We present a novel approach to symbolic regression using vision-capable large language models (LLMs) and the ideas behind Google DeepMind's Funsearch. The LLM is given a plot of a univariate function and tasked with proposing an ansatz for that function. The free parameters of the ansatz are fitted using standard numerical optimisers, and a collection of such ansätze make up the population of a genetic algorithm. Unlike other symbolic regression techniques, our method does not require the specification of a set of functions to be used in regression, but with appropriate prompt engineering, we can arbitrarily condition the generative step. By using Kolmogorov Arnold Networks (KANs), we demonstrate that ``univariate is all you need'' for symbolic regression, and extend this method to multivariate functions by learning the univariate function on each edge of a trained KAN. The combined expression is then simplified by further processing with a language model.
View on arXiv@article{harvey2025_2505.07956,
title={ Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks },
author={ Thomas R. Harvey and Fabian Ruehle and Cristofero S. Fraser-Taliente and James Halverson },
journal={arXiv preprint arXiv:2505.07956},
year={ 2025 }
}