# RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation

arXiv: https://arxiv.org/abs/2606.01862 · HTML: https://mlsanity.com/paper/2606.01862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 1 Jun 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, 24 of 24)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://mlsanity.com/paper/2305.18323/) (2023)
- [AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://mlsanity.com/paper/2402.09742/) (2024)
- [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024)
- [Penetrative AI: Making LLMs Comprehend the Physical World](https://mlsanity.com/paper/2310.09605/) (2023)
- [An Agentic System for Rare Disease Diagnosis with Traceable Reasoning](https://mlsanity.com/paper/2506.20430/) (2025)
- [Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications](https://mlsanity.com/paper/2409.05314/) (2024)
- [AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applications](https://mlsanity.com/paper/2503.05346/) (2025)
- [Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review](https://mlsanity.com/paper/2502.11518/) (2025)
- [Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree](https://mlsanity.com/paper/2508.03038/) (2025)
- [WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks](https://mlsanity.com/paper/2603.00501/) (2026)
- [RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token Reprogrammings](https://mlsanity.com/paper/2501.17888/) (2025)
- [RF-GPT: Teaching AI to See the Wireless World](https://mlsanity.com/paper/2602.14833/) (2026)
- [Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents](https://mlsanity.com/paper/2501.13954/) (2025)
- [Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems](https://mlsanity.com/paper/2506.24009/) (2025)
- [FlexiFly: Interfacing the Physical World with Foundation Models Empowered by Reconfigurable Drone Systems](https://mlsanity.com/paper/2403.12853/) (2024)
- [PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence](https://mlsanity.com/paper/2605.18067/) (2026)
- [NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways](https://mlsanity.com/paper/2403.09861/) (2024)
- [TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds](https://mlsanity.com/paper/1907.02063/) (2019)
- [Embodied Intelligent Wireless (EIW): Synesthesia of Machines Empowered Wireless Communications](https://mlsanity.com/paper/2511.22845/) (2025)
- [Enabling Agile Ambient IoT Networking via a Parameterized Hybrid Radio](https://mlsanity.com/paper/2605.18314/) (2026)

## Abstract (arXiv)

Translating user intent into physical radio signals is the last critical step in wireless prototyping. It chains protocol planning, baseband synthesis, and hardware configuration. Large language models and multi-agent systems have reshaped software engineering, raising the question of whether they can solve this problem. Yet current models fail at this task, even when augmented with domain tools. Because the stages run sequentially, an error at any stage propagates downstream, so the end-to-end success rate collapses toward zero even when each stage looks locally competent. We introduce RadioMaster, a fully autonomous multi-agent framework that drives user input to verified emissions transmitted over the air. It rests on three synergistic pillars. RadioWiki grounds generation in domain knowledge to suppress hallucination. RadioAgent decomposes the fragile pipeline into independently executable and locally recoverable stages. RadioEmulator gates deployment behind closed-loop physical-layer verification. We further build RadioBench, the first benchmark for autonomous radio signal generation. Extensive real-world evaluations show that RadioMaster substantially outperforms state-of-the-art baselines in configuration viability and signal fidelity, while reducing configuration time by up to 28x.