[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82494-en":3,"doc-seo-82494-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82494,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","An LLM-Based Framework for Intent-Driven Network Topology Design","Designing deployable, resilient network topologies from natural-language requirements remains difficult for network automation. This work examines whether large language models can produce structurally valid, constraint-compliant topologies via a constraint-driven pipeline that combines hierarchical modeling with systematic validation. The framework is benchmarked through multimodel comparison of proprietary and open-weight LLMs on four realistic scenarios from a public dataset. Structural fidelity is measured with node/edge F1 against references, while resilience is evaluated using server and content connectivity metrics.","An LLM-Based Framework for Intent-Driven Network Topology Design  \nKholoud El Habbouli, Fen Zhou and Stéphane Huet  \n† CERI-LIA, University of Avignon, France  \n[Emails : {firstname.lastname@univ-avignon.fr}](Emails : {firstname.lastname@univ-avignon.fr})  \narXiv :2607 .00292v 1 [ cs .NI] 1 Jul 2026  \nAbstract—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.  \nIndex Terms—Intent-Based Networking, Network Resilience, Large Language Model (LLM), Topology Synthesis, Structural Fidelity.  \nI. INTRODUCTION  \nRecent large-scale outages in cloud and 5G infrastructures have exposed a critical limitation in current network automation systems: correct configuration alone does not guarantee operational resilience. As network scale and complexity increase, ensuring reliable service delivery requires reasoning not only at the configuration level, but also at the level of network topology design.  \nExisting approaches in Intent-Based Networking (IBN) [1] and Large Language Model (LLM)-driven automation primarily focus on translating high-level intents into device configurations, assuming predefined and valid network topologies. Frameworks  \nsuch as NetConfEval [2] and S-Witch [3] treat topology as an input rather than a design output, which limits their applicability in scenarios requiring explicit structural constraints and redundancy guarantees. As a result, LLM-based systems may generate structurally inconsistent or invalid network representations when reasoning about connectivity and interface compatibility.  \nTo address this limitation, we argue that resilience must be considered at the topology design stage, prior to configuration synthesis. We introduce a Resilience-by-Design perspective, where network structures are generated under explicit constraints ensuring consistency and redundancy before deployment. This decouples topology synthesis from configuration generation, enabling a structured and verifiable design process. A key challenge in this domain is the lack of benchmarks for evaluating LLM-based network topology synthesis under realistic structural and resilience constraints. To address this gap, we introduce a benchmark composed of intent-to-topology scenarios, together with an evaluation methodology for systematically assessing and comparing LLM-generated network topologies.  \nThis paper makes the following contributions:  \n• To the best of our knowledge, we are the first to propose an LLM-based framework for intentdriven network topology design that comprehensively incorporates network structure, heterogeneous network devices and interface diversity, as well as resilience requirements.  \n• We propose a benchmark for intent-driven topology synthesis under realistic scenarios.  \n• We design an evaluation methodology combining node and edge F1-scores with connectivity-  \nbased resilience metrics.  \nII. RELATED WORK  \nRecent LLM-based network automation approaches primarily focus on translating natural language intents into device-level con","cbCaim0mklwWoulM","https://ap.wps.com/l/cbCaim0mklwWoulM","pdf",632022,1,9,"English","en",105,"# Introduction\n## Motivation and key limitation\n## Resilience-by-Design perspective\n## Contributions\n# Related Work\n## Configuration-centric LLM automation\n## Network resilience foundations\n## Graph generation with LLMs","[{\"question\":\"What problem does the framework address in network automation?\",\"answer\":\"It targets the gap where correct configuration alone does not ensure operational resilience, requiring reasoning at the topology design level rather than only at the configuration level.\"},{\"question\":\"How does the proposed framework generate intent-driven topologies?\",\"answer\":\"It uses a constraint-driven pipeline that combines hierarchical modeling with systematic validation to produce structurally valid, constraint-compliant network topologies.\"},{\"question\":\"How are LLM-generated topologies evaluated for correctness and resilience?\",\"answer\":\"Structural correctness is assessed with node and edge F1-scores against reference topologies, while resilience is evaluated through server and content connectivity 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