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Multi-Objective Causal Bayesian Optimization revolutionizes decision-making in healthcare and engineering, integrating causal graphs with Bayesian methods to efficiently navigate trade-offs, ensuring smarter, interpretable solutions for complex systems.

GOLLuM trains LLMs using Bayesian uncertainty signals, raising high-yield Buchwald-Hartwig discovery from 24% to 43% in 50 experiments across 19 benchmarks.