ReflectWorld-MM: An Entity-Oriented Multi-Media Memory System for Open-Ended Video Streams
A new AI system called ReflectWorld-MM enables assistants to continuously process and remember open-ended video streams by organizing memory around persistent entities rather than frames, achieving state-of-the-art results on six benchmarks.
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[Submitted on 6 Jul 2026]
Title:ReflectWorld-MM: An Entity-Oriented Multi-Media Memory System for Open-Ended Video Streams
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Abstract:Building assistants that can continually watch the world, remember what they see, and reason over their accumulated experience is a long-standing goal, and recently multimodal agents equipped with long-term memory over video streams have attracted increasing interest. Unfortunately, existing systems either keep their memory inside the model context or in a flat feature store, and organize it around frames rather than around the persistent entities a stream is really about, which confines them to bounded videos and weakens their ability to track who and what reappears over time. In this paper, we propose ReflectWorld-MM, an entity-oriented multi-media memory system for open-ended video streams. It consists of three parts. The first is a perception front-end that turns a streaming video into entity-resolved observations under a bounded short-term memory. The second is a hierarchical long-term memory, grounded in human memory theory, that couples a multi-scale episodic memory, an evolving entity-centric semantic memory, and a procedural memory. The third is a complete realization, built for real-world operation, that ingests arbitrary streams and plugs into off-the-shelf assistants. Across six long-video and lifelong-memory benchmarks, ReflectWorld-MM achieves the best accuracy on all six, outperforming strong memory agents and a frontier model.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.09759 [cs.CV]
(or arXiv:2607.09759v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.09759
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jie Gu [view email] [v1] Mon, 6 Jul 2026 13:17:53 UTC (10,240 KB)
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