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Constraint-First Information Dynamics and Deterministic Recovery This video presents an overview of Constraint-First Information Dynamics, a theoretical framework formalized in UFT-ID. The framework reframes stability in informational systems as a consequence of enforced geometric constraints, rather than as an outcome of learning, optimization, or probabilistic inference. In this model, systems evolve within an overcomplete state space, where most configurations are invalid. Stability is defined by a privileged admissible subspace that preserves structural invariants such as logical consistency, conservation rules, or typing constraints. When a system drifts outside this admissible region, it accumulates quadratic tension, representing the cost of violating constraints. Once this tension reaches a critical threshold, a deterministic recovery occurs. The system is projected back into a valid state using a fixed lexicographic selection rule, ensuring a unique, invariant-preserving outcome. Observable events are interpreted not as stochastic actions or decisions, but as the automatic release of accumulated constraint tension. The framework draws a structural analogy to quantum error correction, extending its logic to general informational and computational systems. Rather than proposing a physical unification, this work offers a universal architectural language for understanding how invariant enforcement governs stability across physical and computational domains. This video accompanies a formal preprint and supplementary explanatory materials. Preprint + DATASET: Slade, T. (2026). UFT-ID 2.0: Constraint-First Information Dynamics and Deterministic Recovery. Zenodo. https://doi.org/10.5281/zenodo.18176261 Related Work: Slade, T. (2026). A Unified Framework for Unified Field Theory of Information Dynamics Constraint-First Dynamics and Deterministic Recovery. Zenodo. https://doi.org/10.5281/zenodo.18176454