An Analysis of Backpropagation in Large Language Models Through the Lens of the Universal Laws of Nature

Abstract This paper explores the relationship between backpropagation—the core learning algorithm behind large language models (LLMs)—and the three universal laws of nature proposed by Angelito Malicse as a complete solution to the problem of free will. These laws include: (1) the law of karma (cause and effect plus systemic integrity), (2) the law of balance in nature, and (3) the law of feedback mechanisms. The analysis shows that backpropagation naturally aligns with each of these principles, suggesting that its widespread success in artificial intelligence systems may be partly due to its implicit grounding in fundamental universal laws.