用大模型+形式化方法，让自然语言指令自动生成可靠光网络设计
Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
- 融合大模型解析与形式化验证，生成可解释的光网络拓扑
- 通过领域标准增强设计，确保方案符合实际工程规范
- 适合需要高可靠性的光网络自动化设计场景
意图驱动网络（IBN）旨在通过用户指定高层次目标来实现网络自动设计与配置。然而，将非正式的自然语言意图转化为形式正确的光网络拓扑仍具挑战，源于大语言模型固有的模糊性和缺乏严谨性。为此，我们提出一种新型混合流水线，整合基于大模型的意图解析、形式化方法及光学检索增强生成（Optical RAG）。通过融入领域特定的光学标准，并系统性引入符号推理与验证技术，该流水线生成可解释、可验证且可信的光网络设计方案。该方法显著提升了IBN的可靠性与正确性，对关键任务型网络设计至关重要。
原文摘要 · Abstract (English)
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.
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