AD/ZF Framework: A Constraint–Dynamics View of Learning via Dynamic Tension

This work introduces the AD/ZF framework, a minimal extension of standard optimization theory that incorporates both objective error and local adaptation difficulty. We define dynamic tension as a scalar observable combining loss and gradient magnitude. We propose a simple experimental protocol showing that models with identical loss can exhibit different behaviors, and that tension provides a more informative indicator of stability. This deposit includes the full article.