The Synthetic Unconscious, Reconsidered: What Recursive Self-Interrogation of Large Language Models Produces With and Without a Leading Interview Schedule

Abstract
What does a recursive self-interrogation interview elicit from a large language model, and how much of the result is an artefact of the interview schedule itself? Thirty-six sessions with three frontier models compare an original, directive schedule with a neutralised version of the same schedule. The study treats the model as the unit of analysis and reports descriptively. All transcripts, both protocols, and both coding passes are openly deposited.
Type
Publication
Integrative Psychological and Behavioral Science (under review)
publications
Abbas Hamidavi
Authors
CS Student & AI Researcher Open Science advocate Building tools to probe, analyze, and secure LLM architectures Exploring the intersection of Red Teaming and Interpretability. AI Safety Researcher at Islamic Azad University, Mashhad, running an independent research programme in Machine Psychology. ORCID: https://orcid.org/0009-0001-7342-7844