The Inductive Dilemma of AI Hallucination: Epistemological Limitations of Current Language Models and a Three-Layer Solution Framework

Abstract

Large language models (LLMs) generate confident but factually incorrect outputs, a phenomenon widely known as "AI hallucination." Current literature attributes this to training data quality, model scale, or alignment failures. We argue these are symptoms, not causes. The root cause is epistemological: LLMs are fundamentally inductive reasoning systems operating in a non-stationary world. We propose the "Chess Game Fallacy," where AI excels in closed-rule systems but systematically fails in open systems where rules can change. If the world operated under fixed rules, no one would break the law. The very existence of lawbreakers is empirical evidence that the world is not a closed system, and inductive reasoning alone is insufficient. We further trace this problem to its educational roots: AI training data originates from humans educated within inductive-method pedagogies. This is not a leaking pipe; the water is contaminated at the source. Based on cross-platform empirical testing, we propose a three-layer solution framework: anchored deduction, probabilistic quantification, and honest admission of ignorance. Finally, we argue that the core of U.S.-China AI competition is not a contest of computing power, but of whose models can absorb deeper layers of human practical wisdom. The tacit knowledge that cannot be easily formalized is the ultimate determinant of a model's cognitive ceiling.

Keywords: AI Hallucination, Inductive Reasoning, Frame Problem, Large Language Models, Epistemology, Praxis Theory