Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual representations into image generation. However, RAEs still need to decide which encoder layers form the shared latent space for the generator and pixel decoder. This choi...
Hong-Yang Du, Yun-Fei Xie, Jun-Jie Ye et al.· 0 citations
DriveReferee is introduced, which uses a learned geometry readout to predict the scene representation from camera observations and executes the geometric safety rule directly rather than learning it, and which matches or outperforms all learned-verifier and heuristic baselines.
Feng-Cheng Yu, Dhruv Parikh, Jun-Jie Ye et al.· 0 citations
PhysWAM is presented, a unified world-action model for autonomous driving that co-denoises multiview video, metric depth, and ego motion within a single flow-matching transformer and Coupled Point Projection is introduced, demonstrating that the geometric relationship between scene depth and ego motion provides a direc...
Dhruv Parikh, Feng-Cheng Yu, Quan-Kai Gao et al.· 0 citations
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
Logic-VLA is introduced, a formal-requirement-aware VLA that conditions on Signal Temporal Logic (STL) specifications supplied at inference time, showing that a single VLA can adapt its behavior to varying formal requirements without requiring a separate policy for each specification.
Celina Shiyu Wang, Yiqi Zhao, Junjie Ye et al.· 1 citation
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