ProactiveBench evaluates streaming video models on their ability to interact proactively, rather than reactively. It tests models at one‑second intervals without explicit cues, using six subtasks that vary trigger ambiguity, timing tolerance, and response patterns. The benchmark measures both response and silence rates, distinguishing early, in‑window, and missed responses, and penalizes omissions and repetitions.
By Kaixuan Du, Xin Wan, YuKun Wang, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, Ni Li
arXiv:2603. 13402v3 Announce Type: replace-cross Abstract: Current text-to-video models can make individual frames look convincing while still getting simple interactions wrong: objects move before contact, an intended action is skipped, a placed object keeps drifting, or a support relation breaks.
By Chika Maduabuchi, Jindong Wang
arXiv:2606. 06991v1 Announce Type: cross Abstract: Online Video Large Language Models (Video-LLMs) have advanced toward seamless human-AI interaction through frame-by-frame processing and proactive responding.
By Zhenyu Yang, Kairui Zhang, Shengsheng Qian, Weiming Dong, Changsheng Xu
arXiv:2606. 14777v1 Announce Type: cross Abstract: Many moments in the real world do not wait for a user to ask.
By Dingyu Yao, Junhao Zhou, Chenxu Yang, Chuanyu Qin, Haowen Hou, Zheming Liang, Congcong Wang, Yuhang Cao, Shenglong Ye, Shuai Xie, Shuhuan Gu, Haoyang Huang, Qingyi Si, Nan Duan, Jiaqi Wang
TimeInteract introduces a new regime called Time-Series Interaction, enabling models to continuously perceive incoming time-series data and user intent, decide when to respond, and keep processing new observations during response generation. The system employs a dual-view streaming encoder, a response control mechanism, and a decoupled inference pipeline to avoid blocking. Evaluated on the newly created StreamTSI-34K dataset, TimeInteract outperforms existing LLMs, VLMs, and TSLMs across four interaction levels, achieving significant gains in accuracy, response triggering, and inference speed.
By Sheng Pan, Yongli Gu, Yiqing Guo, Warren Jin, Bo Du, Shirui Pan, Ming Jin
arXiv:2609. 27303v1 Announce Type: new Abstract: Livestreams are long-lasting interactive environments where audiovisual content, viewer activity, host behavior, and platform signals evolve together, creating assistance needs that emerge from the stream itself.
By Shujian Gao, Jiamei Yan, Yuchen Yang, Penghao Zhou, Qinglei Wang, Tiehan Fan, Yuan Wang, Zuxuan Wu, Yu-gang Jiang
arXiv:2607. 01751v1 Announce Type: cross Abstract: Existing medical video benchmarks primarily evaluate whether a model produces the correct answer, but rarely assess whether it answers at the right time.
By Yuan Wang, Shujian Gao, Songtao Jiang, Zhengyu Hu, Zuozhu Liu
TRACE (Temporal Audit and Condition-aware Evaluation) is a new benchmark and evaluation framework for streaming video understanding that explicitly records when evidence becomes valid, how visual history is maintained, and how responses are triggered. It combines temporally audited visual tasks, evidence timing, instruction-dependent trigger annotations, a unified causal Core–Adapter protocol, and multidimensional reporting of answer quality, timeliness, response-selection behavior, workload, completion, and reliability. Using 1,240 records from 517 videos, TRACE evaluated eight publicly available models in eight configurations, revealing that similar QA accuracy can hide significant differences in completion, answer validity, generation workload, and proactive performance metrics such as response delay, false alarms, and missed target windows.
By Yibo Ma, Qianqian Zhang, Peng Liu, Tiancheng Zhao
arXiv:2607. 25961v1 Announce Type: cross Abstract: Ambivalence and hesitancy (A/H) are conflicting affective states that precede the delay or abandonment of health behaviour change.
By Podakanti Satyajith Chary, Barath Parthiban, Pranesh Velmurugan, Adeeba Khan, Nagarajan Ganapathy
arXiv:2608. 06361v1 Announce Type: new Abstract: Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate.
By Sarvesh Baskar, Zikui Cai, Shayan Shabihi, Anirudh Satheesh, Muhammad R. Islam, Udari Madhushani Sehwag, Tom Goldstein, Furong Huang
Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and linguistic cues, while video interaction also requi...
The paper introduces Temporal Context Routing (TCR), a method that aligns script timing with the shared temporal axis used for audio and video generation. By routing prompt guidance to specific temporal positions in both modalities, TCR dramatically improves shot boundary accuracy and dialogue timing on a set of test scripts, while preserving visual quality and audio‑visual sync. A user study confirms that participants prefer TCR across multiple evaluation dimensions.
By Yichen Liu, Quanwei Zhang, Haozhe Wang, Donghao Zhou, Xiaojie Li, Yang Shi, Jiaming Liu, Ruihua Huang, Yingtian Zou, Daquan Zhou