LightMem-Ego: Your AI Memory for Everyday Life
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| Authors | Yijun Chen et al. |
| Year | 2026 |
| HF Upvotes | 32 |
| arXiv | 2607.11487 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Personal AI assistants on mobile and wearable devices continuously perceive users' daily lives through visual and audio streams. However, answering queries about past experiences requires lightweight multimodal memory that can continuously accumulate, organize, and retrieve long-term experiences, which remains challenging. To address this challenge, we present LightMem-Ego, a lightweight streaming multimodal memory system for everyday-life assistance. The system continuously captures egocentric visual and audio streams, aligns them on a shared timeline, and organizes them into a hierarchical memory consisting of current, short-term, and long-term memory. Given a user query, LightMem-Ego dynamically routes retrieval to the appropriate memory level and generates answers grounded in multimodal evidence. The demonstration can be deployed on smartphones and AI glasses, supporting object finding, conversation recall, life summarization, routine discovery, and personalized assistance. Code is available at https://github.com/zjunlp/LightMem-Ego.
Engineering Breakdown
The Problem
However, answering queries about past experiences requires lightweight multimodal memory that can continuously accumulate, organize, and retrieve long-term experiences, which remains challenging.
The Approach
To address this challenge, we present LightMem-Ego, a lightweight streaming multimodal memory system for everyday-life assistance.
Key Results
Code is available at https://github.com/zjunlp/LightMem-Ego.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Lightmemego
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