DropKV introduces a new KV‑cache eviction framework that decouples residual‑output perturbation into independent per‑token scores, avoiding the NP‑hard combinatorial optimization of prior methods. The approach offers a provable constant‑factor approximation under a cone condition verified on three long‑context LLMs, and a high‑performance Triton‑based implementation that yields a 19.8× speedup in scoring kernels and up to 9.9% end‑to‑end prefill speedup. Benchmarks on RULER and LongBench show DropKV consistently outperforms state‑of‑the‑art baselines at the same cache budgets, achieving the lowest inference latency among eviction strategies while remaining faster than dynamic cache baselines.
By Aozhong Zhang, Selcuk Gurses, Yanxia Deng, Naigang Wang, Chi-Chun Liu, Davis Wertheimer, Derrick Liu, Xin Li, Zi Yang, Felix X. -F. Ye, Penghang Yin
arXiv:2607. 27600v1 Announce Type: new Abstract: Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.
By Stephen Gould, Anton van den Hengel
The paper introduces EpiKV, an epiphany‑aware key–value cache eviction strategy that avoids using the attention matrix. It leverages hidden‑state shifts and recent query–key relevance to rank cached tokens, matching or surpassing the performance of existing attention‑based eviction methods while remaining compatible with fast inference kernels. Experiments on multiple benchmarks show that EpiKV improves inference throughput without sacrificing accuracy.
By Steven Kolawole, Virginia Smith
arXiv:2606. 17872v1 Announce Type: cross Abstract: Large language models (LLMs) outperform earlier architectures on generative inference and long-context tasks, but their large size introduces significant challenges in memory usage, energy cost, and on-device deployment.
By Ning Ni, Yingjie Lao
arXiv:2606. 29563v1 Announce Type: cross Abstract: Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs.
By Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi, Golnoosh Samei
Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiting sustainable deployment on resource-constrained hardware. Existing KV cache eviction methods typically apply heuristic token scoring over all heads in GQA-based LLMs.