VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
Abstract
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.
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š¢ VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
š¤ Current LVLMs struggle with complex reasoning like multi-hop geometry problems. How can AI agents utilize and construct more useful visual hints?
š Key insight: When LVLMs perform reasoning, they need not only "WHAT to do" but also a mental model of "WHAT WILL HAPPEN after each action"! This brings LVLMs more powerful reasoning performance. #NextLevelAI š¤
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