title: Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
summary: By extending the advantage of chain-of-thought (CoT) reasoning in human-like
  step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning
  has recently garnered significant research attention, especially in the
  integration with multimodal large language models (MLLMs). Existing MCoT
  studies design various methodologies and innovative reasoning paradigms to
  address the unique challenges of image, video, speech, audio, 3D, and
  structured data across different modalities, achieving extensive success in
  applications such as robotics, healthcare, autonomous driving, and multimodal
  generation. However, MCoT still presents distinct challenges and opportunities
  that require further focus to ensure consistent thriving in this field, where,
  unfortunately, an up-to-date review of this domain is lacking. To bridge this
  gap, we present the first systematic survey of MCoT reasoning, elucidating the
  relevant foundational concepts and definitions. We offer a comprehensive
  taxonomy and an in-depth analysis of current methodologies from diverse
  perspectives across various application scenarios. Furthermore, we provide
  insights into existing challenges and future research directions, aiming to
  foster innovation toward multimodal AGI.
paper_authors._id: 67d939f6fa59a8b15aa931a8
paper_authors.name: Yaoting Wang
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paper_authors.fullname: Yaoting Wang
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paper_authors.user: Gh0stAR
paper_authors._id: 67d939f6fa59a8b15aa931a9
paper_authors.name: Shengqiong Wu
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paper_authors.fullname: Shengqiong Wu
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paper_authors.user: ChocoWu
paper_authors._id: 67d939f6fa59a8b15aa931aa
paper_authors.name: Yuecheng Zhang
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paper_authors.fullname: zhang yuechen
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paper_authors.user: julianjuaner
paper_authors._id: 67d939f6fa59a8b15aa931ab
paper_authors.name: William Wang
paper_authors._id: 67d939f6fa59a8b15aa931ac
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_authors._id: 67d939f6fa59a8b15aa931ad
paper_authors.name: Jiebo Luo
paper_authors._id: 67d939f6fa59a8b15aa931ae
paper_authors.name: Hao Fei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T09:30:49.867Z
paper_authors._id: 647773a1168cb428e00e9a8f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647773a1168cb428e00e9a8f/NiRR3ScY6Plzjibfwy1hC.jpeg
paper_authors.fullname: Hao Fei
paper_authors.type: user
paper_authors.user: scofield7419
paper_title: Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
paper_summary: By extending the advantage of chain-of-thought (CoT) reasoning in human-like
  step-by-step processes to multimodal contexts, multimodal CoT (MCoT) reasoning
  has recently garnered significant research attention, especially in the
  integration with multimodal large language models (MLLMs). Existing MCoT
  studies design various methodologies and innovative reasoning paradigms to
  address the unique challenges of image, video, speech, audio, 3D, and
  structured data across different modalities, achieving extensive success in
  applications such as robotics, healthcare, autonomous driving, and multimodal
  generation. However, MCoT still presents distinct challenges and opportunities
  that require further focus to ensure consistent thriving in this field, where,
  unfortunately, an up-to-date review of this domain is lacking. To bridge this
  gap, we present the first systematic survey of MCoT reasoning, elucidating the
  relevant foundational concepts and definitions. We offer a comprehensive
  taxonomy and an in-depth analysis of current methodologies from diverse
  perspectives across various application scenarios. Furthermore, we provide
  insights into existing challenges and future research directions, aiming to
  foster innovation toward multimodal AGI.
paper_ai_keywords: chain-of-thought (CoT) reasoning multimodal CoT (MCoT) reasoning multimodal large language models (MLLMs) image video speech audio 3D structured data robotics healthcare autonomous driving multimodal generation multimodal AGI