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 to

generate 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 recursive

attractors**, 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 AI

safety and alignment**