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LightMem-Ego is a lightweight streaming multimodal memory system for everyday life assistance that continuously captures egocentric visual and audio streams, organizes them into hierarchical memory, and retrieves grounded answers to user queries about past experiences, deployable on smartphones and AI glasses.
EgoCS-400K is a large-scale egocentric Counter-Strike dataset with over 400,000 first-person videos and 10,000 hours of gameplay, providing temporally aligned video-action-language trajectories for world model research.