@inproceedings{zhang-etal-2026-hermes,
title = "{HERMES}: {KV} Cache as Hierarchical Memory for Efficient Streaming Video Understanding",
author = "Zhang, Haowei and
Yang, Shudong and
Fu, Jinlan and
Ng, See-Kiong and
Qiu, Xipeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.381/",
pages = "8411--8430",
ISBN = "979-8-89176-390-6",
abstract = "Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. However, extending these capabilities to streaming video inputs, remains challenging, as existing models struggle to simultaneously maintain stable understanding performance, real-time responses, and low GPU memory overhead. To address this challenge, we propose HERMES, a novel training-free architecture for real-time and accurate understanding of video streams. Based on a mechanistic attention investigation, we conceptualize KV cache as a hierarchical memory framework that encapsulates video information across multiple granularities. During inference, HERMES reuses a compact KV cache, enabling efficient streaming understanding under resource constraints. Notably, HERMES requires no auxiliary computations upon the arrival of user queries, thereby guaranteeing real-time responses for continuous video stream interactions. HERMES achieves 10$\times$ faster TTFT compared to prior SOTA. Even when reducing video tokens by up to 68{\%} compared with uniform sampling, HERMES achieves superior or comparable accuracy across all benchmarks, with up to 11.4{\%} gains on streaming datasets."
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%0 Conference Proceedings
%T HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding
%A Zhang, Haowei
%A Yang, Shudong
%A Fu, Jinlan
%A Ng, See-Kiong
%A Qiu, Xipeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-hermes
%X Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. However, extending these capabilities to streaming video inputs, remains challenging, as existing models struggle to simultaneously maintain stable understanding performance, real-time responses, and low GPU memory overhead. To address this challenge, we propose HERMES, a novel training-free architecture for real-time and accurate understanding of video streams. Based on a mechanistic attention investigation, we conceptualize KV cache as a hierarchical memory framework that encapsulates video information across multiple granularities. During inference, HERMES reuses a compact KV cache, enabling efficient streaming understanding under resource constraints. Notably, HERMES requires no auxiliary computations upon the arrival of user queries, thereby guaranteeing real-time responses for continuous video stream interactions. HERMES achieves 10\times faster TTFT compared to prior SOTA. Even when reducing video tokens by up to 68% compared with uniform sampling, HERMES achieves superior or comparable accuracy across all benchmarks, with up to 11.4% gains on streaming datasets.
%U https://aclanthology.org/2026.acl-long.381/
%P 8411-8430
Markdown (Informal)
[HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding](https://aclanthology.org/2026.acl-long.381/) (Zhang et al., ACL 2026)
ACL