Making Latent Evolution Explicit: Operator-Structured Transitions for World Acti

arXiv:2605.00412v1 Announce Type: new Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and

arXiv:2603.28955v1 Announce Type: new Abstract: This paper presents the World-Action Model (WAM), an action-regularized world model that jointly reasons over future visual

arXiv:2608.20936v1 Announce Type: new Abstract: World models for continuous control are commonly trained for a fixed physical system and can degrade when known morphology

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2610.21612v1 Announce Type: new Abstract: Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However,

arXiv:2608.20735v1 Announce Type: new Abstract: Manipulating moving objects requires a policy to anticipate contact events, yet vision-language-action (VLA) policies are commonly

arXiv:2603.18266v1 Announce Type: cross Abstract: Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the

arXiv:2604.26733v1 Announce Type: new Abstract: Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is