Structural Normalization and Deep Descent: An Abstract Framework

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Authors: Daniel Augusto Jorge Zafaranich
Cited by: 0

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