Unlocking MLLM Reasoning: A Deep Dive into Multimodal Chain-of-Thought (MCoT)
By Wenxin Zhu, Andong Chen, Yuchen Song, Kehai Chen, Conghui Zhu, Ziyan Chen, Tiejun Zhao
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.CV updates on arXiv.org.
Summary
The presented paper offers a systematic review of "Multimodal Chain-of-Thought" (MCoT), a critical approach aimed at enhancing the complex reasoning capabilities of Multimodal Large Language Models (MLLMs). While MLLMs have achieved remarkable success in perception tasks, they still face challenges related to opaque reasoning paths and insufficient generalization. MCoT, by extending the transparent and interpretable reasoning paradigms of Chain-of-Thought from language models to the multimodal domain, seeks to address these limitations. The review meticulously covers MCoT's theoretical underpinnings, mainstream methodologies (across CoT paradigms, post-training, and inference stages), evaluation benchmarks, application scenarios, current challenges, and future research directions.
Why It Matters
This deep dive into Multimodal Chain-of-Thought (MCoT) is not merely an academic exercise; it represents a crucial inflection point for the entire AI industry. For professionals in the AI space, understanding MCoT is paramount because it directly addresses the fundamental leap required for AI to move beyond sophisticated pattern recognition to true, complex reasoning. The "Why It Matters" here is multi-faceted:
First, transparency and interpretability are no longer optional. As MLLMs become more integrated into critical applications like healthcare, autonomous systems, and finance, the ability to understand how an AI arrives at a conclusion is vital for debugging, trust, accountability, and regulatory compliance. MCoT promises to shed light on these opaque reasoning paths, transforming MLLMs from black boxes into more explainable, auditable systems.
Second, it signals a paradigm shift in AI capability. Current MLLMs, while impressive, often struggle with tasks requiring step-by-step logical deduction or complex problem-solving that spans visual, auditory, and textual information. MCoT is a concerted effort to imbue these models with a structured, human-like reasoning process, enabling them to tackle more nuanced, real-world challenges that demand genuine intelligence, not just associative recall. This advancement unlocks new possibilities for AI agents that can truly understand context, plan actions, and justify decisions in dynamic, multimodal environments.
Finally, this review provides a roadmap for future innovation. By systematically analyzing current methods, benchmarks, and challenges, the paper effectively highlights the most promising research avenues and bottlenecks. For engineers and researchers, it's a guide to where to focus efforts for breakthroughs in MLLM development, pointing towards novel architectures, training methodologies, and evaluation strategies. The underlying trend is clear: the pursuit of Artificial General Intelligence (AGI) increasingly hinges on robust, interpretable, and generalizable reasoning across diverse data modalities, and MCoT is a front-runner in making that aspiration a tangible reality.