Intelligent network operation and maintenance systems in modern networks continuously generate large volumes of multi-modal operational data. However, Wi-Fi fault diagnosis under heterogeneous operational environments remains insufficiently understood. We build a real-world Wi-Fi testbed deployed in campus working environments with an automated fault injection system, and collect a multi-modal Wi-Fi fault dataset containing over 10,000 fault samples across diverse wireless scenarios. To the best of our knowledge, this is among the first publicly available datasets jointly capturing heterogeneous cross-layer operational observations for Wi-Fi fault diagnosis. Based on this dataset, we establish a unified benchmark spanning multiple diagnosis tasks, operational modalities, and representative diagnosis paradigms. Experimental results indicate that effectively leveraging heterogeneous operational data remains challenging for existing diagnosis approaches. We further evaluate emerging LLM-based approaches and develop a reasoningoriented evaluation framework to assess the consistency between generated diagnostic analyses and actual network conditions. Our findings suggest several important considerations for future multi-modal Wi-Fi diagnosis.

翻译：现代网络中的智能运维系统持续生成大规模多模态运维数据。然而，异质运行环境下的Wi-Fi故障诊断仍缺乏充分理解。我们在校园工作场景中部署了实际Wi-Fi测试平台，配备自动化故障注入系统，并收集了一个包含超过10,000个故障样本的多模态Wi-Fi故障数据集，覆盖多样化的无线场景。据我们所知，这是首批面向Wi-Fi故障诊断的公开数据集之一，联合捕获了异质跨层运行观测数据。基于该数据集，我们构建了一个统一基准，涵盖多个诊断任务、运行模态和代表性诊断范式。实验结果表明，现有诊断方法在有效利用异质运维数据方面仍面临挑战。我们进一步评估了新兴的基于大语言模型的方法，并开发了一个面向推理的评估框架，以衡量生成的诊断分析与实际网络条件之间的一致性。我们的发现为未来的多模态Wi-Fi诊断提供了若干重要考量。