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 andtool use.That lets an agent say the right things about itself evenwhen the grounded constraints that should make thosestatements action-guiding are not jointly active at themoment of action selection.To address this problem, we apply Stack Theory'sdistinction between apparent and genuine agent identity.This separates window-level co-occurrence, understood ascorrelational evidence,from co-instantiation at a single objective step, whichconstitutes a minimal synthesis conditionnecessary (though not sufficient) for identity constraintsto jointly govern behaviour.We then instantiate Stack Theory's WeakSync and StrongSyncpostulates on grounded identity statements.This yields two persistence scores that can be computedfrom instrumented scaffold traces.We connect these scores to five operational identitymetrics and map common scaffolds into an identitymorphospace that exposes predictable tradeoffs.The result is a conservative toolkit for identityevaluation.It separates talking like a stable self from beingorganized like one.