Self-Preserving Flow (SPF): Canonical Private Master v1.2

This work presents the canonical scientific formulation of Self-Preserving Flow (SPF), a meta-formal framework for modeling stability in recursively adaptive intelligent systems. SPF defines intelligent stability as historically recoverable semantic continuity preserved jointly across state evolution and semantic constraint evolution. Unlike conventional robustness frameworks centered on local correctness, behavioral consistency, runtime admissibility, and execution control, SPF models long-horizon stability as a lineage-preserving property over recursive transformation. The theory introduces a three-layer architecture (DPA, SCL, Meta-SCL) where DPA governs adaptive state transition, SCL governs first-order semantic admissibility, and Meta-SCL governs lawful revision of admissibility constraints themselves. The central contribution is formalizing second-order continuity as a necessary condition for open-world recursive intelligence.