Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
This work addresses the problem of ambiguous and unreliable network design for users and operators in optical networking, representing a domain-specific incremental improvement.
The paper tackles the challenge of translating informal natural-language intents into formally correct optical network topologies in Intent-Based Networking (IBN) by proposing a novel hybrid pipeline that integrates LLM-based intent parsing, formal methods, and Optical Retrieval-Augmented Generation (RAG). This approach generates explainable, verifiable, and trustworthy optical network designs, significantly advancing IBN by ensuring reliability and correctness for mission-critical tasks.
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.