Recursive Capacity Law

The Recursive Capacity Law is the relation f(n) = (n + 1)² by which structural capacity grows quadratically with each recursive step, and which decides when that capacity exceeds the containment of the Toroidal Substrate. The book cites it in the chapter-3 summary: the law determines when accumulated computational capacity exceeds Toroidal Substrate containment, triggering dimensional reorganization at the threshold step n* = ceil(√χ_∂ − 1). It is the engine of Toroidal Genesis and Data Nova Escalation, the reason, in the book's account, that dimensional expansion happens through quantized structural transitions rather than smooth inflation. When the data it counts and that data's null together fill the Bifold's record, the null reorganizes into the closed-loop Toroidal Substrate. f(n) = (n + 1)², where f(n) is the structural capacity after n recursive steps and n the recursion step, both dimensionless [∅]. It connects to Recursive Capacity Growth, Containment Crossing Condition, The UniSpheral Data Nova Threshold Law, Toroidal Genesis, Data Nova Escalation and Toroidal Substrate.