BEWA: A Bayesian Epistemology-Weighted Artificial Intellige-nce Framework for Scientific Inference

The proliferation of scientific literature and the accelerating complexity of epis-temic discourse have outpaced the evaluative capacities of both human scholars and conventional artificial intelligence systems. In response, we propose Bayesian Epistemology with Weighted Authority (BEWA), a computational architecture for truth-oriented knowledge modelling. BEWA formalises belief as a probabilistic relation over structured claims, indexed to authors, contexts, and replication his-tory, and updated via evidence-driven Bayesian mechanisms. Integrating canonical authorial identification, dynamic belief networks, replication-weighted citation metrics, and epistemic decay protocols, the system constructs an evolving belief state that prioritises truth utility while resisting social and citation-based distortions. By anchoring every propositional unit in structured metadata and linking updates to semantic replication and contradiction analysis, BEWA enables automated, principled reasoning across a corpus of scientific knowledge. This work advances the theoretical foundations and practical frameworks necessary for autonomous epistemic agents to assess, revise, and propagate beliefs in dynamic scientific environments.