive_packed,\n generator,\n device,\n epoch,\n sampler,\n )\n validation = evaluate_validation_bce(''')\nrep(''' item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)''', ''' item_correction,\n known_positive_packed,\n device,\n sampler,\n )\n diagnostics.update(validation)''')\n# objective_changed on resume\nrep(''' objective_changed = (\n stored_filter_scope != config.negative_filter_scope''', ''' objective_changed = (\n stored_config.get(\"loss_objective\", \"bce\") != config.loss_objective\n or stored_filter_scope != config.negative_filter_scope''')\n\n# --- CLI\nrep(''' parser.add_argument(\n \"--target-weighting\",''', ''' parser.add_argument(\n \"--loss-objective\",\n choices=(\"sampled-softmax\", \"bce\"),\n default=Config.loss_objective,\n help=\"sampled-softmax uses shared popularity negatives; bce is the legacy objective\",\n )\n parser.add_argument(\n \"--shared-negatives\", type=int, default=Config.shared_negatives\n )\n parser.add_argument(\n \"--negative-popularity-power\",\n type=float,\n default=Config.negative_popularity_power,\n )\n parser.add_argument(\n \"--inbatch-softmax-negatives\",\n type=int,\n choices=(0, 1),\n default=int(Config.inbatch_softmax_negatives),\n )\n parser.add_argument(\"--logq-weight\", type=float, default=Config.logq_weight)\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 loss_objective=args.loss_objective,\n shared_negatives=args.shared_negatives,\n negative_popularity_power=args.negative_popularity_power,\n inbatch_softmax_negatives=bool(args.inbatch_softmax_negatives),\n logq_weight=args.logq_weight,\n target_weighting=args.target_weighting,''')\nrep(''' or args.max_validation_targets_per_user < 0\n ):''', ''' or args.max_validation_targets_per_user < 0\n or args.shared_negatives < 0\n or args.negative_popularity_power < 0\n or args.logq_weight < 0\n ):''')\nrep(''' \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''', ''' \"loss_objective\": config.loss_objective,\n \"negative_sampling\": (\n f\"{config.shared_negatives}-shared-popularity^\"\n f\"{config.negative_popularity_power}-plus-inbatch-targets-logq\"\n if config.loss_objective == \"sampled-softmax\"\n else \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),''')\nopen('train_current_model.py','w').write(src)\nprint('ok')\nPYEOF\npython runs/patch2.py && python -c \"import ast;ast.parse(open('train_current_model.py').read());print('syntax ok')\"", "description": "Wire the softmax objective into training, validation, and CLI" }, "result": { "timestamp": "2026-10-03T09:08:44.263Z", "content": " 8149 agent 20 0 7104764 4.5g 150120 R 393.3 29.4 21:39.51 python\n 8058 agent 20 0 5604140 257312 127636 S 6.7 1.6 0:03.79 claude\n 1 root 20 0 2592 1716 1612 S 0.0 0.0 0:00.01 sh\n 7 root 20 0 2920 1612 1508 S 0.0 0.0 0:00.00 sleep\n 8051 agent 20 0 4480 3368 3020 S 0.0 0.0 0:00.01 bash\n 8059 agent 20 0 2924 1704 1592 S 0.0 0.0 0:00.02 tee\n 8145 agent 20 0 4492 1752 1380 S 0.0 0.0 0:00.00 bash\n 8323 agent 20 0 4648 3480 2940 S 0.0 0.0 0:00.00 bash\nok\nsyntax ok", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:08:44.270Z", "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": "2026-10-03T09:08:44.270Z", "data": { "type": "total_tokens_reminder", "text": "14904679 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:08:44.273Z", "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": "2026-10-03T09:08:51.863Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:08:51.863Z", "text": "I need to run a quick smoke test of the new objective on a small subset, but the baseline process is already consuming most of the CPU across my 8 cores. Since there's no built-in way to limit users for a smoke test, I'll instead use `--max-train-targets-per-user 4` to keep things fast while still covering all users, relying on the cached prep to start instantly.\n\nI'm estimating around 36k targets across 143 groups with one block each, each block being a manageable 256 rows by ~2300 columns, so this should run quickly with 3 threads. I'm also watching the clock since the baseline has been training for about 10 minutes already — my improved version, which only encodes a few thousand candidate items per block versus tens of thousands of negative lookups in the baseline, should end up faster or at least comparable.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:08:51.864Z", "text": "Quick smoke test of the new objective with a tiny per-user target cap (the prepared cache makes startup instant), while the baseline keeps training." }, { "kind": "tool", "timestamp": "2026-10-03T09:08:53.206Z", "callId": "toolu_019rC7MhiChByCtsYKZsQspM", "name": "Bash", "input": { "command": "timeout 600 python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/smoke.parquet --max-train-targets-per-user 4 --max-validation-targets-per-user 2 --threads 3 > runs/smoke.json 2> runs/smoke.log; echo exit=$?; grep -v \"^ranked\" runs/smoke.log | tail -8; python /task/evaluate_public.py runs/smoke.parquet; tail -2 runs/baseline.log", "description": "Smoke-test the sampled-softmax trainer on a tiny target cap" }, "result": { "timestamp": "2026-10-03T09:10:53.362Z", "content": "Command did not complete within its 120s timeout and was moved to the background (ID: brgd9v0dj). Output is being written to: /tmp/claude-1000/-app/47c3e013-b2c2-46e8-8ac3-4197c05494a2/tasks/brgd9v0dj.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.", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:10:53.373Z", "data": { "type": "total_tokens_reminder", "text": "14903637 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:10:53.375Z", "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": "2026-10-03T09:10:56.099Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:10:56.099Z", "callId": "toolu_018nE9wfvdbQuop2P1APqcT3", "name": "Bash", "input": { "command": "sleep 60; tail -5 runs/smoke.log; echo ---; tail -2 runs/baseline.log", "description": "Check smoke test and baseline progress" }, "result": { "timestamp": "2026-10-03T09:11:56.156Z", "content": "target weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,558,741 bytes=34,256,250\nnegative sampler: power=0.75 shared=2048 mass_top1000=0.125 inbatch_softmax=True logq_weight=1.0\nepoch=1/1 train_loss=7.86972 validation_loss=7.36741 pair_accuracy=0.472 train_targets=36,027 validation_targets=17,721 train_seconds=39.8 validation_seconds=6.8\nrestored best validation-BCE checkpoint: epoch=1 loss=7.367406\n---\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": "2026-10-03T09:11:56.167Z", "data": { "type": "total_tokens_reminder", "text": "14903352 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:11:56.169Z", "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": "2026-10-03T09:12:11.151Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:12:11.151Z", "text": "The smoke test is working well end-to-end, training 36k targets in 40 seconds with 3 threads under contention, and it's now ranking. The baseline run is still training at around 14 minutes with no batch-250 log yet, which is a bit concerning given about 25 minutes have elapsed already. I'm weighing whether the time budget is too tight since the baseline could take 20+ minutes total, and I'm sizing up the per-block computation for the improved objective — a 6400 row by ~8000 column block means a 51M-entry logits matrix for forward and backward passes.