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Banger paper from Harvard. AutoScientists drops the central planner entirely. Agents interpret shared experimental data, self-organize around promising directions, evaluate proposals before resource allocation, and document successes AND failures. Decentralized AI co-scientists with failure documentation as a first-class step. Validated across three concrete domains. Biomedical ML reaches 74.4% mean leaderboard percentile. Language model training converges 1.9x faster. Protein fitness prediction lifts +12.5% on specific assays and +6.5% broader. The strongest argument so far that the AI-scientist bottleneck is governance rather than raw capability. Paper: https://arxiv.org/abs/2605.28655 Learn to build effective AI agents in our academy: https://academy.dair.ai/