Zhongyang Mao
Papers
1
Total Citations
30
H-Index
1
About
Zhongyang Mao is a leading researcher in heterogeneous multi-agent systems (HMAS) and artificial intelligence, with a focus on integrating Large Language Models (LLMs) into autonomous coordination frameworks. His most-cited work, "AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models" (2025, 30 citations), introduces a pioneering framework that synergizes LLMs with classical control methods to enable seamless collaboration among diverse agents—such as drones, ground robots, and automated devices—in complex, real-world environments. This contribution addresses critical challenges in task allocation and real-time execution, significantly advancing the field of multi-agent robotics. Mao’s research bridges the gap between high-level reasoning and low-level control, offering scalable solutions for applications in disaster response, autonomous logistics, and smart infrastructure. His work has garnered attention for its practical impact, with the AutoHMA-LLM paper quickly accumulating citations and inspiring further studies in hybrid AI systems. By combining theoretical rigor with applied innovation, Mao continues to shape the future of intelligent, coordinated autonomous systems, making him a key figure for students and researchers exploring the intersection of LLMs and multi-agent robotics.
Research Focus
Key Achievements
Top Papers
- 1