MemEvo presents an automated approach to discover memory mechanisms for streaming video understanding. It frames memory design as a search over executable programs expressed in a lightweight domain-specific language, and uses a pretrained large language model to generate and refine candidate memory programs. The framework evaluates each candidate deterministically while keeping the underlying vision-language model frozen, ultimately producing a training-free, bounded-memory mechanism that shows strong performance on StreamingBench and OVO-Bench.
By Guohong Liu, Jialei Ye, Shanhui Zhao, Yunxin Liu, Yuanchun Li
The paper introduces LatentStream, a progressive latent working memory framework for streaming video understanding. It replaces the traditional store‑and‑retrieve approach with a retrieve‑and‑internalize strategy, organizing visual history into short, mid, and long‑term levels and progressively expanding memory receptive fields to internalize evidence into a compact latent memory. The method also employs confidence‑guided optimization to refine memory tokens, achieving state‑of‑the‑art results on online and offline video benchmarks.
The paper introduces LatentStream, a progressive latent working memory framework for streaming video understanding that replaces the traditional store‑and‑retrieve paradigm with a retrieve‑and‑internalize approach. It organizes visual history into short, mid, and long‑term levels using Jenks‑guided adaptive consolidation, then expands memory receptive fields to iteratively retrieve and internalize evidence into a compact latent memory. A confidence‑guided optimization further refines this memory, leading to state‑of‑the‑art performance on online and offline video benchmarks.
By Hongyu Qu, Guangming Yao, Ling Xing, Xiaobin Hu, Rongxing Ding, Guibin Zhang, Fan Zhang, Yi Yuan, Xiangbo Shu, Shuicheng Yan
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.
By Haonan Ge, Yiwei Wang, Hang Wu, Yujun Cai
arXiv:2608.27881v1 Announce Type: new
Abstract: Recently, many streaming video understanding methods have been proposed by constructing an external memory to store historical data for computational r...
By Yuxin Liu, Peiqin Zhuang, Yali Wang
arXiv:2608.30294v1 Announce Type: new
Abstract: Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to co...
By Xinru Jiang, Lin Zhao, Xi Xiao, Yunbei Zhang, Janet Wang, Chenrui Ma, Haolin Li, Yanzhi Wang, Yifan Gong, Octavia Camps
arXiv:2610.01192v1 Announce Type: new
Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between...
By Yi Chen, MingMing Yu, Rui-Qi Wang, Boran Wang, Xiaohang Cao, Chu Tang, Jingmin Chen, Jie Gu
EventMemAgent is an active online video agent that uses a hierarchical memory module to handle continuous perception and long‑range reasoning in streaming video. The framework employs a short‑term memory layer to detect event boundaries and sample frames within a fixed buffer, while a long‑term memory layer archives observations event‑by‑event. It also incorporates a multi‑granular perception toolkit and Agentic Reinforcement Learning to internalize reasoning and tool‑use strategies, achieving competitive results on online video benchmarks.
By Siwei Wen, Zhangcheng Wang, Xingjian Zhang, Lei Huang, Wenjun Wu
arXiv:2608. 07663v1 Announce Type: cross Abstract: When videos extend from hours to days, directly processing them end-to-end becomes impractical for current Multi-modal Large Language Models (MLLMs).
By Yeeun Choi, Youngbeom Yoo, Joon-Young Lee, Hyolim Kang, Seon Joo Kim
arXiv:2609.00291v1 Announce Type: new
Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily...
By Ce Zhang, Jing Bi, Jinxi He, Jianshu Zhang, Jingyang Lin, Yunzhong Xiao, Minghao Fu, Yaqi Xie, Zhentao Xie, Weicong Chen, Katia Sycara, Ming Zhou
arXiv:2607. 09759v1 Announce Type: cross Abstract: Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing goal, and recently multimodal agents equipped with long-term memory over video streams have attracted increasing interest.
By Xiaokang Ma, Yifan Sun, Zhihong Jin, Jie Gu, Yudong Luo, Shenyi Shao, Chu Tang, Jingmin Chen, Li Pu
arXiv:2606. 17798v1 Announce Type: cross Abstract: Despite the remarkable progress of Video Large Language Models (Video-LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long-horizon contextual memory.
By Zhenyu Yang, Kairui Zhang, Bing Wang, Shengsheng Qian, Changsheng Xu