Controlling Emergence in Artificial Intelligence Systems : Toward a Restricted theory Based on Anticipation, the Vigilance Threshold, and Flexible Constraint.

The advancement of artificial intelligence systems poses a particular challenge for control: how can their ability to produce novel solutions be preserved while reducing the risks associated with behaviors that have not been fully anticipated?We propose approaching certain forms of behavioral novelty through a candidate architecture describing a cycle that includes generative constraint, incubation, a possible early-warning zone, emergence, and an evaluation phase under flexible constraint.The model builds on previous work on critical transitions, emergent-capability evaluation, frontier capability thresholds, monitoring, and AI control, while proposing that these elements be temporally organized around the process of emergence.Three propositions structure this approach: some transitions may exhibit measurable precursor signals; the convergence of these signals may make it possible to define a vigilance threshold that triggers an adaptation of the control regime before the novelty is fully manifested; and, following emergence, temporarily limiting the scope and irreversibility of actions may allow the novelty to be evaluated without immediately suppressing the capability that produced it.The objective is not to claim novelty for early-warning evaluation, thresholds, or adaptive safeguards taken separately. It is to examine their organization within an emergence-control cycle, the separation of heightened vigilance from automatic restriction, and flexible constraint as a post-emergence regime intended to preserve novel compatible possibilities while limiting their immediate consequences.