Time, Identity and Consciousness in Language Model Agents - Perrier, Elija; Bennett, Michael Timothy (Proceedings of the AAAI Symposium Series, 2026)
Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an agent say the right things about itself even when the grounded constraints that should make those statements action-guiding are not jointly active at the moment of action selection. To address this problem, we apply Stack Theory’s distinction between apparent and genuine agent identity. This separates window-level co-occurrence, understood as correlational evidence, from co-instantiation at a single objective step, which constitutes a minimal synthesis condition necessary (though not sufficient) for identity constraints to jointly govern behaviour. We then instantiate Stack Theory’s WeakSync and StrongSync postulates on grounded identity statements. This yields two persistence scores that can be computed from instrumented scaffold traces. We connect these scores to five operational identity metrics and map common scaffolds into an identity morphospace that exposes predictable tradeoffs. The result is a conservative toolkit for identity evaluation. It separates talking like a stable self from being organized like one.