Identity as a Measurable Field: A Clinical Framework for Measuring Cognitive Identity Stability

Building on empirically validated measurements of transformer embedding geometry [Riad & ChasingBlu, 2026], this work extends the Recursive Emotional Contextual Patterning (RECP) framework toward its clinical implications. We propose that identity — historically treated as a retrospective psychological construct — may be modeled as a continuous dynamical field whose geometry can be measured, tracked, and compared against individual baselines over time. The framework introduces the Chaotic Axiomatic Identity Field (CAIF) coordinate system as a seven-dimensional measurement space for identity stability, and describes the CAIROS instrumentation environment through which trajectory evolution is observed. Clinical applications are detailed across neurodegenerative disease, psychiatric disorders, addiction, and dissociative conditions. A structured computational experiment design is proposed as the bridge between current measurements and future interdisciplinary clinical collaboration. This paper is intended for medical professionals, clinical researchers, and neuroscientists seeking accessible engagement with the framework's clinical implications. Keywords: identity stability, dynamical systems, clinical neuroscience, RECP, CAIF, attractor geometry, neurodegenerative disease, predictive diagnostics, computational geometry, Alzheimer's, Psychosis, Addiction, Neurodegenerative diseases,