By framing grokking as computational glass relaxation, this work explains grokking from the perspective of Boltzmann entropy and proposes a physics-based grokking-resistant optimizer.

This paper proposes a novel AIGI detection method based on Multi-granularity Local Entropy Patterns (MLEP), which captures scale- and location-invariant entropy features to improve accuracy and generalization across diverse generative models.