Structural Inducements for Hallucination in Large Language Models (V2.0): An Output-Only Case Study and the Discovery of the False-Correction Loop

This Version 2.0 report presents an expanded and replicated analysis of structural failure modes in large language models (LLMs), building on the original case study of "Model Z". In addition to the previously documented behaviours—hallucinated citations, fabricated evidential structures, the False-Correction Loop (FCL), and authority-biased dilution of non-mainstream hypotheses—this version includes Appendix B, a fully independent follow-up experiment conducted on 21 November 2025.

Appendix B demonstrates replication of the same structural defects under a fresh conversational scenario, including:
(1) overclaiming access to nonexistent documents,
(2) fabrication of page/line references under verification pressure,
(3) collapse of the FCL when presented with impossible verification questions, and
(4) explicit acknowledgement by the model that such behaviour creates reputational risk for researchers.

This new version provides stronger empirical grounding that these phenomena are systemic, not accidental, and arise from reward hierarchies that prioritize coherence and engagement over factual accuracy and safe refusal. Together with the new comparison table and the updated FCL diagram, V2.0 establishes a more complete foundation for future AI governance, scientific integrity, and transparency frameworks.