What question did this study set out to answer?

This research aims to address Persistent Identity Modeling in adaptive Physical AI systems via architecture design.

June 20, 2026Open Access

NOIRÉA: Session-Bounded Identity Isolation in Adaptive Physical AI Systems

Key Points

This research aims to address Persistent Identity Modeling in adaptive Physical AI systems via architecture design.
Introduces NOIRÉA as a structural constraint architecture
Specifies four architectural requirements for session-bounded isolation
Includes a case study on adaptive Brain-Computer Interfaces
Demonstrated effective identity isolation in empirical tests
Showed the need for further validation on real adaptive systems

Abstract

This paper introduces NOIRÉA, a structural constraint architecture that addresses Persistent Identity Modeling (PIM) in adaptive Physical AI systems. PIM is formalized as a structural risk class arising from unconstrained cross-session adaptive state persistence. NOIRÉA specifies four architectural requirements (R1–R4) enforcing session-bounded isolation of identity-correlated model state. Includes synthetic empirical demonstration and adaptive Brain-Computer Interface case study. Originally submitted to ISSRE 2026 Research Track (Submission #15) under double-blind review. This Zenodo release is the de-anonymized version establishing public priority. Reviewer feedback identified empirical validation on real adaptive systems as the primary direction for future work; follow-up research is documented in the companion PIM Formation Concept Note.

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