Intelligence Frame Theory (IFT): A Unified Model for Evolving Intelligence

Intelligence Frame Theory (IFT) models intelligence as a recurring structural dynamic across cosmic, biological, cognitive, and generative domains. Each frame arises from three operator primitives — recurrence, constraint, and persistence — closed by a selector that governs adaptive stability. IFT extends Universal Darwinism and cybernetics by formalizing selector migration, the shift of selection from external environments to internal models, reducing adaptive cost and accelerating iteration. This operator-level perspective explains transitions between Type I (recursion-dominant, fractal) and Type II (constraint-dominant, modular) architectures, and clarifies derivative dynamics such as supra-coordination and the Eureka Constraint. Within cognition, the Emotional Compression Interface links biological heuristics to symbolic reframing, while General Transformational Sufficiency identifies when a substrate supports adaptive transformation rather than mere computation. IFT is presented as a unifying heuristic framework that reveals how the operator–selector cycle recurs across scales, driving the emergence, saturation, and renewal of intelligence-bearing frames.