KVSculpt: KV Cache Compression as Distillation
KV cache compression is critical for efficient long-context LLM inference. Approaches that reduce the per-pair footprint -- quantization and low-rank decomposition -- are orthogonal to those that reduce the sequence length of the cache. Along the sequence-length dimension, existing methods range from pure eviction -- selecting which KV pairs to keep -- to merging, which combines similar pairs into fewer ones. Both remain anchored to the original cache entries. We propose KVSculpt, which moves to the other end of this spectrum: instead of selecting or combining original pairs, we optimize a smaller set of unconstrained KV pairs in continuous embedding space to preserve each layer's attenti...
| Authors | Bo Jiang, Sian Jin |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 29 March 2026 |
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