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Transient distractions can severely degrade scene reconstruction, but MU-GeNeRF effectively mitigates their impact, achieving results on par with specialized methods.
Endowing VLMs with intrinsic 3D geometric awareness and physical interaction cues via XEmbodied substantially boosts performance on spatial reasoning and embodied tasks, surpassing existing 2D image-text pretrained models.
Latent reasoning can beat explicit Chain-of-Thought – but only if you force it to learn causal dynamics via a visual world model, not just language.
Forget static coordination – robots that chat and dynamically re-plan can achieve a whopping 69% improvement in collaborative navigation success.
Autonomous driving models can now achieve remarkable zero-shot generalization by leveraging the power of large-scale video generation models to jointly predict future actions and visuals.
Autonomous driving models no longer need to compromise between spatial perception and semantic reasoning: UniDriveVLA's expert decoupling unlocks state-of-the-art performance across a range of driving tasks.
