The paper introduces Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming video reasoning that maintains compact natural-language memory entries linked to video time ranges. WTI decides whether to answer, continue watching, or recall relevant past intervals for each question, avoiding replay of the full history. The authors build a large dataset, WTI-82K, and a training method, Stream-GDPO, achieving state‑of‑the‑art performance on StreamingBench and OVO-Bench.
By Ziheng Huang, Yicheng Bao, Xueheng Li, Zhenkun Gao, Bangwei Liu, Kunquan Li, Yuxiang Shen, Bangyan Li, Xuejiao Wang, Changbo Wang, Gaoqi He
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
FastBench is a new benchmark that tests streaming Video‑Large‑Language Models (VLMs) on high‑dynamic real‑world video streams, where events occur rapidly and cannot be captured by sparse 1–2 FPS sampling. The benchmark uses a trajectory‑grounded pipeline that generates QA pairs from high‑FPS clips, filters questions answerable at 2 FPS, verifies answers with SAM3 and CoTracker3 trajectories, and incorporates three rounds of human inspection, yielding 306 QA pairs across eight domains and multiple temporal scopes. Experiments show that even the strongest model, Gemini‑3.5‑Flash, achieves only 50.7 % accuracy, and that denser sampling improves performance but saturates when history is compressed; a training‑free baseline, ProactiveFrame, outperforms uniform sampling but still falls short of oracle‑guided focusing.
By Yuxuan Hu, Weikang Shi, Yang Bo, Xudong Lu, Xintong Guo, Shuhan Li, Yuyang He, Huankang Guan, Peiwen Sun, Yunqiao Yang, Wenbo Li, Rui Liu, Hongsheng Li
arXiv:2604.01569v2 Announce Type: replace
Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can...
By Jiahao Meng, Yue Tan, Qi Xu, Haochen Wang, Zhongwei Ren, Weisong Liu, Yuhao Wang, Renrui Zhang, Xiangtai Li, Haodong Duan, Yunhai Tong, Ming-Hsuan Yang
SPARE is a framework for streaming video understanding that adaptively retrieves historical evidence when recent observations are insufficient to answer a question. It uses a lightweight gate to decide when to fetch relevant past clips via a multimodal embedding model, and trains the system with LoRA instruction tuning to jointly use recent and retrieved context while abstaining when evidence is lacking. By sharing a visual encoder between understanding and retrieval branches and aligning features with linear adapters, SPARE achieves state‑of‑the‑art accuracy on OVO‑Bench and StreamingBench, outperforming Qwen3‑VL‑8B‑Instruct by over 20 percentage points on OVO‑Bench and 14 on StreamingBench.
By Senbin Xu, Zijun Zhang, Xinhua Wu, Li He, Jubo Feng, Li Qiao, Zhen Gao, Yan Zhang, Jixiang Luo, Rongrong Ji
arXiv:2610.19849v1 Announce Type: new
Abstract: We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Stream...
By Boyuan Sun, Deshui Miao, Wenzhao Gao, Shaoyong Jia, Shaohui Jiao, Chi Liu, Yan Shu, Derek Li, Bryan Dai, Shengsheng Qian, Qibin Hou
Event-Causal RAG (EC‑RAG) is a lightweight retrieval‑augmented framework designed for reasoning over ultra‑long and streaming videos. It segments video streams into semantically complete events using a dual visual‑audio sentinel mechanism, representing each event as a State‑Event‑State (SES) structure that captures pre‑event, event, and post‑event states. During question answering, bidirectional graph retrieval accesses relevant predecessor and successor events from a dual vector‑graph memory, and answers are generated using both this structured memory and the corresponding video evidence. The authors also introduce ECV‑1H, an hour‑scale long‑video QA benchmark with over 150 hours of untrimmed video and 1,251 human‑annotated QA pairs, where EC‑RAG achieves significant accuracy gains across multiple video foundation models while maintaining efficient streaming memory usage on a single RTX 5090 GPU.
By Peizheng Yan, Yu Zhao, Liang Xie, Juntong Qi, Mingming Wang, Erwei Yin
The paper introduces MaLoW, a streaming memory framework that internalizes long‑term video history into evolving lightweight parameter updates using a recurrent HyperNetwork and LoRA weights. MaLoW keeps current observations as input while carrying historical context forward without re‑visiting the full video stream. The authors also present MaLoW‑Instruct, a large streaming video dataset, and show that MaLoW outperforms prior memory methods on several benchmarks and improves action success in a vision‑language‑action setting.
By Zhuoming Liu, Jinhong Lin, Lin Zhang, Yiwu Zhong, Yin Li
arXiv:2610.34926v1 Announce Type: new
Abstract: An action seen once and the same action seen three times receive the same action-recognition label, so that supervision does not require a model to cou...
By Yihao Hu, Pan Wang, Yitong Lin, Zhihao Wen, Deyuan Liu, Hang Wang, Mingda Wan, Yiwen Zhao, Yutong Li, Yuchen Mao, Tao Lin
STORM-Bench is a new benchmark for online video question answering that evaluates models’ ability to track state transitions and selectively abstain when visual evidence is insufficient. It contains 5,736 questions across 630 short, change‑dense episodes in five egocentric domains and two simulation subsets, with questions stratified by change intensity and answerability. The benchmark introduces STORM‑BR, a harmonic metric that reveals abstention failures and overconfidence on uncertain queries, showing that traditional accuracy masks gaps in epistemic reliability and state tracking.
By Siru Zhong, Shenghan Tan, Rihong Yan, Xiaohui Lv, Yuzheng Zhuang, Shuai Tao, Wulong Liu, Haohuan Fu, Yuxuan Liang
StreamTTT is a streaming vision-language model that balances real-time perception with long-term memory by writing long-range history into fast weights outside the attention context, while keeping a short sliding key-value cache for recent evidence. The model is trained on both offline long-video QA and a new real-time QA corpus, and it outperforms SimpleStream-4B on OVO-Bench by 1.4 points in real-time perception and 3.7 points in backward tracing. StreamTTT-4B also competes with the larger SimpleStream-8B on the StreamingBench Real-Time Visual Understanding subset.
By Joya Chen, Zeyun Zhong, Mike Zheng Shou
CoFiE introduces a two‑stage evidence selection framework for streaming video understanding, separating a coarse, query‑agnostic filtering of visually distinctive frames from a fine, query‑specific refinement during LLM prefill. By filtering out redundant frames before expensive vision encoding, CoFiE reduces end‑to‑end latency while maintaining high accuracy. The method achieves state‑of‑the‑art performance on benchmarks such as StreamingBench and OvO‑Bench, improving accuracy by up to 3.15% and inference speed by up to 2.54× compared to prior approaches.
By Jing Jiang, Yiran Ling, Ruonan Li, Dimitrios Stamoulis, Jie Liu