LLM-Feynman: Leveraging Large Language Models for Universal Scientific Formula and Theory Discovery

Understand

Distilling underlying principles from data has historically driven scientific breakthroughs.

• However, conventional data-driven machine learning often produces complex models that lack interpretability and generalization due to insufficient domain expertise.
• Here, we present LLM-Feynman, a novel framework that leverages large language models (LLMs) alongside systematic optimization to derive concise, interpretable formulas from data and domain knowledge.
• Our method integrates automated feature engineering, LLM-guided symbolic regression with self-evaluation, and Monte Carlo tree search to enhance formula discovery and clarity.