LLM Multi-Agent Systems: Challenges and Open Problems
It addresses foundational issues in multi-agent systems for AI and distributed computing, but is incremental as it reviews existing challenges rather than proposing new solutions.
This paper identifies key challenges in multi-agent systems, including task allocation, iterative reasoning, context management, and memory enhancement, and explores their potential applications in blockchain and distributed systems.
This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing memory management to support the intricate interactions within multi-agent systems. We also explore potential applications of multi-agent systems in blockchain systems to shed light on their future development and application in real-world distributed systems.