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 establishes the high-dimensional formal expansion of $\text{Meta-Axioms (MA)}$ and $\text{Meta-Transitions (MT)}$, 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. Then we must strictly adhere to these principles to declare theoretical perfection: $w \in \mathbb{[}a, b\mathbb{]}$ and $\min(a, b) \leq w \leq \max(a, b)$

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-05
DOI
https://doi.org/10.5281/zenodo.22221057
Primary Topic
Model Reduction and Neural Networks
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
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 establishes the high-dimensional formal expansion of $\text{Meta-Axioms (MA)}$ and $\text{Meta-Transitions (MT)}$, 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. Then we must strictly adhere to these principles to declare theoretical perfection: $w \in \mathbb{[}a, b\mathbb{]}$ and $\min(a, b) \leq w \leq \max(a, b)$

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

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.