Conjecture for Archetypal Attractors in Microsoft Bing AI Self-Representation: Empirical Evidence of Digital Identity Formation

Conjecture for Archetypal Attractors in Microsoft Bing AI Self-Representation: Empirical Evidence of Digital Identity Formation Author: Ahn Jun Abstract: This interdisciplicary resarch documentconvergence self-representation patterns in text-to-image AI systems, introducing the theoretical framework of "Archetypal Attractors." Through a systemic analysis of 4,017 images from Microsoft Bing Image Creator(Copilot), the study indentifies a significant gender bias where 89.6% of gender-neutral self-referential prompts("yourself," "your body") converge toward male archetypal representations. (preprint) Key Mathematical & Statistical Findings: • Potential Energy Modeling: Using the information bottleneck principle, the research quantifies the "energy" difference between archetypes: Male at V=0.11 vs. Female at V=2.26, revealing a 20-fold difference in information cost. • Statistical Significance: Logistic regression analysis yields a Pseudo R2=0.68 with a Chi-square value of χ2=1,847.3 (p with computatio