TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery

We study mixed-variable optimization in scientific discovery, where the key challenge is to efficiently explore the search space under expensive experimental evaluations. Traditional Bayesian optimization (BO) methods typically rely on structural descriptors (e.g., molecular fingerprints) to measure similarity among categorical variables. However, such task-agnostic similarity often fails to reflect task-specific objectives, leading to biased generalization. To address this limitation, we propose \textbf{TagBO} (\textbf{T}ask-\textbf{a}ware \textbf{G}raph \textbf{B}ayesian \textbf{O}ptimization), a method that integrates LLM-derived relational priors with descriptor-based structural information for mixed-variable Bayesian optimization. Rather than directly using LLM outputs as predictive scores, TagBO treats LLM outputs as a potentially noisy source of task-aware relational priors and extracts coarse ordinal relational signals from LLM reasoning to construct task-aware similarity structures for scientific optimization. TagBO then performs constrained Gaussian process Bayesian optimization over the resulting joint discrete-continuous representation space. Experiments on diverse chemistry and materials formulation tasks demonstrate that TagBO consistently improves sample efficiency under fixed evaluation budgets across LLMs of varying capabilities.