Data-Nova Progression Rate

The Data-Nova Progression Rate is the speed at which a system moves through the four Data-Nova stages, which the book makes inversely proportional to the number of accessible modes. In Zinf-limit systems the Four-Nova Pathway runs in its fastest form. Each stage, topological or inside-out closure (DN1), a persistent physical realm with stable occupancy (DN2), cohesion through global phase-lock across the minimal mode set (DN3), and temporal or causal lock with the group-speed bound v_g ≤ 𝒞→ achieved system-wide (DN4), is reached more quickly when few modes compete: small closure volume lowers the energy needed, few competing phases speed coherence, and simple causal relations speed temporal stabilization. The book concludes that the Zinf torus is the most efficient substrate for recursive intelligence emergence, turning geometric scarcity into accelerated coherence. Among the Chapter 8.8 predictions, progression rate ∝ 1/(N⥥)² is to be tracked through complexity metrics during recursive intelligence development. The formal statement is DN▷⥂ ∝ 1/N⚚ ∝ 1/(N⥥)², where N⚚ is the number of accessible modes, with N⚚ ∼ (N⥥)², and N⥥ the minimum cells per loop for a stable sinusoidal mode. All quantities are dimensionless [∅]; the book's check is consistent. It connects to the Four-Nova Pathway, Data Nova, Computational Throughput, Causal Margin and Zinf-Limit Universe.