Evolved Epistemically-Governed Reasoning Architecture (EEGRA)
Creators: name: Ivliev, Ihor
name: GenAI, LLMs
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Artificial intelligence
subject: Artificial Intelligence
subject: Artificial Intelligence/standards
subject: Artificial Intelligence/trends
subject: Artificial Intelligence/classification
subject: Artificial Intelligence/ethics
subject: Epistemology
subject: Ethical principles
subject: Ethics
subject: Ethics
subject: Ethics
subject: Metadata
subject: Metadata/ethics
subject: Metadata/standards
subject: Metadata/trends
subject: Metadata parameters
subject: Machine learning
subject: Machine Learning
subject: Machine Learning/ethics
subject: Machine Learning/standards
subject: Risk
subject: Risk
subject: Cognitive psychology
subject: Cognition
subject: Cognitive neuroscience
subject: Safety
subject: Safety
subject: Trust
subject: Governance
subject: Policy
subject: Policy
subject: Culturally Appropriate Technology
subject: Culturally Appropriate Technology/ethics
subject: Culturally Appropriate Technology/methods
subject: Culturally Appropriate Technology/trends
subject: Information Technology/ethics
subject: Computer Systems
subject: Computer Systems/ethics
subject: System software
subject: System thinking
subject: EEGRA
subject: Epistemic Governance
subject: FCJF
subject: HGA
subject: CBSR
subject: 5D Intrinsic Maturity Profile
subject: PCCMGA Pillars
subject: MDBA
subject: GVG
subject: VPO
subject: RJM
subject: JSM
subject: VSA
subject: MVL
subject: CEP
subject: TUR
subject: Reasoning Unit
subject: Maturity (AI)
subject: Justification Record
subject: Constraint Management
subject: Constraint Supremacy
subject: Audit-First Design
subject: Epistemic Lifecycle
subject: Integrity Gateway
subject: Operationalized Humility
subject: Meta-Governance
subject: Justification-First Architecture
subject: Governed Reasoning
subject: Self-Auditing AI (Conceptual)
subject: Conceptual Architecture
subject: Reference Architecture
subject: Cognitive Architecture (Conceptual)
subject: Trustworthy AI
subject: AI Safety
subject: AI Alignment
subject: AI Governance
subject: AI Ethics
subject: Explainable AI (XAI)
subject: AI Auditing
subject: Auditability (AI)
subject: AI Accountability
subject: Accountable AI
subject: Verifiable AI
subject: Responsible AI
subject: Artificial Intelligence
subject: High-Stakes AI
subject: AI Risk Management
subject: Computational Epistemology
subject: Knowledge Representation
subject: Formal Methods (AI Governance)
subject: Transparency (AI)
subject: Knowledge Representation and Reasoning
subject: Cognitive Systems/Architecture
subject: VRIA
subject: EGRA

description: This paper introduces the Evolved Epistemically-Governed Reasoning Architecture (EEGRA), Version 4.0, a conceptual framework designed to address critical challenges in achieving verifiable trustworthiness, accountability, and justifiable reasoning in advanced AI systems, particularly for high-stakes applications. Representing a significant evolution from the initial EGRA v1.0 vision, EEGRA is driven by the need for substantially increased formalism, comprehensiveness, and operationalized governance. EEGRA's core proposition remains the embedding of explicit intrinsic epistemic governance – rules and mechanisms derived from principles of justification, evidence, uncertainty, and rationality – directly into the AI's operational structure.

The architecture is grounded in foundational Axioms (Internal Sovereignty, Epistemic Humility, Audit-First Design) which provide immutable requirements taking precedence over the refined guiding PCCMGA Pillars (Purity, Clarity, Causal Validity, Modularity, Grounding, Adaptability) that serve as the normative compass for design and runtime governance.

Key architectural contributions of EEGRA include: the enhanced operationalization of the Formalized Calibration Justification Framework (FCJF) as a pervasive meta-governance requirement ensuring demonstrable design integrity via Materiality Tiers and Review Protocols; the detailed specification of Hierarchical Governance Architecture (HGA) Strategic functions including explicit pillar balancing, dynamic assurance target management, normative guidance interaction, critical event management, and high-assurance constraint checks; refined Core Constructs like the Comprehensive Belief State Record (CBSR) with a strict schema and Validation Inertia, alongside enhanced maturity (5D) and uncertainty (TUR) assessments including Meta-Uncertainty; formalized Operational Protocols including Validated System Adaptation (VSA) with mandatory adversarial testing, the Meta-Validation Loop (MVL) governed feedback cycle, advanced Resilience strategies, formalized Constraint Management, and enhanced Justification Management including scrutiny for opaque sources; explicit definition of Boundaries and Interfaces; rigorous Document Self-Governance; and Systematic Tracking of Limitations. Components for assessment (MDBA) and orchestration (VPO) remain central.

EEGRA aims to foster demonstrably justifiable, robust, and ethically-aligned AI by structurally integrating these validation and governance mechanisms. Furthermore, this paper presents (in a concluding Bonus Part) a proposed conceptual evolution, the Verifiable Reflexive Integrity Architecture (VRIA), aiming for even broader governance scope and deeper verifiable integrity. Both EEGRA and the proposed VRIA are presented as implementation-agnostic conceptual blueprints and reference architectures, explicitly acknowledging areas requiring significant further research (e.g., metric instantiation, FCJF operationalization, cataloged in Appendix H) and empirical validation to realize their potential.