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Multi-Agent Consensus Seeking via Large Language Models
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Multi-Agent Consensus Seeking via Large Language Models
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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: windylab.github.io/ConsensusLLM/.
Forward citations
Cited by 15 Pith papers
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Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation
Stronger reasoning models in LLMs reduce behavioral negotiation by defaulting to authority outcomes in multi-agent settings, unlike structured scaffolds that enable concessions.
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Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation
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Real-Time Group Dynamics with LLM Facilitation: Evidence from a Charity Allocation Task
LLM facilitation in group charity allocation leaves consensus and participation equity unchanged while shifting specific allocations up to 5.5 points and increasing perceived trust.
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Insider Attacks in Multi-Agent LLM Consensus Systems
A malicious agent in multi-agent LLM consensus systems can be trained via a surrogate world model and RL to reduce consensus rates and prolong disagreement more effectively than direct prompt attacks.
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"Theater of Mind" for LLMs: A Cognitive Architecture Based on Global Workspace Theory
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Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
Temperature and persona variations shape consensus speed in LLM multi-agent coding but produce no robust accuracy gains over single agents on human-annotated tutoring transcripts.
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HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planning
HEART coordinates role-specialized LLM agents to decompose instructions, validate reachability and constraints, and synthesize executable robotic plans, showing higher success than single-LLM baselines on household tasks.
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