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Key research priorities for multi-agent embodied autonomous driving (MAEAD): verifiable shared-state maintenance, robust intent and plan alignment, and safe coordinated action under communication and computing constraints in real-world deployment are motivated.
Harness VLA boosts the performance of frozen VLA models by 38.6 percentage points on challenging manipulation tasks without the need for finetuning.
CoTIR achieves superior image restoration by internalizing reasoning processes, outperforming traditional methods even in complex degradation settings.
Achieve SOTA 3D scene reconstruction from collaborative driving views without calibration by treating multiple vehicles as a single, unstructured multi-camera system.
Finally, a way to train LLM agents to reason step-by-step without needing humans to check every intermediate thought.
Visual RL agents can recover near-perfect performance even under severe, dynamically changing visual corruptions by learning to disentangle task-relevant foreground from perturbation artifacts.
On-device LLM inference gets a massive speed and energy boost by adaptively streaming only the most expensive parts of the KV cache from the cloud.
