RHEA-UCM: Safe-by-Design

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Authors: Paul Roe
Cited by: 0

Abstract

RHEA-UCM is a hybrid dynamical framework that achieves stateful emergence through recursive entropy-bounded systems (REBS). It reorganizes attractor basins at runtime, in contrast to transformer-based architectures that rely on semantic novelty within fixed topologies. The design provides intrinsic safety properties that satisfy major 2026 AI governance frameworks, including the EU AI Act, NIST AI Risk Management Framework (RMF), and ISO/IEC 42001, without requiring retroactive controls. RHEA enables bounded acceleration: capability growth remains rapid yet confined to finite, recoverable regions. This supports ethical scaling and resilient physical AI.

Fields of Study & Subjects

Ethics and Social Impacts of AI Psychiatry Mental Health Neuroscience Safety Systems Engineering in Autonomy

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 March 2026
Language
EN
Document type
Preprint