让多模态大模型像人一样思考，透明推理路径提升智能水平
From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models
- 引入多模态思维链（MCoT），让模型逐步推理
- 系统梳理方法、评估体系与实际应用场景
- 适合研究多模态推理与可解释AI的学者参考
多模态大语言模型（MLLMs）在感知任务中表现优异，但复杂推理能力仍受限于推理路径不透明和泛化能力不足。思维链（CoT）在语言模型中已证明能提升推理透明度与可解释性，将其拓展至多模态领域具有重要潜力。本文系统综述了多模态思维链（MCoT）的研究进展，从技术演进与任务需求出发分析其理论动因，从思维链范式、后训练阶段与推理阶段三方面介绍主流方法并剖析其机制。同时总结现有评估基准与指标，探讨应用场景。最后分析当前挑战，并展望未来研究方向。
原文摘要 · Abstract (English)
With the remarkable success of Multimodal Large Language Models (MLLMs) in perception tasks, enhancing their complex reasoning capabilities has emerged as a critical research focus. Existing models still suffer from challenges such as opaque reasoning paths and insufficient generalization ability. Chain-of-Thought (CoT) reasoning, which has demonstrated significant efficacy in language models by enhancing reasoning transparency and output interpretability, holds promise for improving model reasoning capabilities when extended to the multimodal domain. This paper provides a systematic review centered on "Multimodal Chain-of-Thought" (MCoT). First, it analyzes the background and theoretical motivations for its inception from the perspectives of technical evolution and task demands. Then, it introduces mainstream MCoT methods from three aspects: CoT paradigms, the post-training stage, and the inference stage, while also analyzing their underlying mechanisms. Furthermore, the paper summarizes existing evaluation benchmarks and metrics, and discusses the application scenarios of MCoT. Finally, it analyzes the challenges currently facing MCoT and provides an outlook on its future research directions.
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