2025/06/19 by Wright, Craig S. · 1 citation
Computer Science · #03B70 #68P20 #68T27 #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Computation and Language (cs.CL) #Databases (cs.DB) #Explainable Artificial Intelligence (XAI) #F.4.1 #FOS: Computer and information sciences #FOS: Mathematics #H.2.8 #I.2.3 #Logic (math.LO) #Logic in Computer Science (cs.LO) #Logic, Reasoning, and Knowledge
paper · pdf · doi:10.48550/arxiv.2506.16015
openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The exponential expansion of scientific literature has surpassed the epistemic processing capabilities of both human experts and current artificial intelligence systems. This paper introduces Bayesian Epistemology with Weighted Authority (BEWA), a formally structured architecture that operationalises belief as a dynamic, probabilistically coherent function over structured scientific claims. Each claim is contextualised, author-attributed, and evaluated through a system of replication scores, citation weighting, and temporal decay. Belief updates are performed via evidence-conditioned Bayesian inference, contradiction processing, and epistemic decay mechanisms. The architecture supports graph-based claim propagation, authorial credibility modelling, cryptographic anchoring, and zero-knowledge audit verification. By formalising scientific reasoning into a computationally verifiable epistemic network, BEWA advances the foundation for machine reasoning systems that promote truth utility, rational belief convergence, and audit-resilient integrity across dynamic scientific domains.