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This post breaks down how gradients propagate through layers in Laplace neural network and how to avoid vanishing and exploding gradients. With a focus on the equations behind backpropagation in Laplace domain, it offers a clear, research-driven explanation of how learning is sustained inside Laplace neural networks.

What does it really mean to “model a world”? In artificial intelligence, this question goes beyond data and algorithms, it touches on how machines can represent, reason, and adapt to dynamic environments. This post explores fresh perspectives on world modeling, offering insights into why it matters for the future of intelligent systems and where current approaches fall short.