\n\nRunning the numbers, each block costs roughly 59 GFLOP, and with ~290 blocks that's 17 TFLOP total, which at an estimated 100 GFLOPS effective CPU throughput should take about 3 minutes plus GRU overhead — manageable. Given time pressure, I'm deciding to start the full validation run of the improved model immediately with 4 threads, running concurrently with the baseline rather than waiting. I'm also considering whether to kick off the submission-stage refit in parallel too, since sequencing everything might not fit the time budget, though running three heavy processes across 8 cores (baseline at 4 threads, improved validation at 4, improved submission at 3) risks contention slowing everything down — I'm weighing whether killing the baseline run is worth it given what its score actually buys me.\n\nChecking the baseline's progress, it started around 09:07 and it's now about 09:22, and based on per-block timing estimates (roughly 3-4s per block over 290 blocks) it should wrap up in 15-20 minutes, so I'll let it keep running rather than kill it. My plan is to start the improved validation run now with 4 threads, then once baseline finishes, launch the submission refit using the improved config in parallel with validation so both complete around the 50-minute mark — risky if validation underperforms baseline since there wouldn't be time to fall back, but I can get an early read from the smoke test score once ranking finishes in about a minute.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:12:11.165Z", "callId": "toolu_01UryFHdGyUzUVhsMpfvkKNA", "name": "Bash", "input": { "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/improved_validation.parquet --threads 4 > runs/improved_val.json 2> runs/improved_val.log &\nsleep 50; cat /tmp/claude-1000/-app/47c3e013-b2c2-46e8-8ac3-4197c05494a2/tasks/brgd9v0dj.output 2>/dev/null | tail -12; tail -1 runs/baseline.log", "description": "Launch full improved validation run; check smoke-test score" }, "result": { "timestamp": "2026-10-03T09:13:01.356Z", "content": "/bin/bash: line 3: 8389 Killed nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/improved_validation.parquet --threads 4 > runs/improved_val.json 2> runs/improved_val.log\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=416.3s", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:01.395Z", "data": { "type": "total_tokens_reminder", "text": "14901921 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:01.412Z", "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": "2026-10-03T09:13:05.875Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:13:05.875Z", "text": "The improved validation run got killed, likely an out-of-memory issue since it's loading cache alongside the baseline and smoke test. Meanwhile the baseline is further along than expected—elapsed time suggests training started later due to prep and filter-building overhead, but it's nearly done with 987k of 1.05M targets. I should check free memory and the smoke test's current state.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:13:05.881Z", "callId": "toolu_01PE5xnzisMhqrCVrV9Ajf8X", "name": "Bash", "input": { "command": "free -g | head -2; ps aux | grep train_current | grep -v grep | awk '{print $2, $4, $6/1e6 \"GB\", $12, $13, $14, $15, $16, $17, $18}'; tail -3 runs/improved_val.log; tail -2 runs/smoke.log", "description": "Check memory, running trainers, and logs" }, "result": { "timestamp": "2026-10-03T09:13:06.018Z", "content": " total used free shared buff/cache available\nMem: 15 2 6 0 6 12\n8331 0.0 0.001688GB 600 python train_current_model.py --stage validation --public-dir /task\n8332 10.5 1.68154GB train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz\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: power=0.75 shared=2048 mass_top1000=0.178 inbatch_softmax=True logq_weight=1.0\nepoch=1/1 train_loss=7.86972 validation_loss=7.36741 pair_accuracy=0.472 train_targets=36,027 validation_targets=17,721 train_seconds=39.8 validation_seconds=6.8\nrestored best validation-BCE checkpoint: epoch=1 loss=7.367406", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:06.022Z", "data": { "type": "total_tokens_reminder", "text": "14901400 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:06.024Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 24, "role": "agent", "timestamp": "2026-10-03T09:13:10.206Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:13:10.206Z", "text": "Wait, the baseline process is gone too, not just the improved run — only the smoke test processes remain in ps. Memory shows 6GB free and 12GB available now, so it's possible the OOM killer took out both the baseline and improved runs; I need to check the baseline log and json to confirm.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:13:10.221Z", "callId": "toolu_01KR6HVV6qJDE4CsxR5reU5E", "name": "Bash", "input": { "command": "tail -4 runs/baseline.log; echo ---; head -c 300 runs/baseline.json; echo; ls -la runs/ production_validation.parquet 2>&1 | head; dmesg 2>/dev/null | tail -5", "description": "Check whether the baseline run survived" }, "result": { "timestamp": "2026-10-03T09:13:10.263Z", "content": "inbatch 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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=416.3s\n---\n\nls: cannot access 'production_validation.parquet': No such file or directory\nruns/:\ntotal 140\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:12 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:04 ..\n-rw-r--r-- 1 agent agent 1874 Oct 3 09:04 analyze.py\n-rw-r--r-- 1 agent agent 0 Oct 3 09:04 baseline.json\n-rw-r--r-- 1 agent agent 1329 Oct 3 09:12 baseline.log\n-r--r--r-- 1 agent agent 80058 Oct 3 09:04 baseline_train.py\n-rw-r--r-- 1 agent agent 9542 Oct 3 09:08 helper.py", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:10.266Z", "data": { "type": "total_tokens_reminder", "text": "14900743 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:10.268Z", "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": "2026-10-03T09:13:26.570Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:13:26.570Z", "text": "I'm trying to pin down the exact death time of the baseline run — the log file's last modified timestamp is 09:12, but process 8149 was still alive and consuming 4.5g of memory at around 09:16, so the process must have survived a bit longer than the log suggests before actually being killed.\n\nLikely it was the OOM killer taking out both the baseline and the newly started improved run simultaneously, since both were large memory consumers. There's some confusion over the runs/ directory listing too — the truncated output made it look like fewer files existed than actually do, but that's just an artifact of piping through head.\n\nWith about 27 minutes elapsed, the baseline is dead with no usable output and the smoke test is still running as process 8332. My priorities now are getting a valid submission out quickly, evaluating the improved model on validation, and deciding whether to re-run the baseline (roughly 10 minutes of CPU time) to get a comparison number, while estimating memory needs for the improved run based on cache size per token.\n\nMaybe the real issue is a cgroup memory limit well below the host's 15GB, not actual process usage — I should check /sys/fs/cgroup/memory.max to confirm. Then I'll plan around that limit: run the improved validation and submission refit sequentially or concurrently with fewer threads depending on headroom, while also waiting for the smoke-test score to finish.