I introduce the Law of Entropic Regression, a formal framework explaining why deterministic learning systems face inherent limits in convergence due to the asymmetric expansion of the error-space entropy.

To overcome this limitation, I define the Machine Unlearning operator and integrate it with conventional learning within a Machine Meta-Learning framework, achieving true asymptotic convergence.

The paper is openly available at OSF: https://doi.org/10.17605/OSF.IO/UXTJ9

Additionally, I provide a Jupyter Notebook demonstrating the Meta-Learning simulation using a 2D "moons" dataset. The simulation results confirm the framework's effectiveness:

Simulation finished Final correct ratio: 99.30% Final error ratio : 0.70% Final entropy : 0.0602 bits

These results illustrate how the combined learning and unlearning operators drive the global error toward zero while maintaining bounded informational entropy.

I welcome feedback from the community on potential applications and improvements.