Xi-Yang Wang

Preprint Aug 2026

BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation

Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back to refine the action model. However, the bottleneck of this loop lies in the simulators'inability to generate behaviorally plausible response...

Jia-Qi Wang, Zhuo Zhang, Hai-Ning Guan et al. · 0 citations

Preprint Aug 2026

BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation

Modern driving action models are increasingly improved in a self-improvement loop, where a learned world simulator imagines future observations and the resulting data is fed back to refine the action model. However, the bottleneck of this loop lies in the simulators'inability to generate behaviorally plausible response...

Jiaqi Wang, Zhuo Zhang, Hai-Ning Guan et al. · 0 citations

#computer vision Preprint Aug 2026

WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving

WA-JEPA is presented, a V-JEPA-native world-action model designed for autonomous driving planning that employs hybrid future-masked pre-training and recast future prediction as conditional flow matching over latent futures, which substantially improves the model's ability to generate plausible future latents for downst...

Xin-Lin Wang, Yu Xiang, Yuheng Zhou et al. · 0 citations