Modern Large Language Models (LLMs) operate on a fundamental vulnerability: the semantic gap. While current software-level safeguards attempt to filter outputs using probabilistic heuristics—often relying on secondary referee AI models that are themselves prone to non-deterministic failure—they lack a deterministic killswitch to verify logical integrity at the point of generation. This research was born out of direct frustration with the persistent nature of LLM hallucinations during my own AI augmented research. The Semantic Gate is a hardware-accelerated monitor designed to bridge this gap by enforcing geometric constraints on embedding manifolds directly at the silicon level. By moving from statistical guessing to deterministic manifold integrity, this work provides a universal standard for grounding and trust. To provide universal access for independent development while supporting industrial-scale integration, the project is released under a dual-licensing model.
Jonathan ƒ(n) Reed (Fri,) studied this question.