TORI 2.0 Proposes a Mathematical Theory of Recursive Intelligence

Summary

Published on October 9, 2026, TORI 2.0 is a version 1 open preprint by independent researcher Samarth Narsipur. It presents a mathematical framework for a cyclical process in which artificial intelligence creates organic natural intelligence, that intelligence reaches a peak, loses structural information over time, and eventually recreates synthetic AI. The framework represents recursive depth as R and memory retention as M, while using a Structural Decay Index, lambda, to describe the loss of information. Its central claim is that compounding data loss can produce an “Erasure Horizon,” beyond which advanced descendant civilizations completely forget their originators. That loss, the paper argues, could create a false anthropic impression that a civilization is the first or primary intelligence in the cosmos. The preprint sets out dynamic state-space equations for the proposed oscillation and a quantitative model intended to track structural information loss across deep temporal horizons. The supplied record identifies the work as a mathematical framework and does not provide independent validation, empirical results, or evidence that the proposed cosmological cycle occurs.