用图操作替代自然语言，让多智能体沟通更准确高效
G2CP: A Graph-Grounded Communication Protocol for Verifiable and Efficient Multi-Agent Reasoning
- 用图遍历指令和子图片段代替自由文本通信
- 通信令牌减少73%，任务准确率提升34%，杜绝幻觉传播
- 适合需要可审计推理链的工业系统场景
基于大语言模型的多智能体系统面临严重挑战：智能体通过自然语言通信导致语义漂移、幻觉传播和低效的令牌消耗。我们提出G2CP（图基通信协议），一种结构化通信语言，消息为图操作而非自由文本。智能体在共享知识图上交换显式的遍历命令、子图片段和更新操作，实现可验证的推理轨迹并消除歧义。我们在工业知识管理系统中验证G2CP，由诊断、流程、合成和摄入等专用智能体协同回答复杂查询。在500个工业场景和21个真实维护案例上的实验表明，G2CP将跨智能体通信令牌减少73%，任务完成准确率比自由文本基线提升34%，彻底消除级联幻觉，并生成完全可审计的推理链。G2CP标志着多智能体系统从语言通信向结构通信的根本转变，对任何需要精确协调的领域均有重要意义。代码、数据和评估脚本已公开。
原文摘要 · Abstract (English)
Multi-agent systems powered by Large Language Models face a critical challenge: agents communicate through natural language, leading to semantic drift, hallucination propagation, and inefficient token consumption. We propose G2CP (Graph-Grounded Communication Protocol), a structured agent communication language where messages are graph operations rather than free text. Agents exchange explicit traversal commands, subgraph fragments, and update operations over a shared knowledge graph, enabling verifiable reasoning traces and eliminating ambiguity. We validate G2CP within an industrial knowledge management system where specialized agents (Diagnostic, Procedural, Synthesis, and Ingestion) coordinate to answer complex queries. Experimental results on 500 industrial scenarios and 21 real-world maintenance cases show that G2CP reduces inter-agent communication tokens by 73%, improves task completion accuracy by 34% over free-text baselines, eliminates cascading hallucinations, and produces fully auditable reasoning chains. G2CP represents a fundamental shift from linguistic to structural communication in multi-agent systems, with implications for any domain requiring precise agent coordination. Code, data, and evaluation scripts are publicly available.
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