| _version_ | 1866901923210199040 |
|---|---|
| author | Adversa LCC Just, Joshua Roger Joseph |
| author_facet | Adversa LCC Just, Joshua Roger Joseph |
| contents | <p><strong>Title:</strong> Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems</p> <p><strong>Description:</strong> 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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>Keywords:</strong> 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</p> <p><strong>Resource type:</strong> Publication — Working Paper</p> <p><strong>Related identifiers:</strong></p> <ul> <li>Is supplemented by: 10.5281/zenodo.18907106 (ABP prior art)</li> </ul> <p><strong>Version:</strong> 1.0</p> <p>Creative Commons Attribution Non Commercial No Derivatives 4.0 International</p> <p>All rights are held by Adversa LLC</p> <p><strong>Notes:</strong> ABP core framework available to researchers upon request with proper NDA. Swarm verification architecture withheld. Contact author directly for research collaboration or licensing discussion. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18907106 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems Adversa LCC Just, Joshua Roger Joseph <p><strong>Title:</strong> Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems</p> <p><strong>Description:</strong> 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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>Keywords:</strong> 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</p> <p><strong>Resource type:</strong> Publication — Working Paper</p> <p><strong>Related identifiers:</strong></p> <ul> <li>Is supplemented by: 10.5281/zenodo.18907106 (ABP prior art)</li> </ul> <p><strong>Version:</strong> 1.0</p> <p>Creative Commons Attribution Non Commercial No Derivatives 4.0 International</p> <p>All rights are held by Adversa LLC</p> <p><strong>Notes:</strong> ABP core framework available to researchers upon request with proper NDA. Swarm verification architecture withheld. Contact author directly for research collaboration or licensing discussion. </p> |
| title | Model Inversion: A Failure Mode Distinct from Model Collapse in Self-Modifying AI Systems |
| url | https://doi.org/10.5281/zenodo.18907106 |