Position: Hippocampal Explicit Memory Is the Cornerstone for AGI

观点：海马体显性记忆是实现通用人工智能（AGI）的基石

Abstract: 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.

摘要： 大语言模型（LLMs）在各项任务中展现出了卓越的能力，这提高了人们对通用人工智能（AGI）的期望。本篇观点论文认为，整合显性记忆是推动大语言模型迈向通用人工智能的基石。

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.

其核心原因在于，大语言模型的底层学习机制与人类的隐性记忆高度相似。然而，通用人工智能所必需的高阶认知功能（如长期战略规划、元认知和符号推理）严重依赖于海马体的显性记忆，而这些功能无法仅通过隐性统计学习产生。

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.

借鉴神经科学的研究发现，我提出了这一观点，并补充了人工显性记忆系统所需的计算要求，旨在促进相关领域的进一步研究，并为显性记忆的整合奠定基础。