@inproceedings{ni-etal-2026-refreekv,
    title = "{R}e{F}ree{KV}: Towards Threshold-Free {KV} Cache Compression",
    author = "Ni, Xuanfan  and
      Xu, Liyan  and
      Lyu, Chenyang  and
      Wang, Longyue  and
      Yu, Mo  and
      Liu, Lemao  and
      Meng, Fandong  and
      Zhou, Jie  and
      Li, Piji",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1541/",
    pages = "30827--30841",
    ISBN = "979-8-89176-395-1",
    abstract = "To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning.While these techniques can accomplish lossless memory reduction on many datasets, they often hinge on an under-emphasized condition: an input/domain-specific threshold for KV cache budget needs to be pre-determined to achieve the optimal performance.However, such input-sensitive design may be considerably limited in real-world scenarios, as open-domain inputs span diverse domains, lengths and difficulty levels, without clear boundaries for threshold selection.As a result, the dependence of such input-sensitive threshold can be a fundamental limitation that causes large degradation on arbitrary inputs.In this work, we propose a new objective that lifts the threshold constraints for robust KV compression, advocating for ``threshold-free'' methods that adaptively adjust budget allocation while preserving full-cache performance.We then propose a novel method, ReFreeKV, serving as the first instantiation of this objective. Extensive experiments across 13 datasets with diverse context lengths, task types, and model sizes demonstrate its efficacy and efficiency. Our code is publicly released at https://github.com/Patrick-Ni/ReFreeKV."
}