SaiVLA-JEPA: Evaluating Future Latent Prediction in JEPA-Conditioned VLA Control
arXiv:2610.38508v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn to predict representations rather than pixels, offering a compact way to model how visual scenes evolve. We ask whether this predictive objective improves closed-loop control when a vision-language-action (VLA) policy already uses V-JEPA features. We call our JEPA-conditioned flow policy family SaiVLA-JEPA. It combines a vision-language model with a diffusion transformer action expert and re…