MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism

As video content explodes across surveillance, medicine, sports analytics, and film, the ability for AI to understand hours-long footage becomes increasingly critical. Current vision-language models choke on extended video because every frame demands processing, creating an unsustainable computational burden. MemDreamer sidesteps this by separating the act of watching from the act of reasoning, building a structured memory that the model navigates intelligently rather than consuming all at once. This approach closely mirrors how humans recall and reason about long experiences. Applications span medical procedure review, legal evidence analysis, long-form documentary understanding, and autonomous systems that must remember hours of environmental history. Authors: Cong Chen, Guo Gan, Kaixiang Ji, ChaoYang Zhang, Zhen Yang, Guangming Yao, Hao Chen, Jingdong Chen, Yi Yuan, Chunhua Shen Paper: https://arxiv.org/abs/2606.07512v1