@inproceedings{chen-etal-2026-last,
title = "Last But Not Least: Boundary Attention {C}alibrati{ON} for Multimodal {KV} Cache Compression",
author = "Chen, Tianhao and
Wu, Yuheng and
Yao, Kelu and
Xu, Xiaogang and
Hu, Xiaobin and
Lee, Dongman",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.331/",
pages = "7619--7639",
ISBN = "979-8-89176-459-0",
abstract = "Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with long visual contexts. Existing compression methods rely on observation window attention for stable token importance estimation, yet this aggregation can dilute sparse critical evidence and discard answer-relevant tokens under aggressive compression. We identify last query attention as a complementary signal for recovering such evidence, though its irrelevant signals may introduce additional noise. We propose BACON, a plug-and-play method that calibrates observation window attention with last query evidence while suppressing noise through intra-layer coherence and inter-layer persistence. Across diverse benchmarks, models, budgets, and compression methods, BACON improves multimodal KV-cache compression by 7.5{\%} on average under the most aggressive budget, with gains up to 30.9{\%}."
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%0 Conference Proceedings
%T Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression
%A Chen, Tianhao
%A Wu, Yuheng
%A Yao, Kelu
%A Xu, Xiaogang
%A Hu, Xiaobin
%A Lee, Dongman
%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 chen-etal-2026-last
%X Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with long visual contexts. Existing compression methods rely on observation window attention for stable token importance estimation, yet this aggregation can dilute sparse critical evidence and discard answer-relevant tokens under aggressive compression. We identify last query attention as a complementary signal for recovering such evidence, though its irrelevant signals may introduce additional noise. We propose BACON, a plug-and-play method that calibrates observation window attention with last query evidence while suppressing noise through intra-layer coherence and inter-layer persistence. Across diverse benchmarks, models, budgets, and compression methods, BACON improves multimodal KV-cache compression by 7.5% on average under the most aggressive budget, with gains up to 30.9%.
%U https://aclanthology.org/2026.emnlp-main.331/
%P 7619-7639
Markdown (Informal)
[Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression](https://aclanthology.org/2026.emnlp-main.331/) (Chen et al., EMNLP 2026)
ACL