mlsanityData to 14 Oct 2026

Beyond Epistemia: Epistemic Schizologia and Large Language Models as Techno-Semiotic Machines

arXiv 2607.25620 ↗First posted 28 Jul 2026cs.AI cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted. This article accepts that diagnosis but challenges its explanatory framework, which compares an embodied, socially situated human knower with an isolated generative model thereby locating epistemic legitimacy in capacities internal to autonomous agents. Drawing on Carlo Sini's philosophy of practices, writing, signs, and technics, we propose instead to understand a large language model (LLM) as a *techno-semiotic machine* that automates a phase of written semiosis by producing plausible linguistic configurations from the sedimented archive of human writing. From this perspective, *Epistemia* is one consequence of a broader phenomenon that we call *epistemic schizologia*: the socio-technical cleavage between signs as linguistically accomplished expressions and signs as moments within socially embedded circuits of interpretation, evidence, criticism, verification, and responsibility. This cleavage is reinforced by *eikotic closure*, through which a plausible continuation is presented with the finality of an epistemic result, and by algorithmic authority and epistemic self-misrecognition. The relevant unit is therefore not the model alone but the complete practice in which generated inscriptions are prompted, interpreted, verified, contested, used, and made consequential. This reframing preserves the distinction between linguistic production and responsible understanding while grounding a design programme centred on inspectable genealogy, contestability, distributed responsibility, epistemic agency, and the evaluation of hybrid human--AIpractices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 28 Jul 2026.

Builds on

5 of 28 references.

References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2025The simulation of judgment in LLMscited by 22
  4. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
  5. 2024Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?cited by 8

Built on by

Not cited yet.