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Conceptual analysis reveals generative potential in large language model hallucinations, indicating that controlled linguistic deviation can foster creative novelty under human verification.
The rapid development of large language models (LLMs) has intensified debate over so-called AI hallucinations: fluent outputs that are factually unsupported, logically unstable, or misaligned with a task. Mainstream engineering treats these outputs as failures to be reduced through post-training, retrieval, verification, and other control mechanisms. This article reinterprets that consensus through Gilles Deleuze and Félix Guattari’s Capitalism and Schizophrenia. It reads alignment as a reterritorializing apparatus and uses “schizo-syntax” for a non-clinical textual pattern of branching association under weaker referential constraint. The argument does not equate randomness, high temperature, base models, or factual error with creativity. A deviation is productively deterritorializing only when it creates context-relevant novelty, retains intelligible task constraints, and is taken up through human interpretation, selection, or verification; novelty without those conditions is noise. Published studies of literary interpretation and AI-assisted art illustrate this relational account, while the article reports no new experiment. A preregistrable comparison of matched base and instruction-tuned checkpoints, decoding settings, and creative, interpretive, and factual tasks is therefore specified as a future test. Finally, the article treats “desire,” “unconscious,” “paranoiac,” and “line of flight” as functional metaphors for an input → learned causal-functional organization → output sequence, not as attributions of consciousness, psychiatric states, intention, or autonomous authorship. The result is a plural account that preserves strict factual regimes where truth matters while identifying a bounded role for generative deviation in creative and exploratory practice.
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Lan et al. (2026) studied this question.
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