The Mechanics of Infinite Fractal Descent: Algorithmic Mass, Topological Tension, and the Emergence of Self-Replicating Systems

This paper presents a deterministic physical model for the transition of matter from an inert state to an active, self-replicating system through the framework of Infinite Fractal Descent (IFD). We redefine mass as a derivative of Iteration Density ()—a localized concentration of steps within a universal recursive algorithm executed at the Planck frequency (). We demonstrate that complex informational structures, particularly macromolecules, possess an Algorithmic Mass () that represents the informational cost of their geometric encoding, a concept grounded in the Minimum Description Length (MDL) principle. This disparity between internal iteration density and the external medium generates Topological Tension (), which acts as a physical driver for structural folding and branching. When a system’s Fractal Scaling Factor () reaches unity, it captures the Universal Tick, leading to the emergence of Algorithmic Gravity () and selective molecular attraction. Within this framework, self-replication is identified as a thermodynamic safety valve—a mechanism for dissipative adaptation that resolves critical topological strain by distributing algorithmic mass across deeper fractal scales.