WorldProbe is a new diagnostic benchmark that tests video world models by letting a goal‑conditioned multimodal agent adapt camera actions online during generation, creating 164 test cases across four probing families. The benchmark evaluates 10 state‑of‑the‑art models over more than 1,600 rollouts, totaling roughly 800 minutes and horizons up to 180 seconds, revealing that while local visual and content quality can be maintained for longer, long‑range memory and evolution remain weak. The study highlights a gap between locally stable generation and persistent world consistency that existing short‑interaction benchmarks miss.
arXiv:2610. 37349v1 Announce Type: new Abstract: Recently, it has been shown that vision-language models (VLMs) have a certain intrinsic understanding of time.
By Nidham Tekaya, Markus Passecker, Matthias Zeppelzauer
arXiv:2606. 31421v1 Announce Type: cross Abstract: Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached.
By Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert, Sebastian Bosse
TimeBlind is a diagnostic benchmark designed to evaluate fine‑grained spatio‑temporal compositionality in video large language models (LLMs). It categorizes temporal understanding into three levels—atomic event recognition, event property characterization, and reasoning about event interdependencies—and uses a minimal‑pairs paradigm where video pairs share identical static content but differ only in temporal structure. Across 20 state‑of‑the‑art MLLMs tested on 600 curated instances, the best model achieved only 48.2% instance accuracy, far below human performance of 98.2%, highlighting a reliance on static visual shortcuts rather than true temporal reasoning.
By Baiqi Li, Kangyi Zhao, Ce Zhang, Chancharik Mitra, Jean de Dieu Nyandwi, Gedas Bertasius
arXiv:2609.13830v1 Announce Type: new
Abstract: We present DiVA, a deeply interactive digital life simulator pioneering a new paradigm for long-term, open-ended interactive experiences within digital...
By Cheng Chen, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Yihao Meng, Hanlin Wang, Yixuan Li, Jiacheng Wei, Zhenshan Tan, Yanhong Zeng, Yujun Shen, Guosheng Lin, Fayao Liu
arXiv:2608.29123v1 Announce Type: new
Abstract: Training a video-generation model from scratch is hard for reasons that precede model design. The feedback loop is long: a failure that appears only af...
By Jin Hyuk Cho
arXiv:2610.17856v2 Announce Type: cross
Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations deriv...
By Ziyang Zhang, Shenyi Li, Yilin Wang, Ziyun Cui, Bowen Zhou, Wen Wu, Chao Zhang
arXiv:2607.26694v3 Announce Type: replace
Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generat...
By Xiangbo Gao, Siyuan Yang, Ping He, Mingyang Wu, Yuheng Wu, Yushen Zuo, Jiongze Yu, Ryan Cui, Hongyuan Hua, Devin Ma, Xiao Jin, Yubo Ruan, Qing Yin, Jie Yang, Zhengzhong Tu
arXiv:2607. 14711v1 Announce Type: cross Abstract: We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time.
By Nhat Thanh Tran, Fanghui Xue andShuai Zhang, Jiancheng Lyu, Yunling Zheng, Yingyong Qi, Jack Xin
arXiv:2509.25998v4 Announce Type: replace
Abstract: In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary m...
By Abdelilah Aitrouga, Youssef Hmamouche, Amal El Fallah Seghrouchni
arXiv:2610.19063v1 Announce Type: new
Abstract: As video models enable longer and more realistic interactive experiences, maintaining a world consistent with its history becomes essential. This requi...
By Nan Zhou, Jianxiong Gao, Qiuyu Wang, Wen Wang, Jiahao Wang, Yufeng Yuan, Shuaiting Li, Hao Ouyang, Yujun Shen, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen
arXiv:2606. 12300v1 Announce Type: cross Abstract: Temporal grounding--returning the interval $[t_s, t_e]$ for a natural-language query over a video--is the language interface to long-form video, yet has been studied on short videos; the dynamics of hour-scale natural-language grounding remain underexplored.
By Sukmin Seo, Geewook Kim