MLL-Lab/ENACT

ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction ENACT is a benchmark dataset for evaluating embodied cognition in vision–language models via egocentric world modeling. It probes whether models can reason about how the world changes under sequences of actions, using long-horizon household activities in a mobile manipulation setting. Project page: https://enact-embodied-cognition.github.io/ Code & evaluation: https://github.com/mll-lab-nu/ENACT… See the full description on the dataset page: https://huggingface.co/datasets/MLL-Lab/ENACT.

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