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

In three linesLLM-AutoSciLab proposes a closed-loop scientific discovery framework coupling hypothesis generation, hypothesis-conditioned experiment selection, and mechanism refinement. Evaluated on ActiveSciBench (57 enzyme-kinetics tasks, 45 gene-regulatory-network tasks), the system achieves 67.6% symbolic accuracy and 2-5x better sample efficiency than competing baselines.

Summary generated by Claude — human-verified