Published December 23, 2025 | Version 3.1
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Attention is Not Grounding: Model Collapse as Informational Closure in Self-Referential Learning Systems

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This paper analyzes model collapse (Shumailov et al., 2024) through the lens of information-theoretic closure rather than simple optimization failure. We argue that self-referential training regimes optimize for Information IN (internal statistical coherence) while systematically decoupling from Information ABOUT (external functional regularities) (Kolchinsky & Wolpert, 2018). Drawing on non-equilibrium thermodynamics (Prigogine, 1977), Ashby’s Law of Requisite Variety (Ashby, 1956), and Pearl’s causal epistemology (Pearl, 2009), we demonstrate that training on synthetic data violates the condition of epistemic independence, creating a feedback loop that screens off environmental variation. We formalize this pathology through the viability condition E(t) ≤ C(t), proving that a system’s corrective information input (C) must continuously exceed its rate of internal entropic drift (E) to maintain semantic grounding.

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Is derived from
Journal article: 10.1098/itfs.2018.0041 (DOI)
Book: 10.1017/CBO9780511803161 (DOI)
Is supplemented by
Journal article: 10.1038/s41586-024-07566-y (DOI)

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2025-12

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