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Initial arm configurations can dramatically skew hand selection in humanoid manipulation, revealing a hidden layer of complexity in VLA policy performance.
VMamba's unique encoding strategy enables it to outperform MambaOut in high-resolution tasks by leveraging the organization of semantic evidence across token magnitude and direction.
