GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration
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Abstract (arXiv)
Communication network engineering in enterprise environments is traditionally a complex, time-consuming, and error-prone manual process. Most research on network engineering automation has concentrated on configuration synthesis, often overlooking changes in the physical network topology. This paper introduces GeNet, a multimodal co-pilot for enterprise network engineers. GeNet is a novel framework that leverages a large language model (LLM) to streamline network design workflows. It uses visual and textual modalities to interpret and update network topologies and device configurations based on user intents. GeNet was evaluated on enterprise network scenarios adapted from Cisco certification exercises. Our results demonstrate GeNet's ability to interpret network topology images accurately, potentially reducing network engineers' efforts and accelerating network design processes in enterprise environments. Furthermore, we show the importance of precise topology understanding when handling intents that require modifications to the network's topology.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 11 Jul 2024.
Builds on
14 of 48 references.
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Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2024FastRAG: Retrieval Augmented Generation for Semi-structured Datacited by 3
- 2026SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshootingcited by 3
- 2025LLM-Based Config Synthesis requires Disambiguationcited by 0
- 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Designcited by 0
- 2026TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologiescited by 0
- 2026NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configurationcited by 0
- 2026NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration24 Aug 2026
- 2026TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies13 Aug 2026
- 2026SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting6 May 2026
- 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design26 Sep 2025
- 2025LLM-Based Config Synthesis requires Disambiguation16 Jul 2025
- 2024FastRAG: Retrieval Augmented Generation for Semi-structured Data21 Nov 2024
Similar, not cited
Close in wording, no citation either way
- 2025INTA: Intent-Based Translation for Network Configuration with LLM Agents
- 2026Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
- 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
- 2024Hermes: A Large Language Model Framework on the Journey to Autonomous Networks
- 2025Automated Network Protocol Testing with LLM Agents
- 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
- 2026The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
- 2026RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines