Large Discovery Models Pair a Generative Proposer With a Bayesian Surrogate, Beating LLM-Only Reflection Across Three Domains
LLM likelihoods and self-assessments are unreliable proxies for expensive real objectives, especially on novel candidates outside the observed distribution. The Large Discovery Model couples a generative proposer with a Bayesian non-parametric reward surrogate that predicts performance and quantifies uncertainty, feeding an uncertainty-aware value back into generation, refinement and selection while continually updating both the discovery memory and the surrogate as each experimental observation arrives. Against LLM-only reflection and traditional statistical search it delivers 2.4x greater reduction in validation BPB on neural-network training, an 18.2% relative decrease in antibody binding energy, and over 60% relative gains on molecular multi-objective optimization.
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