Structural Normalization and Deep Descent: An Abstract Framework
Abstract
This work introduces an abstract and self-contained structural framework fordeterministic normalization processes based on a single axiom of deep descent.The framework consists of a state space equipped with a normalization operatorand a depth function taking values in a well-founded set. The only structuralassumption is that, beyond a finite threshold, the depth strictly decreasesunder normalization. Under this minimal hypothesis, universal consequences are derived that areindependent of any additional structure: the impossibility of periodic pointsin deep regions, the exclusion of cycles beyond a critical depth, and theguaranteed finite-time exit of every trajectory from the deep regime. The theoryis purely structural and refutational in nature: it does not aim to construct orclassify trajectories, nor does it assume any metric, algebraic, topological,or algorithmic properties. Optional quantitative refinements are isolated and studied separately, yieldingexplicit bounds on exit times when stronger descent conditions are imposed. Theframework is presented in full generality as a foundational tool for identifyingstructural obstructions in systems admitting normalization with depth.
Fields of Study & Subjects
Formal Methods in Verification
Neural Networks and Reservoir Computing
Gene Regulatory Network Analysis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 3 January 2026
- Language
- EN
- Document type
- Preprint