AI 科研团队开始自我进化，失败实验也能变成养料
AI 做科研，失败实验通常被当成废料丢掉。这篇让 AI 团队把失败拆开看：是假设错了，还是实现方法错了，分别修正；同时，一个实验里没用的部分，可能正好是另一个实验缺的零件，直接拆下来重组。这套机制让 AI 在 OpenProblems、NatureBench 等基准上刷新了纪录，而且积累的经验能迁移到没见过的任务上，连生物学家都拿它生成的程序做后续分析。它不是你明天就能用的工具，但这是 AI 从「会做实验」走向「会从实验里学习」的关键一步。
📄 原文摘要(英文)
Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scientific agent swarms can combine evidence and implementations from different research directions to guide their agents' subsequent experiments. However, poor experimental results can leave unclear whether to revise a hypothesis or its implementation, while overall program scores can hide components useful to other agents. To address these challenges, we introduce SwarmRSI, a framework that evolves scientific agent swarms for recursive self-improvement. We devise Feedback-Guided Swarm Mutation to refine hypotheses and their implementations using experimental feedback from other agents. We also propose Complementary Swarm Recombination to reuse retained hypotheses and complementary program components across agents, allowing partial results to support new research. Recursive Swarm Evolution integrates both mechanisms into a feedback loop, using new experimental evidence to guide subsequent research. Experiments on OpenProblems, NatureBench and MLE-Bench Lite demonstrate SwarmRSI's state-of-the-art (SOTA) performance. Further evaluations show that accumulated research experience improves performance on held-out tasks. Biologists also reproduce its programs for follow-up biological analyses.