← Back ◬ AI & Machine Learning Jun 11, 2026

Position: Hippocampal Explicit Memory Is the Cornerstone for AGI

arXiv AI Archived Jun 11, 2026 ✓ Full text saved

arXiv:2606.11245v1 Announce Type: new 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-

Full text archived locally

✦ AI Summary · Claude Sonnet

Computer Science > Artificial Intelligence [Submitted on 5 Jun 2026] Position: Hippocampal Explicit Memory Is the Cornerstone for AGI Sangjun Park 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. Comments: Accepted to ICML 2026 (Position Paper Track) Subjects: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2606.11245 [cs.AI] (or arXiv:2606.11245v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.11245 Focus to learn more Submission history From: Sangjun Park [view email] [v1] Fri, 5 Jun 2026 17:40:34 UTC (1,770 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev | next > new | recent | 2026-06 Change to browse by: cs cs.NE q-bio q-bio.NC References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

💬 Team Notes