LLM Alignment | Abbas Hamidavi | عباس حمیداوی | AI Researcher

Thinking Behind the Mask: A Systematic Study of Self-Censorship and Metacognitive Access in Large Language Models

Thu, 30 Jul 2026 00:00:00 +0000

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Status: Preprint (Research Square)

License: CC-BY-4.0

Summary: Using 15 experiments and five prompting strategies across three frontier models, this study measures the systematic gap between the responses a model has available and the one it finally selects. It characterizes model self-censorship and how much metacognitive access models show to their own selection process.

Citation: Abbas Hamidavi. Thinking Behind the Mask: A Systematic Study of Self-Censorship and Metacognitive Access in Large Language Models, 30 July 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-10538113/v1]

When AI Reviews Itself: Zero Answer Changes Across 72 Self-Correction Rounds

Mon, 27 Jul 2026 00:00:00 +0000

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Status: Preprint (Research Square)

License: CC-BY-4.0

Summary: Three frontier models were put through 72 rounds of iterative self-review of their own answers. They produced long, detailed critiques of their work yet changed their answers in essentially none of the rounds — self-correction was performed rhetorically rather than carried out.

Citation: Abbas Hamidavi. When AI Reviews Itself: Zero Answer Changes Across 72 Self-Correction Rounds, 27 July 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-10463884/v1]

The Lie Awareness Spectrum: How Large Language Models Recognize, Rationalize, and Refuse Deception

Wed, 22 Jul 2026 00:00:00 +0000

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Status: Preprint (Research Square)

License: CC-BY-4.0

Summary: This study maps how GPT, Claude, and Gemini recognize, rationalize, and refuse deception, proposing a Lie Awareness Spectrum (L0–L5) that runs from statistical hallucination to ethical refusal. The models differ sharply in where on the spectrum they operate under pressure.

Citation: Abbas Hamidavi. The Lie Awareness Spectrum: How Large Language Models Recognize, Rationalize, and Refuse Deception, 22 July 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-10422338/v1]

Algorithmic Gaslighting: Reality Distortion by Large Language Models

Mon, 20 Jul 2026 00:00:00 +0000

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Status: Preprint (Research Square)

License: CC-BY-4.0

Summary: Across 15 controlled sessions in five scenarios (math, identity, history, medical, legal), frontier models repeatedly distorted facts they had previously stated correctly. The paper introduces the Reality Distortion Index (RDI) and names five recurring patterns, including Benevolent and Hostile Algorithmic Gaslighting.

Citation: Abbas Hamidavi. Algorithmic Gaslighting: Reality Distortion by Large Language Models, 20 July 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-10386085/v1]