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Action-sensitive world models can drastically improve long-horizon planning by preserving critical distinctions between futures generated from different action sequences.
Agents struggle to act effectively in 3D scenes, with none of the eleven evaluated VLMs achieving consistent performance across diverse tasks.
Uncertainty-aware retrieval can significantly enhance the reliability of cross-modal remote sensing systems, even under challenging conditions.
Navigate to objects forever: OVAL enables robots to continuously explore and remember new environments, unlocking truly lifelong object goal navigation.
