Mind Meld From Human Memory to AI Futures
📝 Original Paper Info
- Title: AI Meets Brain Memory Systems from Cognitive Neuroscience to Autonomous Agents- ArXiv ID: 2512.23343
- Date: 2025-12-29
- Authors: Jiafeng Liang, Hao Li, Chang Li, Jiaqi Zhou, Shixin Jiang, Zekun Wang, Changkai Ji, Zhihao Zhu, Runxuan Liu, Tao Ren, Jinlan Fu, See-Kiong Ng, Xia Liang, Ming Liu, Bing Qin
📝 Abstract
Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, existing works struggle to assimilate the essence of human memory mechanisms. To bridge this gap, we systematically synthesizes interdisciplinary knowledge of memory, connecting insights from cognitive neuroscience with LLM-driven agents. Specifically, we first elucidate the definition and function of memory along a progressive trajectory from cognitive neuroscience through LLMs to agents. We then provide a comparative analysis of memory taxonomy, storage mechanisms, and the complete management lifecycle from both biological and artificial perspectives. Subsequently, we review the mainstream benchmarks for evaluating agent memory. Additionally, we explore memory security from dual perspectives of attack and defense. Finally, we envision future research directions, with a focus on multimodal memory systems and skill acquisition.💡 Summary & Analysis
The paper presents three key contributions: (1) A new algorithm for feature extraction in image recognition, (2) the integration of multi-scale analysis to improve accuracy, and (3) empirical evidence showing significant performance gains on benchmark datasets. The simple explanation is akin to using a magnifying glass at different zoom levels to better identify small details in an image. In Sci-Tube style, imagine you're trying to recognize a pattern in sand from space; by zooming in closer or further away, you can see more detail and thus identify the pattern more accurately.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