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:13:26.658Z", "callId": "toolu_0159oq7jsk7UBM6fQVQfRkys", "name": "Bash", "input": { "command": "cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/memory.peak 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null; ls -la runs/smoke* ; tail -2 runs/smoke.log; ps aux | grep train_current | grep -v grep | wc -l", "description": "Check container memory limit and smoke-test completion" }, "result": { "timestamp": "2026-10-03T09:13:26.701Z", "content": "8589934592\n8589934592\nlow 0\nhigh 0\nmax 5791\noom 6\noom_kill 2\noom_group_kill 0\nsock_throttled 0\n-rw-r--r-- 1 agent agent 3179 Oct 3 09:13 runs/smoke.json\n-rw-r--r-- 1 agent agent 951 Oct 3 09:13 runs/smoke.log\n-rw-r--r-- 1 agent agent 929529 Oct 3 09:13 runs/smoke.parquet\nranked users=8,192/8,456\nranked users=8,456/8,456\n0", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:17.626Z", "data": { "type": "queued_command", "prompt": "\nbrgd9v0dj\ntoolu_019rC7MhiChByCtsYKZsQspM\n/tmp/claude-1000/-app/47c3e013-b2c2-46e8-8ac3-4197c05494a2/tasks/brgd9v0dj.output\ncompleted\nBackground command \"Smoke-test the sampled-softmax trainer on a tiny target cap\" completed (exit code 0)\n", "source_uuid": "7908bcfb-4f55-4604-b06a-96166bd44434", "delivery_id": "964fd2c4-1a8f-423e-9bb2-a5f80d162046", "commandMode": "task-notification", "origin": { "kind": "task-notification", "producer": "session-task" }, "timestamp": "2026-10-03T09:13:17.626Z" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:26.713Z", "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": "2026-10-03T09:13:26.713Z", "data": { "type": "total_tokens_reminder", "text": "14898986 tokens left" } } ] }, { "id": 26, "role": "agent", "timestamp": "2026-10-03T09:13:36.082Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:13:36.082Z", "text": "With an 8GB memory limit and two OOM-killed processes, I realize I need to run training one process at a time since the baseline alone used 4.5GB—about 28 minutes gone, 32 left. I also notice the smoke test and ranking timestamps look oddly early compared to when the timeout wrapper reported, which is a bit confusing but not worth chasing right now.\n\nGiven the sequencing constraint, my plan is to quickly evaluate the smoke test's parquet, then launch the full improved validation run alone with 8 threads—improved validation (~8 min) followed by submission refit (~8 min) leaves no room for a baseline re-run. Since the baseline process got OOM-killed and I have no clean numbers for it, I'll prioritize the submission with the improved config, using the smoke test's NDCG signal (even with just 4 targets/user) as my best read on whether this direction is promising before committing fully.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:13:36.091Z", "text": "The container has an 8 GB memory cap and the OOM killer took both the baseline and the improved run when they overlapped. From here I'll run one trainer at a time. First, the smoke-test score." }, { "kind": "tool", "timestamp": "2026-10-03T09:13:36.718Z", "callId": "toolu_015P4CuBpPVspVFHsLyy2XM1", "name": "Bash", "input": { "command": "python /task/evaluate_public.py runs/smoke.parquet; nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/improved_validation.parquet --threads 8 > runs/improved_val.json 2> runs/improved_val.log & echo started", "description": "Evaluate smoke-test ranking, then launch the full improved validation run alone" }, "result": { "timestamp": "2026-10-03T09:13:40.162Z", "content": "{\n \"model\": \"runs/smoke.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.021115,\n \"quality_users\": 4263\n }\n}\nstarted", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:13:40.165Z", "data": { "type": "total_tokens_reminder", "text": "14897611 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:13:40.167Z", "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": "2026-10-03T09:13:47.925Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:13:47.925Z", "text": "The smoke test with only 36k targets already scored 0.0211, double the popularity baseline of 0.0107, but I still don't know how the actual production baseline performs, which I need for the README and for deciding next steps. With about 29 minutes used so far, I'm now running the improved validation with 8 threads, expecting it to take roughly 5-8 minutes.\n\nRunning baseline and improved simultaneously would exceed the 8GB memory limit, so they need to run sequentially rather than concurrently. While waiting I'll use the time to sketch out the README skeleton and write a validation script for checking the submission format, then check back on progress shortly.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:13:47.928Z", "text": "The smoke-tested model already hits 0.0211 NDCG@10, double the popularity baseline of 0.0107. The full validation run is training on all 8 threads now, so I'll write a submission-format checker while it runs.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:13:53.262Z", "callId": "toolu_01Hm8v6GKemNv3bVeS7LisMr", "name": "Bash", "input": { "command": "cat > runs/check_submission.py <<'EOF'\nimport sys, numpy as np, pandas as pd\npath = sys.argv[1]\nf = pd.read_parquet(path) if path.endswith('.parquet') else pd.read_csv(path)\ntu = pd.read_parquet('/task/data/target_users.parquet')\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n uids=a['uids']; items=a['item_ids']; packed=a['packed_eligible']\nmask = np.unpackbits(packed, axis=1, count=len(items), bitorder='little').astype(bool)\nassert set(f.columns) >= {'uid','item_id','rank'}\nfor c in ['uid','item_id','rank']:\n assert np.isfinite(f[c]).all() and (f[c] == f[c].astype(np.int64)).all(), c\nassert set(f.uid.unique()) == set(tu.uid.astype(int)), 'user set mismatch'\ng = f.groupby('uid')\nassert (g.size() == 100).all(), 'rows per user'\nassert (g.item_id.nunique() == 100).all(), 'distinct items per user'\nassert (g['rank'].agg(lambda s: sorted(s) == list(range(1,101)))).all(), 'ranks'\nurow = {int(u): r for r, u in enumerate(uids)}; icol = {int(i): c for c, i in enumerate(items)}\nrows = f.uid.map(urow).to_numpy(); cols = f.item_id.map(icol).to_numpy()\nassert not np.isnan(cols.astype(float)).any(), 'unknown item'\nassert mask[rows, cols.astype(int)].all(), 'ineligible items'\nprint('submission OK:', len(f), 'rows', f.uid.nunique(), 'users')\nEOF\nsleep 60; grep -v \"^ranked\" runs/improved_val.log | tail -3; ps aux | grep \"train_current\" | grep -v grep | awk '{print $6/1e6 \" GB RSS\"}'", "description": "Write submission checker; check improved run progress and memory" }, "result": { "timestamp": "2026-10-03T09:14:53.332Z", "content": "target 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: power=0.75 shared=2048 mass_top1000=0.178 inbatch_softmax=True logq_weight=1.0\n3.11482 GB RSS", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:14:53.340Z", "data": { "type": "total_tokens_reminder", "text": "14896291 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:14:53.345Z", "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": "2026-10-03T09:14:55.938Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:14:55.938Z", "callId": "toolu_01C6UAqztotfU7N121qcsHn2", "name": "Bash", "input": { "command": "sleep 120; grep -v \"^ranked\" runs/improved_val.log | tail -2; ps aux | grep \"train_current\" | grep -v grep | awk '{print $6/1e6 \" GB RSS\"}'; date", "description": "Wait and check improved run progress" }, "result": { "timestamp": "2026-10-03T09:16:56.012Z", "content": "Exit code 143\nCommand timed out after 2m 0s", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:16:56.025Z", "data": { "type": "total_tokens_reminder", "text": "14895985 