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GAE: Graph-Augmented Evolution Boosts LLM-Driven Scientific Discovery

Jul 14, 2026

A new framework called GAE (Graph-Augmented Evolution) integrates graph neural networks, reinforcement learning, and online fine-tuning to enhance large language model (LLM)-guided evolutionary program search. In experiments on symbolic regression for nonlinear oscillators, GAE efficiently discovers closed-form equations and achieves state-of-the-art out-of-distribution performance compared to static LLM-driven baselines.

Why it matters: GAE offers a significant advance in automated scientific discovery by enabling more directed and adaptive search, potentially accelerating the discovery of physical equations and other scientific insights.

Full story at: arXiv Machine Learning ↗

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