Reasoning BO: Enhancing Bayesian Optimization With the Reasoning Power of LLMs
Many real‐world scientific and industrial applications require the optimization of expensive black‐box functions. Bayesian optimization (BO) provides an effective framework but often struggles with local optima and lacks interpretability. This paper introduces reasoning BO, a novel framework leveraging reasoning models to guide BO sampling while incorporating multi‐agent systems and knowledge graphs for online knowledge accumulation. We evaluate our approach across 10 diverse tasks, including synthetic functions and complex real‐world chemical optimizations. Reasoning BO progressively refines sampling strategies through real‐time insights and hypothesis evolution, identifying high‐performing regions effectively. In the direct arylation task, our method significantly outperformed traditional BO, increasing yield from 25.20% to 60.07%. Furthermore, we demonstrate that smaller LLMs, after post‐training, can achieve performance comparable to larger counterparts. This framework establishes an intelligent cost‐effective optimization system for scientific discovery, combining LLM reasoning with structured knowledge management.