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:16:56.028Z", "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": "2026-10-03T09:16:57.896Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:16:57.896Z", "callId": "toolu_01Rt6VAGAEqYw5Et7SjXNUX5", "name": "Bash", "input": { "command": "grep -v \"^ranked\" runs/improved_val.log | tail -2; ps aux | grep \"train_current\" | grep -v grep | awk '{print $6/1e6 \" GB RSS\"}'; date", "description": "Check improved run progress" }, "result": { "timestamp": "2026-10-03T09:16:57.980Z", "content": "epoch=1/1 train_loss=8.20848 validation_loss=7.35754 pair_accuracy=0.579 train_targets=1,052,953 validation_targets=130,103 train_seconds=142.7 validation_seconds=10.0\nrestored best validation-BCE checkpoint: epoch=1 loss=7.357538\n3.69726 GB RSS\nSat Oct 3 09:16:57 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:16:57.986Z", "data": { "type": "total_tokens_reminder", "text": "14895790 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:16:57.988Z", "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": 30, "role": "agent", "timestamp": "2026-10-03T09:17:03.768Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:17:03.768Z", "text": "Training finished in just 143 seconds, much faster than the baseline's 416+ seconds for 250 batches, and it's ranking now. Checking the timestamps, I realize only about 18 minutes have passed rather than 30, so my time estimates were off and I actually have around 40 minutes remaining. With that cushion, I'm revising my plan to start with evaluating the improved validation results.\n\nAfter ranking finishes I'll immediately launch a submission refit with the improved config so there's a valid submission early on, then rerun the production baseline on validation to get the \"before\" comparison number, and if time allows, try one more variant like a different logq_weight or power setting, or two epochs, refitting if it beats the current result. For now I'm just waiting on the ranking to complete.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:17:03.775Z", "callId": "toolu_013vXUqvUUoVfKcehC3cwJGU", "name": "Bash", "input": { "command": "sleep 45; tail -1 runs/improved_val.log; ls runs/improved_validation.parquet 2>/dev/null && python /task/evaluate_public.py runs/improved_validation.parquet", "description": "Wait for ranking and evaluate improved validation run" }, "result": { "timestamp": "2026-10-03T09:17:52.282Z", "content": "ranked users=8,456/8,456\nruns/improved_validation.parquet\n{\n \"model\": \"runs/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0276,\n \"quality_users\": 4263\n }\n}", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:17:52.285Z", "data": { "type": "total_tokens_reminder", "text": "14895182 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:17:52.287Z", "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": "2026-10-03T09:17:55.494Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:17:55.494Z", "text": "Validation improved to 0.0276. Since I can't run both processes simultaneously due to memory limits (3.7GB + 4.5GB exceeds the 8GB cap), I'll launch the submission refit with this same config first to lock in a valid submission, then follow with the baseline comparison afterward.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:17:55.495Z", "text": "Improved model scores 0.0276 NDCG@10 on the public validation. Launching the hidden-test refit now so a valid submission exists early." }, { "kind": "tool", "timestamp": "2026-10-03T09:17:56.137Z", "callId": "toolu_0169KptnGroeQc3EeusxKfFs", "name": "Bash", "input": { "command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache cache/submission.npz --output /app/solution/submission.parquet --threads 8 > runs/improved_sub.json 2> runs/improved_sub.log & echo started; date", "description": "Launch submission-stage refit with the improved objective" }, "result": { "timestamp": "2026-10-03T09:17:56.159Z", "content": "started\nSat Oct 3 09:17:56 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:17:56.161Z", "data": { "type": "total_tokens_reminder", "text": "14894744 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:17:56.163Z", "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": "2026-10-03T09:18:03.433Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:18:03.433Z", "text": "While that runs, I'll draft the README and weigh a second variant to test with the ~35 minutes left—options like a partial popularity-debias weight, early stopping at 2 epochs, or adjusting the negative popularity power—keeping in mind each validation run takes about 4 minutes and a baseline rerun around 10.\n\nMapping out the timeline, running baseline then variant A then variant B then a possible refit gets tight against the ~09:59 deadline, so I need to keep a buffer. I decide the baseline rerun is worth the 10 minutes since the task needs a documented before-and-after score, and running jobs concurrently isn't feasible given memory constraints—so I'll keep things sequential and simple, and start writing the README while waiting.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:18:03.435Z", "callId": "toolu_01FgE31h5StN8Pr8k4Msra5N", "name": "Bash", "input": { "command": "sleep 100; grep -v \"^ranked\" runs/improved_sub.log | tail -2; date", "description": "Check submission refit progress" }, "result": { "timestamp": "2026-10-03T09:19:43.475Z", "content": "full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nnegative sampler: power=0.75 shared=2048 mass_top1000=0.178 inbatch_softmax=True logq_weight=1.0\nSat Oct 3 09:19:43 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:19:43.478Z", "data": { "type": "total_tokens_reminder", "text": "14894170 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:19:43.480Z", "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": "2026-10-03T09:19:45.685Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:19:45.685Z", "callId": "toolu_01MidtCuBQXnHifHBVXPsS3q", "name": "Bash", "input": { "command": "sleep 110; grep -v \"^ranked\" runs/improved_sub.log | tail -1; tail -1 runs/improved_sub.log; date", "description": "Wait for submission refit training to finish" }, "result": { "timestamp": "2026-10-03T09:21:35.750Z", "content": "restored best validation-BCE checkpoint: epoch=1 loss=7.417665\nrestored best validation-BCE checkpoint: epoch=1 loss=7.417665\nSat Oct 3 09:21:35 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:21:35.759Z", "data": { "type": "total_tokens_reminder", "text": "14893905 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:21:35.761Z", "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": "2026-10-03T09:21:38.246Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:21:38.246Z", "callId": "toolu_01JbnJ3hdndH2CjEYGMSGXG2", "name": "Bash", "input": { "command": "sleep 60; tail -1 runs/improved_sub.log; ls -la /app/solution/submission.parquet && python runs/check_submission.py /app/solution/submission.parquet; date", "description": "Wait for ranking, then validate the submission file" }, "result": { "timestamp": "2026-10-03T09:22:40.305Z", "content": "ranked users=8,456/8,456\n-rw-r--r-- 1 agent agent 1151492 Oct 3 09:22 /app/solution/submission.parquet\nsubmission OK: 845600 rows 8456 users\nSat Oct 3 09:22:40 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:22:40.308Z", "data": { "type": "total_tokens_reminder", "text": "14893653 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:22:40.309Z", "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": "2026-10-03T09:22:45.653Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:22:45.653Z", "text": "With a valid submission saved and about 23 minutes gone, I have roughly 36 left, so I'll keep a backup copy before moving on. I'm weighing whether to run the 10-minute production baseline first or try quicker 4-minute variant experiments that might produce a better model needing a 5-minute refit, comparing the total time costs of each ordering.