What question did this study set out to answer?

The aim is to transform AI from mere prediction systems into architectures capable of achieving semantic meaning.

July 27, 2026Open Access

Semantic AI Architecture

Key Points

The aim is to transform AI from mere prediction systems into architectures capable of achieving semantic meaning.
Proposed an eight-layer infrastructure for Semantic AI based on quaternion algebra and Bateson’s orders of learning.
Described operational elements like a State Topology Registry and a Quaternion Governor to manage knowledge and interpretive processes.
Demonstrated the structure's capability to support distinct operational modes for thinking, dreaming, and hallucination.
The architecture successfully decouples meaning from learning, enabling AI to achieve interpretive closure.
It supports a range of cognitive functions, such as thinking and dreaming, distinctively within its framework.

Abstract

Current artificial intelligence architectures, from transformers to diffusion models, are sophisticated engines of statistical optimization. They minimize prediction error over continuous time, but they remain structurally incapable of achieving meaning. We propose a paradigm shift: from prediction engines to closure engines. Drawing upon a formal axiomatic framework grounded in quaternion algebra and Bateson’s orders of learning (Bateson, 1972; Isong, 2026a), we present an eight-layer infrastructure for a Semantic AI that is event-driven, time-independent, and capable of genuine interpretive closure. The architecture features: (1) a persistent State Topology Registry (Tn) as the relational substrate of knowledge; (2) a Quaternion Governor (qn) that coarse-grains topological invariants; (3) a Tension Monitor (rn) that measures interpretive open-loop magnitude; (4) an Endogenous Generator () that produces virtual symbols from internal tension; (5) a Volitional Agency Policy () that chooses interpretive stance, intensity, and source interrogation; (6) a Signal-Dependent Relaxation operator (Cn); (7) a Topology Rewriter triggered by Learning-II events (SO4 reconfiguration); and (8) an Expressive Output Channel (Exp) that externalizes achieved closure. We demonstrate that this architecture decouples meaning (n) from learning (n), while formally supporting thinking, dreaming, hallucination, and expression as distinct operational modes. This paper serves as the technical blueprint for building AI that does not merely mimic syntax but achieves semantic closure.

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