Author

V. Guizilini

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Preprint Sep 2026

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation provides no explicit closed-loop geometric verdict for generated trajectories, making verification important during both training and deploym...

Feng-Cheng Yu, Dhruv Parikh, Jun-Jie Ye et al. · 0 citations

Preprint Sep 2026

PhysWAM: Physically Consistent World Action Model for Autonomous Driving

World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constraint on these predictions. We present PhysWAM, a unified world-action model for autonomous driving that co-denoises multiview video, metric...

Dhruv Parikh, Feng-Cheng Yu, Quan-Kai Gao et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Rolling-WAM: World Action Models with Rolling Imagination

Rolling-WAM is presented, a formulation that distributes joint denoising across successive replanning cycles and delivers a 4.5x steady-state replanning speedup over standard joint WAMs.

Ying Zhou, Jun-Jie Ye, Yi-Qi Zhao et al. · 0 citations