Researchers have published a comprehensive survey of wireless foundation models (WFMs) for AI-native sixth-generation (6G) networks. WFMs learn generalized representations from large-scale heterogeneous wireless data and can be adapted to various communication tasks with minimal supervision. The authors introduce a taxonomy to organize the field, review representative architectures and pre-training strategies, and highlight emerging applications such as physical-layer signal processing and network intelligence. They also discuss key challenges and future research directions for scalable, trustworthy, and general-purpose wireless intelligence. --- Why it matters: This survey matters because it provides a unified reference for researchers and practitioners developing next-generation intelligent wireless systems, which is crucial for the development of AI-native 6G networks. Source: https://arxiv.org/abs/2608.14694