Improving Multi-Agent Debate with Sparse Communication Topology
Multi-agent debate has proven effective in improving large language models quality for reasoning and factuality tasks. While various role-playing strategies in multi-agent debates have been explored, in terms of the communication among agents, existing approaches adopt a brute force algorithm -- each agent can communicate with all other agents. In this paper, we systematically investigate the effect of communication connectivity in multi-agent systems. Our experiments on GPT and Mistral models reveal that multi-agent debates leveraging sparse communication topology can achieve comparable or superior performance while significantly reducing computational costs. Furthermore, we extend the multi-agent debate framework to multimodal reasoning and alignment labeling tasks, showcasing its broad applicability and effectiveness. Our findings underscore the importance of communication connectivity on enhancing the efficiency and effectiveness of the "society of minds" approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Multimodal ReasoningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
RUMAD: Reinforcement-Unifying Multi-Agent Debate
Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus formation, and computational efficiency…
Zero-shot GeneralizationComputational EfficiencyReinforcement LearningDynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus
Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation. However, existing multi-agent debate and col…
Mathematical ReasoningLogical ReasoningPEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate
Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduce persistent positional biases, amplify unreliable agents, and cause h…
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems
Optimizing communication topology in LLM-based multi-agent system is critical for enabling collective intelligence. Existing methods mainly rely on spatio-temporal interaction paradigms, where the sequential execution of…
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems
The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and effectiveness of collective decision-makin…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model