# The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms

arXiv: https://arxiv.org/abs/2604.19803 · HTML: https://mlsanity.com/paper/2604.19803/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 7 of 7)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution](https://mlsanity.com/paper/2509.19349/) (2025)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Evolving Code with A Large Language Model](https://mlsanity.com/paper/2401.07102/) (2024)
- [LLM4AD: A Platform for Algorithm Design with Large Language Model](https://mlsanity.com/paper/2412.17287/) (2024)
- [DeepCode: Open Agentic Coding](https://mlsanity.com/paper/2512.07921/) (2025)

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

- [GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing](https://mlsanity.com/paper/2605.27360/) (2026)
- [Autonomous Discovery of Wireless Communications Algorithms](https://mlsanity.com/paper/2607.17762/) (2026)
- [Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role](https://mlsanity.com/paper/2608.26093/) (2026)

## Abstract (arXiv)

Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper, we explore the ability of agentic AI to autonomously design wireless communication algorithms. To that end, we implement a dedicated framework that leverages large language models (LLMs) to iteratively generate, evaluate, and refine candidate algorithms. We evaluate the framework on three tasks spanning the physical (PHY) and medium access control (MAC) layers: statistics-agnostic channel estimation, channel estimation with known covariance, and link adaptation. Our results show that, in a matter of hours, the framework produces algorithms that are competitive with and, in some cases, outperforming conventional baselines. Moreover, unlike neural network-based approaches, the generated algorithms are fully explainable and extensible. This work represents a first step toward the autonomous discovery of novel wireless communication algorithms, and we look forward to the progress our community makes in this direction.