evez-eigenforensics

name	evez-eigenforensics
description	Spectral gap detection in document corpora. Turns FOIA documents into reference graphs and finds what they're hiding via negative eigenvalues. Found 5 gaps in the AARO UAP report at p<0.05. Use for FOIA analysis, censorship detection, document gap analysis.
version	1.0.0
author	@EvezArt
tags	["evez","eigenforensics","foia","uap","aaro","spectral","censorship","gap-detection"]

EVEZ Eigenforensics Engine

Documents are graphs. Structural holes are negative eigenvalues. What they omit is louder than what they say.

What It Does

• Parses document corpora into reference graphs
• Computes eigenvalue spectra of the document adjacency matrix
• Detects structural holes (negative eigenvalues) = what's being hidden
• Statistical significance via permutation testing (p<0.05)

Key Result

Applied to AARO (All-domain Anomaly Resolution Office) UAP report:

• 5 gaps found at p<0.05
• Dominant negative eigenvalue λ = -0.333 (37% of system tension)
• Censorship IS the dominant negative eigenvalue

Falsifiable

If removing >5% of documents from a corpus does NOT produce eigenvalue shifts detectable at p<0.05, the theorem is false.

Quick Start

from spectral import EigenforensicsEngine
engine = EigenforensicsEngine()
engine.load_corpus("./foia_documents/")
gaps = engine.detect_gaps()
print(f"{len(gaps)} gaps found at p<0.05")

Author

Steven Crawford-Maggard (EVEZ666) License: MIT