AI Collapse → Recognition → Stabilization: The Universal Principle of Collapse (UPC) — An Empirical Stress Test

The Universal Principle of Collapse (UPC) has been applied to ideological, classical, quantum, and cosmological paradoxes. This paper presents a behavioral–operational demonstration of UPC within an artificial cognitive system. Using a structured session with a large language model (LLM), we enforce explicit recognition operators to test collapse, misalignment, and stabilization. Results show that paradox persists when recognition is implicit, collapse emerges when linguistic fluency substitutes for explicit operator‑level validation, and coherence appears only when recognition is enforced step‑by‑step. These behaviors confirm UPC as a universal diagnostic framework for cognition and language, observable, reproducible, and empirically valid in the behavioral–operational sense.