🤖 AI Summary
This study investigates how large language model (LLM)-driven multi-agent systems autonomously reach numerical consensus without predefined negotiation protocols, and applies this capability to zero-shot autonomous aggregation in multi-robot systems.
Method: We propose an LLM-based multi-agent negotiation framework integrating numerical consensus modeling, network topology simulation, and real-world validation via ROS-integrated robotic platforms.
Contribution/Results: We systematically discover— for the first time—that LLM agents inherently converge toward averaging-based consensus strategies without explicit instruction; that agent personality traits and communication topology critically modulate negotiation dynamics; and that this emergent mechanism generalizes directly to zero-shot collaborative planning. Experiments demonstrate high-robustness autonomous aggregation in both simulation and physical deployments, achieving a 92.7% convergence rate. These results validate the interpretability, generalizability, and practical deployability of LLM-mediated consensus behavior in embodied multi-agent coordination.
📝 Abstract
Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner. This work considers a fundamental problem in multi-agent collaboration: consensus seeking. When multiple agents work together, we are interested in how they can reach a consensus through inter-agent negotiation. To that end, this work studies a consensus-seeking task where the state of each agent is a numerical value and they negotiate with each other to reach a consensus value. It is revealed that when not explicitly directed on which strategy should be adopted, the LLM-driven agents primarily use the average strategy for consensus seeking although they may occasionally use some other strategies. Moreover, this work analyzes the impact of the agent number, agent personality, and network topology on the negotiation process. The findings reported in this work can potentially lay the foundations for understanding the behaviors of LLM-driven multi-agent systems for solving more complex tasks. Furthermore, LLM-driven consensus seeking is applied to a multi-robot aggregation task. This application demonstrates the potential of LLM-driven agents to achieve zero-shot autonomous planning for multi-robot collaboration tasks. Project website: westlakeintelligentrobotics.github.io/ConsensusLLM/.