Technical Implementation of Memory Enhancement Generation
The MemoryAugmentedGeneration (MAG) API enables continuous learning of models through a four-stage processing flow:
- Memory encoding: transforming input information into a storable vector representation
- Memory deposition: write optimization based on timeliness filtering
- Memory activation: contextually relevant retrieval enhancement
- Memory fusion: information integration under attentional mechanisms
In an open-domain quizzing task, the technique improved answer accuracy from 671 TP3T to 891 TP3T. typical use cases include:
- Session memory retention for intelligent customer service systems
- Cross-document reasoning in scientific literature
- Long-term preference modeling for personalized recommender systems
The API supports both gRPC and WebSocket communication protocols, with P99 latency controlled within 300ms.
This answer comes from the articleMemOS: An Open Source System for Enhancing the Memory Capacity of Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " MAG interface enables LLM to acquire continuous learning capabilities
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