Architectural Specification of a Local Multi-Scale Contour for Stable Recursive Self-Improvement

Abstract

This document describes a practical architecture for stable recursive self-improvement limited to programming, algorithms, and mathematics. The system runs as a bare core and does not require broad world knowledge or data-center resources. It is designed for 1 to 5 high-end local workstations (Mac Studio class or equivalent with 128–192 GB unified memory). The design combines aggressive evolutionary search with strong conservative safeguards: multiple time scales, a shadow reference copy, orthogonality based on execution traces, an immutable control core, and specification changes that depend on measured success density.