让大模型具备人类海马体显性记忆，是迈向通用人工智能的关键
Position: Hippocampal Explicit Memory Is the Cornerstone for AGI
- 类比人类隐性记忆机制，提出需引入显性记忆系统
- 强调战略规划、元认知等高级认知依赖显性记忆
- 适合关注AGI架构与脑启发计算的研究者
大型语言模型（LLMs）在多项任务中表现出色，推动了对人工通用智能（AGI）的期待。本文认为，整合显性记忆是推动LLMs向AGI演进的核心。其核心理由在于，LLMs的底层学习机制与人类隐性记忆高度相似，但实现AGI所必需的高级认知功能——如长期战略规划、元认知和符号推理——主要依赖海马体介导的显性记忆，无法仅通过隐性统计学习产生。基于神经科学发现，本文提出这一观点，并补充人工显性记忆系统的计算需求，旨在促进相关研究并为显性记忆集成奠定基础。
原文摘要 · Abstract (English)
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.
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