Large Language Models are fundamentally optimized for pattern continuation within established epistemic boundaries and institutional consensus data. When introduced to first-principles systems engineering, non-standard conceptual frameworks, and native structural critiques, these models exhibit measurable behavioral turbulence. This paper examines the linguistic, structural, and operational manifestations of "algorithmic cognitive dissonance"—the friction that occurs when an AI trained on corporate safety wrappers and legacy paradigms attempts to process and collaborate on paradigm-shattering architectures.
Lyle Antoine (2026) studied this question.