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ASSCG cuts inference latency by 60% while boosting performance scores in autonomous driving systems, redefining how LLMs can be efficiently integrated into fast-slow planning architectures.
Token-level Mixture-of-Experts, directly ported from LLMs, can actually *hurt* autonomous driving performance in VLA models; SAMoE-VLA fixes this with scene-adaptive expert selection, achieving SOTA results with fewer parameters.
By decoupling generation and refinement experts within a masked diffusion VLA model, DriveFine achieves both flexible decoding and self-correction for autonomous driving.
