Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, because the mapping is ill-posed in non-line-of-sight (NLOS) conditions and must generalize to unseen environments. Distortion-minimizing regressors return the conditional mean, which over-smooths the spectrum and erases the multipath structure that downstream tasks need. We cast the task as a perception-distortion problem and propose RadioDiff-v2, a dual-branch one-dimensional diffusion transformer trained with flow matching. It couples periodic angular encoding, adaptive layer-normalization conditioning, a Fourier angular mixer, and joint velocity and clean-signal heads. A per-metric estimator portfolio reads every deployment quantity from this single model, so that samples carry the distribution, the clean-signal head supplies a regression-grade point estimate, Bayes-optimal rules select beams, and the conditional likelihood localizes the receiver. We prove that a concentrated conditional yields a straight probability-flow trajectory that one step integrates exactly, identifying deterministic transport as the correct inductive bias. On a zero-shot test of 99 environments and one million links, RadioDiff-v2 leads every baseline on every metric, with a 0.39 dB Wasserstein-1 distance, per-bin error below the regression baseline, a 2.43 dB eight-beam NLOS sweep loss, and a 20.6-pixel localization error with four base stations. Code is available at https://github.com/UNIC-Lab/RadioDiff-v2.
RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信工程国家重点实验室和西安电子科技大学电信工程学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
Xidian University（西安电子科技大学）；School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；University of Waterloo（滑铁卢大学）
RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction
State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University（信息与通信国家重点实验室和西安电子科技大学通信工程学院）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Science and Engineering (SSE), Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen), and Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen)（科学与工程学院、深圳未来网络智能研究院、广东省未来网络智能重点实验室，香港中文大学（深圳））；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
Structure-prior Informed Diffusion Model for Graph Source Localization with Limited Data
Shenzhen Key Laboratory of Ubiquitous Data Enabling, Shenzhen International Graduate School Tsinghua University Shenzhen China（深圳 ubiquitous 数据赋能关键实验室，深圳国际研究生院，清华大学，深圳，中国）；Department of Electronic Engineering Tsinghua University Beijing China（电子工程系，清华大学，北京，中国）；Pengcheng Laboratory Shenzhen China（鹏城实验室，深圳，中国）；Pengcheng Laboratory（鹏城实验室）；Tsinghua University（清华大学）
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Guangdong University of Technology（广东工业大学）
Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Hubei Luojia Laboratory, Wuhan University（湖北珞珈实验室）；Chinese Antarctic Center of Surveying and Mapping, Wuhan University（中国极地测绘南极科考中心）
FakeLocator: Robust Localization of GAN-Based Face Manipulations
East China Normal University（华东师范大学）；Shanghai Industrial Control Safety Innovation Technology Co., LTD（上海工业控制安全创新科技有限公司）；Alibaba Group（阿里巴巴集团）；Nanyang Technological University（南洋理工大学）；Zhejiang Sci-Tech University（浙江理工大学）
