Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overcome this, we introduce MemDreamer to decouple perception and reasoning, shifting long-video understanding into an agentic exploration process. As a plug-and-play framework, it incrementally streams videos to construct a Hierarchical Graph Memory, a top-down three-tier architecture for semantic abstraction, anchored by a foundational graph capturing spatiotemporal and causal relations. During inference, the reasoning model employs agentic tool-augmented retrieval, navigating hierarchies, searching nodes, and traversing logical edges via an Observation-Reason-Action loop. Experiments show MemDreamer achieves SOTA results across four mainstream benchmarks, narrowing the gap with human experts to only 3.7 points. It constrains the reasoning context window to merely 2% of full-context ingestion while delivering a 12.5 point absolute accuracy gain. Furthermore, statistical analysis uncovers a strong positive linear correlation between an VLM's performance on logic reasoning and long-video understanding benchmarks, establishing agentic capability scaling as a new paradigm for multimodal comprehension.
MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Department of Computer Science, The University of Texas at Dallas（德克萨斯大学达拉斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）
TMLR Group Department of Computer Science Hong Kong Baptist University（香港 Baptist 大学计算机科学系 TMLR 组）；RIKEN AIP；Cooperative Medianet Innovation Center Shanghai Jiao Tong University（上海交通大学合作中立创新中心）；Stanford University（斯坦福大学）；Sydney AI Centre The University of Sydney（悉尼大学悉尼AI中心）；The University of Tokyo（东京大学）
Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Informatics, Xiamen University（厦门大学信息学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
SANEmerg: An Emergent Communication Framework for Semantic-aware Agentic AI Networking
1 School of Elect. Inform. \& Commun., Huazhong Univ. of Science \& Technology, China；2 Peng Cheng Laboratory, Shenzhen, China；3 Pazhou Laboratory (Huangpu), Guangzhou, China；4 Department of Electrical；Computer Engineering, the University of Arizona
Native Active Perception as Reasoning for Omni-Modal Understanding
The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；Qwen Team, Alibaba Group（阿里巴巴集团Qwen团队）
