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An angular effective learning rate is derived that accounts for the parameter-update angle, parameter norm, and update norm, and shows that the conventional norm-based measure is a special case under parameter-update orthogonality.
This work argues that agentic risk is progressive: it can enter at four loci of the agent control loop--skill admission, invocation-time intent, execution-time effect, and post-action consequence--while a denied dangerous objective can reappear across surface forms, tools, or turns.
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