LATIDIA · Investigación

L2G-SR: Aprender a guiar la regresión simbólica basada en LLM

arXiv:2610.20685v1 Announce Type: new Abstract: La regresión simbólica busca una ecuación que explique los datos observados. El error numérico se usa comúnmente para guiar la búsqueda, pero solo puede favorecer a un sustituto preciso

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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