An LLM-Based Framework for Intent-Driven Network Topology Design
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Computer Science > Networking and Internet Architecture
Title:An LLM-Based Framework for Intent-Driven Network Topology Design
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.
| Comments: | submitted to IEEE CNSM 2026 |
| 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:2607.00292v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00292
arXiv-issued DOI via DataCite (pending registration)
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