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
- Sermsak Yingsomsuk (ORCID: https://orcid.org/0009-0009-5385-0723)
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