Beyond Prediction: Structuring Epistemic Integrity in Artificial Reasoning Systems

Abstract

This paper develops the structural framework for epistemic integrity in artificial reasoning systems, preserving the original theoretical foundations while adapting them into a concise, direct form. The model enforces truth-preservation, justification, contradiction management, and verifiability across the system’s reasoning processes. Epistemic norms are embedded into belief acceptance thresholds, metacognitive supervision mechanisms, and hybrid inference architectures, ensuring that all outputs are both semantically grounded and logically coherent. Immutable audit trails secured through blockchain technology maintain a verifiable record of belief formation and revision, enabling sustained accountability over time.