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A single, static adversarial patch can cripple world action models across diverse tasks and architectures by inducing a stable, global representation shift, even without querying the target policy.
Visual-Seeker outperforms proprietary models by actively engaging with visual details, redefining multimodal search capabilities.
Forget painstakingly engineering robot behaviors: DreamZero learns directly from video of other robots or even humans, adapting to new tasks and bodies with just minutes of data.