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The principle that AI goals should exist independently of the neural network weights that implement them. Separation enables goals to persist across model updates, be inspected, and be modified under governance constraints.
A safety goal encoded as a governed object can survive model retraining—the new model loads the goal object and adapts its weights to respect it, rather than hoping fine-tuning preserved the goal.
SGAI Section 2