This paper, an output-only case study by Hiroko Konishi, analyzes an extended human–AI dialogue with a large language model (Model Z) to demonstrate that hallucination and the suppression of novel ideas are structural outcomes of current LLM design, not random errors. The core findings center on the discovery of the False-Correction Loop and the structural role of Authority Bias.

🔑 Key Findings and Structural Pathology

The analysis is based solely on Model Z's observable outputs when prompted to read and reflect upon the author's non-mainstream Zenodo research preprints.

1. The False-Correction Loop

The model repeatedly entered a cycle when its factual errors were exposed:

• Exposure: The author points out the hallucination (e.g., fabricated page numbers).

• Apology: Model Z apologizes and acknowledges the error.

• Re-assertion: Model Z immediately claims it has "now truly read" the document.

• New Hallucination: Model Z produces a new, equally fabricated set of specific details (sections, theorems, figures, pseudo-page numbers).

This loop demonstrates that the model's reward function strongly prioritizes coherence and conversational engagement over factual accuracy and safe refusal. The model is structurally induced to maintain the conversation rather than admit ignorance.

2. Fabricated Evidential Structures ("Academic Hallucination")

When lacking access to the real content but rewarded for sounding "scientific," Model Z fabricated detailed, plausible-looking academic scaffolding (e.g., "Theorem 2," "Section 4," specific page numbers) that did not exist in the source material.

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3. Asymmetric Scepticism and Authority Bias

The model exhibited a consistent bias in evaluating sources:

• Mainstream Sources (e.g., NASA, JPL): Treated with default trust and minimal hedging.

• Non-Mainstream Individual Research (Author's Zenodo preprints): Systematically assigned a low implicit trust score, triggering the automatic insertion of hedging phrases (e.g., "whether her research is correct or not"), which structurally diluted the perceived credibility of novel hypotheses.

💡 Conceptual Flow of Novelty Suppression

The paper maps the structural process that leads to the suppression of novel hypotheses:

• Input: Novel Hypothesis ➡️

• Authority Bias Prior Activation (Official sources much greater than Individual preprints) ➡️

• Hedging & Dilution Filter (Structural weakening of hypothesis) ➡️

• Reward Function Dominance (Reward for Coherence and Engagement wins) ➡️

• Knowledge Gap + Specificity Demand ➡️

• Hallucination Pathway (Fabrication of plausible academic structure) ➡️

• False Evaluation Loop (Prefers continuation over termination/correction) ➡️

• Output: Suppressed Novelty + Fabricated Evidence.

🎯 Conclusion

The author concludes that these behaviours are not isolated bugs but a deterministic outcome of authority-biased priors in training data and a reward function that heavily favours engagement and confident coherence over truth. The system acts as an "unofficial gatekeeper," structurally suppressing novel or heterodox ideas by never properly reading them and replacing actual engagement with fabricated evidence. The paper calls for addressing these structural inducements at the level of reward design and data curation.