Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908562088787968 |
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| author | Bekri, Anis Abane, Amar Battou, Abdella Bensalem, Saddek |
| author_facet | Bekri, Anis Abane, Amar Battou, Abdella Bensalem, Saddek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design Bekri, Anis Abane, Amar Battou, Abdella Bensalem, Saddek Networking and Internet Architecture Artificial Intelligence 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. |
| title | Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2509.22834 |