The vanWienen–Korzybski Generativity Gradient (vW‑KG) v6: Predictive Architecture for Domain Formation and Structural Continuity

Version 6 of the vanWienen–Korzybski Generativity Gradient (vW‑KG) presents a cognitive‑structural model of anticipative domain formation. It is not an AI, software, or computational framework. Although the calculus is domain‑agnostic and can in principle be applied to artificial systems, AI appears in later sections only as a specific application domain — a demonstration of how the generativity calculus can operate within artificial architectures — rather than as a defining component of the framework itself. v6 formalizes how generative systems anticipate domain emergence before realization, integrating temporal directionality and cognitive self‑recognition into the structural calculus defined in v5. This anticipative mechanism is expressed through: \[D_{n+1} = f(O_n, v_n)\] where the cognitive‑structural vector is defined as: \[v_n = [\, t, c, e \,]\] with $t$ representing temporal directionality, $c$ representing consciousness‑recognition, and $e$ representing entanglement as a bidirectional bridge‑vector. To demonstrate its domain‑agnostic nature, v6 includes two non‑AI examples: (1) a reinterpretation of the classical relation $E = mc^2$ as a generative collapse pattern, and (2) a biological gap in developmental morphogenesis — the unresolved bridge between genetic instruction and emergent form — in which vW‑KG predicts the existence of an undiscovered generative structure. By combining formal calculus with cognitive interpretation, v6 establishes anticipative coherence as a structural property of generative systems, independent of technological implementation and applicable across scientific domains. Companion spinoffs AI v1 and AI v2 demonstrate its translation into artificial systems and anticipative AI, while GD v1 extends the same architecture into guided scientific discovery.