RT by @_akhaliq: Large Discovery Models: learning where to search next An LLM proposes, a Bayesian…

TL;DR - Large Discovery Models combine LLM-generated proposals with a Bayesian uncertainty-scoring surrogate in an iterative search loop. The approach reports gains across program discovery, proteins, and molecules, suggesting more efficient exploration of large candidate spaces.

• The LLM proposes candidates while the Bayesian surrogate estimates uncertainty and guides where to search next.
• The iterative loop reportedly cuts validation error by 2.4×.
• Reported domain gains include an 18% improvement in binding energy and more than 60% improvement in molecular objectives.
• Results span multiple discovery settings, though the provided content does not specify methods, baselines, or datasets.