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    <abstract>Query-conditioned sparse KV selection reduces long-context inference cost by attending to a subset of historical KV entries at each decoding step. Existing selectors must wait for the target-layer query before scoring candidates and generating indices, placing routing on the immediate pre-attention path. We introduce EarlySelectKV, a training-free transformation that uses proxy queries from earlier hidden states to precompute selected KV indices while retaining the real target-layer query for final attention. Tests with RocketKV, Quest, and Loki show no consistent pattern of quality degradation across the evaluated settings. In the primary RocketKV evaluation, ES-RocketKV differs by +0.018 points on LongBench, approximately \ensuremath-0.01 points on RULER, and +0.25 percentage points on representative NIAH/Passkey settings. In a TensorRT-LLM deployment with CPU-offloaded routing, ES-RocketKV improves end-to-end output throughput by up to 12.4% and reduces GPU-resident memory by 11.1% of the combined KV/K-summary pool. This makes routing timing an explicit optimization target alongside KV subset selection in sparse long-context decoding. Code is available at https://github.com/sea-with-sakura/EarlySelectKV.</abstract>
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%0 Conference Proceedings
%T EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies
%A Liu, Yuhao
%A Li, Yuehao
%A Zhang, Linghe
%A Huang, Gaobin
%A Li, Junran
%A Wang, Hongchang
%A Jian, Haifang
%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 liu-etal-2026-earlyselectkv
%X Query-conditioned sparse KV selection reduces long-context inference cost by attending to a subset of historical KV entries at each decoding step. Existing selectors must wait for the target-layer query before scoring candidates and generating indices, placing routing on the immediate pre-attention path. We introduce EarlySelectKV, a training-free transformation that uses proxy queries from earlier hidden states to precompute selected KV indices while retaining the real target-layer query for final attention. Tests with RocketKV, Quest, and Loki show no consistent pattern of quality degradation across the evaluated settings. In the primary RocketKV evaluation, ES-RocketKV differs by +0.018 points on LongBench, approximately \ensuremath-0.01 points on RULER, and +0.25 percentage points on representative NIAH/Passkey settings. In a TensorRT-LLM deployment with CPU-offloaded routing, ES-RocketKV improves end-to-end output throughput by up to 12.4% and reduces GPU-resident memory by 11.1% of the combined KV/K-summary pool. This makes routing timing an explicit optimization target alongside KV subset selection in sparse long-context decoding. Code is available at https://github.com/sea-with-sakura/EarlySelectKV.
%U https://aclanthology.org/2026.emnlp-main.229/
%P 5272-5291

Markdown (Informal)

[EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies](https://aclanthology.org/2026.emnlp-main.229/) (Liu et al., EMNLP 2026)

• EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies (Liu et al., EMNLP 2026)

ACL

• Yuhao Liu, Yuehao Li, Linghe Zhang, Gaobin Huang, Junran Li, Hongchang Wang, and Haifang Jian. 2026. EarlySelectKV: Anticipatory KV Selection via Cross-Layer Query Proxies. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5272–5291, Budapest, Hungary. Association for Computational Linguistics.