From Scientific Observations to Mechanisms: Benchmarking Hypothesis Generation by AI Scientists

Excerpt

arXiv:2610.05197v1 Announce Type: new Abstract: Data-driven mechanistic hypotheses are essential to scientific discovery because they explain how underlying processes produce observed phenomena. AI agents and AI scientists increasingly support scientific data analysis. However, their ability to turn empirical findings into mechanistic hypotheses remains insufficiently examined. To address this gap, we introduce MechHypoBench, the first benchmark for evaluating whether AI agents and AI scientists