Loading
Life sciences · Preprint
arXiv · October 26, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Preprint.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Attention over tens of thousands of visual tokens dominates the inference of video diffusion transformers (DiTs). Existing methods reduce this cost through sparsity, low precision, or both, but use a single precision for all kept tiles. We observe that the output error introduced by approximation varies widely across tiles. Many tiles can be skipped with little error, others must be computed but gain little beyond low precision, and a few retain substantial error even at low precision. We present Triage, a training-free attention method that assigns each tile one of three approximation tiers: skipping it by replacing it with its block means, skimming it in NVFP4, or refining it with NVFP4 residual correction. A lightweight router predicts each tile's error under each tier from block statistics and moves a tile to a more accurate tier only when the predicted error reduction justifies the extra compute, with a single price setting this trade-off. A fused kernel executes all three tiers in a single pass on the FP4 tensor cores of RTX Blackwell GPUs. Across Wan2.2, HunyuanVideo, and LTX-2.5, Triage accelerates end-to-end generation by up to 2.32x over dense FlashAttention, and in each of five settings it runs faster than every baseline while reaching 0.3-3.0 dB higher PSNR than the highest-PSNR baseline.