Score: 2

Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design

Published: September 26, 2025 | arXiv ID: 2509.22834v1

By: Anis Bekri , Amar Abane , Abdella Battou and more

BigTech Affiliations: NIST

Potential Business Impact:

Makes computer networks build themselves correctly.

Business Areas:
Natural Language Processing Artificial Intelligence, Data and Analytics, Software

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.

Country of Origin
πŸ‡«πŸ‡· πŸ‡ΊπŸ‡Έ France, United States

Page Count
8 pages

Category
Computer Science:
Networking and Internet Architecture