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This arXiv preprint proposes L2G-SR, an LLM-based symbolic regression method that adds a learned structural verifier, trained on tree edit distance labels, to the usual numerical-error signal for selecting candidate equations. Reported results on LLM-SRBench are state-of-the-art, but they come from a single benchmark and self-reported comparisons, so readers should check the evaluation setup and baselines before drawing broad conclusions. The main takeaway for AI and ML readers is the idea that structural similarity can complement fit-based search when recovering the right equation, not just an accurate approximation.
L2G-SR: Learning to Guide LLM-based Symbolic Regression
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.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.20685 [cs.AI]
(or arXiv:2610.20685v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.20685 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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