The Multi-Dimensional Tensor Framework (nD) of The Core Algebraic Kernels: Meta-Axioms (MA) and Meta-Transitions (MT) Breaking the Bivalent Lattice Boundaries

This paper establishing the high-dimensional formal expansion of Meta-Axioms (MA) and Meta-Transitions (MT), advancing the ungrounded 1D scalar kernels into continuous, symmetric tensor spaces spanning the extended real number line $\overline{\mathbb{R}}^n$. Shifting away from the static metrics of scalar limits, we address the non-linear, multi-layered layer configurations through which nature manifests its contextual dimensions. By formalizing the operational continuum operator $\text{OP}_w(a, b)$ and directional dimension-reduction metrics, we demonstrate that prior informational networks natively decouple from traditional discrete constraints and ascend to differentiable tensor fields. This framework unifies multi-context evaluations with irreversible process dynamics, enabling direct, un-degraded computational execution over continuous physical magnitudes without boundary saturation or systemic semantic explosion.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-01
DOI
https://doi.org/10.5281/zenodo.22221058
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

The Multi-Dimensional Tensor Framework (nD) of The Core Algebraic Kernels: Meta-Axioms (MA) and Meta-Transitions (MT) Breaking the Bivalent Lattice Boundaries

Sermsak Yingsomsuk
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

The Multi-Dimensional Tensor Framework (nD) of The Core Algebraic Kernels: Meta-Axioms (MA) and Meta-Transitions (MT) Breaking the Bivalent Lattice Boundaries

Sermsak Yingsomsuk
preprint en

Abstract

This paper establishing the high-dimensional formal expansion of Meta-Axioms (MA) and Meta-Transitions (MT), advancing the ungrounded 1D scalar kernels into continuous, symmetric tensor spaces spanning the extended real number line $\overline{\mathbb{R}}^n$. Shifting away from the static metrics of scalar limits, we address the non-linear, multi-layered layer configurations through which nature manifests its contextual dimensions. By formalizing the operational continuum operator $\text{OP}_w(a, b)$ and directional dimension-reduction metrics, we demonstrate that prior informational networks natively decouple from traditional discrete constraints and ascend to differentiable tensor fields. This framework unifies multi-context evaluations with irreversible process dynamics, enabling direct, un-degraded computational execution over continuous physical magnitudes without boundary saturation or systemic semantic explosion.

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
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