[Submitted on 14 Oct 2026]
Title:L2G-SR: Learning to Guide LLM-based Symbolic Regression
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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.
Comments: 40 pages, including references and appendices
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.20685 [cs.AI]
(or arXiv:2610.20685v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.20685
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Letian Huang [view email] [v1] Wed, 14 Oct 2026 16:39:39 UTC (867 KB)
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