连接语言模型与形式化方法以实现意图驱动的光网络设计
Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
- National Institute of Standards and Technology（美国国家标准技术研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对LLM难以将自然语言意图严谨转换为光网络拓扑的问题，提出融合意图解析、形式化方法与光学RAG的混合流水线，以生成可验证、可信的自动化设计。
AI中文摘要：
基于意图的网络（IBN）旨在通过允许用户指定驱动自动化网络设计和配置的高层目标来简化网络管理。然而，由于大型语言模型（LLM）固有的歧义性和缺乏严谨性，将非正式的自然语言意图转换为形式正确的光网络拓扑仍然具有挑战性。为解决这一问题，我们提出了一种新颖的混合流水线，集成了基于LLM的意图解析、形式化方法和光学检索增强生成（RAG）。通过利用特定领域的光学标准丰富设计决策，并系统性地纳入符号推理和验证技术，我们的流水线可生成可解释、可验证且值得信赖的光网络设计。该方法通过确保可靠性和正确性显著推进了IBN，这对关键任务网络场景至关重要。
英文摘要：
Intent-Based Networking (IBN) aims to simplify network management by enabling users to specify high-level goals that drive automated network design and configuration. However, translating informal natural-language intents into formally correct optical network topologies remains challenging due to inherent ambiguity and lack of rigor in Large Language Models (LLMs). To address this, we propose a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). By enriching design decisions with domain-specific optical standards and systematically incorporating symbolic reasoning and verification techniques, our pipeline generates explainable, verifiable, and trustworthy optical network designs. This approach significantly advances IBN by ensuring reliability and correctness, essential for mission-critical networking tasks.
