We present Integrity-OS, a novel architecture for preventing hallucinations in Large Language Models through real-time dissonance detection and prefrontal inhibition. Inspired by neuroscience (anterior cingulate cortex and prefrontal cortex mechanisms), our system achieves 0.5% hallucination rate (99.1% reduction from 33.7% baseline) across 1000 multi-domain tests, while reducing energy consumption by 46.6% through early termination. KEY RESULTS:- Baseline (GPT-2): 33.7% hallucination rate- Protected (Integrity-OS): 0.5% hallucination rate- Improvement: 99.1% relative reduction (334/337 hallucinations prevented)- Domains: 10 (programming, science, technology, history, geography, biology, physics, literature, music, art)- Energy savings: 46.6% average- Consistency: 0.3-0.8% per domain APPROACH:Unlike training-based approaches (RLHF, Constitutional AI), Integrity-OS provides architectural guarantees by continuously monitoring conflicts between generated outputs and a verified knowledge graph. IMPLEMENTATION:Full open-source implementation will be available on GitHub with automated Wikipedia crawler for reproducible research. GitHub: https://github.com/marchesse/integrity-os (publishing shortly after this upload)License: MIT (free for academic and commercial use) For the benefit of humanity.
Marc Hesse (Sun,) studied this question.