LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs

ArXi:2605.24043v1 Announce Type: cross Scientific discovery is a closed-loop process in which hypotheses guide data acquisition and observations refine the hypothesis space. Yet most approaches reduce discovery to supervised learning over fixed datasets, where limited observations can multiple plausible mechanisms that fit locally but fail to generalize. Thus, the key challenge is selecting informative observations to resolve uncertainty, shifting the focus from static inference to adaptive data acquisition.