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

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 an understanding of the ever-changing environment. To overcome these challenges, holistic memory modeling is required to efficiently retrieve and store relevant information across sessions for accurate 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 principles, we propose CAIM, a cognitive AI memory framework that models key aspects of human memory through a multi-agent architecture. Specialized LLM-based agents handle memory-related functions such as retrieval, contextual relevance evaluation, and memory maintenance. We compare CAIM against existing approaches, focusing on metrics such as retrieval accuracy, response correctness, and contextual coherence. The results demonstrate that CAIM outperforms baseline frameworks in different metrics, highlighting its context awareness and demonstrating its contribution to memory mechanisms for improving long-term human-AI interactions.