Toward Multimodal Agent Intelligence: Perception, Reasoning, Generation and Interaction

Abstract

The pursuit of Artificial General Intelligence (AGI) necessitates the development of agents that can understand and interact with the world in a manner akin to humans. A cornerstone of this endeavor is multimodal agent intelligence, which equips agents with the ability to process, comprehend, and act upon information from a multitude of sensory channels, such as vision, language, and audio. This survey provides a comprehensive overview of the field, charting a course through the core components required to build such sophisticated systems. We begin by establishing the foundations of multimodal intelligence, defining key concepts and tracing its evolution. We then delve into the three pillars of agent capability: Multimodal Perception, which covers how agents see, hear, and read the world; Multimodal Reasoning and Learning, which explores how they think, infer, and acquire new knowledge from diverse data streams; and Multimodal Generation and Interaction, which examines how they communicate and create content across different modalities. By systematically reviewing state-of-the-art techniques, benchmark datasets, and architectural paradigms in each of these areas, we map the current landscape of research. Finally, we synthesize the major open challenges, including robustness, interpretability, data scarcity, and ethical considerations, and propose promising future directions. This survey aims to serve as a valuable resource for researchers and practitioners, illuminating the path toward creating more capable, collaborative, and human-like intelligent agents.