Our novel neural-reparameterization for topology optimization generates stress-optimal structures by training a convolutional KAN network. The framework is completely data-free, outperforming MMA and CNN models. For more, see: S. Yan, J. Jelovica (2025) KATO: Neural-reparameterized topology optimization using convolutional Kolmogorov-Arnold network for stress minimization, International Journal for Numerical Methods in Engineering, Vol. 126:e70034,