To improve the reasoning and question-answering capabilities of Large
Language Models (LLMs), several multi-agent approaches have been introduced.
While these methods enhance performance, the application of collective
intelligence-based approaches to complex network structures and the dynamics of
agent interactions remain underexplored. This work extends the concept of
multi-agent debate to more general network topologies, measuring the
question-answering accuracy, influence, consensus, and the effects of bias on
the collective. The results show that random networks perform similarly to
fully connected networks despite using significantly fewer tokens. Furthermore,
a strong consensus among agents in correlates with correct answers, whereas
divided responses typically indicate incorrect answers. Analysing the influence
of the agents reveals a balance between self-reflection and interconnectedness;
self-reflection aids when local interactions are incorrect, and local
interactions aid when the agent itself is incorrect. Additionally, bias plays a
strong role in system performance with correctly biased hub nodes boosting
performance. These insights suggest that using random networks or scale-free
networks with knowledgeable agents placed in central positions can enhance the
overall performance of multi-agent systems.

本研究将多智能体辩论的概念扩展到更一般的网络拓扑结构，衡量了问题回答的准确性、影响力、共识以及偏见对集体的影响。结果表明，随机网络与全连接网络效果相似，但使用的令牌数量显著较少。此外，智能体之间存在平衡，自我反思在局部互动出错时有助于解答正确，而局部互动在智能体本身出错时起到帮助作用。偏见在系统性能中起到重要作用，具有正确偏见的中心节点能提升系统性能。这些发现表明，在多智能体系统中使用随机网络或具有知识的智能体位于中心位置的无标度网络可以提高整体性能。