Iterated Agent for Symbolic Regression
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915542306127872 |
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| author | Song, Zhuo-Yang Cai, Zeyu Zhang, Shutao Wei, Jiashen Pan, Jichen Qiu, Shi Cao, Qing-Hong Hou, Tie-Jiun Liu, Xiaohui Luo, Ming-xing Zhu, Hua Xing |
| author_facet | Song, Zhuo-Yang Cai, Zeyu Zhang, Shutao Wei, Jiashen Pan, Jichen Qiu, Shi Cao, Qing-Hong Hou, Tie-Jiun Liu, Xiaohui Luo, Ming-xing Zhu, Hua Xing |
| contents | Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpretable models. This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search. By generating candidate expressions guided by natural-language rationales, our method biases discovery towards models that are not only accurate but also conceptually coherent and interpretable. We demonstrate IdeaSearchFitter's efficacy across diverse challenges: it achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines; discovers mechanistically aligned models with good accuracy-complexity trade-offs on real-world data; and derives compact, physically-motivated parametrizations for Parton Distribution Functions in a frontier high-energy physics application. IdeaSearchFitter is a specialized module within our broader iterated agent framework, IdeaSearch, which is publicly available at https://www.ideasearch.cn/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterated Agent for Symbolic Regression Song, Zhuo-Yang Cai, Zeyu Zhang, Shutao Wei, Jiashen Pan, Jichen Qiu, Shi Cao, Qing-Hong Hou, Tie-Jiun Liu, Xiaohui Luo, Ming-xing Zhu, Hua Xing Computational Physics Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning High Energy Physics - Phenomenology Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpretable models. This paper introduces IdeaSearchFitter, a framework that employs Large Language Models (LLMs) as semantic operators within an evolutionary search. By generating candidate expressions guided by natural-language rationales, our method biases discovery towards models that are not only accurate but also conceptually coherent and interpretable. We demonstrate IdeaSearchFitter's efficacy across diverse challenges: it achieves competitive, noise-robust performance on the Feynman Symbolic Regression Database (FSReD), outperforming several strong baselines; discovers mechanistically aligned models with good accuracy-complexity trade-offs on real-world data; and derives compact, physically-motivated parametrizations for Parton Distribution Functions in a frontier high-energy physics application. IdeaSearchFitter is a specialized module within our broader iterated agent framework, IdeaSearch, which is publicly available at https://www.ideasearch.cn/. |
| title | Iterated Agent for Symbolic Regression |
| topic | Computational Physics Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2510.08317 |