A Scenario-Driven Cognitive Approach to Next-Generation AI Memory
This work addresses the need for robust memory systems in artificial general intelligence, though it appears incremental as it builds on existing cognitive and memory concepts.
The authors tackled the problem of limited adaptability and insufficient multimodal integration in current AI memory architectures by proposing a scenario-driven methodology that extracts functional requirements from cognitive scenarios, leading to the development of the COgnitive Layered Memory Architecture (COLMA) framework for lifelong learning and human-like reasoning in AGI.
As artificial intelligence advances toward artificial general intelligence (AGI), the need for robust and human-like memory systems has become increasingly evident. Current memory architectures often suffer from limited adaptability, insufficient multimodal integration, and an inability to support continuous learning. To address these limitations, we propose a scenario-driven methodology that extracts essential functional requirements from representative cognitive scenarios, leading to a unified set of design principles for next-generation AI memory systems. Based on this approach, we introduce the \textbf{COgnitive Layered Memory Architecture (COLMA)}, a novel framework that integrates cognitive scenarios, memory processes, and storage mechanisms into a cohesive design. COLMA provides a structured foundation for developing AI systems capable of lifelong learning and human-like reasoning, thereby contributing to the pragmatic development of AGI.