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

arXiv: https://arxiv.org/abs/2407.08249 · HTML: https://mlsanity.com/paper/2407.08249/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents](https://mlsanity.com/paper/2311.05437/) (2023)
- [OtterHD: A High-Resolution Multi-modality Model](https://mlsanity.com/paper/2311.04219/) (2023)
- [SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs](https://mlsanity.com/paper/2308.03349/) (2023)
- [What do LLMs need to Synthesize Correct Router Configurations?](https://mlsanity.com/paper/2307.04945/) (2023)
- [Can LLMs Understand Computer Networks? Towards a Virtual System Administrator](https://mlsanity.com/paper/2404.12689/) (2024)
- [An Empirical Study of NetOps Capability of Pre-Trained Large Language Models](https://mlsanity.com/paper/2309.05557/) (2023)
- [Neural language models for network configuration: Opportunities and reality check](https://mlsanity.com/paper/2205.01398/) (2022)

## Built on by (most cited first, 6 of 6)

- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [FastRAG: Retrieval Augmented Generation for Semi-structured Data](https://mlsanity.com/paper/2411.13773/) (2024)
- [Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design](https://mlsanity.com/paper/2509.22834/) (2025)
- [NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration](https://mlsanity.com/paper/2608.23179/) (2026)
- [LLM-Based Config Synthesis requires Disambiguation](https://mlsanity.com/paper/2507.12443/) (2025)
- [TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies](https://mlsanity.com/paper/2608.13389/) (2026)

## 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.