An LLM-Based Framework for Intent-Driven Network Topology Design

[2607.00292] An LLM-Based Framework for Intent-Driven Network Topology Design Skip to main content Search Submit Donate Log in Computer Science > Networking and Internet Architecture arXiv:2607.00292 (cs) [Submitted on 1 Jul 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: An LLM-Based Framework for Intent-Driven Network Topology Design Authors: Kholoud El-Habbouli , Fen Zhou , Stephane Huet View PDF HTML (experimental) Abstract: Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This work investigates the ability of Large Language Models (LLMs) to generate structurally valid and constraint-compliant network topologies through a constraint-driven pipeline combining hierarchical modeling and systematic validation. The framework is evaluated via a multimodel comparison of proprietary and open-weight LLMs across four realistic network scenarios released as a public dataset. We assess structural correctness using node and edge F1-scores against reference topologies, and evaluate resilience through server and content connectivity metrics. In addition, we analyze common failure modes, including interface mismatches and directional inconsistencies in generated topologies. Overall, this work provides a systematic benchmark for understanding how LLMs handle structural and resilience constraints in topology synthesis, and supports informed model selection for AI-driven network design. Subjects: Networking and Internet Architecture (cs.NI) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2607.00292 [cs.NI] (or arXiv:2607.00292v2 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2607.00292 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Fen Zhou Mr [ view email ] [v1] Wed, 1 Jul 2026 00:38:36 UTC (585 KB) [v2] Thu, 27 Aug 2026 20:44:25 UTC (585 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.NI < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI cs.CL References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• An LLM-Based Framework for Intent-Driven Network Topology Design#682741
• An LLM-Based Framework for Intent-Driven Network Topology Design#329048
• Customizing an LLM for Enterprise Software Engineering#200584
• Campus Network Topology Discovery and Distributed Firewall Policy Generation#1027033
• M-ISIS: Multi Topology (MT) Routing in Intermediate System to Intermediate Systems (IS-ISs)#420314