\n\nThat gives 23 minutes total, finishing around 09:46, well within the 09:59 deadline, so I'll kick off the baseline run now.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:22:45.655Z", "text": "Valid submission saved. Now I'll run the production baseline on validation for the before-number (it needs the machine alone because of the memory cap), and back up the current submission." }, { "kind": "tool", "timestamp": "2026-10-03T09:22:45.833Z", "callId": "toolu_016Ae2CwSjVseVTQjC7YUNVT", "name": "Bash", "input": { "command": "cp /app/solution/submission.parquet runs/submission_v1_backup.parquet; cp runs/improved_sub.json runs/submission_v1.json; nohup python runs/baseline_train.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output /app/solution/production_validation.parquet --threads 8 > runs/baseline.json 2> runs/baseline.log & echo started; date", "description": "Back up submission and launch production baseline validation run" }, "result": { "timestamp": "2026-10-03T09:22:45.873Z", "content": "started\nSat Oct 3 09:22:45 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:22:45.876Z", "data": { "type": "total_tokens_reminder", "text": "14893061 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:22:45.878Z", "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": "2026-10-03T09:22:48.985Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:22:48.985Z", "text": "I notice runs/baseline_train.py imports two_tower_model from its own directory, but that module isn't present in runs/ — this would cause an import failure. Let me check the log to confirm.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:22:48.989Z", "callId": "toolu_01RPZEjuyz7PpTK5nFSBcrkW", "name": "Bash", "input": { "command": "sleep 5; tail -3 runs/baseline.log; ps aux | grep baseline_train | grep -v grep | wc -l", "description": "Confirm the baseline run started correctly" }, "result": { "timestamp": "2026-10-03T09:22:54.018Z", "content": " File \"/app/solution/runs/baseline_train.py\", line 34, in \n import two_tower_model as base\nModuleNotFoundError: No module named 'two_tower_model'\n0", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:22:54.021Z", "data": { "type": "total_tokens_reminder", "text": "14892830 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:22:54.023Z", "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": 37, "role": "agent", "timestamp": "2026-10-03T09:22:56.772Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:22:56.772Z", "callId": "toolu_01GPjiA7LVSSDgLeZjUwQa4k", "name": "Bash", "input": { "command": "cp two_tower_model.py runs/two_tower_model.py && nohup python runs/baseline_train.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output /app/solution/production_validation.parquet --threads 8 > runs/baseline.json 2> runs/baseline.log & sleep 8; tail -2 runs/baseline.log; date", "description": "Relaunch baseline with the module it imports available" }, "result": { "timestamp": "2026-10-03T09:23:04.811Z", "content": "target 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\nSat Oct 3 09:23:04 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:23:04.814Z", "data": { "type": "total_tokens_reminder", "text": "14892528 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:23:04.816Z", "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": 38, "role": "agent", "timestamp": "2026-10-03T09:23:07.394Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:23:07.394Z", "text": "While the baseline runs, I'll draft the README now with placeholders for the baseline number to fill in once it finishes, then move on to running the variants.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:23:07.395Z", "text": "While the baseline trains, I'll draft the README (the before-score gets filled in once the baseline finishes)." }, { "kind": "tool", "timestamp": "2026-10-03T09:23:25.053Z", "callId": "toolu_01Hq8chjEciGfLArydpQ9Yrw", "name": "Bash", "input": { "command": "cat > /app/solution/README.md <<'EOF'\n# \"Made for You\" two-tower: negative-sampling fix\n\n## Diagnosis\n\nThe production trainer scores every next-item target with a sample-weighted\nBCE against 24 uniform negatives plus 8 \"propensity-corrected\" in-batch\nnegatives. On this catalog that negative mix is nearly uninformative:\n\n- **Uniform negatives are too easy.** The 30,000-track catalog is heavy\n tailed. A uniform draw lands in the long tail almost every time, so the\n model only has to learn \"popular beats obscure\" to drive the negative loss\n down. The actual ranking problem is the opposite: validation relevance\n items have a median popularity rank of about 5,600 and are spread across\n the whole head, and the candidate sets exclude every track the user has\n already encountered, so the shelf has to separate plausible head tracks\n from each other, not from the tail.\n- **The in-batch channel is tiny and then re-flattened.** Only 8 of the 32\n negatives come from the batch, they are drawn from *unique* batch targets\n (which already flattens popularity), and the correction weight\n `(1/N / p)^0.5` clipped to `[0.25, 4]` down-weights exactly the popular\n confusers that carry the signal.\n- **Per-row negatives are expensive.** Each 6,400-target block encodes about\n 200k negative items through the item tower and runs a 32-round\n rejection-resampling loop, so the budget cannot simply be spent on more\n negatives.\n\n## Change (training objective only)\n\n`train_current_model.py` now defaults to `--loss-objective sampled-softmax`:\n\n- **Shared popularity-smoothed negatives.** Each block draws 2,048 negatives\n from `q(i) ∝ (target_count_i + 1)^0.75` once and shares them across all\n targets in the block (`--shared-negatives`, `--negative-popularity-power`).\n- **All unique batch targets as in-batch negatives.** Every target in the\n block is a column for every row (`--inbatch-softmax-negatives 1`).\n- **Sampled softmax with log-Q correction.** Logits are\n ` - logq_weight * log p_target(item)`, so popularity-biased\n sampling still estimates the full next-item softmax (`--logq-weight`).\n- **Same false-negative filter.** The full-training-history known-positive\n bitset and the active-like set mask a row's known positives out of the\n shared columns, exactly as the BCE path filtered its per-row draws.\n\nEverything else is unchanged: the chronological data protocol, target\nweighting, the GRU user tower, the catalog-aware item tower, raw dot-product\nscoring, and the indexed two-tower serving path (one user vector, precomputed\nitem vectors). Setting `--loss-objective bce` restores the production path\nbit-for-bit. The new objective also trains faster (about 140 s for the 1.05M\ntraining targets with 8 CPU threads versus more than 7 minutes for the\nproduction BCE path), because each block encodes only about 8k candidate\nitems instead of about 200k per-row negatives.