Virtual Invasion Dynamics: Mutual Invasion of Self-Organizing Attribute Clusters — From Boundary Generation to Actual Condensation: A Formal Outline of an Interdisciplinary Meta-Framework

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-14
DOI
https://doi.org/10.5281/zenodo.22743458
Primary Topic
Neural Networks and Applications
Type
preprint
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preprint

Virtual Invasion Dynamics: Mutual Invasion of Self-Organizing Attribute Clusters — From Boundary Generation to Actual Condensation: A Formal Outline of an Interdisciplinary Meta-Framework

Trace Drift
Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Applications
preprint

Virtual Invasion Dynamics: Mutual Invasion of Self-Organizing Attribute Clusters — From Boundary Generation to Actual Condensation: A Formal Outline of an Interdisciplinary Meta-Framework

Trace Drift
preprint en

Abstract

This paper presents the Virtual Invasion Dynamics (VID) framework, a meta-theory describing "how interaction is possible." The framework's core claims are: interaction precedes entities; entities are emergent products of interaction processes reaching condensation thresholds; time is not a background parameter but the accumulation of irreversible marks left by invasion; boundaries are products of condensation rather than prerequisites. The framework comprises a global constraint layer (attribute lattice L, partial order ≼, Möbius ring field theory, time projection, self-referential structure, dynamic category Inv) and a local dynamics layer (virtual invasion process operator I, phase transition criterion Ψ, condensation operator G, invasion-response function H). This paper provides eight closure clauses and validates the framework's operability on three instances: the chicken-egg problem (pure logical demonstration), Marin d2048 training (real data validation, condensation moment n* ≈ 5%), and the N-S proof event (qualitative analysis). The framework is at the formal outline stage; unclosed items are marked. Version v0.2 update: Three previously unclosed items are resolved: iterative convergence of the condensation threshold ρ_c (proved via Banach fixed-point theorem), the observation window k (fixed at k = |L|, justified by minimal complete coverage), and the dimensional system (completed in Appendix A). Remaining unclosed items: constructive proof of Fix(F), and precise definition of ∇Φ_I in metric-free regions.

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