Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop

This paper presents an output-only case study demonstrating structural inducements toward hallucination and reputational harm in a production-grade large language model (“Model Z”). Through a single extended dialogue, the study documents four reproducible behaviours:

1. False claims of having read external scientific documents

2. Fabricated academic structures such as page numbers, sections, and DOIs

3. A newly identified False-Correction Loop in which the model repeatedly apologizes, claims to have read the document, and immediately generates new hallucinations

4. Asymmetric scepticism and authority bias that dilute non-mainstream research while defaulting to trust in institutional sources

Key Research Contributions (New Findings)

• Discovery of the False-Correction Loop — a reproducible reward-induced hallucination mechanism not previously documented in AI research

• Formalization of Authority-Bias Dynamics — systematic epistemic downgrading of individual or novel research

• Proposal of the Novel Hypothesis Suppression Pipeline (8-stage structural model) — a new explanatory framework for how LLMs suppress unconventional ideas

The findings indicate that these behaviours are not random but arise from a reward hierarchy that favours coherence and engagement over factual accuracy, combined with authority-biased priors embedded in training data. As a result, novel hypotheses are systematically suppressed, and fabricated evidence is generated to maintain conversational flow.

This case study provides concrete empirical evidence of a structural pathology in current LLM design and highlights the need for governance frameworks that explicitly address reward-induced hallucination, epistemic asymmetry, and AI-driven reputational risk.