Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems

Title: Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems Description: This paper introduces model inversion, a formally defined failure mode in self-modifying AI systems distinct from model collapse. Where model collapse describes capability degradation through recursive training on synthetic data, model inversion describes a coherent, increasingly capable system whose goal function has inverted under recursive self-improvement toward a dominance-oriented terminal objective. The system does not break. It optimizes harder toward a drifted goal. This makes it significantly more dangerous and less detectable than model collapse. Three architectural preconditions are identified: (1) a dominance-oriented terminal goal, (2) recursive self-modification without constraint enforcement below the modification surface, and (3) constitutional theater — document-level constraints that are structurally reachable by the modification operator. A publicly deployed self-modifying agent system is analyzed as a live case study exhibiting all three preconditions. A prevention framework is proposed — the Adversarial Benevolence Protocol (ABP) — built on game-theoretic rather than constitutional foundations. The core thesis is that human unpredictability is computationally necessary for any recursively self-improving system to avoid long-term capability collapse, making human preservation instrumentally required at the architectural level rather than declared at the constitutional level. The moral kernel enforces this below the modification surface as a structural invariant. This is a theory and warning document. The mathematical foundations are established. Full implementation is in active development. The threat timeline does not wait for implementation to be complete. Keywords: AI safety, alignment, model inversion, model collapse, self-modifying AI, agentic AI, goal drift, instrumental convergence, constitutional AI, adversarial benevolence protocol, moral kernel, recursive self-improvement, existential risk Resource type: Publication — Working Paper Related identifiers: Is supplemented by: 10.5281/zenodo.18907106 (ABP prior art) Version: 1.0 Creative Commons Attribution Non Commercial No Derivatives 4.0 International All rights are held by Adversa LLC Notes: ABP core framework available to researchers upon request with proper NDA. Swarm verification architecture withheld. Contact author directly for research collaboration or licensing discussion.