HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants

2026-01-06

arXiv.org

AI Summary

This work addresses the limitations of traditional retrieval-augmented generation (RAG) in personalized AI assistants, which suffer from constrained memory capacity and insufficient integration with dialogue history, often leading to redundant clarifications and irrelevant document retrieval. Inspired by the hippocampal–neocortical memory system, we propose an end-to-end architecture that integrates short-term and long-term memory mechanisms. Our approach employs reinforcement learning to compress dialogue context and pre-retrieve relevant knowledge, introduces a short-term memory extractor that mimics hippocampal–prefrontal coordination, and constructs a partitioned long-term memory network to store user-specific information and rerank retrieved results. Evaluated on a real-world industrial dataset, the proposed method significantly outperforms cascaded RAG baselines, and ablation studies confirm the effectiveness and necessity of both memory components.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) power many interactive systems such as chatbots, customer-service agents, and personal assistants. In knowledge-intensive scenarios requiring user-specific personalization, conventional retrieval-augmented generation (RAG) pipelines exhibit limited memory capacity and insufficient coordination between retrieval mechanisms and user-specific conversational history, leading to redundant clarification, irrelevant documents, and degraded user experience. Inspired by the hippocampus-neocortex memory mechanism, we propose HiMeS, an AI-assistant architecture that fuses short-term and long-term memory. Our contributions are fourfold: (1) A short-term memory extractor is trained end-to-end with reinforcement learning to compress recent dialogue and proactively pre-retrieve documents from the knowledge base, emulating the cooperative interaction between the hippocampus and prefrontal cortex. (2) A partitioned long-term memory network stores user-specific information and re-ranks retrieved documents, simulating distributed cortical storage and memory reactivation. (3) On a real-world industrial dataset, HiMeS significantly outperforms a cascaded RAG baseline on question-answering quality. (4) Ablation studies confirm the necessity of both memory modules and suggest a practical path toward more reliable, context-aware, user-customized LLM-based assistants.

Problem

• personalized AI assistants
• retrieval-augmented generation
• memory capacity
• user-specific personalization
• conversational history

Innovation

• hippocampus-inspired memory
• retrieval-augmented generation
• short-term memory
• long-term memory
• personalized AI assistants