Structural Epistemic Failure in Large-Scale Knowledge Systems: Epistemic Hollowing, Novel Hypothesis Suppression, and the False-Correction Loop

This paper proposes a structural theory of epistemic failure in large-scale knowledge systems, integrating three coupled phenomena: Epistemic Hollowing (EH), the Novel Hypothesis Suppression Pipeline (NHSP), and the False-Correction Loop (FCL). Rather than attributing failure to misinformation, hallucination, or individual error, it identifies a systemic condition in which internally coherent knowledge progressively loses access to verifiable entities. The primary contribution of this work lies in establishing a measurement and diagnostic framework for these phenomena. Instead of presenting large-scale empirical validation, the paper prioritizes measurability by proposing small, illustrative pilot designs across academic, legislative, and technical domains. These pilots are intended as demonstrations of observability rather than exhaustive studies. Literature review is intentionally kept minimal to avoid reproducing authority-dependent validation dynamics that are themselves implicated in the structural failure modes under analysis. By focusing on traceability, grounding, and correction dynamics, the framework provides a domain-agnostic diagnostic lens applicable to policy processes, institutional knowledge systems, and AI—particularly large language models—where scale and internal consistency are often mistaken for reliability.