用大模型+贝叶斯实验设计，高效发现物理机制模型

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.