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

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 support 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. To address this, we propose LLM-AutoSciLab, a closed-loop framework that couples hypothesis generation with hypothesis-conditioned experiment selection and mechanism refinement. Rather than fitting models to passively collected data, LLM-AutoSciLab iteratively proposes plausible hypotheses, selects informative experiments to distinguish among them or refine them, and updates its state based on the resulting evidence. To evaluate dynamic, closed-loop scientific discovery with active data acquisition, we introduce ActiveSciBench-Chem (57 enzyme-kinetics domains) and ActiveSciBench-GRN (45 gene-regulatory-network tasks), benchmarks that model discovery as a budget-constrained process requiring adaptive experiment design, variable selection, and recovery of true mechanisms. Across NewtonBench, ActiveSciBench-Chem, and ActiveSciBench-GRN, LLM-AutoSciLab outperforms prior methods, achieving 67.6% and 35.1% symbolic accuracy and 31.1% exact graph recovery, respectively. Moreover, hypothesis-guided experimentation is 2x--5x more sample-efficient than the strongest competing baselines.