Author

Jianhui Liu

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Preprint Sep 2026

EVO-WAM: Evolving World Action Models through Video-Action Verification

Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action models (WAMs) use broad video priors to jointly predict future videos and actions, offering a potential source of supervision for adapting to new tasks. However, generate...

Shi-Long Zhou, Xiong-Hao Wu, Wen-Bo Li et al. · 0 citations

Preprint Sep 2026

Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning

FactoSR is presented, a factorized reinforcement learning framework that explicitly interpret the dimensions collapsed by visual projection, and suggests that reinforcing explicit, factorized 4D consistency is a critical step toward evolving VLMs into robust, world-aware reasoners.

Yi-Jun Yang, Shenghe Zheng, Wen-Bo Li et al. · 0 citations

Preprint Aug 2026

ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training

This work introduces ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video, and adopts an asynchronous Slow-Fast dual-system architecture to make generative WAMs practical for real-time control.

Xiong-Hao Wu, Yi-Jun Yang, Shi-Long Zhou et al. · 2 citations