EVEZ Cross-Domain Correlation Engine
Discover hidden correlations between disparate research domains.
When to Use
- Finding novel cross-domain connections nobody else would think to cross-reference
- Detecting emerging technology intersections before they're obvious
- Cross-referencing threat patterns across cybersecurity, finance, and materials science
- Identifying investment signals from undervalued research intersections
- Mining unclaimed patent territory between fields
Architecture
The engine runs an EVEZ OODA loop:
- OBSERVE — Scan domains, collect signals with intensity scores and keywords
- ORIENT — Score cross-domain pairs by keyword overlap × intensity × base novelty
- BRANCH — Generate verifiable correlation events with confidence scores
- ACT — Commit to append-only spine (no edits, no deletes)
- COMPRESS — Hash-chain the cycle into the immutable ledger
Key Concepts
- Spine Protocol: Every event is written once. No updates. No deletes. The history IS the state.
- Correlation Events: Carry unique ID, confidence score, domain classification, and cryptographic hash
- poly_c = τ × ω × topo / 2√N: The EVEZ formula for topological proximity scoring
- MAES Pattern: Inspired by the autonomous discovery of 0.82 correlation between VQC research and FinCEN SAR patterns
Verification
Every correlation event can be:
- Verified by checking the hash chain
- Audited via the append-only spine
- Falsified through the VERIFIED/PENDING/INVESTIGATING status system
Formula
poly_c = τ × ω × topo / 2√N
Where:
- τ = temporal weight (recency of signals)
- ω = domain weight (importance of each domain)
- topo = topological proximity (keyword overlap between domains)
- N = number of observed signals (normalization factor)
References
See scripts/correlation_engine.py for the full implementation.