This revised version includes clarified architectural artifacts, refined terminology, and expanded appendix materials, aimed at improving reproducibility and overall structural clarity."This paper presents Verok V6, a system architecture specification that proposes a complementary paradigm for AI safety via deterministic governance mechanisms operating at inference time. We identify a distinct architectural asymmetry in current AI development: while model capabilities are engineered through rigorous structural designs (e.g., Transformer architectures), safety protocols have predominantly relied on probabilistic training-based methods such as RLHF. Positing that safety requires structural containment analogous to capability engineering, Verok V6 introduces a tri-layer governance framework (Execution, Observation, Governance) that operates independently of model weights. This architecture functions not by altering the model's learning, but by establishing a deterministic "chassis" within which the probabilistic "engine" operates—enabling the safe deployment of maximum model capability in high-stakes environments. Working prototypes developed on Google AI Studio validate architectural feasibility. This unified specification integrates theoretical foundations with practical implementation guidance, demonstrating that deterministic safety guarantees are achievable through structural design while preserving full model capability."
Thanh Phước Anh Nguyễn (Sun,) studied this question.