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New RISR method uses LLMs for scientific equation discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RISR, a novel method for scientific equation discovery that leverages large language models (LLMs) by analyzing residual error patterns. This approach guides the formula discovery process by learning which corrections are most valuable to fit. Evaluated on the LLM-SRBench, RISR demonstrated improved numerical equation recovery, outperforming existing baselines with high accuracy rates at specific error tolerances. AI

IMPACT Enhances scientific discovery by improving the accuracy and efficiency of equation recovery using LLMs.

RANK_REASON This is a research paper detailing a new method for scientific equation discovery 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 RISR method uses LLMs for scientific equation discovery

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This is a research paper detailing a new method for scientific equation discovery 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) · Haobo Li, Wenshuo Zhang, Wenxiao Zhao, Eunseo Jung, Rui Sheng, Yushi Sun, Peiqin Zhuang, Hao Chen, Fenghua Ling ·

    RISR: Residual-Informed Scientific Equation Discovery with Large Language Models

    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…

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