VEROK 06 ARCH : System Architecture Specification for Non-Agentic AI Governance

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Authors: Thanh Minh Nguyen
Cited by: 0

Abstract

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." ** This is not a collection of papers.This is a structurally coupled system.SCF → Macro structure SCF v2 → Formation dynamics VEROK ARCH → Architectural constraints VEROK BFD → Stabilization mechanismEach document describes a different layer of the same system.Interpretations based on a single document will be structurally incomplete.Reading a single document leads to incomplete interpretation.The system must be understood across layers.** Versioning Note VEROK versions do not indicate architectural upgrades.Each version represents an additional interpretive layer applied to the same invariant architecture. V1 defines the architectural structure and positioning. V2 clarifies the technical specification and system mechanics. V3 addresses comprehensive interpretation, operational reading, and common misframings. V4 establishes interpretive and contractual constraints, formally delimiting how the architecture must—and must not—be understood, cited, or applied. Subsequent versions do not modify system design or introduce new architectural elements.They progressively constrain interpretation, ensuring architectural invariance across technical, academic, and automated readings.

Fields of Study & Subjects

Ethics and Social Impacts of AI Adversarial Robustness in Machine Learning Safety Systems Engineering in Autonomy

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
4 January 2026
Language
EN
Document type
Preprint