mlsanityData to 7 Oct 2026

GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration

arXiv 2407.08249 ↗First posted 11 Jul 2024cs.NI cs.AIcited by 6

Not on the map. Why?

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.

References · 14

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  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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  6. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  7. 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
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  11. 2023What do LLMs need to Synthesize Correct Router Configurations?cited by 29
  12. 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administratorcited by 11
2 more
  1. 2023An Empirical Study of NetOps Capability of Pre-Trained Large Language Modelscited by 9
  2. 2022Neural language models for network configuration: Opportunities and reality checkcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025INTA: Intent-Based Translation for Network Configuration with LLM Agents
  2. 2026Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
  3. 2026TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
  4. 2024Hermes: A Large Language Model Framework on the Journey to Autonomous Networks
  5. 2025Automated Network Protocol Testing with LLM Agents
  6. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
  7. 2026The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
  8. 2026RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines