Cross Domain Engine

Discover hidden correlations between disparate research domains using EVEZ OODA loop architecture. Use when finding novel cross-domain connections, detecting emerging technology intersections, cross-referencing threat patterns, identifying investment signals, or mining unclaimed innovations across fields. Covers signal observation, cross-domain scoring, verifiable correlation events, and append-only spine protocol.

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:

1. OBSERVE — Scan domains, collect signals with intensity scores and keywords
2. ORIENT — Score cross-domain pairs by keyword overlap × intensity × base novelty
3. BRANCH — Generate verifiable correlation events with confidence scores
4. ACT — Commit to append-only spine (no edits, no deletes)
5. 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.