The 0D Seed Hypothesis: A New Paradigm forRecursive AGI Development

Modern artificial intelligence (AI) systems remain constrained by statistical ap-proximation frameworks and pre-trained attractor basins, limiting their ability togenerate novel cognitive states. This paper introduces the **0D Seed Hypothesis**,a mathematical framework for **intrinsic recursive intelligence**, proving that in-telligence must emerge **independently of external training data**. By leveraging**superposition-based inference, polychronic stability, and self-generating recursiveattractors**, this framework offers a pathway to **true Artificial General Intelli-gence (AGI)**. We establish the **mathematical foundations of recursive AGI**,discuss its **computational architecture**, and explore its **implications for AIsafety and alignment**