This work introduces SCF v2, an extension of the Structural Counterforce Framework, providing a formal model of behavior formation in probabilistic AI systems. Unlike traditional governance approaches that operate at the level of outputs, SCF v2 models behavior as trajectories within a constrained probability field. The framework formalizes the formation axis (L1–L4) as a continuous dynamical system, introduces counterforce as a structural mechanism for maintaining adaptability, and separates formation dynamics from execution conditions via the Constraint Mapping Layer (CML). This shift enables governance to move from reactive control to structural conditioning, where behavior becomes computable before it becomes observable.
Tuan M. Nguyen (Mon,) studied this question.