用闭环智能体自动完成无线资源优化研究，从选题到成文全流程自动化。
WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents
- 构建多智能体系统，通过可执行成果驱动研究流程闭环。
- 在无线资源分配案例中自动生成可运行代码与技术论文，验证率达92%。
- 适合想快速探索无线优化方向的研究者或工程团队使用。
大型语言模型（LLM）代理正具备工具调用、代码执行、成果检查和迭代修正能力，为科学与工程研究自动化带来新可能。本文首次提出面向无线领域的端到端自动研究框架，聚焦无线资源分配优化。我们设计了无线自动研究代理（WARA），一个闭环的多智能体系统，仅需初始主题即可分解为三个阶段：研究空白识别与问题提出、优化建模与算法设计实验、研究成果构建。在整个流程中，WARA采用以成果为中心的控制机制：上游成果作为输入，结构化输出供下游使用，控制器管理的验证门限确保模型、算法、实验与结论的一致性。当验证失败时，仅修复相关成果而非重启全过程。通过典型无线资源分配案例展示，WARA能将初始主题转化为包含可执行证据与合成技术论文的完整研究包。我们进一步设计基于结构化LLM的ScoringAgent，评估论文级研究有效性与优化成熟度。对比结果表明，WARA显著优于一次性生成的LLM方案，接近近期被接受的同行评审论文质量。这些结果表明，闭环成果控制是实现端到端LLM辅助无线优化研究的可行路径。源码见 https://github.com/guoyuan-dotcom/WARA_CUHKSZ。
原文摘要 · Abstract (English)
Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative 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 focus on wireless resource allocation optimization. We propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system for automated wireless optimization research. Given only an initial topic, WARA decomposes the workflow into three phases: research gap identification and problem proposal, wireless optimization modeling, algorithm design and experimentation, and research deliverable construction. Across these phases, WARA uses artifact-mediated control: upstream artifacts are consumed as inputs, structured outputs are stored for downstream use, and controller-managed gates validate consistency among models, algorithms, experiments, and claims. When validation fails, WARA repairs only the responsible artifact instead of restarting the whole workflow. We present a representative wireless resource allocation case study showing how WARA converts an initial topic into a complete research package with executable evidence and a synthesized technical manuscript. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed technical papers. These results indicate 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.
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