Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning should differ across driving situations. Our key insight is that while scene complexity informs reasoning depth, dynamic risk is equally critical for deciding how to reason...
Xiao-Lei Chen, Zhuo-Lin He, Yuxuan Liang et al.· 0 citations
Large Vision-Language Models (LVLMs) face significant computational inefficiencies caused by the large number of visual tokens. Existing visual token pruning methods mainly focus on either retaining individually important tokens or selecting mutually diverse ones. In this work, we revisit visual token pruning from a co...
This work proposes Visual Difference DeepStack (VD-DeepStack), which explicitly conditions language reasoning on query-reference visual differences on query-reference visual differences, and supports mitigating the visual comparison-reasoning gap through the joint design of comparison representations and their integrat...
Meng-Yang Zhao, Zhuo-Lin He, Hai-Yang Yu et al.· 1 citation
CRISP is proposed, a pre-LLM yet text-driven visual token pruning framework that preserves both instruction-relevant evidence and essential scene context that serves as a practical solution for efficient LVLM inference, especially in resource-constrained scenarios.
Xu Li, Yi Zheng, Mengyang Zhao et al.· arXiv.org· 1 citation
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