WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents

Abstract: Large language model (LLM) agents are increasingly capable of external tool use, code generation and execution, intermediate artifact inspection, and iterative output revision, creating new opportunities for automating scientific and engineering 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 for automated wireless optimization research. Given only an initial research topic as input, WARA decomposes the research workflow into three phases: 1) research gap identification and problem proposal, 2) wireless optimization problem modeling, solution algorithm design, and experimentation, and 3) construction of research deliverables. For each phase, we design artifact-mediated control mechanisms, where declared upstream artifacts are consumed as inputs to produce structured outputs for downstream use. A controller-managed gate is then applied in each phase to validate the generated artifacts and ensure consistency among models, algorithms, experiments, and claims. When an artifact fails validation, only the corresponding artifact needs to be repaired, rather than restarting the entire workflow. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity, and the comparative evaluations show that WARA substantially outperforms one-shot LLM generation while approaching the quality profile of recently accepted peer-reviewed technical papers. These results demonstrate that closed-loop artifact control is a promising path toward end-to-end LLM-assisted wireless optimization research. The source code is available at https://github.com/guoyuan-dotcom/WARA_CUHKSZ.