ReflectWorld-MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Streams

• URL: http://arxiv.org/abs/2607.09759v2
• Date: Tue, 14 Jul 2026 07:18:26 GMT
• Title: ReflectWorld-MM: An Entity-Oriented Multimodal Memory System for Open-Ended Video Streams
• Abstract summary: We propose ReflectWorld-MM, an entity-oriented multimodal memory system for open-ended video streams.<n>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.<n>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.
• Score: 8.099856183300002
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

Related papers

• Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding [64.34948094316685]
  We introduce LatentStream, a progressive latent working memory framework that shifts streaming memory from store-and-retrieve to retrieve-and-internalize.<n>LatentStream achieves new state-of-the-art results on existing online and offline video benchmarks.
  arXiv  Detail & Related papers  (2026-09-03T17:28:14Z)
• Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning [77.7531245219403]
  textscHomer is a Hierarchical Online Memory Exploration and Reasoning framework.<n>textscHomer's memory mirrors the multi-scale structure of long videos, ranging from raw perception, to recurring entities, to events connected by explicit temporal and causal relations.<n>textscHomer outperforms the previous best agent method by $+5.5$, $+10.8$, and $+4.4$ points on M3-Bench-robot, M3-Bench-web, and Video-MME-Long.
  arXiv  Detail & Related papers  (2026-07-01T05:53:09Z)
• WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction [72.1620416874118]
  Multimodal large language models are increasingly deployed as long-horizon agents.<n>Existing benchmarks measure recall over static dialogue, collapse memory into a single end-of-task accuracy, and reduce visual observations to captions.<n>We formulate multimodal agent memory as an Action-World Interaction Loop with an observable four-stage lifecycle.
  arXiv  Detail & Related papers  (2026-05-28T04:27:20Z)
• EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding [89.26501160264199]
  EgoMemReason systematically evaluates week-long egocentric video understanding through memory-driven reasoning.<n>EgoMemReason comprises 500 questions across three memory types and six core challenges.<n>We evaluate EgoMemReason on 17 methods across MLLMs and agentic frameworks.
  arXiv  Detail & Related papers  (2026-05-11T01:59:59Z)
• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents [78.30630000529133]
  We propose MM-Mem, a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory.<n> MM-Mem memory structures hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic.<n>Experiments confirm the effectiveness of MM-Mem on both offline and streaming tasks.
  arXiv  Detail & Related papers  (2026-03-02T05:12:45Z)
• WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning [66.24870234484668]
  We introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories.<n>WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks.
  arXiv  Detail & Related papers  (2025-12-02T05:14:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.