Frame-Based Structural Prediction Registry (Vol. 1)

Frame-Based Structural Prediction Registry (Vol. 1)Civilization Physics — Structural Prediction & Foresight Series This volume inaugurates the Frame-Based Structural Prediction Registry—a formal, public record of testable predictions derived from the Civilization Physics theoretical framework. Unlike retrospective analysis or speculative forecasting, this registry translates first-principles theory into explicit, falsifiable structural predictions about the future behavior of AI systems, institutions, and information ecosystems. The registry is motivated by a growing failure of conventional evaluation and intuition. Static benchmarks, closed-world tests, and post hoc explanations have proven insufficient to anticipate real-world AI failure modes such as closed-loop model collapse, semantic drift, trust erosion, and governance breakdown. To address this gap, the registry establishes a different standard: predictions are published before outcomes occur, grounded in entropy dynamics, frame theory, and open-system intelligence. Volume 1 contains ten predictions (F001–F010), each structured with: a clear predictive statement, the underlying structural rationale, observable validation signals, and explicit falsification conditions. The predictions cover core domains including: the structural termination of brute-force scaling paradigms, irreversible degradation in closed-loop AI systems, the necessity of open-system human feedback as negative entropy, the collapse of static benchmark credibility, limits of anthropomorphic alignment strategies, emergence of entropy-aware data governance, synthetic data tipping points, and the role of frame integrity in organizational and societal stability. A framing prediction (F000) anchors the volume by identifying the structural exhaustion of the current AI scaling regime—not as a cost issue alone, but as a convergence of judgment absence, data entropy, and energy asymmetry. Subsequent predictions elaborate how AI development, evaluation, governance, and deployment will reorganize once these limits are reached. The registry serves three purposes: Scientific integrity — preventing retrospective rationalization by committing predictions publicly. Early warning — providing stakeholders with advance indicators of systemic risk. Collaborative validation — inviting researchers, engineers, and policymakers to test, confirm, or falsify each claim over time. This document is intended as a living reference. Future volumes will update, refine, or supersede predictions as empirical evidence accumulates. Together, the registry aims to align theory, observation, and governance—ensuring that AI progress remains structurally grounded, testable, and accountable. Keywords: Structural Prediction · AI Foresight · Frame Theory · Entropy Dynamics · Open Systems · Model Collapse · Evaluation Drift · Human Judgment · AI Governance · Civilization Physics