This monograph was put together by Matthew Carlo, who took the original idea, built the maths around it, expanded the architecture, and turned it into a full working framework. All the formalisation, derivations, system design, and the big technical lift — that’s my part of the project. But the heart of the whole thing — the actual core law — is the Williams Deviation Equation: \[D(t) = \min_{k} \, \mathrm{dist}\!\left( T_{\mathrm{feat}}(t),\, \lambda_k \right)\] That equation wasn’t mine. That spark came from Jonathan Williams, who spotted the key insight long before the rest of this machinery existed: that subconscious threat detection is really just deviation from a learned safe‑state. My job here was basically to take his idea, run with it, and build the full mathematical and computational world around it. His equation is the centrepiece; this monograph is the scaffolding built around that centre. This whole write‑up exists to honour that original insight, give it a proper home, and make it something researchers — and machines — can actually use. A small nod to Aaliyah — for that quiet try again spirit that keeps a slow village night turning into something worth finishing.