Multi-scale active inference in coupled bio-computational systems: from predictive coherence to phase transitions in hybrid agency
Abstract This paper introduces a unified framework for understanding agency as an emergent, multi-scale property of coupled bio-computational systems. Extending classical active inference beyond isolated agents, we propose that cognition, adaptation, and systemic stability arise from the minimization of multi-domain incoherence across informational, biological, and physical substrates. By integrating exception-driven learning dynamics (TAE), human–AI predictive coupling (CPEA), and field-based physical constraints (METFI), we develop a generalized model in which learning is inherently non-linear and characterized by phase transitions. We formalize the conditions under which distributed agency emerges, define metrics for phenotyping hybrid systems, and propose an experimental program grounded in EEG–AI coupling. This work establishes a conceptual and computational pathway toward scalable hybrid intelligence systems and provides a foundation for reinterpreting cognition as a coherence-ma...