bayesian-surprise

This paper addresses the limitation of static surprisal in LLM-based scientific discovery by introducing evidence-informed non-stationary beliefs, and proposes belief-update filtering and diversity maximization to improve discovery, achieving 30.62% higher non-stationary surprisal across five domains.