Monster Symmetry as a Safety Architecture in Generative Systems

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This paper proposes a structural safety framework where Monster group symmetry governs generative behavior. Equivariance preserves high performance, while symmetry breaking naturally induces degradation, providing intrinsic protection without external prohibitions or ethical constraints. This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.