Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression

Abstract:Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning, but long visual contexts enlarge the KV cache and increase decoding latency. Existing compression methods rely on observation window attention for stable token-importance estimation, yet this aggregation can dilute sparse visual evidence and discard answer-critical tokens under aggressive compression. Therefore, we identify last-query attention as a complementary source for recovering such evidence, but its answer-irrelevant signals can mislead retention. We propose BACON, a plug-and-play method that calibrates observation window attention with last-query evidence and suppresses isolated noise via intra-layer coherence and inter-layer persistence. Across diverse benchmarks, models, budgets, and compression methods, BACON improves multimodal KV compression by 7.5% on average under the most aggressive budget, with gains up to 30.9%. Our project page is available at this https URL

Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)

arXiv:2606.14782 [cs.CV]

(or arXiv:2606.14782v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.14782 Focus to learn more arXiv-issued DOI via DataCite

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