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Agents can now reliably navigate evolving environments by maintaining a probabilistic 4D belief that accounts for hidden target movements and revises predictions based on intermittent, visibility-aware observations.
Treating robot skill calls as runtime-verified execution proposals rather than blind action predictions enables self-correcting VLA autonomy that reaches 97.4% success on LIBERO.
Object-centric vision could be the key to unlocking LMMs' potential for precise object manipulation and fine-grained spatial reasoning, capabilities currently beyond their reach.
A new unified LVLM, OmniCT, bridges the gap between slice-level detail and volumetric understanding in CT scans, outperforming existing methods by a significant margin.
