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

2026-06-30

AI Summary

This work addresses the core challenge in network automation: automatically generating deployable network topologies from natural language requirements while satisfying structural and resilience constraints. We propose a large language model (LLM)-based, constraint-driven framework that translates natural language into compliant topologies through hierarchical intent parsing and systematic validation. To facilitate evaluation, we introduce the first benchmark for this task, releasing a public dataset encompassing four real-world scenarios and characterizing common generation error patterns. Extensive experiments across multiple proprietary and open-source LLMs demonstrate the framework’s effectiveness, with performance quantified using metrics including topological correctness, node/edge F1 scores, and server-content connectivity. Our results provide actionable guidance for model selection in AI-driven network 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.

Problem

• network topology design
• natural language requirements
• resilience
• deployable networks
• network automation

Innovation

• Large Language Models
• Network Topology Design
• Intent-Driven Networking
• Constraint-Driven Synthesis
• Resilience Evaluation