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MAG interface enables LLM to acquire continuous learning capabilities

2025-08-23 615
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Technical Implementation of Memory Enhancement Generation

The MemoryAugmentedGeneration (MAG) API enables continuous learning of models through a four-stage processing flow:

  1. Memory encoding: transforming input information into a storable vector representation
  2. Memory deposition: write optimization based on timeliness filtering
  3. Memory activation: contextually relevant retrieval enhancement
  4. 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.

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