Core Viewpoint - The article discusses the emergence of Multimodal Chain of Thought (MCoT) as a significant advancement in AI, enabling it to process and reason across various modalities such as images, audio, and text, thereby enhancing its reasoning capabilities to be more human-like [1][4][17]. Summary by Sections MCoT Overview - MCoT represents a shift from traditional Chain of Thought (CoT) by integrating multiple sensory inputs, allowing AI to perform complex reasoning tasks that reflect real-world scenarios [2][3][4]. - The development of MCoT is a collaborative effort from researchers at several prestigious institutions, addressing the lack of comprehensive reviews in this field [5]. MCoT Methodology - MCoT's success relies on a systematic methodology comprising six technical pillars, enhancing the precision and fluency of academic expression [7]. 1. Reasoning Construction Perspective - Prompt-based: Utilizes carefully designed multimodal instruction templates to guide models in generating reasoning chains in few-shot scenarios [8]. - Plan-based: Constructs dynamic reasoning paths, allowing models to explore multiple hypotheses and select optimal solutions [8]. - Learning-based: Embeds reasoning tasks during training to enhance the model's intrinsic reasoning capabilities [8]. 2. Structured Reasoning Perspective - Asynchronous Modality Modeling: Decouples perception and reasoning modules to improve modular efficiency [10]. - Defined Procedure Staging: Employs predefined procedural rules to ensure orderly reasoning processes [10]. - Autonomous Procedure Staging: Dynamically generates sub-task sequences based on task requirements [10]. 3. Information Enhancement Perspective - Expert Tools Integration: Combines specialized tools to improve task accuracy and practicality [12]. - World Knowledge Retrieval: Utilizes retrieval-augmented generation techniques to enrich model background information [12]. - In-context Knowledge Retrieval: Analyzes entity relationships within task contexts to enhance logical consistency [12]. 4. Target Granularity Perspective - Introduces multimodal thinking processes to improve interpretability and intuitiveness in reasoning tasks [14]. - Coarse Understanding: Focuses on macro-level scene understanding [14]. - Semantic Grounding: Achieves mid-level analysis by detecting specific object locations [14]. - Fine-grained Understanding: Conducts micro-level analysis for precise segmentation [14]. 5. Multimodal Rationale - Emphasizes the importance of reasoning across multiple modalities to enhance AI's cognitive capabilities [15]. 6. Testing and Expansion Perspective - Slow-Thinking Mechanism: Encourages deep reasoning through long-chain examples and diverse reasoning paths [16]. - Reinforcement Learning Optimization: Guides reasoning processes with reward functions to improve performance in complex tasks [16]. Applications and Future Challenges - MCoT is already influencing various sectors, including robotics, autonomous driving, healthcare, creative generation, and education [17][25]. - Key challenges for MCoT's future development include: 1. Efficient use of computational resources, requiring algorithm improvements and hardware optimization [18][19]. 2. The chain effect of reasoning errors, necessitating real-time error detection and correction algorithms [20][21]. 3. Ethical concerns regarding content credibility, prompting the need for content verification frameworks [22][23]. 4. The diversity of task scenarios, which calls for cross-domain evaluation systems to enhance MCoT's applicability [24].