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Researchers have proposed a radical new framework for understanding artificial intelligence, suggesting that seemingly distinct concepts like attention mechanisms, diffusion models, reasoning processes, and training methodologies may be fundamentally interconnected. The paper argues against viewing intelligent models as a collection of separate components, instead suggesting they emerge from a single, underlying framework that may even have parallels to quantum mechanics. This unified approach purports to derive and explain the workings of attention itself. The authors are actively seeking community feedback on this groundbreaking theory. The initial discussion can be found on Reddit: https://old.reddit.com/r/artificial/comments/1m852pv/unifying_probabilistic_learning_in_transformers/.