Radio maps describe how wireless signals propagate across space and are essential for wireless communication, sensing, and network planning. However, constructing accurate radio maps traditionally requires either dense measurements or computationally expensive physical simulations, which limits scalability and real-time deployment. Recent advances in generative artificial intelligence offer a promising alternative, but existing approaches lack fine-grained control and physical consistency when applied to real-world wireless environments. Here we present \textbf{ControlRadio}, a controllable generative framework that produces radio maps from natural-language descriptions and environmental layouts, including building structures and transmitter locations. Joint semantic and spatial conditioning enables interpretable, propagation-plausible generation, while a controlled latent prior and layout-aware conditioning improve stability and structural consistency. Extensive experiments demonstrate that ControlRadio achieves state-of-the-art accuracy and strong generalization across diverse urban scenarios, while reducing computation time by more than four orders of magnitude compared with conventional simulation-based methods. Such results suggest a new paradigm for scalable and controllable wireless environment modeling, with broad implications for next-generation communication systems and data-driven radio sensing.
Kangjun Liu, Xiying Pan, Shuhang Zhang et al.· 0 citations
Pre-trained 3D vision-language models have demonstrated strong semantic generalization and robustness to distribution shifts. However, the implications of semantic robustness for geometric stability remain unclear. This study revisits the adversarial robustness of 3D vision-language models when confronted with adversarial point clouds. Focusing on zero-shot classification, this study demonstrates that these models exhibit heightened sensitivity to small coordinate perturbations. The behavior of adversarial perturbations is further analyzed under widely used point-cloud preprocessing mechanisms, revealing that naive filtering or reconstruction mainly suppresses irregular perturbations produced by vanilla gradient-based attacks and provides limited protection against stronger attack methods. To this end, a refined adversarial objective is introduced with two complementary priors that encourage adversarial point clouds to remain smooth and geometrically plausible: a statistical prior that regularizes the sampling distribution, and a geometric prior that promotes consistency with a plausible object-surface manifold. These findings highlight the need for a more rigorous security evaluation of 3D vision-language models.
Xuanxiang Lin, Yan Huang, Longkun Zou et al.· IEEE Access· 0 citations
Local Margin Restoration (LMR) is proposed, a lightweight, one-step TTA framework that consistently outperforms state-of-the-art TTA baselines, proving exceptionally robust and efficient even in challenging low-batch test-time regimes.
Yan Huang, Guowei Wang, Xu Wang et al.· 0 citations