AI 自己当科学家：边做实验边总结规律
科学家发现新规律，靠的是反复做实验、从结果里提炼因果。现在 AI 也能这么干了：Principia 让大模型在推理过程中，先对输入和结果提出因果假设，再把多个假设里反复出现的规律归纳成「科学原理」，然后用这个原理去做下一步实验、拿到新结果、再修正假设和原理，循环往复。在分子优化、蛋白质工程、基因扰动等基准上，它比靠内部知识、外部资料或经验总结的基线都强，而且生成的原理和真实文献一致。它不是你明天能用上的工具，但这是 AI 从「答对题」走向「自己发现规律」的一个真实信号。
📄 原文摘要(英文)
Scientific discovery requires identifying the scientific principles underlying observed phenomena to guide further investigation. As these empirical principles are often unknown in complex discovery processes, they are constructed via a hypotheses-to-principle pipeline to obtain recurring causalities from progressively accumulated observations during test-time discovery. However, constructing this knowledge requires substantial domain expertise, time, and effort, making this translation stage a bottleneck for scientific discovery. To address this limitation, we propose Principia, an LLM-based framework that constructs and continually refines scientific principles at test time. Drawing on how human researchers develop principles, Principia forms comparative causal hypotheses from related inputs and their observed outcomes, then synthesizes recurring relationships across these hypotheses into inductive principles. By applying the constructed hypotheses and inductive principles to the task, it obtains new observations, revises the hypotheses used based on these new observations, and reconstructs principles from the updated hypotheses, allowing scientific knowledge to continuously evolve throughout test-time discovery. Across benchmarks spanning molecular optimization, protein engineering, gene perturbation, and molecular property prediction, Principia outperforms baselines relying on internal knowledge, external sources, or experience summaries, while generating principles that are plausible and consistent with the scientific literature, demonstrating its effectiveness in constructing and refining task-specific hypotheses and principles at test time for scientific discovery. Our code is available at https://github.com/DongkiKim95/Principia.