The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability
Hikmat Karimov, Rahid Zahid Alekberli
Why It Matters
What makes this one worth your time
This work offers a potentially rigorous approach to AI safety by grounding ethical violations in physical and informational principles, which could lead to more reliable autonomous systems.
A novel framework linking thermodynamics and stochastic control for AI safety and stability.
Summary
The paper introduces the Kerimov-Alekberli model, an information-geometric framework that connects non-equilibrium thermodynamics with stochastic control to address AI safety and system stability. It uses the Kullback-Leibler divergence and Fisher Information Metric to detect systemic anomalies and validates the model on datasets like NSL-KDD and UAV simulations.
Key contributions
- Introduction of the Kerimov-Alekberli model for AI safety.
- Establishing a formal isomorphism between non-equilibrium thermodynamics and stochastic control.
- Validation of the model on NSL-KDD dataset and UAV simulations.
Notable insights
- Linking non-equilibrium thermodynamics with stochastic control to redefine AI safety.
- Using the Fisher Information Metric to dynamically govern the Kullback-Leibler divergence threshold.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.24083v1 Announce Type: new Abstract: This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium thermodynamics to stochastic control for the ethical alignment of autonomous systems. By establishing a formal isomorphism between non-equilibrium thermodynamics and stochastic control, we define systemic anomalies as deviations from a Riemannian manifold. The model utilizes the Kullback-Leibler divergence as the primary metric, governed by a dynamic threshold derived from the Fisher Information Metric. We further ground this framework in the Landauer Principle, proving that adversarial perturbations perform measurable physical work by increasing the system's informational entropy. Validation on the NSL-KDD dataset and unmanned aerial vehicle trajectory simulations demonstrated that our model achieves effective real-time detection via the FPT trigger, with strong performance metrics (e.g., high accuracy and low FPR) on benchmark datasets. This study provides a rigorous physical foundation for AI safety, transitioning from heuristic, rule-based ethical frameworks to a thermodynamics-based stability paradigm by grounding ethical violations in quantifiable physical work and entropic information.