The widespread assumption that logical coherence implies truth is increasingly challenged in the context of contemporary artificial intelligence systems. This paper examines the philosophical claim that what is logically consistent is not necessarily true, and investigates its implications for the behavior and evaluation of Large Language Models (LLMs). Unlike traditional reasoning systems grounded in formal logic or empirical verification, LLMs generate outputs based on probabilistic pattern recognition, optimizing for linguistic coherence rather than factual accuracy. As a result, these models can produce arguments that are internally consistent and highly persuasive, yet fundamentally detached from reality. This work argues that LLMs do not fail at truth-seeking; rather, they are not inherently designed for it. Instead, they simulate reasoning by reproducing patterns of logical structure present in their training data, creating an "illusion of reasoning" that can obscure the distinction between valid argumentation and true claims. The paper further explores how this distinction affects the evaluation of knowledge, particularly in contexts where coherence, clarity, and rhetorical strength are mistakenly treated as indicators of correctness. By analyzing the epistemic limitations of coherence-based systems, this paper highlights a critical gap between logical form and factual grounding in AI-generated content. It concludes by proposing a conceptual framework for separating coherence from truth in the design and assessment of intelligent systems, emphasizing the need for hybrid approaches that integrate logical consistency with mechanisms of external validation.
Authors: Momen Ghazouani
Publish Year: 2026
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