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:2610.28405v1 Announce Type: new
Abstract: Recent advances in multimodal large language models (MLLMs) have substantially improved spoken interaction and video understanding. Existing online and...
By Yuhang Chen, Guangyue Liu, Shilong Liu
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:2607. 03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks.
By Ante Wang, Jiaqi Fu, Xuanyi Chen, Ruotian Ma, Zhaopeng Tu, Weizhi Ma, Yang Liu
arXiv:2610.28402v1 Announce Type: new
Abstract: As streaming video generation advances, enabling real-time interaction with generated video is becoming increasingly important. We introduce StreamEval...
By StreamEval Team
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...
ProAct is a dual‑system framework for real‑time embodied social interaction that separates a low‑latency Behavioral System, which streams multimodal interaction and generates continuous non‑verbal motion, from a slower Cognitive System that performs long‑horizon social reasoning and produces proactive intentions. The Cognitive System uses an efficient memory mechanism and a user‑motivation prediction module to decide when to intervene, while the Behavioral System translates these intentions into fluid motion via an intention‑conditioned streaming flow‑matching generator with a disentangled ControlNet branch. The framework is deployed on a physical humanoid robot and validated through real‑world user studies, motion‑generation benchmarks, and a new ProActBench benchmark for proactive trigger detection and restraint.
By Zeyi Zhang, Zixi Kang, Ruijie Zhao, Yusen Feng, Biao Jiang, Hanyu Ji, Libin Liu
The paper introduces Temporally-Grounded Language Generation (TGLG), a benchmark that tests vision‑language models on their ability to produce semantically accurate and temporally precise utterances in real‑time settings. It identifies perceptual updating and contingency awareness as key capabilities, curates datasets from sports broadcasting and egocentric interactions, and proposes the TRACE metric to jointly evaluate semantic similarity and temporal alignment. The authors also present VLM‑TSI, a model that interleaves visual and linguistic tokens in a time‑synchronized manner, achieving better performance than a strong baseline yet still showing modest overall results, underscoring the challenge of real‑time VLMs.
By Keunwoo Peter Yu, Joyce Chai
LiveProBench evaluates streaming video models on their ability to interact proactively, assessing whether they respond at appropriate times without explicit cues. The benchmark tests models at one‑second intervals across six subtasks that vary trigger ambiguity and timing tolerance, measuring response accuracy, silence rates, and duplicate responses. Results show that many models issue premature responses more often than missed ones, highlighting a significant shortfall in human‑like temporal decision making.
By Kaixuan Du, Xin Wan, Hang Zhang, Meng Cao, Dai Guan, Ming Chen, YuKun Wang
arXiv:2609.13814v1 Announce Type: new
Abstract: Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and li...
By Ruixiang Zhao, Hualei Wang, Renhe Sun, Enzhi Zhou, Jincenzi Wu, Xujie Song, Kexin Shi, Zihang Liu, Pengcheng Zhu, Jiayi Zhou, Baoyue Zhang, Changhao Zhang, Zitong Wang, Jinhong Wang, Tong Niu, Jingjing Liu, Junan Lin, Haolin He, Hengshuo Chu, Yuhui Chen, Jian Liu, Yuge Huang, Junliang Xing, Yuntao Wang, Weiqiang Wang, Chun Yu, Yuanchun Shi
arXiv:2606. 07639v1 Announce Type: cross Abstract: Video understanding is shifting from the offline paradigm -- taking a fully recorded video as input and producing a single answer after it ends -- toward real-time interaction, in which the model perceives new frames while still replying, revises its answer as new evidence appears, and remains silent when there is nothing to say.
By Pengyu Wang, Chenkun Tan, Shaojun Zhou, Wei Huang, Qirui Zhou, Zhan Huang, Zhen Ye, Jijun Cheng, Xiaomeng Qian, Yanxin Chen, Xingyang He, Huazheng Zeng, Chenghao Wang, Pengfei Wang, Hongkai Wang, Shanqing Gao, Yixian Tian, Chenghao Liu, Xinghao Wang, Botian Jiang, Xipeng Qiu
arXiv:2606. 05121v1 Announce Type: cross Abstract: Audio is an inherently interactive modality, yet today's Large Audio Language Models (LALMs) are offline, and streaming audio models each handle only a single task such as streaming ASR or voice chatting.
By Zhifei Xie, Zihang Liu, Ze An, Xiaobin Hu, Yue Liao, Ziyang Ma, Dongchao Yang, Mingbao Lin, Deheng Ye, Shuicheng Yan, Chunyan Miao