2 papers
cs.CV2026
TAPDreamer: Transferable Adversarial Patches for World Action Models
Xuanyu Lu, Fengqing Jiang, Kaiyuan Zheng +8
World models learn to predict how their environment will evolve, making them an important foundation for general-purpose robotic control. Yet world action models depend on camera i…
cs.CV2026
MIND: Benchmarking Memory Consistency and Action Control in World Models
Yixuan Ye, Xuanyu Lu, Yuxin Jiang +7
World models aim to understand, remember, and predict dynamic visual environments, yet a unified benchmark for evaluating their fundamental abilities remains lacking. To address th…