Bayesian experimental optimization

A Gaussian-process objective turns an LLM's overconfidence into a calibrated error bar

GOLLuM fits a language model and a Gaussian process under a single probabilistic objective, so the model learns from being confidently wrong. Its authors report matching classical Bayesian optimisation on over 40 per cent fewer experiments.