LLM-Assisted Bayesian Experiment Design Cuts Trial Count by 5x
rohanpaul_ai · x · 2026-08-30
The paper introduces the Model Discovery Agent (MDA), which uses LLMs to generate explanatory hypotheses for data and then employs standard Bayesian scoring to select the experiment that best distinguishes between them.
This approach avoids wasting resources on experiments that merely confirm existing beliefs. On a physics benchmark, the MDA-assisted model passed in 93% of runs, compared to 31% for the LLM alone. MDA also replicated a published result using only about 8 experiments instead of approximately 41.
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