Large Language Model (LLM)-based Multi-Agent Systems (MAS) enhance complex problem solving through multi-agent collaboration, but often incur substantially higher costs than single-agent systems. Recent MAS routing methods aim to balance performance and overhead by dynamically selecting agent roles and language models. However, these approaches typically rely on a homogeneous collaboration mode, where all agents follow the same interaction pattern, limiting collaboration flexibility across different roles. Motivated by Social Capital Theory, which emphasizes that different roles benefit from distinct forms of collaboration, we propose SC-MAS, a framework for constructing heterogeneous and cost-efficient multi-agent systems. SC-MAS models MAS as directed graphs, where edges explicitly represent pairwise collaboration strategies, allowing different agent pairs to interact through tailored communication patterns. Given an input query, a unified controller progressively constructs an executable MAS by selecting task-relevant agent roles, assigning edge-level collaboration strategies, and allocating appropriate LLM backbones to individual agents. Experiments on multiple benchmarks demonstrate the effectiveness of SC-MAS. In particular, SC-MAS improves accuracy by 3.35% on MMLU while reducing inference cost by 15.38%, and achieves a 3.53% accuracy gain with a 12.13% cost reduction on MBPP. These results validate the feasibility of SC-MAS and highlight the effectiveness of heterogeneous collaboration in multi-agent systems.

翻译：基于大型语言模型（LLM）的多智能体系统（MAS）通过多智能体协作增强了复杂问题的解决能力，但其成本通常远高于单智能体系统。近期的MAS路由方法旨在通过动态选择智能体角色和语言模型来平衡性能与开销。然而，这些方法通常依赖于同构协作模式，即所有智能体遵循相同的交互模式，限制了不同角色之间的协作灵活性。受社会资本理论启发——该理论强调不同角色受益于不同形式的协作，我们提出了SC-MAS，一个用于构建异构且成本高效的多智能体系统的框架。SC-MAS将MAS建模为有向图，其中边显式表示成对协作策略，允许不同的智能体对通过定制的通信模式进行交互。给定输入查询，一个统一的控制器通过选择任务相关的智能体角色、分配边级协作策略并为各个智能体分配合适的LLM骨干网络，逐步构建一个可执行的MAS。在多个基准测试上的实验证明了SC-MAS的有效性。具体而言，SC-MAS在MMLU上将准确率提高了3.35%，同时将推理成本降低了15.38%；在MBPP上实现了3.53%的准确率提升和12.13%的成本降低。这些结果验证了SC-MAS的可行性，并凸显了异构协作在多智能体系统中的有效性。