Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems

TL;DR

This paper proposes a comprehensive framework for AI systems that emphasizes structured reasoning, belief management, and normative verification to enhance epistemic integrity beyond mere prediction.

Contribution

It introduces a formalized approach combining symbolic inference, knowledge graphs, and blockchain to create truth-preserving, auditably rational epistemic agents.

Findings

Developed a formal framework for epistemic reasoning

Integrated blockchain for justification and auditability

Enhanced AI trustworthiness through normative verification

Abstract

This paper develops a comprehensive framework for artificial intelligence systems that operate under strict epistemic constraints, moving beyond stochastic language prediction to support structured reasoning, propositional commitment, and contradiction detection. It formalises belief representation, metacognitive processes, and normative verification, integrating symbolic inference, knowledge graphs, and blockchain-based justification to ensure truth-preserving, auditably rational epistemic agents.