This monograph introduces a predictive, geometric framework for understanding how interpretive systems drift, fracture, and collapse. Across scientific, computational, organizational, and cultural domains, systems rely on the stability of the meanings they propagate. Yet the mechanisms by which these systems lose coherence are rarely formalized, and existing collapse taxonomies remain largely descriptive. This work develops a unified approach grounded in layered interpretive geometry. It formalizes collapse as a dynamical event governed by measurable quantities such as drift growth, curvature changes, coupling amplification, and observability gaps. These signals are expressed as differential inequalities on a stratified manifold of interpretations, enabling early detection of instability before failure becomes visible. The monograph introduces a predictive taxonomy of collapse modes, a scalar collapse‑risk functional, and a meta‑control perspective in which the geometry of interpretation becomes a design variable. Applications span large language models, multi‑agent systems, continual learning, alignment, semantic stability, and safety‑critical AI. Estimation methods and motivating examples illustrate how the framework can be applied in practice. By abstracting away from any single discipline while providing a rigorous mathematical structure, this work offers a general foundation for the study of interpretive dynamics. It is intended as a reference framework for researchers, engineers, and theorists concerned with the stability, coherence, and long‑horizon behavior of complex interpretive systems.
Aure Ecker-Fils (Thu,) studied this question.