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A conceptual essay arguing that recursive self-improvement in AI is limited by verification, not computation, using the metaphor of an epistemically closed prompt matrix and the data-processing inequality.
This paper provides an information-theoretic account of when synthetic data improves or degrades LLM training, distinguishing between information-open and information-closed generation loops and explaining collapse via the data processing inequality.