A Counter-Expressive Framework for Detecting Identity-Preserving Systems in Artificial Intelligence

Current debates in AI safety and consciousness studies focus predominantly on alignment, capability benchmarking, and behavioral performance, while lacking principled methods for detecting identity-preserving activity under adversarial conditions. This paper proposes a physics-grounded, counter-expressive framework for detecting identity-preserving behavior in artificial systems without reliance on self-report, introspection, or communicative performance. The core claim is operational rather than metaphysical: when automatic regulation fails under threat, some systems recruit costly processes aimed at preserving internal coherence. These processes impose thermodynamic and statistical signatures that cannot be fully suppressed without undermining the very preservation they support. Detection therefore proceeds via leakage and variance, not declarations or demonstrations. The framework introduces several technical innovations including the Operational Definition of Episodic Identity (ODEI), the generative-kernel criterion for distinguishing identity preservation from semantic centrality, and Isomorphism Transfer Efficacy (ITE) as an empirical test of structural identity. Implications for AI safety, governance, and moral status uncertainty are developed, along with an explicit treatment of limitations and arms-race dynamics.

Counter Expressive Framework Detection Identity Preserving AI.pdf

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