On the Limits of Learned Importance Scoring for KV Cache Compression
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915742295785472 |
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| author | Steele, Brady |
| author_facet | Steele, Brady |
| contents | We investigate learned KV cache compression through Speculative Importance Prediction (SIP), a 1.7M parameter non-query-aware scorer that predicts token importance from KV representations alone. Despite architectural sophistication (multi-horizon lookahead, cross-attention), SIP does not outperform simple baselines, including random selection, across 5 seeds, 4 retention levels, and 3 tasks. Key findings: (1) position-based heuristics (keep first 4 + last N tokens) match or exceed learned approaches; (2) prefill attention provides equivalent signal to complex learned scorers; (3) marginal information in KV representations beyond position and prefill attention appears limited for importance prediction. We hypothesize that circular dependence between future queries and generation trajectories contributes to this difficulty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the Limits of Learned Importance Scoring for KV Cache Compression Steele, Brady Machine Learning Artificial Intelligence I.2.6; I.2.7; I.2.8 We investigate learned KV cache compression through Speculative Importance Prediction (SIP), a 1.7M parameter non-query-aware scorer that predicts token importance from KV representations alone. Despite architectural sophistication (multi-horizon lookahead, cross-attention), SIP does not outperform simple baselines, including random selection, across 5 seeds, 4 retention levels, and 3 tasks. Key findings: (1) position-based heuristics (keep first 4 + last N tokens) match or exceed learned approaches; (2) prefill attention provides equivalent signal to complex learned scorers; (3) marginal information in KV representations beyond position and prefill attention appears limited for importance prediction. We hypothesize that circular dependence between future queries and generation trajectories contributes to this difficulty. |
| title | On the Limits of Learned Importance Scoring for KV Cache Compression |
| topic | Machine Learning Artificial Intelligence I.2.6; I.2.7; I.2.8 |
| url | https://arxiv.org/abs/2601.14279 |