发表机构
The Chinese University of Hong Kong (Shenzhen); National University of Singapore; Sun Yat-Sen University; Southeast University（香港中文大学（深圳）; 新加坡国立大学; 中山大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于LLM智能体的可信赖无线自动研究框架，通过类型化契约、版本控制与验证器保障流程，案例表明其能生成创新思路并产出高质量手稿。
AI 中文摘要
大语言模型（LLM）智能体赋能的自动研究（AutoResearch）正在各科学学科中引起越来越多的关注。在该范式中，LLM被用于知识综合、多步规划、代码生成、工具调用和迭代优化，从而自动化研究生命周期，从假设生成、实验到分析和手稿准备。无线通信特别适合这种范式，这是因为该领域的进展往往依赖于基础极限分析、系统优化和协议设计，所有这些都得到了成熟的数学、仿真和优化工具链以及标准、测量和数字孪生平台的支持。然而，将自主智能体整合到严谨的无线研究流程中需要可追溯的过程和可验证的证据。本文提出了一种通用的可信赖无线自动研究框架。该框架采用类型化研究契约、版本控制工件、独立验证器和受限智能体权限，将假设生成与系统建模、数学公式化、算法设计、代码生成、仿真、可复现声明和手稿准备连接起来。我们针对无人机（UAV）的集成感知与通信（ISAC）进行了案例研究。结果表明，所提出的框架能够生成创新的研究思路，并且在适当的专家干预下，能够产出一份评估分数与相关IEEE会议和期刊论文相当或更高的手稿。这些发现证明了LLM智能体在编排权威科学工具和版本化研究工件方面的有效性，同时强调了人机协作的重要性。
英文摘要
Large language model (LLM) agent-enabled AutoResearch is attracting growing interest across scientific disciplines, in which LLMs are leveraged for knowledge synthesis, multistep planning, code generation, tool invocation, and iterative refinement, thus automating the research lifecycle, from hypothesis generation and experimentation to analysis and manuscript preparation. Wireless communications is particularly suitable to this paradigm. This is due to the fact that advances in this field often rely on fundamental-limit analysis, system optimization, and protocol design, all supported by mature mathematical, simulation, and optimization toolchains, as well as standards, measurement, and digital-twin platforms. However, integrating autonomous agents into rigorous wireless research workflows requires traceable processes and verifiable evidence. This article presents a general trustworthy wireless AutoResearch framework. This framework employs typed research contracts, version-controlled artifacts, independent validators, and bounded agent authority to connect hypothesis generation with system modeling, mathematical formu lation, algorithm design, code generation, simulation, reproducible claims, and manuscript preparation. We conduct a case study on integrated sensing and communication (ISAC) for unmanned aerial vehicles (UAVs). It is shown that the proposed framework can generate innovative research ideas and, with appropriate expert intervention, produce a manuscript whose evaluation score is comparable to or higher than those of related IEEE conference and letter papers. These findings demonstrate the effectiveness of LLM agents in orchestrating authoritative scientific tools and versioned research artifacts, while underscoring the importance of human-AI collaboration.
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