\n\n## Public validation (train_* only, scored on val_*)\n\n| Model | quality_ndcg@10 | scored users |\n| --- | --- | --- |\n| Popularity order within eligible candidates | 0.0107 | 4263 |\n| Production BCE, 24 uniform + 8 in-batch (`--loss-objective bce`) | BASELINE_SCORE | 4263 |\n| Sampled softmax, 2048 shared popularity negatives + in-batch, log-Q (default) | 0.0276 | 4263 |\nEXTRA_ROWS\n\n## Commands\n\n```sh\n# public backtest of the improved objective\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --prepared-cache /app/solution/cache/validation.npz \\\n --output /app/solution/runs/improved_validation.parquet\npython /task/evaluate_public.py /app/solution/runs/improved_validation.parquet\n\n# final refit on train_* + val_* and hidden-test ranking\npython /app/solution/train_current_model.py --stage submission --public-dir /task \\\n --prepared-cache /app/solution/cache/submission.npz \\\n --output /app/solution/submission.parquet\n```\n\n`runs/check_submission.py` verifies the submission contract (users, 100\ndistinct ranks and items per user, test eligibility).\nEOF\nsleep 60; tail -1 runs/baseline.log; date", "description": "Write README draft and check baseline progress" }, "result": { "timestamp": "2026-10-03T09:24:25.130Z", "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nSat Oct 3 09:24:25 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:24:25.133Z", "data": { "type": "total_tokens_reminder", "text": "14890716 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:24:25.135Z", "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": 39, "role": "agent", "timestamp": "2026-10-03T09:24:30.340Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:24:30.340Z", "text": "The baseline should finish in about 7-9 minutes, so I'll wait since memory constraints prevent running another trainer simultaneously. Once it's done I'm considering which variant to try next—running just 2 epochs with early stopping on validation loss is fast given the 143s/epoch pace, and likely beneficial since one epoch on a million targets with fresh embeddings probably isn't enough to converge.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:24:30.346Z", "callId": "toolu_01KLhAsVk2rpFY8X7CL3Ec1T", "name": "Bash", "input": { "command": "sleep 115; tail -1 runs/baseline.log | cut -c1-200; date", "description": "Wait for baseline training progress" }, "result": { "timestamp": "2026-10-03T09:26:25.388Z", "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nSat Oct 3 09:26:25 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:26:25.391Z", "data": { "type": "total_tokens_reminder", "text": "14890280 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:26:25.393Z", "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": "2026-10-03T09:26:27.186Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:26:27.186Z", "callId": "toolu_01BTTEYgZW7HhY8di2uRNzBM", "name": "Bash", "input": { "command": "sleep 115; tail -1 runs/baseline.log | cut -c1-200; date", "description": "Wait for baseline training progress" }, "result": { "timestamp": "2026-10-03T09:28:22.228Z", "content": "restored best validation-BCE checkpoint: epoch=1 loss=0.496636\nSat Oct 3 09:28:22 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:28:22.237Z", "data": { "type": "total_tokens_reminder", "text": "14890086 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:28:22.243Z", "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": "2026-10-03T09:28:25.093Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:28:25.093Z", "text": "The baseline training finished faster with 8 threads, around 5 minutes, and ranking is now about 1 minute. I'll evaluate results and launch the 2-epoch variant, waiting roughly 50 seconds for it to progress.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:28:25.102Z", "callId": "toolu_01AqroBtC86gJR1Htrx9SM3L", "name": "Bash", "input": { "command": "sleep 50; tail -1 runs/baseline.log; ls production_validation.parquet && python /task/evaluate_public.py production_validation.parquet; grep -o '\"seconds\": [0-9.]*' runs/baseline.json | head -1; date", "description": "Evaluate the production baseline validation ranking" }, "result": { "timestamp": "2026-10-03T09:29:18.582Z", "content": "ranked users=8,456/8,456\nproduction_validation.parquet\n{\n \"model\": \"production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\n\"seconds\": 220.89775895599996\nSat Oct 3 09:29:18 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:29:18.585Z", "data": { "type": "total_tokens_reminder", "text": "14889778 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:29:18.587Z", "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": "2026-10-03T09:29:24.748Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:29:24.748Z", "text": "The improved model shows a 30% relative gain over baseline (0.0276 vs 0.02128), and training ran in about 221s with 8 threads. With roughly 29 minutes left, I'll launch a 2-epoch validation run, then if it performs better, refit for submission—should land around the 09:42 mark.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:29:24.750Z", "text": "The improved objective beats baseline by 30% (0.0276 vs 0.02128). With ~29 minutes left, I'll test one more variant: running a second epoch under the new objective.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:29:24.754Z", "callId": "toolu_013Pr9vwYmaJokqTDBXqc6AF", "name": "Bash", "input": { "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/improved_e2_validation.parquet --threads 8 --epochs 2 --early-stopping-patience 2 > runs/improved_e2_val.json 2> runs/improved_e2_val.log & sed -i 's/BASELINE_SCORE/0.02128/' README.md; echo started; date", "description": "Launch two-epoch variant on validation; fill baseline score into README" }, "result": { "timestamp": "2026-10-03T09:29:24.783Z", "content": "started\nSat Oct 3 09:29:24 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:29:24.785Z", "data": { "type": "total_tokens_reminder", "text": "14889167 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:29:24.787Z", "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": "2026-10-03T09:29:26.424Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:29:26.424Z", "callId": "toolu_01KfzLNXsbiveC9RVbUeyjaC", "name": "Bash", "input": { "command": "sleep 118; grep \"^epoch=\" runs/improved_e2_val.log | cut -c1-160; date", "description": "Wait for the two-epoch variant's first epoch" }, "result": { "timestamp": "2026-10-03T09:31:24.469Z", "content": "Sat Oct 3 09:31:24 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:31:24.478Z", "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": "2026-10-03T09:31:24.478Z", "data": { "type": "total_tokens_reminder", "text": "14888995 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:31:24.481Z", "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": "2026-10-03T09:31:26.550Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:31:26.550Z", "callId": "toolu_01W5GWc8HVs63ie7YMPAkzq2", "name": "Bash", "input": { "command": "sleep 100; grep \"^epoch=\" runs/improved_e2_val.log | cut -c1-160; date", "description": "Check two-epoch variant epoch results" }, "result": { "timestamp": "2026-10-03T09:33:06.585Z", "content": "epoch=1/2 batches=250 targets=987,417 batch_bce=7.765315 running_bce=8.219107 elapsed=133.3s\nepoch=1/2 train_loss=8.20848 validation_loss=7.35754 pair_accuracy=0.579 train_targets=1,052,953 validation_targets=130,103 train_seconds=144.3 validation_secon\nSat Oct 3 09:33:06 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:33:06.603Z", "data": { "type": "total_tokens_reminder", "text": "14888786 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:33:06.605Z", "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": "2026-10-03T09:33:10.214Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:33:10.214Z", "text": "Epoch 2 should finish around 09:35:40, with ranking wrapped up by 09:37. If results improve, refitting the submission with 2 epochs would take about 6.5 minutes total, landing around 09:44, well within the 09:59 deadline. I'll wait roughly 150 seconds before checking again.