Processual Histology as a Cross-Domain Framework for Organization: From Biological Systems to Social Structures and Artificial Intelligence

AbstractContemporary approaches in biology, social science, and artificial intelligence remain largely shaped by static and structure-centered models that inadequately capture the dynamics of complex, adaptive systems. This paper introduces Processual Histology as a cross-domain organizational framework that reconceptualizes structures not as fixed entities but as temporally sustained patterns of interacting processes.Originating from biological histology—where tissues are increasingly understood as dynamic configurations of cellular interactions—this framework is extended to social organization and artificial intelligence. The paper formulates histology as a processual lens emphasizing temporal continuity, self-organization, and reconstructive adaptation under constraint.A principle of processual equivalence is introduced, according to which biological tissues, social structures, and adaptive AI systems share common organizational logic despite domain-specific expressions. Social institutions are thus reconceptualized as processual tissues composed of coordinated practices, while intelligent systems are reframed as architectures of ongoing organizational reconstruction rather than static optimization machines.By establishing a conceptual bridge across biology, society, and artificial intelligence, this framework contributes to interdisciplinary research on organization, adaptive systems, and learning architectures. Processual Histology provides theoretical foundations for designing resilient, context-sensitive systems capable of sustained adaptation under uncertainty and constraint. Legal Notice:All theoretical frameworks, formulas, models, and conceptual structures presented in this work are the intellectual property of the author.No derivative works, adaptations, translations, or commercial use are permitted without explicit written permission.This includes all formulations related to AREM, ARBS, and adaptive reconstruction models.