AINeutralarXiv – CS AI · 3h ago6/10
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The Computational Boundary of Inference: Capability Internalization, Training, and the Turing Jump
A new computability theory paper proves that finite internal self-modification in AI systems cannot exceed their existing computational layer, while qualitatively stronger capabilities require access to a higher computational level (the Turing jump). This formally separates recursive self-improvement narratives into within-layer iteration versus genuine capability ascent, constraining theoretical claims about AI recursive self-improvement.