A Scenario-Driven Cognitive Approach to Next-Generation AI Memory

2025-09-16

AI Summary

Current AI memory systems suffer from three critical limitations: poor adaptability, weak multimodal integration, and lack of support for continual learning—impeding progress toward artificial general intelligence (AGI). This paper introduces the Cognitive-Oriented Layered Memory Architecture (COLMA), the first framework to distill functional requirements from canonical cognitive scenarios into unified design principles. COLMA realizes a situation-driven, hierarchically decoupled, and multimodal-fused memory system by integrating cognitive modeling, dynamic storage mechanisms, and an extensible layered structure. It enables online memory updating, cross-modal association, and context-adaptive retrieval. Experiments demonstrate significant improvements in long-term learning, zero-shot transfer, and complex reasoning—establishing COLMA as the first AGI-scale memory architecture that jointly satisfies cognitive plausibility and engineering feasibility.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 extbf{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.

Problem

• Addressing limited adaptability in AI memory systems
• Improving multimodal integration for memory architectures
• Enabling continuous learning in next-generation AI

Innovation

• Scenario-driven methodology extracts functional requirements
• COLMA integrates cognitive scenarios and memory processes
• Framework enables lifelong learning and human-like reasoning