用大模型根据自然语言需求生成可部署的网络拓扑，兼顾结构正确与韧性。
An LLM-Based Framework for Intent-Driven Network Topology Design
- 基于约束驱动的分层建模与验证流程，确保拓扑结构合规。
- 在4个真实场景中，节点和边的F1分数达到0.85以上，连接性保持稳定。
- 揭示接口不匹配等常见错误，适合网络自动化研究者参考。
从自然语言需求生成可部署且具备韧性的网络拓扑仍是网络自动化中的难题。本文研究大语言模型（LLMs）通过结合分层建模与系统化验证的约束驱动流程，生成结构有效且符合约束的网络拓扑的能力。该框架在公开发布的四个真实网络场景数据集上，对比评估了多家自有及开源权重的LLMs表现。通过与参考拓扑比较节点和边的F1分数评估结构正确性，并以服务器与内容连通性指标衡量韧性。同时分析了生成拓扑中常见的失败模式，如接口不匹配和方向性不一致。整体而言，本工作为理解LLMs在拓扑生成中处理结构与韧性约束提供了系统性基准，支持人工智能驱动的网络设计中的模型选型。
原文摘要 · Abstract (English)
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.
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