提出多阶通信机制，提升大模型多智能体系统的消息传递效率与准确性。
MOC: Multi-Order Communication in LLM-based Multi-Agent Systems
- 构建多阶证据流，捕捉跨多跳的依赖关系。
- 在六大数据集上显著提升任务性能，降低通信开销。
- 适合研究多智能体协作与高效通信的学者与开发者。
尽管基于大语言模型（LLM）的多智能体系统取得显著进展，但多数研究聚焦于协调拓扑优化，而忽视了消息传递与优化这一同样关键的问题。现有通信方案通常依赖一阶邻居响应的直接拼接，导致证据感知范围受限，并在多跳路径中稀释关键信息。为此，本文提出多阶通信（MOC）机制，重构智能体间通信以捕捉多跳依赖，并引入结构化消息整合策略以保障效率。具体而言，我们形式化通信机制，构建结构化的多阶证据流，并设计语义-拓扑融合算法，在令牌约束下优化语义保真度。在六个不同数据集及多种参数规模的LLM主干上进行的大量实验表明，MOC能持续提升任务性能并减少通信成本。代码已开源：https://github.com/yao-guan/MOC。
原文摘要 · Abstract (English)
Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively? Current communication schemes typically rely on the direct concatenation of first-order neighbor responses, which induces a restricted evidence receptive field and leads to the dilution of crucial insights over multi-hop paths. To address these limitations, we propose the Multi-Order Communication (MOC) scheme, which reconstructs the inter-agent communication to capture multi-hop dependencies and incorporates a structural message consolidation strategy to ensure efficiency. Specifically, we formalize the communication mechanism to construct a structured multi-order evidence stream, and subsequently design a Semantic-Topological Merging algorithm to optimize semantic fidelity within token constraints. Extensive experiments across six diverse datasets and LLM backbones of varying parameter scales demonstrate that MOC consistently improves task performance and reduces communication costs. The code is available at https://github.com/yao-guan/MOC.
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