The risk of KV cache compression
Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compression, which replaces the full cache with a compact summary. Despite its practical importance, the design of such summaries is largely driven by empirical experimentation. On the theoretical side, existing results show that KV cache compression can be impossible in the worst case, but offer little systematic guidance for designing algorithms in regimes where accurate compression is possible. We bridge this gap by characterizing the minimax risk of KV cache compression in terms of the intrinsic compressibility of ...
| Authors | Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno et al. |
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| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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