AI Collapse → Recognition → Stabilization: The Universal Principle of Collapse (UPC) — An Empirical Stress Test
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. Building on prior work extending UPC into artificial cognition, this paper presents a behavioral–operational demonstration of UPC within an AI language model. Using a structured session with a large language model (LLM), we apply explicit recognition operators to probe collapse, misalignment, and stabilization dynamics. The results show that paradox persists when recognition remains implicit; collapse emerges when linguistic fluency substitutes for operator‑level validation; and coherence appears only when recognition is enforced explicitly and sequentially. These behaviors confirm UPC as a universal diagnostic framework for cognition and language — observable, reproducible, and empirically valid in the behavioral–operational sense. Authored by Eloy Escagedo Gutierrez as part of The Universal Principle of Collapse (UPC) Research Project.
- University Research Co (United States) United States
- Universal University Russian Federation
AI collapse, Category collapse, Operator‑level coherence, Semantic drift, Large Language Models (LLMs), Stabilization mechanisms, Reasoning collapse, AI interpretability, Universal Principle of Collapse (UPC), Recognition dynamics, Stepwise validation, Constraint application, AI safety, AI robustness, Empirical stress testing, Diagnostic framework, LLM failure modes
AI collapse, Category collapse, Operator‑level coherence, Semantic drift, Large Language Models (LLMs), Stabilization mechanisms, Reasoning collapse, AI interpretability, Universal Principle of Collapse (UPC), Recognition dynamics, Stepwise validation, Constraint application, AI safety, AI robustness, Empirical stress testing, Diagnostic framework, LLM failure modes
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