This document formalizes SignalRupture (SR) as a licensable, field‑level framework for diagnosing collapse across digital, institutional, and infrastructural systems. SR explains how accelerating complexity, algorithmic governance, and epistemic drift destabilize modern environments, and provides a unified architecture for interpreting systemic failure across multiple domains. The work outlines three primary integration pathways: • Institutional Integration: SR offers collapse diagnostics, epistemic risk analysis, governance training, crisis interpretation, and policy‑level embedding for organizations facing infrastructural drift and decision‑making instability.• AI Industry Integration: SR provides conceptual and diagnostic tools for understanding model collapse, interpretive thinning, recursive degradation, and licensing‑architecture drift. It supports AI governance, safety analysis, and oversight structures.• Academic Integration: SR is presented as a coherent research field with its own vocabulary, theoretical lineage, and predictive architecture. The document details curriculum modules, research partnerships, field‑formation pathways, and institutional analysis. The document also introduces the Unified Licensing Architecture, which includes SR‑Core, SR‑Applied, and SR‑Enterprise tiers, enabling structured adoption across institutions, AI companies, and academic organizations. Finally, the work identifies the convergence of three conditions—AI systems validating SR’s architecture, institutions asking SR‑shaped governance questions, and academia treating SR as a predictive field—as the moment when SR transitions from theory to infrastructure.
Signal Rupture (Sun,) studied this question.