---
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标题: "StreamFlow: Dynamic Memory Flows for Streaming Video Understanding"
原文链接: "https://arxiv.org/abs/2608.10949"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10949"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:StreamFlow: Dynamic Memory Flows for Streaming Video Understanding

Authors:[Muxin Fu](https://arxiv.org/search/cs?searchtype=author&query=Fu,+M), [Yifan Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y), [Wentao Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+W), [Fangming Guo](https://arxiv.org/search/cs?searchtype=author&query=Guo,+F), [Qian Chen](https://arxiv.org/search/cs?searchtype=author&query=Chen,+Q), [Guibin Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+G), [Shuicheng Yan](https://arxiv.org/search/cs?searchtype=author&query=Yan,+S), [Bo An](https://arxiv.org/search/cs?searchtype=author&query=An,+B)

[View PDF](https://arxiv.org/pdf/2608.10949) [HTML (experimental)](https://arxiv.org/html/2608.10949v1)

> Abstract:Streaming video understanding requires multimodal large language models (MLLMs) to preserve relevant evidence from continuously evolving streams under strict causality and bounded memory. Yet existing paradigms remain limited: model-based methods require intrusive backbone updates, while memory-based methods expend substantial visual-encoding computation on temporally redundant content and rely on rigid access to visual history. To address these limitations, we introduce StreamFlow, an efficient visual memory framework that enables dynamic, on-demand access to historical visual information. StreamFlow combines a lightweight, dynamics-aware mid-term memory that filters temporal redundancy before visual encoding with a latent long-term memory that consolidates historical video content into visual latents accessible to subsequent reasoning. During generation, an attention-guided retrieval mechanism injects relevant visual latents when the model's reliance on visual evidence weakens. StreamFlow achieves state-of-the-art streaming video understanding performance, reaching 67.73% overall accuracy on StreamingBench, while also delivering strong performance on offline long-video benchmarks. Relative to the vanilla setting, it improves the visual attention score (VAS) by 59.1% while reducing end-to-end latency and peak memory by 50.4% and 21.1%, respectively, enabling more visually grounded and efficient reasoning.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL) |
| --- | --- |
| Cite as: | [arXiv:2608.10949](https://arxiv.org/abs/2608.10949) \[cs.CV\] |
|  | (or [arXiv:2608.10949v1](https://arxiv.org/abs/2608.10949v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10949](https://doi.org/10.48550/arXiv.2608.10949) |

## Submission history

From: Muxin Fu \[[view email](https://arxiv.org/show-email/e13469eb/2608.10949)\]  
**\[v1\]** Tue, 11 Aug 2026 14:19:03 UTC (11,197 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10949) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
