@inproceedings{jiang-etal-2026-dynamic-hub,
title = "Dynamic Hub-and-Spoke Memory for Streaming Video Understanding",
author = "Jiang, Xinru and
Zhao, Lin and
Xiao, Xi and
Zhang, Yunbei and
Wang, Janet and
Ma, Chenrui and
Li, Haolin and
Wang, Yanzhi and
Gong, Yifan and
Camps, Octavia",
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.2216/",
pages = "47455--47475",
ISBN = "979-8-89176-462-0",
abstract = "Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to compactly remember long-range history while effectively retrieving question-relevant evidence. We propose Dynamic Hub-and-Spoke Memory (D-HSM), a training-free framework that represents distant history as structured textual memory while preserving the recent frames as visual tokens for fine-grained perception. Specifically, D-HSM turns selected historical video chunks into typed textual observations and stores them in an entity-centered hub-and-spoke memory, with entities as hubs and related evidence as spokes. When answering a question, D-HSM dynamically retrieves a compact question-aware memory subset, expands it through hub-and-spoke links, and combines it with the recent visual window for frozen-VLM answer prediction. Extensive experiments on both streaming and long video benchmarks show that D-HSM consistently and substantially improves VLM backbones and outperforms other state-of-the-art online and offline video understanding baselines."
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<abstract>Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to compactly remember long-range history while effectively retrieving question-relevant evidence. We propose Dynamic Hub-and-Spoke Memory (D-HSM), a training-free framework that represents distant history as structured textual memory while preserving the recent frames as visual tokens for fine-grained perception. Specifically, D-HSM turns selected historical video chunks into typed textual observations and stores them in an entity-centered hub-and-spoke memory, with entities as hubs and related evidence as spokes. When answering a question, D-HSM dynamically retrieves a compact question-aware memory subset, expands it through hub-and-spoke links, and combines it with the recent visual window for frozen-VLM answer prediction. Extensive experiments on both streaming and long video benchmarks show that D-HSM consistently and substantially improves VLM backbones and outperforms other state-of-the-art online and offline video understanding baselines.</abstract>
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%0 Conference Proceedings
%T Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
%A Jiang, Xinru
%A Zhao, Lin
%A Xiao, Xi
%A Zhang, Yunbei
%A Wang, Janet
%A Ma, Chenrui
%A Li, Haolin
%A Wang, Yanzhi
%A Gong, Yifan
%A Camps, Octavia
%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 jiang-etal-2026-dynamic-hub
%X Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to compactly remember long-range history while effectively retrieving question-relevant evidence. We propose Dynamic Hub-and-Spoke Memory (D-HSM), a training-free framework that represents distant history as structured textual memory while preserving the recent frames as visual tokens for fine-grained perception. Specifically, D-HSM turns selected historical video chunks into typed textual observations and stores them in an entity-centered hub-and-spoke memory, with entities as hubs and related evidence as spokes. When answering a question, D-HSM dynamically retrieves a compact question-aware memory subset, expands it through hub-and-spoke links, and combines it with the recent visual window for frozen-VLM answer prediction. Extensive experiments on both streaming and long video benchmarks show that D-HSM consistently and substantially improves VLM backbones and outperforms other state-of-the-art online and offline video understanding baselines.
%U https://aclanthology.org/2026.findings-emnlp.2216/
%P 47455-47475
Markdown (Informal)
[Dynamic Hub-and-Spoke Memory for Streaming Video Understanding](https://aclanthology.org/2026.findings-emnlp.2216/) (Jiang et al., Findings 2026)
ACL
- Xinru Jiang, Lin Zhao, Xi Xiao, Yunbei Zhang, Janet Wang, Chenrui Ma, Haolin Li, Yanzhi Wang, Yifan Gong, and Octavia Camps. 2026. Dynamic Hub-and-Spoke Memory for Streaming Video Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47455–47475, Budapest, Hungary. Association for Computational Linguistics.