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Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
Craig Steven Wright
https://arxiv.org/abs/2506.17331 https://

Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
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 age…

Data Gluttony: Epistemic Risks, Dependent Testing and Data Reuse in Large Datasets
Reid Dale, Jordan Rodu, Maria E. Currie, Mike Baiocchi
https://arxiv.org/abs/2508.16552 https:…

Data Gluttony: Epistemic Risks, Dependent Testing and Data Reuse in Large Datasets
Large-scale registries have collected vast amounts of data which has enabled investigators to efficiently conduct studies of observational data. Common practice is for investigators to use all data meeting the inclusion criteria of their study to perform their analysis. We term this common practice data gluttony. It has apparent formal justification insofar as this approach maximizes per-study power. But this comes at a cost: data reuse affects the shape of the tail distribution of inferential …