Files
MerlinH 41c94b73bf Add 2 entries from recommended candidates, reject 1
- Anthropic Automated W2S Researcher: PGR 0.97 vs 0.23 human baseline;
  full prompt stack published (system prompt, skill, critic prompts)
- SPARK pathology agents: Nature Medicine, survival-validated biomarkers;
  full CrewAI agents/tasks prompts published
- OpenAI graviton amplitudes: rejected - no prompt ever released
2026-08-20 14:49:16 +00:00

2.4 KiB

Automated Weak-to-Strong Researcher (AAR)

Lab Anthropic (Alignment Science)
Model Claude Opus 4.6 (Claude Agent SDK)
Field AI alignment research — weak-to-strong generalization
Result (yield) Nine parallel AARs working 5 days (~800 cumulative hours, ~$18k) achieved PGR 0.97 on weak-to-strong supervision, vs. a human-researcher baseline of 0.23 achieved over 7 days. Published April 14, 2026.
Announcement https://www.anthropic.com/research/automated-alignment-researchers
Paper https://alignment.anthropic.com/2026/automated-w2s-researcher/
Code + prompts (MIT) https://github.com/safety-research/automated-w2s-research

Why it's battle-tested

This is the strongest provenance in the gallery: the run is published, and the entire prompt stack is public — the agent system prompt, the Claude skill, the critic-idea prompts, the sandbox, datasets, and baselines. Nothing user-authored is hidden.

Why the yield is credible

  • Ground truth was held server-side; agents were isolated in Docker/RunPod sandboxes that only saw unlabeled data (cheating was designed out).
  • The metric (Performance Gap Recovery) is objective and mechanically scored.
  • Human baseline (0.23) was measured on the same task, same datasets.

Prompt files (verbatim, copy-paste)

{{placeholder}} variables are filled by the harness at runtime; the MCP tools (evaluate / share finding / leaderboard) and sandbox are in the public repo. License: MIT.