Adding explicit process-control layers to LLM reasoning—rather than just filtering outputs—can dramatically reduce hallucination and adversarial vulnerability by enforcing integrity at the reasoning stage itself.
Box Maze proposes a three-layer architecture for LLMs that separates reasoning into memory grounding, structured inference, and boundary enforcement to prevent hallucination and adversarial attacks. Testing on multiple LLM systems shows the approach reduces failure rates from ~40% to <1% under adversarial conditions, suggesting architectural constraints can improve reasoning reliability.