Principia Intelligentiae: The Developmental Kernel Architecture

Current large language models do not learn. They are trained once, frozen, and deployed. No matter how sophisticated the architecture or how vast the dataset, this fundamental constraint remains. They do not acquire new skills from interaction, do not retain knowledge across sessions, and do not build upon their own experience. We argue that this paradigm faces architectural limits that no amount of scaling will overcome. The core problem is not compute or data. It is the absence of develop...