Decoupling Memory and Generation: A Neurologically-Inspired Architecture for Eliminating Factual Hallucinations in AI Systems

Current large language models suffer from hallucinations because memory retrieval and language generation are the same operation within transformer parameters. This leads to two compounding problems: knowledge-class errors cannot be eliminated at the architectural level, and significant computation is wasted generating statistically plausible but factually incorrect content. We propose a new architecture inspired by the Complementary Learning Systems of the human brain, in which a hippocampal module handles factual storage and retrieval, and a neocortical module handles language generation and reasoning. The neocortical module is structurally prevented from generating factual content without input from the hippocampal module, making knowledge-class hallucinations architecturally impossible rather than statistically reduced. This separation also eliminates the computational waste associated with confabulation.