A Review of Multimodal Large Language Models: Fusion Mechanisms and Capability Evolution

Abstract

With the continuous advancement of large language models, MLLMs have gradually emerged as a major research focus in the field of artificial intelligence. Although existing studies have reviewed relevant progress from different perspectives, such as multimodal learning, vision-language models, and domain-specific applications, there is still a lack of systematic analysis of the intrinsic relationships among fusion mechanisms, capability formation, and application expansion in MLLMs. This paper reviews the principal technical paradigms of multimodal fusion, including early fusion, intermediate fusion, late fusion, and hybrid fusion, and compares the structural characteristics and applicable scenarios of different fusion approaches. Building on this analysis, the paper further explores the development of MLLMs from the perspectives of vision-language understanding, multimodal content generation, multimodal interaction and agent-oriented tasks, as well as domain-specific applications, thereby outlining the evolutionary trajectory of their core capabilities and broader application trends.