Abstract

Large language models demonstrate increasingly sophisticated reasoning, synthesis, and abstraction, yet continue to exhibit persistent epistemic failures, including hallucinated references, fabricated facts, and unjustified assertions under uncertainty. These failures are often treated as surface-level errors or alignment shortcomings. This paper argues instead that hallucination reflects a deeper structural limitation: the absence of epistemic closure in stateless generative systems. Building on the Hudson Recursive Information System (HRIS) framework, this work extends the theory of constraint persistence by introducing Epistemic Closure Constraint (ECC) as a necessary condition for long-horizon reliability. We distinguish reasoning competence from epistemic judgment and show that contemporary evaluation paradigms systematically conflate the two. Through analysis of recent benchmarks, hallucination, alignment, and scientific acceleration literature, we demonstrate that successful deployments of frontier models rely on external enforcement of epistemic closure, typically supplied by human collaborators. HRIS VI reframes hallucination not as a defect of knowledge or scale, but as a predictable consequence of optimizing generative systems without architectural mechanisms for abstention, negative knowledge, and admissibility control. The paper concludes by outlining design requirements for long-horizon AI systems in which epistemic refusal is treated as a first-class behavior rather than a failure mode.