# Toward Reproducing Network Research Results Using Large Language Models

arXiv: https://arxiv.org/abs/2309.04716 · HTML: https://mlsanity.com/paper/2309.04716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Prompting Is Programming: A Query Language for Large Language Models](https://mlsanity.com/paper/2212.06094/) (2022)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Repairing Bugs in Python Assignments Using Large Language Models](https://mlsanity.com/paper/2209.14876/) (2022)
- [Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis](https://mlsanity.com/paper/2109.02445/) (2021)
- [Semi-Automated Protocol Disambiguation and Code Generation](https://mlsanity.com/paper/2010.04801/) (2020)
- [Automated Feedback Generation for Competition-Level Code](https://mlsanity.com/paper/2206.01848/) (2022)
- [Rethinking Data-driven Networking with Foundation Models: Challenges and Opportunities](https://mlsanity.com/paper/2211.06494/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [NetArena: Dynamic Benchmarks for AI Agents in Network Automation](https://mlsanity.com/paper/2506.03231/) (2025)
- [MoE2: Optimizing Collaborative Inference for Edge Large Language Models](https://mlsanity.com/paper/2501.09410/) (2025)

## Abstract (arXiv)

Reproducing research results in the networking community is important for both academia and industry. The current best practice typically resorts to three approaches: (1) looking for publicly available prototypes; (2) contacting the authors to get a private prototype; and (3) manually implementing a prototype following the description of the publication. However, most published network research does not have public prototypes and private prototypes are hard to get. As such, most reproducing efforts are spent on manual implementation based on the publications, which is both time and labor consuming and error-prone. In this paper, we boldly propose reproducing network research results using the emerging large language models (LLMs). In particular, we first prove its feasibility with a small-scale experiment, in which four students with essential networking knowledge each reproduces a different networking system published in prominent conferences and journals by prompt engineering ChatGPT. We report the experiment's observations and lessons and discuss future open research questions of this proposal. This work raises no ethical issue.