Author

Xu Li

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Preprint Sep 2026

CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving

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

Preprint Sep 2026

ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs

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...

Xu Li, Yuxuan Liang, Yi Zheng et al. · 0 citations

Jul 2026

CRISP: Pre-LLM Yet Text-Driven Visual Token Pruning for Efficient LVLM Inference

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