Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored. In this paper, we introduce a novel benchmark specifically designed to evaluate how VLMs perceive and reason about chronological information within and across images.
arXiv:2606. 05702v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored.
By Haoyu Zhou, Qing Qing, Caichong Li, Qixin Zhang, Yongcheng Jing, Ziqi Xu, Juncheng Hu, Xikun Zhang, Renqiang Luo
arXiv:2606. 00154v1 Announce Type: cross Abstract: Recent advancements in multimodal large language models (MLLMs) have achieved remarkable progress in multimodal reasoning and code generation, catalyzing a new paradigm for front-end development.
By Fan Wu, Lishuai Dong, Cuiyun Gao, Yujia Chen, Yiming Huang, Yang Xiao, Qing Liao
arXiv:2605. 16223v2 Announce Type: replace-cross Abstract: Generative video models are increasingly used in design animation tasks, yet no standardized evaluation framework exists for this domain.
By Adrienne Deganutti, Dingning Cao, Jaejung Seol, Elad Hirsch, Purvanshi Mehta
arXiv:2602. 10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving.
By Yanan Wang, Renxi Wang, Yongxin Wang, Xuezhi Liang, Fajri Koto, Timothy Baldwin, Xiaodan Liang, Haonan Li
arXiv:2509. 09151v2 Announce Type: replace-cross Abstract: Research in video understanding has advanced rapidly, driven by increasingly diverse datasets and more powerful model architectures.
By Lei Wang, Syuan-Hao Li, Piotr Koniusz, Yongsheng Gao