Auto-replication process in Specific Artificial Intelligence for Artificial Research by Deduction

This paper presents a theoretical and architectural framework for the implementation of auto-replication processes in Specific Artificial Intelligence (SAI) systems dedicated to Artificial Research by Deduction. Auto-replication, in this context, refers to the autonomous ability of a deductive SAI to improve, adapt, and evolve its own reasoning structures, proof mechanisms, and knowledge bases without external intervention. Unlike simple replication, auto-replication implies a dynamic and self-sustaining system capable of assessing the performance of its deductive operations and reproducing enhanced instances of itself to improve precision, efficiency, and epistemic transparency. This capacity supports recursive self-improvement in narrowly defined logical domains and enables the generation of multiple coordinated deductive agents. The paper argues that auto-replication is a critical milestone toward the emergence of Global Artificial Intelligence (GAI), offering a reliable mechanism for scaling provable reasoning and self-correcting research behavior across interconnected SAI modules. By formalizing the principles of auto-replication, the study lays the foundation for robust, autonomous, and logically sound research systems.