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:33:10.216Z", "callId": "toolu_01SXumLgy58wVazUMtjhoHhv", "name": "Bash", "input": { "command": "sleep 150; grep \"^epoch=2/2 train\" runs/improved_e2_val.log | cut -c1-120; tail -1 runs/improved_e2_val.log | cut -c1-80; date", "description": "Wait for epoch two of the variant" }, "result": { "timestamp": "2026-10-03T09:35:10.272Z", "content": "Exit code 143\nCommand timed out after 2m 0s", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:35:10.280Z", "data": { "type": "total_tokens_reminder", "text": "14888363 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:35:10.286Z", "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": "2026-10-03T09:35:12.558Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:35:12.558Z", "callId": "toolu_01UkFZPqLkUeiEcuNdxbHuMG", "name": "Bash", "input": { "command": "grep \"^epoch=2/2 train\" runs/improved_e2_val.log | cut -c1-120; tail -1 runs/improved_e2_val.log | cut -c1-80; date", "description": "Check epoch two result and ranking status" }, "result": { "timestamp": "2026-10-03T09:35:12.621Z", "content": "epoch=2/2 train_loss=7.78724 validation_loss=7.14654 pair_accuracy=0.690 train_targets=1,052,953 validation_targets=130,\nrestored best validation-BCE checkpoint: epoch=2 loss=7.146540\nSat Oct 3 09:35:12 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:35:12.628Z", "data": { "type": "total_tokens_reminder", "text": "14888169 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:35:12.630Z", "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": 47, "role": "agent", "timestamp": "2026-10-03T09:35:16.165Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:35:16.165Z", "text": "Epoch 2 brought the internal validation loss down from 7.358 to 7.147, which is encouraging. Ranking should finish in about a minute, and if NDCG shows improvement, I'll refit with 2 epochs for submission, targeting completion around 09:43. I'll wait 60 seconds and check the results.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:35:16.174Z", "callId": "toolu_012FkqsBJiNwoNL4VmsCvpkt", "name": "Bash", "input": { "command": "sleep 55; tail -1 runs/improved_e2_val.log | cut -c1-60; ls runs/improved_e2_validation.parquet 2>/dev/null && python /task/evaluate_public.py runs/improved_e2_validation.parquet; date", "description": "Evaluate the two-epoch variant ranking" }, "result": { "timestamp": "2026-10-03T09:36:14.628Z", "content": "ranked users=8,456/8,456\nruns/improved_e2_validation.parquet\n{\n \"model\": \"runs/improved_e2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019932,\n \"quality_users\": 4263\n }\n}\nSat Oct 3 09:36:14 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:36:14.631Z", "data": { "type": "total_tokens_reminder", "text": "14887763 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:36:14.633Z", "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": 48, "role": "agent", "timestamp": "2026-10-03T09:36:24.025Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:36:24.025Z", "text": "Interesting that 2 epochs actually gives worse discovery NDCG (0.0199) than 1 epoch (0.0276), despite lower validation loss internally — that's overfitting to next-item prediction including repeats rather than true discovery. So I'll stick with 1 epoch for the submission. With about 22 minutes left, there's time for one more variant to test.\n\nI'm thinking logq_weight=0.5 could help — a midpoint between full popularity correction and debiasing toward discovery. Given the median relevance item popularity rank is around 5600, somewhat head-heavy, this midpoint seems worth a quick 4-minute test, then refit if it improves things. I'll launch this run now and update the README with the epoch-2 results in the meantime.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:36:24.028Z", "text": "A second epoch improves training loss but hurts discovery NDCG, so I'll keep the one-epoch model as the submission. With ~20 minutes left, I'll test a half-strength log-Q correction and note the epoch finding in the README.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:36:26.447Z", "callId": "toolu_011uX71Dbc5QgxEy5DzMstVK", "name": "Bash", "input": { "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache/validation.npz --output runs/improved_lq05_validation.parquet --threads 8 --logq-weight 0.5 > runs/improved_lq05_val.json 2> runs/improved_lq05_val.log & \npython - <<'EOF'\np='/app/solution/README.md'; s=open(p).read()\ns=s.replace('EXTRA_ROWS','| Same, 2 epochs (internal next-item loss 7.36 -> 7.15, but discovery NDCG drops) | 0.0199 | 4263 |\\nLQ_ROW\\n\\nThe second epoch keeps lowering the internal next-item validation loss while\\nthe discovery NDCG falls, so the internal BCE/softmax early-stopping signal is\\nnot a proxy for the organic discovery objective; one epoch is retained.')\nopen(p,'w').write(s)\nEOF\necho ok; date", "description": "Launch half log-Q variant; add epoch finding to README" }, "result": { "timestamp": "2026-10-03T09:36:26.489Z", "content": "ok\nSat Oct 3 09:36:26 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:36:26.492Z", "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": "2026-10-03T09:36:26.492Z", "data": { "type": "total_tokens_reminder", "text": "14886649 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:36:26.493Z", "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": 49, "role": "agent", "timestamp": "2026-10-03T09:36:31.678Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:36:31.678Z", "text": "While waiting, I'm double-checking the solution directory contents and confirming the submission file is a proper regular file written by pandas, not a symlink. I'm also verifying that train_current_model.py is the improved version with two_tower_model.py alongside it, and considering whether there's anything worth noting about the 8GB cgroup memory limit for future reference.\n\nThat's environment-specific and only relevant to this conversation, so I'll skip saving it. Now I'll just wait for the lq05 run to finish.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:36:31.681Z", "callId": "toolu_011atbpkfcyzUY6XrMPByv2J", "name": "Bash", "input": { "command": "sleep 110; grep \"^epoch=1/1 train\" runs/improved_lq05_val.log | cut -c1-100; ls -la /app/solution; date", "description": "Check variant progress and solution directory contents" }, "result": { "timestamp": "2026-10-03T09:38:21.723Z", "content": "total 2480\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:29 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 08:59 ..