[Submitted on 7 Oct 2026]
Title:CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches
View a PDF of the paper titled CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches, by Albert Ge and 7 other authors
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Abstract:Autoregressive video diffusion models maintain a growing attention key-value (KV) cache, making memory a key bottleneck for long video generation. Recently, clustering-based quantization has emerged as an effective method to compress video KV caches by grouping similar key and value vectors then quantizing residual errors. However, when later frames attend to the compressed history, small reconstruction errors can compound as the video grows. Beyond the within-layer similarity exploited by clustering, we find strong dependencies across cache layers in video models. This motivates Coral-KV - Calibrated Offline, Residual-coded, and Adapted Locally - a KV-cache compression framework that uses cache states in each layer to predict keys and values in subsequent layers, then quantizes the remaining prediction residuals. We construct compact block-diagonal-plus-low-rank (block-DPLR) predictors: block-diagonal components model channel relationships within each attention head, while a global low-rank term models dependencies between heads. Coral-KV exposes a flexible fidelity-memory trade-off: it can reuse its predictors across videos or refit lightweight block-diagonal predictors to the current video for higher fidelity. Across extensive evaluations on both HY-WorldPlay and MAGI-1, our reusable configuration improves PSNR over QVG-4 by 0.32 dB and 4.95 dB, respectively, while using 48% and 37% less cache memory. Against QVG-Pro, our video-adapted configuration improves MAGI-1 PSNR by 3.77 dB while using 33.7% less cache memory. Coral-KV also maintains higher reconstruction fidelity over extended generation and reduces temporal discontinuities while retaining comparable generation latency.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.12514 [cs.CV]
(or arXiv:2610.12514v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.12514
arXiv-issued DOI via DataCite
Submission history
From: Albert Ge [view email] [v1] Wed, 7 Oct 2026 18:20:42 UTC (20,031 KB)
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