English(EN) DIGEST: Discovery-Guided LLM Tuning for Symbolic Regression

LLM驱动的DIGEST方法提升符号回归精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种新颖的符号回归方法DIGEST，它将大型语言模型（LLMs）与遗传编程（GP）相结合。DIGEST通过使用LLM派生的策略来播种GP种群，增强了生成灵活性，从而解决了当前方法的局限性。它还包含一个反馈循环，其中GP发现的高质量表达式用于更新LLM策略，使其能够从过去的搜索发现中学习。实验表明，DIGEST在恢复真实表达式方面表现优越，并且比现有方法更有效。 AI

影响该方法有望改进从数据中发现科学表达式，从而可能加速科学领域的研究。

排序理由该集群包含一篇详细介绍符号回归新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍符号回归新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kaifeng Jin, Ruizhong Qiu, Zihao Li, Xiao Lin, Dongqi Fu, Yinglong Xia, Hong Li, Hanghang Tong · 2026-10-28 04:00

DIGEST：用于符号回归的发现式LLM调优

arXiv:2610.33880v1 Announce Type: new Abstract: Symbolic regression (SR) seeks to discover mathematical expressions directly from data, without requiring task-specific scientific descriptions. Despite tremendous progress in both conventional search-based methods and recent LLM-ba…

本文如何被排名

报道来源 [1]

DIGEST：用于符号回归的发现式LLM调优

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