The Epistemology of Hallucinations: The Limits of Truth in Linguistic Models

This article provides an in-depth analysis of the philosophical and technical foundations of hallucinations in large language models (LLMs). The author contrasts classical inference methods—deduction and induction—to demonstrate that AI systems operate in the realm of probability, not absolute truth. A key element of the text is the introduction of auction theory as a model explaining how attentional heads aggregate signals and why this leads to confabulation. The reader will learn how the proper scoring rule and the convex function influence content generation in uncertainty space. This is an essential work for those seeking to understand the epistemic compromise underlying the contemporary cognitive architecture of transformers and the mechanisms of semantic error generation.