arXiv Machine Learning
Oct 12

CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches

CORAL-KV is a new framework for compressing the key‑value (KV) cache used by autoregressive video diffusion models. It predicts keys and values across cache layers using block‑diagonal‑plus‑low‑rank (block‑DPLR) predictors, then quantizes only the residuals, offering a flexible trade‑off between fidelity and memory usage. In extensive tests on HY‑WorldPlay and MAGI‑1, the reusable configuration outperforms QVG‑4 by up to 4.95 dB PSNR while reducing cache memory by 37–48 %, and the video‑adapted configuration surpasses QVG‑Pro by 3.77 dB PSNR with 33.7 % less memory, all while maintaining comparable latency and reducing temporal discontinuities.

By Albert Ge, Akash Haridas, Utkarsh Saxena, Ruijing Yang, Ian Colbert, Frederic Sala, Vikram Appia, Emad Barsoum
arXiv Machine Learning
Oct 15

OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond

arXiv:2605.19660v3 Announce Type: replace Abstract: The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a domina...

By Zunhai Su, Rui Yang, Chao Zhang, Yaxiu Liu, Yifan Zhang, Wei Wu, Jing Xiong, Dayou Du, Xialie Zhuang, Yulei Qian, Jianchao Tan, Yuchen Xie, Yik-Chung Wu, Hongxia Yang, Ngai Wong