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

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Key takeaways

• 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.

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Contribution summary

• We assess structural correctness using node and edge F1-scores against reference topologies, and evaluate resilience through server and content connectivity metrics.
• 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.

Why it matters for eval

• 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.