This theoretical framework explores LLM hallucination as a creative glitch in computational text generation, proposing new insights for AI and aesthetics.
Large language model (LLM) hallucination is almost universally framed as a pathology to be corrected. This paper proposes an alternative theoretical framework, arguing that hallucination is the textual equivalent of a computational glitch—a stochastic deviation that simultaneously reveals the underlying architecture of the system and produces semantically generative output. Drawing on three theoretical traditions—glitch art theory [Menkman, 2010, Betancourt, 2016], poststructuralist authorship theory [Barthes, 1977], and the philosophy of mind [Searle, 1980]—we develop what we term the Stochastic Glitch / Zero-Author (SGZA) framework. This framework holds that: (1) hallucination and creativity are mechanically identical processes, distinguished only by evaluative frame; (2) the LLM constitutes the most literal realization of the Barthesian scriptor —a zero-author that produces meaning-bearing text without intention; and (3) the interpretive labor released by this zero-authorship falls entirely to the reader, in a stronger sense than Barthes originally envisaged. Crucially, we distinguish this from claims that LLMs “are creative”: following Searle’s Chinese Room argument, we maintain that glitch-as-beauty resides in the accident, not in the agent. The SGZA framework offers a productive alternative to both the “bullshit” frame [Hicks et al., 2024] and uncritical celebrations of machine creativity, with implications for AI aesthetics, human–AI collaboration, and the epistemology of generative systems.
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Xavier Vinaixa Roselló (2026) studied this question.
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