Mixed-variable experiments stop leaning on structure alone: LLM ranking priors plus structural smoothing reach near-optimal candidates faster on six chemistry and materials tasks
TagBO treats candidate rankings produced by a large language model as coarse task-aware priors and fuses them with local structural smoothing into a similarity graph, reaching high-quality candidates in fewer evaluations under a fixed budget on six mixed-variable chemistry and materials optimization tasks.