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

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Abstract

AI hallucination is not an engineering bug — it is the epistemological ceiling of inductive reasoning applied to non-stationary environments. This paper traces the problem from Hume’s Problem of Induction through PAC learning bounds to the No Free Lunch theorem, proving that zero hallucination is mathematically impossible in open systems. The root cause is not insufficient data or compute, but the fundamental limitation of pattern-matching on historical observations to predict novel futures.

Key Contributions

• Formal proof (No Induction Completeness Theorem) that hallucination rate is bounded away from zero in open systems
• Mapping of Hume’s epistemological framework to modern ML generalization theory
• Identification of education systems as the upstream source of AI’s inductive bias

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