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
Under tight feature and compute budgets, classical threat detection pipelines often degrade on near-decision-boundary events. Small quantum processors are now available, but existing work inadequately shows whether quantum components improve end-to-end threat detection under the above resource constrained conditions. T...
This work proposes AtomicVLA, a unified planning-and-execution framework that jointly generates task-level plans, atomic skill abstractions, and fine-grained actions, and introduces a flexible routing encoder that automatically assigns dedicated atomic experts to new skills, enabling continual learning.
Likui Zhang, Tao Tang, Zhihao Zhan et al.· arXiv.org· 19 citations· ⚡3
Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained perception tasks under the"Thinking with Images"(TwI) paradigm by iteratively performing various visual tool operations. However, this paradigm relies heavily on frequent external tool calls and repeated image re-encoding, whi...
Xiuwei Chen, Quanlin Chen, Wentao Hu et al.· 0 citations
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