aiqbench analysis
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)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 251 words
Related coverage
Recovery-Directed Symbolic Distillation of Neural Likelihoods
arXiv · Sep 30, 2026
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
arXiv · Aug 3, 2026
Deliberate Evolution: Agentic Reasoning for Sample-Efficient Symbolic Regression with LLMs
arXiv · Jun 5, 2026
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression
arXiv · Oct 14, 2026