Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
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Discrete probabilistic program inference is fixed-parameter tractable under bounded treewidth of primal graphs and exponentially bounded inverse acceptance probability.
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From Mechanistic to Compositional Interpretability
Compositional interpretability defines explanations as commuting syntactic-semantic mapping pairs grounded in compositionality and minimum description length, with compressive refinement and a parsimony theorem guaranteeing concise human-aligned decompositions.
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Fixed-parameter tractable inference for discrete probabilistic programs, via string diagram algebraisation
Discrete probabilistic program inference is fixed-parameter tractable under bounded treewidth of primal graphs and exponentially bounded inverse acceptance probability.