Transforming Fixed Solutions into Executable Knowledge
Author: Phan Thanh Trung Affiliation: Independent Researcher Version: 1.0
The Operatorization Framework is a conceptual and computational framework for transforming fixed knowledge structures into reusable executable operators.
Rather than treating theorems, laws, algorithms, proofs, and expert knowledge as static artifacts, Operatorization investigates how their latent operational behavior can be extracted, formalized, and represented as executable computational entities.
The framework aims to establish a bridge between human knowledge and executable reasoning systems, providing foundations for operator libraries, knowledge execution engines, and future scientific AI architectures.
The Operatorization Experimental Platform can be accessed directly in a web browser.
https://artifactsmaker.github.io/operatorization-framework/oep/
https://artifactsmaker.github.io/operatorization-framework/ogrb
No installation required. No backend required. Runs entirely in-browser.
Traditional knowledge systems primarily store information.
Operatorization focuses on executable behavior.
The framework investigates whether stable knowledge structures can be transformed into reusable operators capable of supporting computational reasoning and knowledge execution.
Knowledge Objects
↓
Structural Extraction
↓
Behavioral Extraction
↓
Constraint Extraction
↓
Operator Construction
↓
Validation
↓
Executable Operators
↓
Operator Libraries
↓
Computational Reasoning
↓
Scientific AI
Scientific and mathematical artifacts containing stable knowledge structures.
Examples include:
- Theorems
- Scientific Laws
- Algorithms
- Formal Proofs
- Expert Knowledge
The process of transforming static knowledge into executable behavioral representations.
Reusable computational entities derived from knowledge objects.
Structured collections of executable operators that can be validated, composed, and reused.
Reasoning systems that utilize executable operators rather than relying solely on symbolic descriptions or statistical correlations.
The framework currently includes three representative operator classes.
Derived from Tuy's Cut methodology.
Behavioral Category:
Selection
Derived from Brauer's Height-Zero principles.
Behavioral Category:
Stabilization
Derived from second-order evolution equations and Chernoff approximation theory.
Behavioral Category:
Evolution
Together these operators illustrate three foundational behavioral classes explored within the Operatorization Framework.
operatorization-framework/
│
├── figures/
├── graphical_abstract/
├── paper/
├── operators/
├── playground/
├── benchmark/
├── oep/
└── ogrb/
The framework introduces:
- Operatorization
- Executable Knowledge
- Knowledge-to-Operator Transformation
- Operator Libraries
- Operator Execution Protocol (OEP)
- Operator-Guided Reasoning Benchmark (OGRB)
These components provide an initial foundation for transforming static knowledge into executable computational systems.
If you use this framework in research, software, or educational materials, please cite:
Phan Thanh Trung.
Operatorization:
A Framework for Transforming Fixed Solutions
into Executable Knowledge.
Zenodo, 2026.
DOI: 10.5281/zenodo.20669008
https://doi.org/10.5281/zenodo.20669008
- Operatorization Framework
- Operator Execution Protocol (OEP)
- Operator-Guided Reasoning Benchmark (OGRB)
- Manuscript Trajectory Framework (MTF)
Current Version:
v1.0
This repository serves as the primary reference implementation and research workspace for the ongoing development of the Operatorization Framework.
Future work includes:
- Expanded Operator Libraries
- Automated Operator Extraction
- Operator Validation Methodologies
- Reasoning System Integration
- Scientific AI Applications
Creative Commons Attribution 4.0 International (CC BY 4.0)
Apache License 2.0
Author: Phan Thanh Trung DOI: 10.5281/zenodo.20669008 Year: 2026