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This paper argues that probability theory is a historically evolving form of rationality, tracing its development from combinatorial games to Bayesian inference and contrasting it with fuzzy logic and deep learning.
This article explores model collapse not as a technical bug but as an epistemic problem: when an AI model's outputs become its own inputs, the model's representation of reality gradually flattens into a self-referential average, raising questions about how we distinguish a model that models the world from one that models only itself.