VeriVLA: Adaptive Test-Time Verification for Flow-Based VLAs with Latent World Models
Flow-based Vision-Language-Action (VLA) policies are inherently random during testing, leading to potentially harmful suboptimal actions in fine manipulation tasks. VeriVLA introduces a training-free two-axis trigger to detect critical states based on motion variation and sample variance. When triggered, a JEPA latent world model ranks action chunks by predicting future latent states and task progress, offering accurate verification without pixel reconstruction.