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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.

FPT Bayesian Nets via Feedback Edges

FPT Bayesian Nets via Feedback Edges

Analyzes parameterized complexity of Bayesian Network Structure Learning using superstructure. Proves fixed-parameter tractability with feedback edge set parameterization. Extends to treewidth with additive representations and polytree learning.

ArXiv AIResearchFeb 12#research#bns-l#v1