arXiv:2609.36760v1 Announce Type: new
Abstract: Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memor...
By Zunhai Su, Yuxuan Sun, Jianchao Tan, Tao Zhang, Ruihan Hu, Yuchen Xie, Xunliang Cai, Ngai Wong
arXiv:2608. 02691v1 Announce Type: cross Abstract: The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quantization increasingly important.
By Vincent-Daniel Yun, Woosang Lim, Minsoo Cheong, Sunwoo Lee, Murali Annavaram, Sai Praneeth Karimireddy, Sungjoo Yoo
RoLaQ is an offline technique that jointly calibrates rotation and quantization range for 2‑bit key‑value caches in multi‑head latent attention models. By refining a shared rotation within subspaces defined by frozen key and value projections and learning coupled quantization thresholds and reconstruction gains, RoLaQ improves prediction matching and attention‑output supervision while keeping model weights frozen. Experiments on three MLA models and six benchmarks show that RoLaQ outperforms other 2‑bit methods, achieving higher average accuracy and, in a DeepSeek‑V2‑Lite implementation, nearly four times the BF16 KV capacity and more than double the decode throughput for 256K input tokens.
By Shiyu Ji, Peng Ling, Chen Lin, Shupeng Li, Yixuan Wang, Qingfu Zhu, Wanxiang Che
arXiv:2610.06927v1 Announce Type: cross
Abstract: The key-value (KV) cache of autoregressive transformers grows linearly with context length and dominates memory at long context. Most training-free r...
By Sara Abdali, Jongwoo Ko, Pashmina Cameron
The paper introduces JoLT, a training‑free compressor that jointly allocates rank and precision for key‑value (KV) cache compression in long‑context language models. JoLT treats grouped prefill caches as fourth‑order tensors, applies partial Tucker decomposition along token and feature modes, and uses a rotated low‑bit quantizer for residuals, all governed by a single Lagrangian dual under a global byte constraint. Across five models from four architecture families, JoLT achieves 2–3× compression with less than 0.2% perplexity loss, and near‑lossless retrieval accuracy on LLaMA‑3.1‑8B at 64K context up to 3× compression.
By Rahul Krishnan, Volker Schulz
arXiv:2606. 31519v1 Announce Type: new Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased.
By Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du