Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models

arXiv:2608.09696v4 Announce Type: replace
Abstract: A primary goal of science is to learn mechanistic or causal world
models from data. These models can be used to explain some
phenomenon of interest. They also provide the ability to answer
interventional “what if'' questions (i.e., to predict the outcome
of an action never taken). Identifying such models usually requires
experiments, because passive data leaves the mechanisms
unidentified. Since experiments are expensive, we need to develop
learning algorithms that are data efficient. We therefore introduce
the Model Discovery Agent (MDA), which combines three ingredients: a
novel SMC$^3$ algorithm, which uses 3 levels of nested sequential
Monte Carlo (over models, parameters, and latents); a large language
model (LLM), which is used as a way to propose new models when the
current hypothesis space is detected to be insufficient (c.f.,
M-open Bayesian inference); and an experiment designer based on
maximizing the Value of Information. On three existing benchmarks
— \DPbench \citep{wiemann2026discoverphysics}, \CHEMbench
\citep{kabra2026autoscilab} and \boxing \citep{gandhi2025boxinggym}
— we show that MDA sets a new SOTA in terms of performance.
Finally, we introduce \HHbench, a new stochastic single-neuron
electrophysiology benchmark, which is significantly harder than
current benchmarks, but on which MDA performs well due to its
noise-robust Bayesian foundations.

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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models