Contact-Order Prediction of Transformation-Induced AI Decision Failure: AI-1 Pre-Empirical Specification

This pre-empirical technical note develops and freezes a proposed contact-order framework for predicting transformation-induced decision failure in artificial intelligence classifiers. For a task-preserving transformation orbit and pairwise decision margin, the framework identifies the first nonzero local contact order and its associated leading coefficient, producing a prospective prediction of the distance to a decision boundary. The construction is extended to competing multiclass boundaries, remainder-controlled prediction intervals, degenerate first-order behavior, turning boundaries, bidirectional transformation orbits, and least-robust transformation directions. The framework is positioned against established work on adversarial decision-boundary distance, gradient and curvature analysis, geometric transformation robustness, and robustness certification rather than claiming those established concepts as new. A frozen MNIST rotation experiment is specified to test whether finite contact-order information provides predictive value beyond confidence, initial decision margin, first-order directional sensitivity, and second-order curvature. Hypotheses H1-H4, prediction and evaluation windows, baseline comparisons, and falsification criteria are fixed prior to observing experimental outcomes. No empirical performance claim is made in this version.