WARA: A Closed-Loop Multi-Agent Framework for Wireless Optimization Autoresearch

Yuan Guo, Yilong Chen, Chao Hu, Xianghao Yu, Liang Hong, Jie Xu

Large language model (LLM) agents have shown growing capabilities in tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a particular focus on wireless resource allocation optimization, an essential area for characterizing the fundamental performance limits of wireless systems and enhancing their practical performance under dynamic channel and network conditions. Specifically, we propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system that transforms an initial research topic into a complete research package. WARA organizes the research workflow into three phases: 1) research gap identification and problem proposal, 2) optimization modeling, algorithm design, and experimentation, and 3) research deliverable construction. Each phase follows an artifact-mediated process, in which structured upstream artifacts are consumed to generate downstream outputs. Controller-managed gates validate these artifacts and maintain consistency among problem formulations, algorithms, experiments, and research claims. When validation fails, WARA repairs only the affected artifact instead of restarting the entire workflow. We further design an LLM-based ScoringAgent to evaluate manuscript-level research validity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed papers. These results demonstrate the potential of closed-loop artifact control for end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