Papers
1
Total Citations
14
H-Index
1
About
Xiaoyuan Fu is a leading researcher in artificial intelligence and multiagent systems, with a focus on enabling efficient cooperation among intelligent machines—such as robots, autonomous vehicles, and drones. Their most-cited work, "GraphComm: Efficient Graph Convolutional Communication for Multiagent Cooperation" (2021, 14 citations), introduces a groundbreaking framework that leverages graph convolutional networks to streamline communication between agents in complex, dynamic environments. This innovation addresses a critical bottleneck in next-generation communication: how to coordinate the "brains" of smart things—the agents and cybertwins residing on end devices and edge servers—so they can collaborate effectively without overwhelming network resources. By modeling agent interactions as graph structures, Fu’s approach reduces redundant data exchange while preserving decision-making quality, a significant leap for real-time applications like autonomous fleets and drone swarms. Their work bridges the gap between AI and communication theory, offering scalable solutions for the Internet of Things and edge computing. With a growing citation impact, Xiaoyuan Fu is recognized as a rising voice in multiagent cooperation, pushing the boundaries of how intelligent systems learn, communicate, and act together in the physical world.
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Top Papers
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