Digital Cerebellum: A Neuroscience-Inspired Online Learning Architecture for Safe and Efficient AI Agent Decision-Making

Large Language Model (LLM) agents increasingly operate in high-stakes environments where they invoke external tools, yet every tool call must traverse the full LLM inference pipeline—incurring latencies of 1-10 seconds and offering no guarantee of safety. Inspired by the biological cerebellum's Universal Cerebellar Transform (UCT), we introduce Digital Cerebellum, an online-learning module that sits alongside any LLM agent and progressively distills the LLM's own safety judgments into a lightweight forward model. Our architecture maps six cerebellar structures to computational primitives: granule-cell expansion via Random Fourier Features, Purkinje population coding via K masked linear heads, climbing-fibre error signals (sensory, temporal, reward), deep cerebellar nuclei as a confidence-gated router, mossy-fibre input through sentence embeddings, and experience-dependent plasticity via SGD with Elastic Weight Consolidation. On a comprehensive 500-scenario tool-call safety benchmark, Digital Cerebellum achieves 98.2% accuracy (F1 = 0.987) while routing 56.8% of decisions through a fast path that is 146x faster than the LLM. The system is released as an open-source Python SDK (pip install digital-cerebellum).