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cs.LG2026
Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
Yuxuan Yin, Chen He, Todd Jacobs +4
Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detect…
cs.CV2026
Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
Boxun Xu, Yuming Du, Zichang Liu +7
We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality while reducing decoding laten…