MAE: Self-Evaluating VLA Action Generation with Markov Attention Entropy

A new arXiv paper (2608.16697) proposes Markov Attention Entropy (MAE), a method for Vision-Language-Action models (VLAs) to self-evaluate their action generation reliability without external supervision. The authors observe that internal visual modality entropy consistently distinguishes successful from failed tasks across heterogeneous VLA architectures. They formulate a Conditional Generative Markov Chain to model the common latent action generation abstraction shared by different VLAs, despite architectural differences. MAE leverages this formulation to compute attention entropy as a self-evaluation metric. The paper addresses a key challenge in VLA deployment: enabling reliable self-assessment without expert annotations or output-only uncertainty estimates. The method is validated across diverse VLA architectures, showing that internal signals can effectively predict task success. This work contributes to making VLAs more robust and trustworthy in real-world applications, particularly in robotics and autonomous systems where reliable action generation is critical.