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cs.CV2026
PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration
Han Wang, Zijun Wang, Shuoshuo Xue +5
Action-conditioned world models are a key component of embodied AI, serving as scalable policy evaluators that reduce reliance on expensive real-world rollouts. To accurately captu…
cs.CV2026
PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models
Bin Hu, Yanwen Ma, Jiehui Huang +14
Recent game world models can synthesize visually plausible, action-conditioned rollouts. However, their interaction behaviors often remain limited to exploratory or wandering traje…