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cs.CV2026
DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving
Enhui Ma, Jiahuan Zhang, Guantian Zheng +10
Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly…
cs.CV2026
Unifying Language-Action Understanding and Generation for Autonomous Driving
Xinyang Wang, Qian Liu, Wenjie Ding +7
Vision-Language-Action (VLA) models are emerging as a promising paradigm for end-to-end autonomous driving, valued for their potential to leverage world knowledge and reason about…
cs.CV2026
Efficient Token Pruning for LLaDA-V
Zhewen Wan, Tianchen Song, Chen Lin +2
Diffusion-based large multimodal models, such as LLaDA-V, have demonstrated impressive capabilities in vision-language understanding and generation. However, their bidirectional at…