Dynamic Hub-and-Spoke Memory for Streaming Video Understanding

• URL: http://arxiv.org/abs/2608.30294v1
• Date: Mon, 31 Aug 2026 06:02:28 GMT
• Title: Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
• Abstract summary: Dynamic Hub-and-Spoke Memory (D-HSM) is a training-free framework that represents distant history as structured textual memory.<n>D-HSM turns selected historical video chunks into typed textual observations and stores them in an entity-centered hub-and-spoke memory.<n>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.
• Score: 36.38729564564716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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