VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
Authors: name: Yikun Wang, name: Siyin Wang, name: Qinyuan Cheng, name: Zhaoye Fei, name: Liang Ding, name: Qipeng Guo, name: Dacheng Tao, name: Xipeng Qiu

Recent advancements in Large Vision-Language Models have showcased remarkable
capabilities. However, they often falter when confronted with complex reasoning
tasks that humans typically address through visual aids and deliberate,
step-by-step thinking. While existing methods have explored text-based slow
thinking or rudimentary visual assistance, they fall short of capturing the
intricate, interleaved nature of human visual-verbal reasoning processes. To
overcome these limitations and inspired by the mechanisms of slow thinking in
human cognition, we introduce VisuoThink, a novel framework that seamlessly
integrates visuospatial and linguistic domains. VisuoThink facilitates
multimodal slow thinking by enabling progressive visual-textual reasoning and
incorporates test-time scaling through look-ahead tree search. Extensive
experiments demonstrate that VisuoThink significantly enhances reasoning
capabilities via inference-time scaling, even without fine-tuning, achieving
state-of-the-art performance in tasks involving geometry and spatial reasoning.