立场论文：海马外显记忆是通往 AGI 的基石
Position: Hippocampal Explicit Memory Is the Cornerstone for AGI
一篇被 ICML 2026 立场论文赛道接收的论文主张，整合外显记忆是推动 LLM 迈向 AGI 的基石。作者认为 LLM 的学习机制与人类内隐记忆高度类似，而长期战略规划、元认知和符号推理等 AGI 所需的高阶认知功能依赖海马外显记忆，无法仅靠内隐统计学习产生。论文结合神经科学发现，提出人工外显记忆系统的计算需求，以期为后续研究奠定基础。
arXiv:2606.11245v2 Announce Type: replace-cross 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. 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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来源：arXiv q-bio.NC 预印本 · arxiv.org