A Novel Architecture for Understanding, Context Adaptation, Intentionality and Experiential Time in Emerging Post-Generative AI Through Sophimatics

Abstract

Contemporary artificial intelligence is dominated by generative systems that excel at extracting patterns but fail to grasp meaning, sense, context, and experiential temporality. This limitation highlights the need for new computational wisdom that combines philosophical insights with advanced models to produce AI systems capable of authentic understanding. Sophimatics, as elaborated upon in this article, is introduced as a science of computational wisdom that rejects the purely syntactic manipulation of symbols characteristic of classical physical symbolic systems and addresses the shortcomings of generative statistical approaches. Building on philosophical foundations of dynamic ontology, intentionality and dialectical reasoning, Sophimatics integrates complex temporality, multidimensional semantic modeling, hybrid symbolic–connectionist logic, and layered memory structures to that the AI can perceive, remember, reason, and act in ethically grounded ways. This article, which is part of a set of papers, summarizes the theoretical framework underlying Sophimatics and outlines the conceptual results of the materials and methods, illustrating the potential of this approach to improve interpretability, contextual adaptation, and ethical deliberation compared to basic generative models. This is followed by a methodology and a complete formal model for translating philosophical categories into an operational model and specific architecture. This article represents Phase 1 of a six-phase research program, providing mathematical foundations for the architectural implementation and empirical validation presented in companion publications. Following this, several use cases are outlined, and then the Discussion Section anticipates the main results and perspectives for post-generative AI solutions within the Sophimatic paradigm.