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New framework L2G-SR enhances LLM-based symbolic regression with structural guidance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed L2G-SR, a novel framework designed to enhance Large Language Model (LLM)-based symbolic regression. This method incorporates a learned structural verifier to guide the search for equations, complementing traditional numerical error metrics. The verifier assesses the structural similarity of candidate formulas to a target equation, using tree edit distance to account for variations in expression forms. L2G-SR has demonstrated state-of-the-art performance on the LLM-SRBench benchmark, improving numerical fit, equation accuracy, and structural similarity. AI

IMPACT This research could lead to more accurate and structurally sound equation discovery from data using LLMs.

RANK_REASON The cluster describes a new research paper detailing a novel framework for symbolic regression using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework L2G-SR enhances LLM-based symbolic regression with structural guidance

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The cluster describes a new research paper detailing a novel framework for symbolic regression using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Letian Huang, Xiangyu Wang, Haowei Lin, Di He ·

    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-S…

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