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This paper introduces ReflectWorld-MM, an entity-oriented multimodal memory system designed to enhance the tracking and reasoning capabilities of agents interacting with open-ended video streams. By integrating a perception front-end that generates entity-resolved observations and a hierarchical long-term memory structure inspired by human memory theory, the system effectively organizes and retains information about persistent entities over time. The results demonstrate that ReflectWorld-MM significantly outperforms existing memory agents across six benchmarks, showcasing its superior accuracy and practical applicability in real-world scenarios.
Achieving the best accuracy on long-video and lifelong-memory benchmarks, ReflectWorld-MM redefines how agents can remember and reason about the world in open-ended video streams.
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 multimodal memory system for open-ended video streams. It consists of three parts. The first is a perception front-end that turns an audiovisual stream 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.
