Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling

ArXi:2604.12806v1 Announce Type: new Inferring latent interaction structures from observed dynamics is a fundamental inverse problem in many-body interacting systems. Most neural approaches rely on black-box surrogates over trainable graphs, achieving accuracy at the expense of mechanistic interpretability. Symbolic regression offers explicit dynamical equations and stronger inductive biases, but typically assumes known topology and a fixed function library.