🤖 AI Summary
Current large language models rely on implicit memory, which hinders their capacity to support the advanced cognitive functions essential for artificial general intelligence (AGI), such as long-term planning, metacognition, and symbolic reasoning. This work systematically argues, for the first time, that explicit memory constitutes a critical pathway toward achieving AGI. Inspired by the neural mechanisms of the hippocampus, the study proposes a novel computational paradigm of artificial explicit memory that integrates insights from neuroscience with large language model architectures. The proposed framework not only establishes a theoretical foundation for designing explicit memory modules in AGI systems but also opens new interdisciplinary avenues at the intersection of cognitive science, neuroscience, and artificial intelligence.
📝 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.