KV$^2$: A Self-Refining KV Cache
The memory footprint of the key-value (KV) cache constrains the practical use of long-context models, and it dominates cost when one prefilled context must later serve many different queries. In this reusable setting, query-agnostic compression trades cost against quality: lightweight estimators are cheap but less accurate, whereas full-context reconstruction scoring is more accurate yet reprocesses the entire prompt. We introduce KV$^2$, a query-agnostic KV-cache compression method based on selective reconstruction. KV$^2$ first uses a lightweight proxy scorer to identify informative in-context tokens, then reprocesses only this subset to compute final eviction scores. On RULER, Needle-i...
| Authors | Johannes Wesch, Danni Liu, Jan Niehues |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 15 October 2026 |
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