NOIRÉA: Session-Bounded Identity Isolation in Adaptive Physical AI Systems
Year: 2026
Type: preprint
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.
Author: Lin, Hui-Pi
Language: English
Topic: Access Control and Trust
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7165017137
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