Author

Chengen Xie

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Preprint Oct 2026

SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models

Driving world action models (WAMs) learn from paired trajectories and future observations. Post-training with reinforcement learning allows the policy to explore alternative trajectories and improve planning using driving rewards. However, driving logs contain future videos only for the recorded trajectories, leaving t...

Chang-Hui Jing, Hao-Chen Tian, Hong-Chen Li et al. · 0 citations

Preprint Aug 2026

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

LD4WAM is presented, which pairs a Latent Dynamics Model trained with semantic reconstruction and real motion alignment with a World Dynamics Action Model built as a mixture-of-transformers (MoT), which preserves full future-video generation and uses learnable queries to distill these latent dynamics from generated fut...

Zhen Shen, Jia-Qi Liang, Jasper Lu et al. · 3 citations