How can LLMs best communicate in multi-agent systems?
Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems
This paper surveys Large Language Model-based Multi-Agent Systems (LLM-MAS), focusing on how agents communicate. It proposes a framework analyzing LLM-MAS at both the system level (architecture, goals) and the internal communication level (strategies, paradigms, objects, content). Key points relevant to LLM-based multi-agent systems include different communication architectures (flat, hierarchical, team, society, hybrid), communication goals (cooperation, competition, mixed), communication strategies (one-by-one, simultaneous, simultaneous with summarizer), communication paradigms (message passing, speech act, blackboard), and communication objects (self, other agents, environment, human), along with diverse content types (natural language, code, structured data, implicit signals), and challenges including optimizing system design, advancing agent competition research, enabling multimodal communication, addressing communication security, and establishing benchmarks for evaluation.