构建统一知识平面，让网络智能体更高效协作。
KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence
- 分离知识获取与智能逻辑，统一管理网络知识
- 支持实时网络问答与边缘AI服务编排，提升响应效率
- 开源实现，助力6G智能网络标准化
大型语言模型（LLMs）和智能体系统的兴起，正推动具备自配置、自优化、自愈能力的自主6G网络发展。然而，当前各智能任务依赖独立的知识检索管道，导致数据冗余和理解不一致。受Open-RAN服务模型统一思路启发，我们提出KP-A：一种专为智能网络设计的统一网络知识平面。通过将网络知识获取与管理从智能逻辑中解耦，KP-A简化了开发流程，降低了智能工程师的维护复杂度。其直观一致的知识接口还增强了智能体间的互操作性。我们在两个典型任务中验证了KP-A的有效性：实时网络知识问答与边缘AI服务编排。所有实现代码均已开源，以支持可复现性和未来标准制定。
原文摘要 · Abstract (English)
The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.
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