Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence (AGI). This position paper argues that integrating explicit memory is the cornerstone for advancing LLMs toward AGI. The key reason is that the underlying learning mechanism of LLMs is highly analogous to human implicit memory. However, higher-order cognitive functions necessary for AGI, such as long-term strategic planning, metacognition, and symbolic reasoning, heavily rely on hippocampal explicit memory and cannot arise solely from implicit statistical learning. Drawing on findings from neuroscience, I advance this perspective and complement it with computational requirements for artificial explicit memory systems, hoping to foster further research and lay the groundwork for explicit memory integration.

翻译：大型语言模型（LLMs）已在各类任务中展现出卓越能力，提升了人们对通用人工智能（AGI）的期望。本文立场论证指出，整合显式记忆是推动LLMs迈向AGI的关键基石。其核心原因在于LLMs的底层学习机制与人类内隐记忆高度相似。然而AGI所需的高阶认知功能，如长期战略规划、元认知和符号推理，严重依赖海马体显式记忆，无法仅通过内隐统计学习产生。基于神经科学的研究发现，本文深化了这一观点，并从计算需求角度对人工显式记忆系统进行补充阐述，旨在推动后续研究并为显式记忆整合奠定基础。