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Transformers are Bayesian Networks

Transformers are Bayesian Networks

This arXiv paper proves Transformers are Bayesian networks via five methods: formal proofs showing sigmoid transformers implement loopy belief propagation, constructive exact BP implementation, uniqueness of BP weights, AND/OR structure matching Pearl's algorithm, and experiments. It argues hallucinations arise from lacking finite grounded concepts, unverifiable without them.