Visual token pruning is an effective way to accelerate vision-language models and is especially useful for vision-language-action (VLA) inference, where many visual tokens must be processed before predicting robot actions. Existing pruning methods usually estimate which tokens can be pruned based on attention scores or...
Tian-Jun Shi, Hao-Tian Xiong, Zi-Yu Gong et al.· 0 citations
This synthesis shows that the open frontier of reasoning in driving agents lies in intermediate representations that can be grounded in the real world, coupled to real-time action, and verified under safety-critical systems.
Zheng-Xu Tang, Xiao-Zhou Zhang, Guo-Feng Cui et al.· 0 citations
To improve deployable small VLMs, KPA is proposed, a knowledge-preserving adaptation framework that combines structured perception-to-decision prompting, SLERP-based expert merging, and RegMoE, a regime-aware LoRA mixture-of-experts module that preserves the pretrained model's open-world knowledge while allocating ligh...
Zheng-Xu Tang, Guo-Feng Cui, Zi-Yu Gong et al.· 0 citations
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