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  "text": "Banger paper from Harvard.\n\nAutoScientists 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.\n\nValidated 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.\n\nThe strongest argument so far that the AI-scientist bottleneck is governance rather than raw capability.\n\nPaper: https://t.co/LtqUsrJ0os\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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