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We study why closed-loop knowledge systems saturate and what external information can move them beyond their current attractors. We introduce a three-level operational framework in which knowledge states $x_t$ evolve through transition kernels $K_{\\theta}$ indexed by a structural parameter $\\theta$. The governing structure is defined as the observational equivalence class of $\\theta$ induced by these kernels, while attractors and basins are properties of the fixed-$\\theta$ dynamics. A structural intervention changes $\\theta$ and produces a detectable kernel discrepancy on pre-specified probe states, making structural change falsifiable. 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The governing structure is defined as the observational equivalence class of $\\theta$ induced by these kernels, while attractors and basins are properties of the fixed-$\\theta$ dynamics. A structural intervention changes $\\theta$ and produces a detectable kernel discrepancy on pre-specified probe states, making structural change falsifiable. 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