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    <abstract>Decoding-time KV cache compression research focuses heavily on designing better token scoring functions, while the temporal rule that aggregates scores across decode steps is often treated as an implementation detail. Under aggressive KV compression, we find that exponential-moving-average (EMA) aggregation makes approximately order-preserving scorer modifications largely indistinguishable at the eviction-set level. Value-norm and entropy variants remain highly correlated with attention and produce nearly unchanged retention sets, whereas KeyDiff, key norm, recency, and a learned scorer alter the ranking and degrade substantially. We associate this stability with the evaluated aggregation, which couples layer weighting and temporal retention. Building on this observation, we introduce InertiaKV, an EMA-based decoding-time eviction method, and InertiaKV-Lazy, its periodic-refresh variant, which yields 1.34–1.46× decode throughput relative to full refresh InertiaKV. We also study Score-Free decoding as a separate empirical operating point: it scores the full context once at the first decode step, freezes that ranking, and incurs an average quality change of +0.03 while removing all subsequent scoring. Across six open-weight backbones and the LongBench, LongBench-v2, and RULER benchmarks, the results identify temporal aggregation and ranking preservation as distinct, consequential design factors; they do not imply that scoring quality is irrelevant in general.</abstract>
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%0 Conference Proceedings
%T What Matters for Aggressive Decoding-Time KV Eviction? Temporal Aggregation and Ranking Preservation
%A Zeng, Bo
%A Zhao, Yu
%A Liu, Yefeng
%A Lu, Zhihong
%A Ni, Xuanfan
%A Wang, Xintong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zeng-etal-2026-matters
%X Decoding-time KV cache compression research focuses heavily on designing better token scoring functions, while the temporal rule that aggregates scores across decode steps is often treated as an implementation detail. Under aggressive KV compression, we find that exponential-moving-average (EMA) aggregation makes approximately order-preserving scorer modifications largely indistinguishable at the eviction-set level. Value-norm and entropy variants remain highly correlated with attention and produce nearly unchanged retention sets, whereas KeyDiff, key norm, recency, and a learned scorer alter the ranking and degrade substantially. We associate this stability with the evaluated aggregation, which couples layer weighting and temporal retention. Building on this observation, we introduce InertiaKV, an EMA-based decoding-time eviction method, and InertiaKV-Lazy, its periodic-refresh variant, which yields 1.34–1.46× decode throughput relative to full refresh InertiaKV. We also study Score-Free decoding as a separate empirical operating point: it scores the full context once at the first decode step, freezes that ranking, and incurs an average quality change of +0.03 while removing all subsequent scoring. Across six open-weight backbones and the LongBench, LongBench-v2, and RULER benchmarks, the results identify temporal aggregation and ranking preservation as distinct, consequential design factors; they do not imply that scoring quality is irrelevant in general.
%U https://aclanthology.org/2026.emnlp-main.2225/
%P 50648-50665

Markdown (Informal)

[What Matters for Aggressive Decoding-Time KV Eviction? Temporal Aggregation and Ranking Preservation](https://aclanthology.org/2026.emnlp-main.2225/) (Zeng et al., EMNLP 2026)

• What Matters for Aggressive Decoding-Time KV Eviction? Temporal Aggregation and Ranking Preservation (Zeng et al., EMNLP 2026)

ACL

• Bo Zeng, Yu Zhao, Yefeng Liu, Zhihong Lu, Xuanfan Ni, and Xintong Wang. 2026. What Matters for Aggressive Decoding-Time KV Eviction? Temporal Aggregation and Ranking Preservation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50648–50665, Budapest, Hungary. Association for Computational Linguistics.