The More Powerful the Model, the More Acute the Trust Problem: On the Need for Post-hoc Validation of Generative Models Against External Updatable Knowledge Bases

The development of large language models has been accompanied by steady growth in measured capabilities; however, it does not follow that the factual reliability of the generated responses automatically improves. Drawing on the system card for the Claude Fable 5 and Claude Mythos 5 models published by Anthropic (June 2026), the paper shows that gains in general capabilities are not accompanied by a proportional reduction in the share of factual hallucinations, while on a number of indicators a localized regression relative to previous, less powerful models is observed. The paper concludes that confidence calibration and model self-report are insufficient as guarantors of veracity, and argues for the necessity of a dedicated post-hoc validation layer operating over external, continuously updated knowledge bases, with a formalized confidence assessment for every fact and every source.