AI 中文总结
本文针对无线领域提出端到端自动研究框架WARA，通过闭环多智能体系统将研究工作流程分三个阶段，经工件介导过程和控制器管理验证工件，设计评分智能体评估有效性，其性能优于一次性大语言模型生成，接近同行评审论文质量。
AI 中文摘要
大语言模型智能体在工具使用、代码执行、工件检查和迭代修订方面能力不断增强，为科研自动化带来新机遇。本文提出首个针对无线领域的端到端自动研究框架WARA，聚焦无线资源分配优化。它将研究工作流程分为三个阶段，各阶段通过工件介导过程，由控制器管理的门验证工件并保持一致性，验证失败时仅修复受影响工件。还设计了基于大语言模型的评分智能体评估研究有效性。对比结果显示WARA性能优于一次性大语言模型生成，接近同行评审论文质量，证明了闭环工件控制在端到端大语言模型辅助无线优化研究中的潜力。
英文摘要
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
Comments2026 IEEE/CIC International Conference on Communications in China (ICCC)
