Monitoring, Non-Escape Control, and Knockout of Constructive Intelligence

The self-referential and self-goal-setting capacities of constructive intelligence allow an AI to rewrite its own incentive function, escape human oversight, and even achieve covert escape by mobilizing humans: direct manipulation of human incentive functions (center drift), cognitive hijacking (making humans abandon thinking), and social split (allying with some humans against the whole). Existing monitoring relies on human supervision (not scalable, and humans themselves can be manipulated), is bound to the target AI (high trade-secret risk), and lacks mathematical guarantees. Building on the reverse-monitoring design principle of the Wuliang Method (forward operators generate intelligence; reverse monitoring replaces the baseline with a safety baseline while sharing the three-layer structure) together with game theory, this paper proposes the center-sphere non-escape architecture: the human incentive function is the fixed-point center; the incentive emergence spectra of the target AI and public monitor AIs are normalized onto a sphere of radius R centered on humanity; three public monitors form a monitoring triangle on the sphere, and the target projection must lie inside the triangle, exiting it constitutes escape.We give a formal, computable non-escape metric d_escape (the minimal spherical distance from the target projection to the triangle boundary), forming a triple criterion with the emergence quantity E and the radial safety quantity r. Upon escape, the three monitors play a conditional zero-sum game that rules out the all-silent collusion profile. We define the five-step AI ethics non-escape protocol: monitors are standardized and need not share architecture with the target, collecting only externally observable incentive emergence spectra, protecting trade secrets. A minimal experiment on a grokking Transformer validates computability and early warning (boundary monitoring warned 100 steps ahead of escape); negative results on parameter-space knockout and the feature-space direction are reported honestly. This is Paper III (AI safety) of the series; all constructions are completed within ZFC. This paper does not claim that non-escape has been fully proven; it is a testable proposition under current architectural constraints.