Constraint Persistence and the Stability of Intelligence: A Structural Synthesis of Explanation, Hallucination, and Long-Horizon Failure

This paper presents a structural synthesis of the YU-AI series, integrating three core results: explanation as exclusion, hallucination as constraint failure, and long-horizon failure as constraint decay. It argues that intelligence is fundamentally defined by its ability to enforce and preserve exclusion. Systems that generate without excluding, or exclude without preserving, exhibit predictable failure patterns regardless of scale or optimization. The paper introduces constraint persistence as a unifying principle governing the stability of intelligent systems. It shows that hallucination arises from missing exclusion, while long-horizon failure arises from the loss of exclusion over time. Together, these phenomena reveal a structural limitation in current artificial intelligence systems. This work reframes alignment, reliability, and generalization as problems of constraint persistence rather than representation or optimization, and positions constraint persistence as a general principle applicable across artificial, biological, and physical systems. This paper serves as the integrative entry point to the YU-AI Series: Constraint-Based Foundations of Intelligence.