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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In Proceedings of the 43rd International Conference on Machine Learning (ICML 2026 Position Paper Track), 2026.

@inproceedings{park:icml26, title={Position: Hippocampal Explicit Memory Is the Cornerstone for AGI}, author={Sangjun Park}, booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML 2026 Position Paper Track)}, month={ }, url="http://nn.cs.utexas.edu/?park:icml26", year={2026} }