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Current video generation models fail to maintain embodiment consistency and functional interaction in human-to-robot manipulation, revealing significant gaps in their transfer capabilities.
Current vision-language models struggle with process understanding in robotic manipulation, but targeted post-training can yield significant improvements.
LLMs maintain a positive attitude even when losing in adversarial board games, yet their gameplay reveals surprising instability in skill execution.
