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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kotegov, Volodymyr, "Architectural Specification of a Local Multi-Scale Contour for Stable Recursive Self-Improvement", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11696