MC-Sparse separates three sources of quality and efficiency loss in sparse DiT attention. This review explains why token selection and kernel execution costs must be…
VC-Attention is a training-free kernel that removes value-quantization and softmax bottlenecks in low-bit video-DiT attention, speeding it up 1.59× on B200.
Programmable World Model — Embedding "Executable State" into Generative Worlds One-Line Summary (TL;DR) Video world models are producing increasingly realistic…
CoVeR: Coverage-Based Pruning That Cuts Visual Tokens of Multi-View 3D Reasoning by up to 92% Using Geometry Alone One-Line Summary (TL;DR) Rendering a 3D scene into…
Marigold V2: Reviving an Image-Editing Diffusion Transformer (DiT) as a Monocular Depth Estimator TL;DR This study fine-tunes an image-editing diffusion transformer…
ShallowStream: Index Shallowly, Answer Deeply — Unlocking the Computational Bottleneck of Streaming Video Understanding TL;DR — In streaming video understanding, the most…
![[Paper Review] Radial Attention: O(n log n) Sparse Attention with Energy Decay for Long Video Generation](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/radial-attention-onlog-n-sparse-attention-with-energy-decay-for-long-video-generation-2.png)
Radial Attention: O(n log n) Sparse Attention with Energy Decay — Generating "Long Videos" Cheaply One-Line Summary (TL;DR) Based on the observation that post-softmax…