Research on Multimodal AGI Empowering the Development of LLM-based Multi-Agent System - Bai, Zirun (International Journal of Computer Science and Information Technology, 2026)
This paper analyzes the multimodal general artificial intelligence-enabled large language model multi-agent system, focusing on exploring the internal logic, technical laws, and practical scope of their integration. By integrating domestic and foreign literature, technical comparisons, and industrial cases from 2022 to 2025, and combining authoritative achievements in the fields of multi-agent and multimodal analysis, the analysis reveals that multimodal fusion can compensate for the shortcomings of traditional text intelligent agent perception, interaction, and collaboration, forming a complete perception–inference–execution chain. After the technology is implemented, the adaptability and stability of the agent are significantly improved, which reduces decision-making bias in individual models. Currently, demand for intelligent physical scenarios has surged, and traditional text agents are difficult to adapt to complex dynamic environments. Existing research mostly focuses on a single technical dimension and lacks systematic fusion analysis. This study fills this gap and provides reliable references and practical support for industrial upgrading. The study also identifies existing challenges and viable development pathways, and the conclusions drawn can provide direct basis for technological iteration, scenario implementation, and industry standardization improvement.