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OPPO, Suzhou Institute for Advanced Research, University of Science and Technology of China, This work was supported by the National Natural Science Foundation of China under Grant No. T2350005, the Fundamental Research Funds for the Central Universities, Sun Yat-sen University under Grant No. 23xkjc008, and in part by the Young Faculty Development Program of the Fundamental Research Funds for the Central Universities under Grant No. 24qnpy206, Sun Yat-sen University, School of Electronics and Communication Engineering.Gelu Liu, Teng Wang, Zhijie Wu, Junliang Wu, Songyuan Li, Xiangwei Zhu are with the School of Electronics and Communication Engineering and Shenzhen Key Laboratory of Navigation and Communication Integration, Sun Yat-sen University, Shenzhen 518107, China (e-mail: liuglu@mail2.sysu.edu.cn; wangt556@mail.sysu.edu.cn; wuzhj39@mail2.sysu.edu.cn; wujliang9@mail2.sysu.edu.cn; lisy287@mail. sysu.edu.cn; zhuxw666@mail.sysu.edu.cn)
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Current multimodal retrieval systems fall flat when faced with realistic visual streams where context is distributed across time, motivating a new agentic paradigm for context-aware image retrieval.
