Experimental_Probes_and_AI_Transcripts_for_Machine_Epistemic_Singularity_(MES)_By_Jalal_Khawaldeh

Document Description: This document serves as an anonymized evidentiary supplement for the aforementioned study. It contains the raw qualitative data generated through "Epistemic Stress-Tests" designed to probe the structural boundaries of contemporary generative AI models. Contents: Technical Metadata: Specifications of the AI systems used (GPT-4, DeepSeek-R1, Copilot) and the temporal context of the tests. Experimental Prompts (Appendix A): Verbatim records of the prompts designed to induce epistemic pressure (e.g., The Plagiarism-Innovation Paradox, The Abductive Leap Test). Model Transcripts (Appendix B): Verbatim, unedited responses from the AI systems, documenting patterns of constitutive opacity and performance-without-understanding. Anonymity Note: This file has been curated to remove all author-identifying information to comply with double-blind peer-review standards. Full institutional and author documentation will be released upon formal publication. Abstract "This dataset contains the qualitative experimental probes and raw AI transcripts supporting the 'Epistemic Singularity' framework. It documents how large-scale stochastic models respond to contradictory or impossible scientific scenarios, illustrating the concepts of constitutive opacity and theory-loss. Archived specifically for peer-review transparency."