[Submitted on 11 Jul 2026]
Title:How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit
View a PDF of the paper titled How Query Visibility Changes KV-Cache Compression Rankings: A Matched-Budget Audit, by Daming Luo and 2 other authors
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Abstract:KV-cache compression methods are predominantly evaluated with the query appended to the context before compression -- a query-aware protocol. Yet the economic case for a compressed KV cache is reuse: compress a document once, answer many future questions against it. In that deployment, compression must happen query-agnostic -- before any question is seen. We present a matched-budget audit of six published compression methods against three trivial baselines on three open 7-9B models (144,300 paired evaluations on RULER-8192; 40,800 on LongBench; 50,000-resample paired bootstrap throughout). Everything is held fixed -- model, compression ratio, instances, decoding -- except the scoring rule. Three findings. (1) Query visibility changes the rankings: under the agnostic protocol, of the five audited methods that share a common attention backend, only KeyDiff beats a best-of-3 trivial baseline consistently (31 of 36 cells), and the most widely deployed method, SnapKV, loses to "keep the start and the recent window" on average (-0.066). (2) The per-method drop between the two protocols is ordered consistently with how visible the question is to each method's scoring signal, legible in its source code: from Delta=+0.198 for SnapKV (the question sits inside its 64-token observation window) down to Delta=+0.011 for KeyDiff (its score contains no query term at all).
Comments: 12 pages, 5 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11942 [cs.LG]
(or arXiv:2607.11942v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.11942
arXiv-issued DOI via DataCite
Submission history
From: Daming Luo [view email] [v1] Sat, 11 Jul 2026 09:30:35 UTC (63 KB)
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