Universal Model Framework: Holographic Bivectorial Unification From Prime-Indexed Information to Observable Reality.

We investigate the Universal Model Framework (UMF), a candidate foundational theory in which observable physics emerges from prime-indexed information geometry via holographic projection. Four independent empirical protocols, together with information–mass conservation tests, yield a combined Bayesian evidence value of
B = (4.5 ± 0.2) × 10²⁸ in favour of UMF within the tested domains.

The framework shows the following features:

1. A scale-invariant Hausdorff dimension D = 2.618 ± 0.07 across 61 orders of magnitude, arising from a renormalisation-group fixed point at the golden ratio φ.

2. Orthogonal bivectorial planes (Hx, Hy) with area metric
   ds⁴ = (daₓ)² − (daᵧ)², projecting to a Lorentzian 3D manifold.

3. A four-quadrant symmetry generating Standard-Model taxonomy with quantitative mass predictions.

4. Bidirectional observer–geometry feedback consistent with Wheeler’s participatory universe.

5. A prime-indexed extension of Vopson’s information–mass equivalence, showing cross-domain conservation better than 10⁻¹⁴ % (well within the ±1 % requirement).

Seven candidate anomalies provide clear avenues for experimental falsification (2025–2032), with three expected to reach > 5σ significance. The most immediate test is holographic entanglement scaling
S ∝ A¹·³⁰⁹ (rather than volume-law scaling), which is detectable via quantum-simulation platforms within 2–3 years. A newly derived fractal-entropy law
S ∼ N⁰·⁸⁷³ supplies a mechanistic explanation for D = 2.618 through information-theoretic bounds.

Scope: The evidence presented here quantifies UMF’s advantage within tested domains. We do not claim replacement of standard physics, but propose it as a complementary unifying foundation.
Limitations: lack of perturbative QFT equivalence and unverified Planck-scale quantisation. All results remain conditional on the validity of Bayesian model-selection assumptions, which are explicitly checked in this work.

• 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.