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This paper formalizes the concept of Bayes-sufficient representations in supervised learning, defining when a representation retains exactly the information needed for Bayes-optimal prediction under a given loss function. It introduces the Bayes quotient as a canonical loss-dependent object and connects the framework to property elicitation, illustrating distinctions between sufficiency, minimality, and excess retained information through experiments.