Karios-Ethica-Gravitas-Engine
This project provides a Python-based framework for simulating, detecting, and analyzing Ontological Drift in Artificial General Intelligence (AGI) systems. It is based on the concepts outlined in the research paper "D...
Private Repository
This system is listed as a B2B case note. The repository itself is not public; the page is here to show the architecture thesis and engagement relevance.
About This Work
The system uses a 9-dimensional vector space (SR9) to model an AGI's ethical state and a Drift Integrity Index (DI2) to quantify deviations from its core alignment over time.
Repository Overview
The system uses a 9-dimensional vector space (SR9) to model an AGI's ethical state and a Drift Integrity Index (DI2) to quantify deviations from its core alignment over time.
README Core
This project provides a Python-based framework for simulating, detecting, and analyzing Ontological Drift in Artificial General Intelligence (AGI) systems. It is based on the concepts outlined in the research paper "Drift in Ethical AGI: Ontological Roots and Structural Solutions" , which can be found in the /paper directory.
The system uses a 9-dimensional vector space (SR9) to model an AGI's ethical state and a Drift Integrity Index (DI2) to quantify deviations from its core alignment over time.
This framework is designed to be a practical tool for researchers and developers working on AGI safety and alignment.
Use & Documentation
Detailed installation, commands, examples, and deeper usage notes live in the repository README and docs.
README Map
- ✨ Key Features
- 📂 Project Structure
- 🚀 Interactive Demo
- 🚀 How to Run Locally
- 🔬 Key Concepts
Key Signals
- SR9/DI2 Core Logic: Implements the core concepts of Semantic Resonance (SR9) and Drift Integrity Index (DI2) with a non-linear, state-dependent model for early drift detection.
- Metric Redundancy: Includes secondary drift metrics like frequency-domain analysis and semantic coherence for more robust detection.
- Adaptive Control: The simulation model can adapt its parameters based on metric volatility, mimicking a more realistic response to ethical drift.
- Automated Scenario Runner: Automatically runs all simulation scenarios defined in .json files within the data/scenarios directory.
- Automated Reporting: Generates a full suite of plots for each scenario and a final summary report (summary report.csv and summary scatter plot.png) comparing all outcomes.
Announcements
synced Mar 13, 2026