Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on Invertible Compression and Learnable Prediction

TL;DR

The paper introduces ApCM, a neural memory model that enhances LLMs by integrating invertible compression and learnable prediction for better runtime memory management.

Contribution

It presents a new neural memory architecture combining invertible compression with auxiliary prediction to improve dynamic memory handling in language models.

Findings

Demonstrates improved memory efficiency in LLMs.

Enhances model adaptability to personalized interactions.

Shows superior performance over existing memory mechanisms.

Abstract

Current large language models (LLMs) generally lack an effective runtime memory mechanism,making it difficult to adapt to dynamic and personalized interaction requirements. To address this issue, this paper proposes a novel neural memory storage architecture--the Auxiliary Prediction Compression Memory Model (ApCM Model).