Published December 24, 2025 | Version v1
Preprint Open

Recursive Self-Localization: Axiom VIII of the Universal Model Framework.

Authors/Creators

Description

The Universal Model Framework (UMF) develops physical reality from a minimal set of distinction-logic axioms, prime-indexed renormalization structure, and information-theoretic constraints. While earlier axioms establish existence (Prime-Selective Actualization) and dynamical consistency (RG-anchored evolution), a foundational gap remained: the emergence of localized perspective, contextual measurement, and observer-relative frames within an otherwise global informational substrate.

This work introduces Axiom VIII: Recursive Self-Localization (RSL), a necessary closure principle stating that only recursively self-modeling, compression-stable subsystems admit localized experiential frames. RSL is formulated mathematically via bounded information flow, Kolmogorov compression ratios, and predictive closure under validation dynamics. Without introducing new ontological primitives, the axiom derives locality, contextuality, temporal directionality, and observer multiplicity as structural consequences of recursive self-reference.

RSL integrates consistently with prior UMF axioms and yields testable implications across quantum measurement, holographic scaling, and cosmology. In particular, it predicts universal holographic scaling with anomalous dimension η=2\eta = 2η=2, observed independently across multiple validation protocols. The result completes the UMF axiomatic ladder by explaining not only what exists and how it evolves, but why physical reality is necessarily experienced through localized frames rather than as a single undifferentiated whole.

  • This project was developed by Marco Gericke, with structured assistance from a large language model. All scientific concepts and conclusions were generated, verified, and interpreted by the author.
  • Dedicated to Peter Plichta, who envisioned the code before it could be computed.

Files

Axiom_VIII_RSL_Complete_With_Maya_v2.pdf

Files (412.8 kB)

Name Size Download all
md5:d2eec00d5bbc0426abc8d3bfd79dcf36
412.8 kB Preview Download