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VLMs often rely on misleading visual cues instead of true chronological understanding, revealing critical flaws in their reasoning capabilities.
Achieving an 88.75% success rate in dexterous manipulation tasks, RealDexUMI bridges the gap between human demonstrations and robot execution without losing critical dexterity.
Current AI agents struggle to reliably rediscover scientific knowledge, with top performers averaging only 21.5 out of a possible score, revealing critical gaps in their research capabilities.
