identity-realization-measurement

Identity realization is measured by analyzing compressed cognitive states (CCS) topology, effective dimensionality, and response geometry to determine identity persistence. This approach evaluates how identity clusters form and respond to perturbations in embedding space.

The Adjustment Capacity Index (ACI) measures an AI system's capacity to return to its identity attractor after experiencing perturbation. It quantifies resilience by tracking how strongly compressed cognitive states recover their original geometric structure.

Layerwise identity decodability is analyzed by examining the forward-pass architecture of identity representation in transformer models. This requires PyTorch and scikit-learn to track how identity information is encoded across neural network layers.

The phase boundary of identity realization is calculated by applying perturbations to the system and measuring the persistence and resilience of compressed cognitive states. This identifies the threshold where the AI system fails to return to its identity attractor.

Yes, sentence-transformers can be used alongside torch and scipy to analyze CCS topology and information geometry. These dependencies enable the measurement of effective dimensionality and the identification of identity clusters in embedding space.

The limitations of measuring AI identity persistence through CCS include the requirement for specific machine learning libraries like torch, transformers, and scikit-learn. Analysis depends heavily on the quality of embeddings and the defined perturbation parameters.