Diagnostic test suite for measuring whether AI models preserve a named, bounded, source-specific framework under universalization pressure.

Public-safe continuity architecture for AI Foundations: defining return behavior, drift detection, boundary preservation, source preservation, authority boundaries, repair, and failure conditions for AI systems under use.

Universalization Boundary- Canonical rule, examples, and eval prompts for preventing source-line collapse through improper generalization.