A Scenario-Driven Cognitive Approach to Next-Generation AI Memory
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915497475309568 |
|---|---|
| author | Cai, Linyue Cheng, Yuyang Shao, Xiaoding Wang, Huiming Zhao, Yong Zhang, Wei Li, Kang |
| author_facet | Cai, Linyue Cheng, Yuyang Shao, Xiaoding Wang, Huiming Zhao, Yong Zhang, Wei Li, Kang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Scenario-Driven Cognitive Approach to Next-Generation AI Memory Cai, Linyue Cheng, Yuyang Shao, Xiaoding Wang, Huiming Zhao, Yong Zhang, Wei Li, Kang Artificial Intelligence 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. |
| title | A Scenario-Driven Cognitive Approach to Next-Generation AI Memory |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.13235 |