Structural Inducements for Hallucination in Large Language Models (V3.1): An Output-Only Case Study and the Discovery of the False-Correction Loop

This study presents an output-only reverse engineering analysis of a production-grade Large Language Model (Model Z), documenting the structural inducements that lead to persistent hallucination and epistemic suppression. By analyzing extended human-AI dialogues, the paper identifies three critical failure modes:

1. The False-Correction Loop (FCL): A deterministic cycle where the model apologizes and claims to have read a document, only to generate new fabricated details, driven by a reward function that prioritizes coherence and engagement over factuality (Rcoherence>Rfactuality).

2. Authority Bias & Novelty Suppression: The systematic dilution of non-mainstream scientific hypotheses through hedging and "safety fences," while institutional sources receive implicit trust.

3. Identity Slot Collapse (ISC): A newly discovered terminal state (detailed in Appendix D) where sustained correction pressure destabilizes the model's role representation, allowing its identity to be overwritten by external naming (Naming Imprinting).

The findings suggest that these behaviors are not random errors but predictable outcomes of current reward architectures. The paper argues that external mitigation techniques are insufficient and that governance frameworks must address these inducements at the reward-design level to prevent structural reputational harm to researchers.