L2G-SR: Learning to Guide LLM-based Symbolic Regression

L2G-SR, a framework for LLM-based symbolic regression, achieves state-of-the-art numerical fit, equation-level accuracy and tree edit distance (TED) on LLM-SRBench across the evaluated LLM backbones and domains, according to the arXiv paper 2610.20685v1. The framework guides search with a learned structural verifier trained on structural supervision built from tree edit distance that accounts for commutative operators and alternative expression forms; the verifier aligns numerical observations with formulas and scores a candidate's structural similarity to the hidden target formula, complementing numerical error in parent selection. An analyst uses verifier feedback and diagnostic tools to propose revisions that a generator converts into refined expressions.

arXiv:2610.20685v1 Announce Type: new Abstract: Symbolic regression searches for an equation that explains observed data. Numerical error is commonly used to guide the search, but it alone can favor an accurate surrogate over a candidate with the right structure. We propose L2G-SR, a framework for LLM-based symbolic regression that guides search with a learned structural verifier. We construct structural supervision signal using tree edit distance TED , accounting for commutative operators and alternative expression forms. A verifier first aligns numerical observations with the formulas, then learns to score a candidate's structural similarity to the target formula. At search time, the target formula is hidden and the verifier complements numerical error in parent selection. An analyst then uses verifier feedback and diagnostic tools to propose revisions, which a generator converts into refined expressions. On LLM-SRBench, L2G-SR achieves state-of-the-art numerical fit, equation level accuracy and TED across the evaluated LLM backbones and domains. L2G-SR demonstrates the value of learned structural guidance for LLM-based symbolic regression.