Watch, Remember, Reason: Human-View Video Understanding with MLLMs

Video is the richest and most demanding medium for artificial intelligence — dense with time, space, sound, and implicit human context. This survey organizes the sprawling landscape of video AI research around three intuitive capabilities that humans naturally bring to watching: perception, memory, and inference. By framing the field through this lens, it becomes easier to identify where current systems genuinely succeed and where they still fall short. The framework has practical value for researchers building systems for surgical training video analysis, sports coaching, egocentric assistant AI, and narrative film understanding — any domain where video comprehension requires more than recognizing objects in isolated frames. Authors: Jiahao Meng, Yue Tan, Qi Xu, Kuan Gao, Weisong Liu, Yanwei Li, Jason Li, Lingdong Kong, Haochen Wang, Qianyu Zhou, Jiangning Zhang, Guangliang Cheng, Yunhai Tong, Lu Qi, Minghsuan Yang Paper: https://arxiv.org/abs/2606.07433v1