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Achieving up to 60% success in robot manipulation by aligning language and action predictions without sacrificing pretrained visual representations reveals a breakthrough in VLA policy training.
Current visual world models show a dramatic decline in performance when faced with unconventional and impossible physical interactions, highlighting a critical gap in their generalization capabilities.
LMMs can't MacGyver their way out of a paper bag: they struggle to creatively repurpose objects in visually complex environments, revealing a critical gap in grounded reasoning beyond pattern recognition.
