提出面向AGI的场景驱动记忆架构，实现类人持续学习。
A Scenario-Driven Cognitive Approach to Next-Generation AI Memory
- 从认知场景提取需求，构建统一设计原则
- 提出分层记忆框架COLMA，整合认知与存储机制
- 适合研究AGI长期学习与类人推理的学者参考
随着人工智能向通用人工智能（AGI）演进，构建稳健且类人记忆系统的需求日益迫切。当前记忆架构普遍存在适应性差、多模态融合不足及无法支持持续学习等问题。为此，我们提出一种场景驱动的方法，通过典型认知场景提炼核心功能需求，形成下一代AI记忆系统的设计原则。基于此，提出新型分层记忆架构——COgnitive Layered Memory Architecture（COLMA），将认知场景、记忆过程与存储机制有机整合，为实现终身学习与类人推理的AI系统提供结构化基础，推动AGI的实用化发展。
原文摘要 · Abstract (English)
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.
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