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Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought

arXiv — cs.CV•Tuesday, October 28, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
Recent advancements in large vision-language models (LVLMs) have significantly improved how machines understand and interpret multimodal tasks. The introduction of multimodal chain-of-thought (MCoT) techniques, particularly Textual-MCoT and Interleaved-MCoT, has enhanced both performance and interpretability. These methods allow for more effective processing of combined text and images, making it easier for AI to generate coherent outputs. This progress is crucial as it paves the way for more sophisticated AI applications that can better understand human communication and creativity.
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