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English(EN) RISR: Residual-Informed Scientific Equation Discovery with Large Language Models

新的RISR方法利用LLM进行科学方程发现

研究人员开发了RISR,一种利用大型语言模型(LLM)通过分析残差误差模式来进行科学方程发现的新方法。该方法通过学习哪些修正最适合来指导公式发现过程。在LLM-SRBench上进行评估,RISR在数值方程恢复方面表现出改进,在特定的误差容差下以高精度优于现有基线。 AI

影响 通过提高LLM在方程恢复方面的准确性和效率,增强科学发现能力。

排序理由 这是一篇详细介绍使用LLM进行科学方程发现的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的RISR方法利用LLM进行科学方程发现

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这是一篇详细介绍使用LLM进行科学方程发现的新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haobo Li, Wenshuo Zhang, Wenxiao Zhao, Eunseo Jung, Rui Sheng, Yushi Sun, Peiqin Zhuang, Hao Chen, Fenghua Ling ·

    RISR:基于大型语言模型的残差信息科学方程发现

    arXiv:2610.11387v1 Announce Type: cross Abstract: Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across inputs. We introduce RISR, a residual-informed method that uses these error patt…