While initial applications of artificial intelligence (AI) in wireless communications over the past decade have demonstrated considerable potential using specialized models for targeted communication tasks, the revolutionary demands of sixth-generation (6G) networks for holographic communications, ubiquitous sensing, and native intelligence are propelling a necessary evolution towards AI-native wireless networks. The arrival of large AI models paves the way for the next phase of Wireless AI, driven by wireless foundation models (WFMs). In particular, pre-training on universal electromagnetic (EM) principles equips WFMs with the essential adaptability for a multitude of demanding 6G applications. However, existing large AI models face critical limitations, including pre-training strategies disconnected from EM-compliant constraints leading to physically inconsistent predictions, a lack of embedded understanding of wave propagation physics, and the inaccessibility of massive labeled datasets for comprehensive EM-aware training. To address these challenges, this article presents an electromagnetic information theory-guided self-supervised pre-training (EIT-SPT) framework designed to systematically inject EM physics into WFMs. The EIT-SPT framework aims to infuse WFMs with intrinsic EM knowledge, thereby enhancing their physical consistency, generalization capabilities across varied EM landscapes, and overall data efficiency. Building upon the proposed EIT-SPT framework, this article first elaborates on diverse potential applications in 6G scenarios of WFMs, then validates the efficacy of the proposed framework through illustrative case studies, and finally summarizes critical open research challenges and future directions for WFMs.

翻译：过去十年间，人工智能在无线通信中的初步应用已展现出显著潜力，但其基于针对特定通信任务的专用模型。第六代（6G）网络对全息通信、泛在感知及原生智能的革命性需求，正推动无线网络向AI原生的必然演进。大规模AI模型的出现为无线基础模型引领的无线智能下一阶段铺平了道路，尤其通过对通用电磁原理的预训练，无线基础模型获得了应对诸多严苛6G应用场景所需的关键适应性。然而，现有大规模AI模型面临根本性局限：与电磁合规约束脱节的预训练策略导致物理不一致的预测、缺乏对波传播物理机制的嵌入式理解，以及无法获取海量标注数据集以实现全面电磁感知训练。针对上述挑战，本文提出电磁信息论引导的自监督预训练框架，旨在系统性地将电磁物理定律注入无线基础模型。该框架通过赋予无线基础模型内在电磁知识，增强其物理一致性、跨变电磁环境的泛化能力及整体数据效率。基于所提出的电磁信息论引导的自监督预训练框架，本文首先阐释无线基础模型在6G场景中的多元潜在应用，继而通过典型案例研究验证框架有效性，最终总结无线基础模型的关键开放性研究挑战与未来发展方向。