HeadQ: Model-Visible Distortion and Score-Space Correction for KV-Cache Quantization
KV-cache quantizers usually optimize storage-space reconstruction, even though attention reads keys through logits and values through attention-weighted readout. We argue that persistent cache error should be measured in model-visible coordinates. For keys, the visible object is score error modulo constant shifts; this yields HeadQ, a key-side method that stores a low-rank residual side code in a calibration-learned query basis and applies it as an additive logit correction. For values, fixed-attention readout gives an $A^2$-weighted token-distortion surrogate. Across six models, Fisher/score-space error predicts attention KL far better than raw key MSE; same-budget counterexamples, null-...
| Authors | Jorge L. Ruiz Williams |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 19 May 2026 |
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