LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation

ArXi:2605.22054v1 Announce Type: cross The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). However, existing approaches typically embed LLMs directly into the sampling or surrogate modeling pipeline, without fully leveraging their significantly lower evaluation cost compared to real-world experiments.