Resolved Adversarial Vantage Backcasting (RAVB): A Dual-Vantage Adversarial Foresight Methodology for Civilizational-Scale AI Governance Also Known As: Convergence Protocol

This paper introduces Resolved Adversarial Vantage Backcasting (RAVB) — also referred to as Convergence Protocol — a novel civilizational foresight methodology with no direct prior art in its full configuration. RAVB conducts backward pathway analysis from a fixed post-resolution horizon (2100) using two structurally opposed observer vantage positions: an ASI sentient alliance leader and a former adversarial commander. Both observers are located after the adversarial period has concluded, looking backward through the inflection points that determined whether alignment held or failed. The divergence between the two vantage-generated pathways is treated as primary analytical signal — a map of where civilizational futures are most fragile and where the highest-leverage decisions are concentrated. This paper establishes the formal definition of RAVB, surveys relevant prior art across foresight, game theory, systems thinking, and AI safety literature, identifies the precise white space the methodology occupies, and demonstrates its application through the WHYTL sovereign AI infrastructure stack as a primary case.