vanWienen–Korzybski Predictive Extension (vW‑KG‑PX)

The vanWienen–Korzybski Predictive Extension (vW‑KG‑PX) introduces the predictive layer of the vW‑KG generative architecture. While vW‑KG‑Gradient (v3) formalizes the structural conditions under which domains arise, stabilize, and transform, vW‑KG‑PX operationalizes these conditions into a generative predictive framework. PX provides a domain‑bound predictive mechanism that does not rely on universal equations, but instead derives prediction from the internal generativity of each domain. This approach reframes prediction as an emergent, architecture‑driven process rather than a cross‑domain reduction. The framework introduces three predictive patterns: the Generative Prediction Pattern (GPP), the Domain‑Bound Predictive Pattern (DBPP), and the Field‑Dependent Predictive Pattern (FDP). Together, these patterns describe how generative gradients, asymmetries, and domain‑specific constraints interact to produce stable, falsifiable predictions. The Predictive Extension is not a standalone theory; it is the operational layer of the vW‑KG architecture. It demonstrates how generativity becomes measurable, how domain‑bound lawfulness can be expressed without reductionism, and how predictive structure emerges from the same generative principles that produce domains themselves. This preprint is part of the broader vW‑KG corpus, which includes the vW‑KG‑Gradient (architectural layer), UGSF (meta‑generative case), and CE (computational embodiment). PX extends this corpus by providing the first formal bridge between generative architecture and predictive capability.