TGEO - empírico

TGEO (Teleological Gradient Operators) is introduced as a structural framework for stabilizing gradient‑based dynamics through global invariants and second‑order teleological terms. Unlike classical optimization, which treats learning as a purely local descent process, TGEO imposes constraints on the evolution of the system itself, reducing oscillations and enforcing regularity in its trajectories. This work presents an external empirical validation across four independent domains—neural network training, perturbation robustness, teleological control, and classical optimization—showing that TGEO systematically improves dynamical stability without modifying model architectures. The results support the view that TGEO provides a generalizable conceptual mechanism for understanding and shaping learning dynamics in artificial systems. “The complete reproducible code for all four experiments is available in the Notes section of the corresponding PhilPapers entry.”