Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
Authors: name: Yaoting Wang, name: Shengqiong Wu, name: Yuecheng Zhang, name: William Wang, name: Ziwei Liu, name: Jiebo Luo, name: Hao Fei

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.