Model Discovery Agent: LLM-Assisted Bayesian Experiment Design
K. Murphy · 2026
What this means for traders
Let the model propose and let explicit, auditable machinery score and decide: when every number behind a decision is computed outside the model, the decision can be checked rather than trusted.
Abstract
Couples an LLM used strictly as a proposer of candidate mechanisms with standard Bayesian machinery: sequential Monte Carlo for posteriors and evidence, and value-of-information to choose the next experiment. On existing physics, chemistry and regression benchmarks it cuts held-out prediction error and recovers the true mechanism in far fewer experiments than the published LLM-driven agents, and it also performs well on a new, harder neuroscience benchmark. The most transferable idea is the separation of roles: the LLM only proposes, while every number behind the decision (posterior, evidence, experiment score, forecast) is computed by Bayesian inference, never by the model. All experiments use a single model, so stability across models is not measured.