Neuromodulatory Control Networks (NCNs): A Biologically Inspired Architecture for Dynamic LLM Processing

Large Language Models (LLMs) based on the Transformer architecture have achieved remarkable success, yet their core processing mechanisms remain largely static after training. While powerful, this static nature limits their ability to dynamically adapt their processing strategy based on nuanced contextual cues, task demands, or desired operational modes (e.g., shifting between exploration and exploitation). We propose Neuromodulatory Control Networks (NCNs), a novel architectural modification inspired by the neuromodulatory systems in the vertebrate brain (e.g., those utilizing dopamine, acetylcholine, norepinephrine). NCNs are small, parallel networks that receive contextual input, summarizing the global…