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A theoretical framework exploring two intertwined properties of advanced AI: generativity (the ability to produce novel, human‑level or superhuman outputs across domains) and recursion (the ability to improve itself, generating better AI that generates even better AI, in a feedback loop). The theory examines how recursive self‑improvement could lead to rapid capability gains (an [intelligence] explosion) and how [generative AI] systems might produce training data for their successors, creating closed‑loop evolution. It also considers risks: loss of control, value misalignment, and the difficulty of verifying recursive safety. The theory is central to discussions of artificial general [intelligence] and existential risk.
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