跳到正文
返回论文列表
cs.AR提交于 已译

CORE: KV缓存的覆盖校准与淘汰质量重分配

CORE: COverage CAlibration and Evicted-Mass REdistribution for KV Cache

Shuxin Liu · Qing Liu · Yi Du · Ou Wu

中文摘要

长上下文解码日益受到键值(KV)缓存内存和带宽的约束。现有的固定预算压缩方法通常将保留与补偿分离,而保留排序既不能指定淘汰的注意力质量也不能指示诱导输出误差的方向。从一个精确分解出发:淘汰误差等于淘汰的注意力质量乘以淘汰质心与保留输出之间的方向差距,强调了集合级覆盖在保留和质量保持内存写入中的重要性。引入CORE——KV缓存的覆盖校准与淘汰质量重分配,它将结合查询效用和对数行列式覆盖的离线分配蒸馏成一个轻量级缓存感知索引器。推理时,一个校准分布驱动两个通道:其Top-B排序保留互补的KV状态,而其排除的分配质量和条件权重参数化潜在内存写入,无需单独的重预测器或在线对数行列式评估。分析提供了一个四 term 预补偿误差证书,描述了非加性覆盖交互,并通过递归和查询自适应归一化建立了与质量无关的写入稳定性。在三个骨干网络上,CORE在90%压缩率下比最强RULER基线高出3.78分;LongBench和重复淘汰评估进一步证明了其强大的有效性和解码效率。

关键要点

  1. 01问题:长上下文解码受KV缓存内存和带宽约束,现有方法未能考虑淘汰注意力质量和误差方向
  2. 02方法:提出淘汰误差的精确分解公式:淘汰误差=淘汰注意力质量×方向差距,揭示集合级覆盖重要性
  3. 03方法:CORE将离线分配的查询效用和对数行列式覆盖蒸馏成轻量级缓存索引器,推理时单分布驱动保留和写入
  4. 04结果:在三个骨干网络上,90%压缩率下比RULER基线最高提升3.78分,LongBench评估验证有效性
  5. 05局限:方法依赖校准分布的质量,对极端压缩率下的性能边界需进一步探索

解读

尚无解读。

原始英文摘要

arXiv:2610.02235v1 Announce Type: cross Abstract: Long-context decoding is increasingly constrained by key--value (KV) cache memory and bandwidth. Existing fixed-budget compression methods typically separate retention from compensation, while a retention ranking specifies neither discarded attention mass nor the direction of induced output error. We start from an exact factorization: eviction error equals evicted attention mass times the directional gap between the evicted centroid and retained output, highlighting the importance of set-level coverage in retention and mass-preserving memory writing. We introduce CORE COverage Calibration and Evicted-Mass REdistribution for KV Cache, which distills an offline allocation combining query utility and log-determinant coverage into a lightweight cache-aware indexer. At inference, one calibrated distribution drives both channels: its Top-$B$ ordering retains complementary KV states, while its excluded allocation mass and conditional weights parameterize latent-memory writes without a separate write-weight predictor or online log-determinant evaluation. Our analysis provides a four-term pre-compensation error certificate, characterizes non-additive coverage interactions, and establishes mass-independent write stability with hierarchical bounds through recurrent and query-adaptive normalization. Across three backbones, CORE exceeds the strongest RULER baseline by up to 3.78 points at 90\% compression; LongBench and repeated-eviction evaluations further demonstrate strong effectiveness and decoding efficiency.

同方向论文 · cs.AR

查看全部 →