Certification Under Evolving Observability: Operativity, Latent Invalidators, and Causal Diagnostic Robustness

A transformation can appear perfectly preserving under every observation available today and still create the conditions under which tomorrow’s observations reveal that preservation was false. We formalize this problem as certification under evolving observability. A complete state carries both an ordinary configuration and a verifier-generating structure; Present Certification (PC) evaluates only currently realizable verifiers, whereas Absolute Preservation Stability (APS) quantifies over verifiers reachable after the transformation and subsequent admissible evolution. Their gap defines Self-Invalidating Preservation (SIP). We prove exact characterizations through latent invalidators and observational partitions, show a singleton-emergent-verifier equivalence with reachability, derive decidability and undecidability transfers, and establish a scoped strict extension over compatibility semantics whose verifier vocabulary is fixed independently of the transformation. We also show that APS naturally reduces to relational safety in an augmented product state, preventing claims of superior decision power over conventional verification. The framework is implemented as a reproducible Python analysis pipeline. Exact enumeration covers 144 finite systems with zero characterization violations; controlled scenarios agree 4/4 with an independent product-state baseline. Four externally grounded evolution cases support causal diagnostic transfer. Under 5–50 non-causal distractor observables, causal upstream selection retains 100% diagnostic accuracy while nearest, cheapest, and random difference baselines fall to 50%, 0%, and 0–25%. The contribution is therefore a transformation-relative certification and diagnostic layer for systems whose future observability evolves.