@inproceedings{chen-etal-2026-contrastkv,
    title = "{C}ontrast{KV}: Robust {KV} Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization",
    author = "Chen, Xingchi  and
      Zong, Peiyuan  and
      Gao, Ziqiang  and
      Li, Qing  and
      Jiang, Yong  and
      Zhu, Fa  and
      Li, Hui",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.417/",
    pages = "9216--9229",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) face significant memory and latency overheads during long-context inference due to the growing KV cache, especially in Knowledge Base Question Answering (KBQA) settings that require support for multiple downstream queries. Query-aware eviction methods do not generalize across queries, while existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high eviction ratios. We propose ContrastKV, a robust query-agnostic KV cache eviction algorithm for multi-query generalization. ContrastKV introduces a contrastive signal fusion mechanism that jointly exploits complementary semantic and non-semantic signals. By contrasting semantic consistency with structural robustness, the method constructs a more reliable eviction criterion that alleviates the blind spots of single-query proxies. The framework integrates efficient signal generation, parallel importance scoring, and multi-level fusion across heads and layers. Experiments show that ContrastKV outperforms state-of-the-art methods, retaining up to 92{\%} accuracy with only 20{\%} of the KV cache budget, while reducing decoding latency by approximately 50{\%} and significantly lowering GPU memory usage."
}