A Unified Framework for Unified Field Theory of Information Dynamics Constraint-First Dynamics and Deterministic Recovery

This preprint presents a unified, constraint-first formulation of Unified Field Theory of Information Dynamics (UFT-ID). The central claim is that informational state spaces are generically overcomplete, while stable and predictable behavior arises from geometric admissibility constraints defining a privileged subspace of legal states. Deviations from this admissible subspace are directly detectable, accumulate a quadratic tension functional, and—upon crossing a critical threshold—trigger deterministic recovery via lexicographic projection. The framework formalizes structural principles shared across classical constraint mechanics, quantum error correction, lattice systems, and hybrid dynamical systems, without asserting physical unification. UFT-ID is presented as an architectural language for constraint-governed dynamics, complementary to learning-based, probabilistic, and agent-centric models, which typically assume invariant enforcement but do not explicitly formalize it. In addition to the primary preprint PDF, this deposit includes supplementary explanatory materials intended to illustrate and contextualize the framework. These materials do not introduce additional empirical claims and are provided solely for conceptual clarification and communication. Included supplementary files are: Constraint_The_Engine_of_Predictability.pdf — an extended conceptual exposition of constraint-first predictability NotebookLM Mind Map.png — a visual synthesis of the framework structure Screenshot images (.png) — reference captures of framework schematics and development artifacts Stability_as_Geometric_Constraint_Enforcement.m4a — narrated audio overview of the core concepts The_Geometry_of_Stability.mp4 — video walkthrough of the framework and schematic interpretation This work supersedes earlier exploratory formulations by providing a stabilized, non-versioned statement of the framework. The contribution is structural and conceptual rather than empirical.