@inproceedings{liu-etal-2026-thought,
title = "Thought-Aware {KV} Cache Compaction for Reasoning via Adaptive Attention Matching",
author = "Liu, Yang and
Chong, Bin and
Zhang, Chongyang and
Zheng, Hao and
Liang, Jiayu and
Kefu, Xu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1605/",
pages = "34181--34195",
ISBN = "979-8-89176-462-0",
abstract = "Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck during decoding. Existing compaction methods treat reasoning trajectories as flat token sequences and apply uniform compression, ignoring the hierarchical structure of CoT reasoning where different steps vary drastically in importance. We propose \textbf{Thought-Aware Attention Matching (TAM)}, which exploits this structure through three mechanisms: (i)thought segmentation that decomposes the trajectory into reasoning blocks, (ii)adaptive budget allocation that assigns compression budget based on each segment{'}s importance and size, and (iii)pivotal token protection that preserves high-attention reasoning anchors. We prove that the allocation rule is optimal under a convex error model and that cumulative error under sequential compaction remains bounded. Experiments on AIME 2024 and MATH-500 with Qwen3-4B show that TAM improves accuracy over uniform compaction at the same memory footprint, with periodic compaction bounding peak memory to 3.1{--}3.2,GB (a 65{\%} reduction) while maintaining competitive accuracy."
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<abstract>Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck during decoding. Existing compaction methods treat reasoning trajectories as flat token sequences and apply uniform compression, ignoring the hierarchical structure of CoT reasoning where different steps vary drastically in importance. We propose Thought-Aware Attention Matching (TAM), which exploits this structure through three mechanisms: (i)thought segmentation that decomposes the trajectory into reasoning blocks, (ii)adaptive budget allocation that assigns compression budget based on each segment’s importance and size, and (iii)pivotal token protection that preserves high-attention reasoning anchors. We prove that the allocation rule is optimal under a convex error model and that cumulative error under sequential compaction remains bounded. Experiments on AIME 2024 and MATH-500 with Qwen3-4B show that TAM improves accuracy over uniform compaction at the same memory footprint, with periodic compaction bounding peak memory to 3.1–3.2,GB (a 65% reduction) while maintaining competitive accuracy.</abstract>
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%0 Conference Proceedings
%T Thought-Aware KV Cache Compaction for Reasoning via Adaptive Attention Matching
%A Liu, Yang
%A Chong, Bin
%A Zhang, Chongyang
%A Zheng, Hao
%A Liang, Jiayu
%A Kefu, Xu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-thought
%X Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck during decoding. Existing compaction methods treat reasoning trajectories as flat token sequences and apply uniform compression, ignoring the hierarchical structure of CoT reasoning where different steps vary drastically in importance. We propose Thought-Aware Attention Matching (TAM), which exploits this structure through three mechanisms: (i)thought segmentation that decomposes the trajectory into reasoning blocks, (ii)adaptive budget allocation that assigns compression budget based on each segment’s importance and size, and (iii)pivotal token protection that preserves high-attention reasoning anchors. We prove that the allocation rule is optimal under a convex error model and that cumulative error under sequential compaction remains bounded. Experiments on AIME 2024 and MATH-500 with Qwen3-4B show that TAM improves accuracy over uniform compaction at the same memory footprint, with periodic compaction bounding peak memory to 3.1–3.2,GB (a 65% reduction) while maintaining competitive accuracy.
%U https://aclanthology.org/2026.findings-emnlp.1605/
%P 34181-34195
Markdown (Informal)
[Thought-Aware KV Cache Compaction for Reasoning via Adaptive Attention Matching](https://aclanthology.org/2026.findings-emnlp.1605/) (Liu et al., Findings 2026)
ACL