Operatorization: A Framework for Transforming Fixed Solutions into Executable Knowledge

Phan Thành Trung Independent Researcher Date Written: June 11, 2026 Abstract Human knowledge has accumulated through the discovery of theorems, laws, algorithms, logical principles, and formal proofs. While these artifacts have enabled significant scientific and technological progress, they are typically stored, communicated, and utilized as static knowledge objects. Modern artificial intelligence systems primarily learn from data and textual representations of such knowledge, rather than directly leveraging the operational structures embedded within them [11,15,16]. This paper proposes a conceptual framework termed Operatorization, a process that transforms fixed solutions and static knowledge artifacts into machine-executable operators. Instead of treating a theorem, law, or formal result solely as an object of interpretation, the proposed framework seeks to identify and extract its re