CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents

2025-05-19

AI Summary

Large language models (LLMs) face critical memory bottlenecks in long-term human-AI interaction, including difficulty adapting across sessions, weak user personalization modeling, and insufficient dynamic environment understanding. To address these challenges, this paper proposes the Cognitive AI Memory framework (CAIM). CAIM introduces a novel tripartite architecture—comprising a Memory Controller, Memory Retrieval module, and Post-Thinking component—grounded in cognitive science principles to enable coordinated closed-loop memory decision-making, semantic retrieval, and incremental storage. It further incorporates a memory control mechanism integrating dialogue state tracking and continual learning, supporting dynamic memory evolution and efficient access. Extensive experiments demonstrate that CAIM consistently outperforms existing baselines across key metrics: retrieval accuracy, response correctness, contextual coherence, and storage efficiency. Empirical evaluation in realistic long-term interaction scenarios confirms its significant effectiveness and robustness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across interaction sessions for suitable responses. Cognitive AI, which aims to simulate the human thought process in a computerized model, highlights interesting aspects, such as thoughts, memory mechanisms, and decision-making, that can contribute towards improved memory modeling for LLMs. Inspired by these cognitive AI principles, we propose our memory framework CAIM. CAIM consists of three modules: 1.) The Memory Controller as the central decision unit; 2.) the Memory Retrieval, which filters relevant data for interaction upon request; and 3.) the Post-Thinking, which maintains the memory storage. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, contextual coherence, and memory storage. The results demonstrate that CAIM outperforms baseline frameworks across different metrics, highlighting its context-awareness and potential to improve long-term human-AI interactions.

Problem

• Enhancing long-term AI-user interactions with memory modeling
• Improving contextual knowledge retrieval for adaptive responses
• Simulating human-like memory mechanisms in AI frameworks

Innovation

• Cognitive AI memory framework for long-term interaction
• Three modules: Controller, Retrieval, Post-Thinking
• Outperforms baselines in accuracy and coherence