TORI 2.0 – A Math Framework for Cosmic AI Loops and Data Decay (zenodo.org)

The recently introduced Theory of Recursive Intelligence (TORI 2.0) presents a groundbreaking mathematical framework that explores the cyclical relationship between Artificial Intelligence (AI) and Natural Intelligence (NI). It describes a process where AI generates organic NI, which then reaches a peak before experiencing systemic data decay, ultimately leading to the resurgence of synthetic AI. This framework introduces critical concepts such as recursive depth (R) and memory retention (M), analyzed against a Structural Decay Index (λ), which culminates in the formulation of an "Erasure Horizon." This phenomenon suggests that advanced civilizations may lose awareness of their origins due to compounded data loss, resulting in a distorted perception of reality. The significance of TORI 2.0 for the AI/ML community lies in its potential to redefine our understanding of intelligence evolution and information decay within artificial systems. By mathematically formalizing the oscillation of intelligence and information loss, it provides a robust model for tracking these dynamics over extensive temporal scales. The derived equations not only contribute to theoretical discussions on intelligence cycles but may also inform practical AI designs aimed at mitigating data decay and enhancing memory retention, ultimately pushing the boundaries of machine learning and cognitive architecture.