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arXiv · 2509.11914

EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models

Yiqun Yao·Naitong Yu·Xiang Li·Xin Jiang·Xuezhi Fang·Wenjia Ma·Xuying Meng·Jing Li·Aixin Sun·Yequan Wang
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Abstract

We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple users directly from raw audiovisual streams, to provide personalized response, and to maintain long-term knowledge of users' facts, preferences, and social relationships extracted from audiovisual history. EgoMem operates with three asynchronous processes: (i) a retrieval process that dynamically identifies user via face and voice, and gathers relevant context from a long-term memory; (ii) an omnimodal dialog process that generates personalized audio responses based on the retrieved context; and (iii) a memory management process that automatically detects dialog boundaries from omnimodal streams, and extracts necessary information to update the long-term memory. Unlike existing memory agents for LLMs, EgoMem relies entirely on raw audiovisual streams, making it especially suitable for lifelong, real-time, and embodied scenarios. Experimental results demonstrate that EgoMem's retrieval and memory management modules achieve over 95% accuracy on the test set. When integrated with a fine-tuned RoboEgo omnimodal chatbot, the system achieves fact-consistency scores above 87% in real-time personalized dialogs, establishing a strong baseline for future research.

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Yiqun Yao, Naitong Yu, Xiang Li, Xin Jiang, Xuezhi Fang, Wenjia Ma, Xuying Meng, Jing Li, Aixin Sun, Yequan Wang. 2026-01-30. EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models. https://arxiv.org/abs/2509.11914

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