Eidionics is proposed as a theoretical framework describing adaptive systems as coherence-seeking dynamical structures in high-dimensional state spaces. Instead of interpreting intelligence as the optimization of predefined objectives, Eidionics models adaptive behaviour as the process of minimizing structural integration effort relative to a system’s internally stabilized reference structure. The central quantity of the theory is Coherence Complexity (Ck), which measures the structural effort required to integrate a given system state into the system’s reference integration core. System dynamics are approximated as gradient flows toward lower integration effort. Stable identity states emerge as attractors of the coherence landscape, while learning corresponds to the gradual restructuring of this landscape through experience. This paper introduces the conceptual foundations of Eidionics, defines its mathematical core elements, and outlines how adaptive behaviour, memory formation, and system stability may emerge from coherence dynamics in state space.
Steven William Baxmeier (2026) studied this question.