[2605.03101] Programmatic Context Augmentation for LLM-based Symbolic Regression
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Computer Science > Artificial Intelligence
arXiv:2605.03101 (cs)
[Submitted on 4 May 2026]
Title:Programmatic Context Augmentation for LLM-based Symbolic Regression
Authors:Hao Liu, Xiao-Wen Yang, Atharva Sehgal, Yixin Wang, Lan-Zhe Guo, Yu-Feng Li, Yisong Yue
View a PDF of the paper titled Programmatic Context Augmentation for LLM-based Symbolic Regression, by Hao Liu and 6 other authors
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Abstract:Symbolic regression (SR), the task of discovering mathematical expressions that best describe a given dataset, remains a fundamental challenge in scientific discovery. Traditional approaches, primarily based on genetic algorithms and related evolutionary methods, have proven useful but suffer from scalability and expressivity limitations. Recently, large language model (LLM)-based evolutionary search methods have been introduced into SR and show promise. However, existing LLM-based approaches typically rely on scalar evaluation metrics, such as mean squared error, as the sole source of feedback during the search process, thereby overlooking the rich information embedded in the dataset. To address this limitation, we propose a novel LLM-based evolutionary search framework that incorporates programmatic context augmentation. By enabling code-based interactions with the dataset, our method can actively perform data analysis and extract informative signals, beyond aggregated evaluation scores. We evaluate our framework on advanced benchmarks, such as LLM-SRBench, and demonstrate superior efficiency and accuracy compared to strong baselines.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.03101 [cs.AI]
(or arXiv:2605.03101v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.03101
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hao Liu [view email] [v1] Mon, 4 May 2026 19:34:03 UTC (815 KB)
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