The paper investigates how temporal aggregation of token scores during decoding-time KV cache compression affects eviction decisions. It shows that using an exponential‑moving‑average (EMA) aggregation keeps ranking changes minimal for many scorer variants, while others like KeyDiff or recency significantly alter rankings and degrade performance. Building on this, the authors propose InertiaKV and its lazy variant, which achieve 1.34–1.46× faster decoding than full refresh, and also evaluate a score‑free approach that slightly improves quality while eliminating further scoring.
NestedKV is a training‑free key‑only KV cache compression technique for long‑context language models that uses global, block‑level, and sliding‑window key anchors to score tokens via multi‑time‑scale cosine anomaly. It combines these rankings with a head‑adaptive outer learner and surprise‑gated token routing, requiring no model modification or additional training. Experiments on Qwen3 and Llama‑3.2 across benchmarks such as RULER, LongBench, and MMLU‑Pro show that NestedKV outperforms existing methods when the retained cache is small, achieving up to 19‑point gains on RULER and LongBench at a 75% retention rate.
By Hong Chen, Xiang Liu, Yubo Gao, Yuxuan Fan, Bo Wang, Yuanlin Chu, Yuanguo Lin, Xuming Hu
arXiv:2607. 15498v1 Announce Type: cross Abstract: The key-value (KV) cache is the main memory bottleneck in long-context large language model (LLM) inference.
By Shahrzad Esmat, Dhawal Shah, Ali Jannesari
arXiv:2609.30738v1 Announce Type: new
Abstract: KV cache eviction methods such as SnapKV and PyramidKV rank tokens solely by mean attention over a small observation window. We study a unified score,...
By Tianfang Xie, Wei Zhu
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
arXiv:2605.23258v2 Announce Type: replace
Abstract: KV cache growth is a major bottleneck for long-context inference in large language models. Existing methods are often dominated by binary eviction...
By Yuping Lin, Jiayuan Ding, Yue Xing, Pengfei He, Jiliang Tang, Subhabrata Mukherjee
The paper introduces Draft‑Guided Eviction (DGE), a training‑free KV‑cache compression technique that postpones token eviction until after drafting the first two answer tokens, thereby preserving the cache entries needed for the answer trajectory. Unlike prior methods that focus on optimizing what to keep, DGE changes the timing of eviction, allowing existing compression schemes such as SnapKV, H2O, and PyramidKV to be applied without altering their eviction scores. Experiments show DGE consistently outperforms previous approaches across multiple instruct‑tuned models, achieving near‑full‑cache performance on LongBench and demonstrating that the improvement stems from trajectory anchoring rather than changes in cache selection.
By Haeyong Kang, Chang D. Yoo
arXiv:2608.23843v1 Announce Type: new
Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compressio...
By Zizhong Wang, Jieying Wang, Zhao Zhang, Jiajia Li
The paper introduces iS-KV, an online low‑rank KV‑cache compression technique that uses block‑incremental SVD to manage memory during long‑horizon autoregressive decoding. Unlike token‑eviction methods, iS‑KV retains all positions in a compact representation by keeping a recent window exact and incrementally folding older states into bounded‑rank bases, synchronizing coordinates as the basis evolves. Experiments on DeepSeek‑R1‑Distill‑Llama‑8B and Qwen3‑8B show that iS‑KV achieves high accuracy (82.6% and 89.2% respectively) while providing 4.06‑fold and 5.64‑fold compression, outperforming token‑eviction baselines under matched memory budgets.
By Yiren Zhao, Guanghui Song, Tianrui Qin, Kejiang Ye, Cheng-zhong Xu, Xitong Gao
arXiv:2610.02953v1 Announce Type: new
Abstract: Long-context LLM serving is increasingly bottlenecked by KV-cache memory, especially in resource-constrained scenarios. Among existing KV-cache compres...
By Zihan Teng, Jiayu Zhao, Wentao Ren, Minhao Fan, Tianrui Ma, Song Chen, Weichen Liu
arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.
By Luca Moschella, Laura Manduchi, Ozan Sener
arXiv:2608. 05326v1 Announce Type: new Abstract: Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache.
By Ayushman Garg, Akshita Gupta, Shaswata Bhattacharya, Abhishek Gupta, Sandeep Kumar, Manoj Kumar