The introduction of long chain-of-thought (CoT) reasoning and external tools has substantially improved the performance of multimodal large language models (MLLMs) on general and reasoning tasks. However, existing fast-slow reasoning paradigms mainly regulate textual reasoning length, while visual computation introduce...
Quan-Lin Chen, Xiu-Wei Chen, Kun Xiang et al.· 0 citations
Reasoning through video generation offers a promising path toward visual intelligence by modeling latent visual states and their dynamics. However, current video generation models often lack explicit guidance on how these states should evolve, leaving generated trajectories prone to physical and structural inconsistenc...
Ze-Hua Ma, Kun Xiang, Yun-Shuang Nie et al.· 0 citations
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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