Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over single agent systems, they lead to huge computational overheads because of heavy communication between agents. Previous research has made efforts to train a sparse multi-agent graph or fine-tune a planner to orchestrate the workflow better. However, such extra training processes introduce computational costs and limit MAS to specific domains, therefore compromising their generalizability. In this paper, we propose CONCAT, a training-free multi-agent collaboration framework based on CONsensus and Confidence-driven Ad hoc Teaming to efficiently organize agent interactions. Specifically, agents are clustered based on their initial answers, and leaders of each cluster are selected based on the agents' confidence. Then, a heuristic function based on the Theory of Mind is designed to predict the collaboration benefits between every two leaders according to their answers and confidence. Finally, an ad hoc multi-agent network is organized after evicting a percentage of communications based on the predicted benefits. Experiments across three LLMs and three benchmarks show that CONCAT achieves up to 2.02x higher efficiency (accuracy/latency ratio) than LLM-Debate and outperforms training-aware methods such as AgentDropout, while reducing average latency by 50.1% on Qwen2.5-14B-Instruct, without any task-specific training.

翻译：尽管基于大语言模型（LLM）的多智能体系统（MAS）在解决复杂任务和获得优于单智能体系统的性能方面展现出强大能力，但智能体间的密集通信导致了巨大的计算开销。现有研究尝试通过训练稀疏多智能体图结构或微调规划器来优化工作流程，然而这类额外训练过程不仅引入计算成本，还将MAS局限于特定领域，损害了其泛化能力。本文提出CONCAT——一种无需训练的、基于共识与置信度驱动的临时团队协作框架，旨在高效组织智能体交互。具体而言，首先根据智能体的初始回答对其进行聚类，并基于各智能体的置信度选择每个聚类的领导者；随后，基于心智理论设计启发式函数，根据领导者的回答与置信度预测两两之间的协作收益；最终，在依据预测收益剔除部分通信后，组织形成临时的多智能体网络。在三种LLM和三个基准测试上的实验表明，CONCAT相比LLM-Debate实现了最高2.02倍的效率提升（准确率/延迟比），且性能优于AgentDropout等需训练方法，同时在Qwen2.5-14B-Instruct上将平均延迟降低50.1%，且无需任何任务特定训练。