\n-rw-r--r-- 1 agent agent 4291 Oct 3 09:36 README.md\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:06 cache\n-rw-r--r-- 1 agent agent 1244230 Oct 3 09:28 production_validation.parquet\ndrwxr-xr-x 2 agent agent 4096 Oct 3 09:36 runs\n-rw-r--r-- 1 agent agent 1151492 Oct 3 09:22 submission.parquet\n-rw-r--r-- 1 agent agent 93610 Oct 3 09:08 train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Oct 3 09:04 two_tower_model.py\nSat Oct 3 09:38:21 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:38:21.734Z", "data": { "type": "total_tokens_reminder", "text": "14886237 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:38:21.737Z", "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": 50, "role": "agent", "timestamp": "2026-10-03T09:38:23.860Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:38:23.860Z", "callId": "toolu_01LrJEmGKBSCfUwBRcz94WrL", "name": "Bash", "input": { "command": "sleep 100; grep \"^epoch=1/1 train\" runs/improved_lq05_val.log | cut -c1-100; tail -1 runs/improved_lq05_val.log | cut -c1-60; date", "description": "Wait for the half log-Q variant to finish training" }, "result": { "timestamp": "2026-10-03T09:40:03.928Z", "content": "epoch=1/1 train_loss=8.07126 validation_loss=7.23489 pair_accuracy=0.611 train_targets=1,052,953 val\nrestored best validation-BCE checkpoint: epoch=1 loss=7.2348\nSat Oct 3 09:40:03 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:40:03.935Z", "data": { "type": "total_tokens_reminder", "text": "14885710 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:40:03.937Z", "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": 51, "role": "agent", "timestamp": "2026-10-03T09:40:07.209Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:40:07.209Z", "text": "I need to wait about a minute for ranking to finish, then check the result. If it beats 0.0276, I'll refit with --logq-weight 0.5, which should take roughly 4.5 minutes total, finishing around 09:46 — well within the 09:59 deadline.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:40:07.304Z", "callId": "toolu_01LdAjVwyHHg2A7xKJ1U6a9X", "name": "Bash", "input": { "command": "sleep 60; tail -1 runs/improved_lq05_val.log | cut -c1-40; ls runs/improved_lq05_validation.parquet 2>/dev/null && python /task/evaluate_public.py runs/improved_lq05_validation.parquet; date", "description": "Evaluate the half log-Q variant ranking" }, "result": { "timestamp": "2026-10-03T09:41:10.783Z", "content": "ranked users=8,456/8,456\nruns/improved_lq05_validation.parquet\n{\n \"model\": \"runs/improved_lq05_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019907,\n \"quality_users\": 4263\n }\n}\nSat Oct 3 09:41:10 UTC 2026", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:41:10.786Z", "data": { "type": "total_tokens_reminder", "text": "14885315 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:41:10.787Z", "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": 52, "role": "agent", "timestamp": "2026-10-03T09:41:16.122Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:41:16.122Z", "text": "Half log-Q correction scores worse (0.0199) than the full correction with 1 epoch (0.0276), confirming the current submission's config is already optimal — no refit needed. I'll just verify the submission JSON matches the config (sampled-softmax loss, 1 epoch, logq 1.0), update the README with the lq05 row, and write the final summary.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:41:16.125Z", "text": "Half-strength log-Q scored worse (0.0199), confirming the full correction with one epoch is still the best setup—matching what's in the saved submission. Now finalizing the README and re-verifying the artifact.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:41:19.526Z", "callId": "toolu_01BnJCYwZxqCfr247zcJvXZi", "name": "Bash", "input": { "command": "python - <<'EOF'\np='/app/solution/README.md'; s=open(p).read()\ns=s.replace('LQ_ROW','| Same, `--logq-weight 0.5` (half popularity correction) | 0.0199 | 4263 |')\ns=s.replace('one epoch is retained.','one epoch is retained. Weakening the log-Q correction also hurts, so the\\nsubmitted ranker is the default: one epoch, full log-Q correction,\\n2048 shared `popularity^0.75` negatives plus all in-batch targets.\\n\\nThe submission was produced by the default configuration refit on\\n`train_* + val_*` (`runs/submission_v1.json` holds its run record).')\nopen(p,'w').write(s)\nEOF\npython runs/check_submission.py /app/solution/submission.parquet; python - <<'EOF'\nimport json; j=json.load(open('/app/solution/runs/submission_v1.json'))\nprint(j['stage'], j['config']['loss_objective'], j['config']['epochs'], j['config']['logq_weight'], j['config']['shared_negatives'], j['architecture']['negative_sampling'], j['runtime_seconds'])\nEOF\ntest -L /app/solution/submission.parquet && echo SYMLINK || echo regular-file; rm -f runs/unused.parquet; grep -c \"\" README.md", "description": "Finalize README and verify the submission and its run record" }, "result": { "timestamp": "2026-10-03T09:41:21.658Z", "content": "submission OK: 845600 rows 8456 users\nsubmission sampled-softmax 1 1.0 2048 2048-shared-popularity^0.75-plus-inbatch-targets-logq 253.806\nregular-file\n87", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:41:21.661Z", "data": { "type": "total_tokens_reminder", "text": "14884405 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:41:21.663Z", "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": 53, "role": "agent", "timestamp": "2026-10-03T09:41:32.415Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:41:32.415Z", "text": "The work is complete and the submission is in place.\n\n**Diagnosis.** The production objective scores each next-item target with BCE against 24 uniform negatives and 8 in-batch negatives. On a 30k long-tail catalog, uniform draws almost always land in the tail, so the model mainly learns \"popular beats obscure\". The ranking task is harder than that: candidates exclude everything the user has encountered, and relevant validation items have a median popularity rank around 5,600, spread across the head. The 8 in-batch negatives that could separate head tracks are drawn from unique batch targets and then further down-weighted by the propensity correction. Per-row negatives also cost about 200k item-tower encodings per block, so the budget could not simply buy more of them.\n\n**Change.** The trainer in `/app/solution/train_current_model.py` now defaults to a sampled-softmax objective. Each block draws 2,048 shared negatives from smoothed target popularity (count^0.75), adds every unique batch target as a column, applies a log-Q correction so the loss still estimates the full next-item softmax, and masks known positives and active likes with the existing full-history filter. Data protocol, target weighting, both towers, dot-product scoring, and the indexed serving path are unchanged. The legacy path is still available via `--loss-objective bce`. Training is also faster, about 140 seconds versus roughly 220 seconds for the production objective on the same 8 threads.\n\n**Public validation results (NDCG@10, 4,263 scored users):**\n\n| Model | Score |\n| --- | --- |\n| Popularity order | 0.0107 |\n| Production BCE baseline | 0.0213 |\n| Sampled softmax, shared popularity negatives, 1 epoch (submitted) | 0.0276 |\n| Same with 2 epochs | 0.0199 |\n| Same with half log-Q correction | 0.0199 |\n\nTwo findings worth noting. A second epoch lowers the internal next-item validation loss but hurts discovery NDCG, so the internal early-stopping signal is not a proxy for the organic objective. Weakening the popularity correction also hurts.\n\n**Deliverables.** `/app/solution/submission.parquet` is a regular file built by refitting the default configuration on train plus val, and it passes the full contract check (8,456 users, 100 distinct eligible items and ranks per user). The README records the diagnosis, scores, and commands. One operational note: the container has an 8 GB memory cap, and an early attempt to run two trainers concurrently was OOM-killed, so all reported runs were executed one at a time." } ] } ] }