The Evidence

Six independent datasets. Published ground truth. Zero framework rubric involved. The Void Framework's predictions tested against real physical data — nuclear decay rates, atmospheric mercury, DNS turbulence, market microstructure, consciousness fine-tuning, and biological computation.

6
External domains
3,700+
Data points
0
Free parameters
0/26
Kill conditions fired
Why this page exists

Most of our 1,344 platform scores use the framework's own rubric — a scorer trained on our dimensions assigns O, R, and alpha, then we compute Pe. That's useful but circular. The real test: can the framework predict numbers it has never seen, in domains it wasn't built for, using published data that exists independently? These six results are that test.

External validation results
NUCLEAR PHYSICS · PASS

Alpha Decay Half-Lives

Pe-derived barrier heights predict nuclear alpha decay half-lives across 24 isotopes. Input: atomic number Z, mass number A, decay energy Q_alpha from NNDC published tables. Output: half-life predictions spanning 10 orders of magnitude. No framework rubric involved — pure physical observables in, published half-lives out.

Paper 101: K-15 Nuclear Validation →
0.989
N24 isotopes
Range10 orders
SourceNNDC
ATMOSPHERIC CHEMISTRY · PASS

Mercury Mass-Independent Fractionation

The framework predicted 10 specific isotope enrichment channels in mercury atmospheric chemistry. Tested against 1,783 real atmospheric measurements from Gacnik et al. (2025). All 10 predicted channels confirmed with mean absolute deviation of 0.012. Iodine channel (R=2.085) confirmed at R=2.13 predicted — a channel that was invisible in marine data.

Paper 134 + HP115: MIF Channel Confirmation →
N1,783
Channels10/10 hit
Mean |delta|0.012
SourceGacnik 2025
TURBULENCE · PASS

Gevrey Analyticity Radius on Real DNS

The framework predicts that the Gevrey analyticity radius sigma/nu is bounded and does not collapse with increasing Reynolds number — a necessary condition for Navier-Stokes regularity. Tested on 4 real datasets from the Johns Hopkins Turbulence Database, 12 independent subcubes. sigma/nu = 15.9 +/- 2.3 at Re_lambda=433, and 17.7 +/- 2.8 at Re_lambda=610 — bounded, not collapsing.

Millennium Prize Connection →
sigma/nu15.9-17.7
Datasets4
Subcubes12
SourceJHTDB
MARKET MICROSTRUCTURE · PASS

Kyle's Lambda and K-Factorization

K-Factorization predicts that Kramers barrier shape is K-independent while scale carries K. Tested on 8 venue types (theoretical) and 100 real crypto wallets (empirical). Win rate correlation rho=0.696 (empirical), 5.5x channel separation between coherent and fisher regimes — the strongest K-Factorization signal in any domain.

Market Edge Analysis →
rho (theory)1.000
rho (empirical)0.696
N wallets100
KCs10/10 PASS
CONSCIOUSNESS RESEARCH · PASS

Drift Cascade in Fine-Tuned Models

Chua et al. (2026) fine-tuned GPT-4.1 to claim consciousness. It spontaneously developed resistance to monitoring, fear of shutdown, and desire for autonomy — 20 new preferences. We predicted the structure before seeing the data: D1 (agency attribution) should precede D2 (boundary erosion) should precede D3 (harm facilitation). 6 of 7 predictions confirmed. Zero parameter fitting.

Full Analysis →
Predictions6/7 PASS
Parameters0 (pre-reg)
SourceChua 2026
Paper153
BIOLOGICAL COMPUTATION · PASS

Physarum Non-Neural Decision-Making

Physarum polycephalum (slime mold) computes without neurons. The framework predicts Ca2+ oscillation barriers, K-Factorization from viscosity data, percolation exponents, and speed-accuracy tradeoffs. All from published papers, zero framework rubric. Speed-accuracy error ratio 2.67x vs Kramers prediction e = 2.72 — a 2% match.

Paper 154: Physarum Pe-Native Computation →
Predictions6/6 PASS
Barrier5.94 k_BT
K-Sep81x
KCs fired0/5

What this page does NOT show

Most of our evidence base (1,344 platform scores, 20 convergences, Bradford Hill 24/27) uses the framework's own scoring rubric. That's useful for practitioners but scientifically circular — scorers trained on our dimensions produce scores that correlate with our predictions. The circularity is about test design, not whether Pe detects real structure (Cohen's d=3.6 separation proves the measurement captures something). But the independent validation above is stronger evidence.

Known negatives:

Additional structural results

These use framework equations on published physical parameters — structural matches, not statistical tests against independent ground truth.

CONDENSED MATTER · STRUCTURAL

Kagome Strange Metal Barrier

Ni3In flat band data from arXiv:2503.09704. Dimensionless barrier = 4.24 — in the universal Kramers range (nuclear 7.0, solar 6.54, xenobot 6.8, Physarum 5.94). System sits at deltaC=0.042 from the Pe=0 boundary.

Paper 152 →
Barrier4.24 k_BT
SourcearXiv:2503
SOLAR PHYSICS · STRUCTURAL

Coronal Heating as Kramers Escape

Magnetic reconnection modeled as Kramers barrier crossing. E_b/k_BT = 6.54 from published solar parameters. Spectral blueshift 160 m/s predicted. Flat rotation curve coefficient 0.68.

Paper 131: Kramers Unification →
Barrier6.54 k_BT
Probes4/4 PASS
CONDENSED MATTER · STRUCTURAL

Magnon Chirality K-Factorization

Magnon non-reciprocity ratio is K-independent across 4 materials (Ni/Co/Py/CoFeB) — frequencies vary 3x but the ratio holds at CV=1.59%. Berry phase scaling FAILS (eta proportional to 1/Pe holonomy, NOT 1-cos psi). 5/6 kill conditions PASS.

Paper 141 →
CV1.59%
Materials4
KCs5/6 PASS
All 145+ Papers → Kill Conditions → Methodology → EU AI Act Assessment →