This paper presents a hierarchical multi-agent LLM architecture to bridge communication gaps between non-technical end users and telecommunications domain experts in private network environments. We propose a cross-domain query translation framework that leverages specialized language models coordinated through multi-agent reflection-based reasoning. The resulting system addresses three critical challenges: (1) accurately classify user queries related to telecommunications network issues using a dual-stage hierarchical approach, (2) preserve user privacy through the anonymization of semantically relevant personally identifiable information (PII) while maintaining diagnostic utility, and (3) translate technical expert responses into user-comprehensible language. Our approach employs ReAct-style agents enhanced with self-reflection mechanisms for iterative output refinement, semantic-preserving anonymization techniques respecting $k$-anonymity and differential privacy principles, and few-shot learning strategies designed for limited training data scenarios. The framework was comprehensively evaluated on 10,000 previously unseen validation scenarios across various vertical industries.

翻译：本文提出了一种分层多智能体大语言模型架构，旨在弥合私有网络环境中非技术终端用户与电信领域专家之间的沟通鸿沟。我们设计了一种跨领域查询翻译框架，该框架利用通过多智能体反思式推理协调的专用语言模型。最终系统解决了三个关键挑战：（1）采用双阶段分层方法准确分类与电信网络问题相关的用户查询；（2）通过对语义相关的个人身份信息进行匿名化处理来保护用户隐私，同时维持诊断效用；（3）将技术专家的响应翻译为用户可理解的语言。我们的方法采用了增强自我反思机制的ReAct风格智能体用于迭代输出优化、遵循$k$-匿名性与差分隐私原则的语义保持匿名化技术，以及针对有限训练数据场景设计的小样本学习策略。该框架在跨多个垂直行业的10,000个未见验证场景上进行了全面评估。