Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
Craig Steven Wright
2025-06-19Knowledge Graphs
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.
Related Papers
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs2025-07-17Topic Modeling and Link-Prediction for Material Property Discovery2025-07-08Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs2025-07-05Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing2025-06-29Active Inference AI Systems for Scientific Discovery2025-06-26Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph2025-06-26Generating Reliable Adverse event Profiles for Health through Automated Integrated Data (GRAPH-AID): A Semi-Automated Ontology Building Approach2025-06-25Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphs2025-06-24