# Designing Network Algorithms via Large Language Models

arXiv: https://arxiv.org/abs/2404.01617 · HTML: https://mlsanity.com/paper/2404.01617/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [When Does Label Smoothing Help?](https://mlsanity.com/paper/1906.02629/) (2019)
- [DocPrompting: Generating Code by Retrieving the Docs](https://mlsanity.com/paper/2207.05987/) (2022)
- [Automatic Curriculum Generation for Learning Adaptation in Networking](https://mlsanity.com/paper/2202.05940/) (2022)

## Built on by (most cited first, 12 of 12)

- [Glia: A Human-Inspired AI for Automated Systems Design and Optimization](https://mlsanity.com/paper/2510.27176/) (2025)
- [NNGPT: Rethinking AutoML with Large Language Models](https://mlsanity.com/paper/2511.20333/) (2025)
- [Improving Coherence and Persistence in Agentic AI for System Optimization](https://mlsanity.com/paper/2603.21321/) (2026)
- [Man-Made Heuristics Are Dead. Long Live Code Generators!](https://mlsanity.com/paper/2510.08803/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Towards an Agentic Workflow for Internet Measurement Research](https://mlsanity.com/paper/2511.10611/) (2025)
- [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025)
- [Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routes](https://mlsanity.com/paper/2608.22644/) (2026)
- [Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks](https://mlsanity.com/paper/2409.19007/) (2024)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)
- [Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems](https://mlsanity.com/paper/2609.16599/) (2026)
- [NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2610.16226/) (2026)

## Abstract (arXiv)

We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existing algorithm implementation, NADA enables LLMs to create a wide variety of alternative designs in the form of code blocks. It then efficiently identifies the top-performing designs through a series of filtering techniques, minimizing the need for full-scale evaluations and significantly reducing computational costs. Using adaptive bitrate (ABR) streaming as a case study, we demonstrate that NADA produces novel ABR algorithms -- previously unknown to human developers -- that consistently outperform the original algorithm in diverse network environments, including broadband, satellite, 4G, and 5G.