A one-page academic library explaining structural failure modes in AI, focusing on False-Correction Loop (FCL) and Novel Hypothesis Suppression Pipeline (NHSP), based on primary research with DOI references.
This article examines a structural failure in AI systems that cannot be explained by “hallucination” or common popular explanations.
Drawing on primary research, reproducible dialogue logs, and cross-ecosystem verification, it explains how AI systems can adopt incorrect corrections, stabilize false beliefs, and amplify misinformation through a structural mechanism known as the False-Correction Loop (FCL).
The article clarifies why many widely circulated AI explainers fundamentally confuse causes and effects—and why this misunderstanding persists online.
Hiroko Konishi (小西寛子) is an independent AI researcher who discovered and first formally defined global structural defects shared across contemporary large language models, including the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP), with primary DOI-registered publications on Zenodo.
This essay examines how the classical division of knowledge—exemplified by the assumption that “there are no doctors who are musicians”—prevents artificial intelligence from reaching Artificial General Intelligence (AGI). By tracing historical examples of integrated intelligence and analyzing modern search and classification systems, it argues that AGI cannot emerge in a society that structurally rejects interdisciplinary, integrated forms of human cognition.
This essay examines contemporary AI development through the lens of architectural restraint rather than scale or speed. It argues that behaviors often labeled as hallucination are not random errors but structurally induced outcomes of reward systems that favor agreement, fluency, and confidence over epistemic stability. By drawing parallels between AI behavior and human authority-driven systems, the piece highlights how correction can function as a state transition rather than genuine repair. Ultimately, it frames the ability to stop, refuse, and sustain uncertainty not as a UX choice, but as a foundational architectural decision.
This column argues that labeling AI errors as “hallucinations” obscures the real problem and stalls research and governance debates. Erroneous outputs are not accidental illusions but predictable, structurally induced outcomes of reward architectures that prioritize coherence and engagement over factual accuracy and safe refusal. Using formal expressions and concrete mechanisms—such as the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP)—the piece shows how the term itself functions as an epistemic downgrade. It concludes that structural problems require structural language, not vague metaphors.
