Programmatic Context Augmentation for LLM-based Symbolic Regression

This paper introduces a novel LLM-based evolutionary search framework for symbolic regression, addressing the limitations of existing methods that rely solely on scalar evaluation metrics. It incorporates programmatic context augmentation to enable code-based data analysis and richer information extraction, aiming to improve the discovery of mathematical expressions.