Applications
Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
arXiv:2604.12806v1 Announce Type: new Abstract: Inferring latent interaction structures from observed dynamics is a fundamental inverse problem in many-body interacting systems. Most neural approaches
arXiv:2604.12806v1 Announce Type: new Abstract: 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. We propose extbf{COSINE} (extbf{C}o-extbf{O}ptimization of extbf{S}ymbolic extbf{I}nteractions and extbf{N}etwork extbf{E}dges), a differentiable framework that jointly discovers interaction graphs and sparse symbolic dynamics. To overcome the limitations of fixed symbolic libraries, COSINE further incorporates an outer-loop large language model that adaptively prunes and expands the hypothesis space using feedback from the inner optimization loop. Experiments on synthetic systems and large-scale real-world epidemic data demonstrate robust structural recovery and compact, mechanism-aligned dynamical expressions. Code: https://anonymous.4open.science/r/COSINE-6D43.
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Source: arXiv cs.LG | 2026-04-15