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This paper introduces YUKTI, a framework that transforms natural-language decision situations into robust, verifiable decisions by using an uncertainty-typed proposition graph, assumption-robust Pareto frontiers with regret bounds, and a multi-stage optimization hand-off. Validated on synthetic and real datasets, it significantly reduces regret compared to naive point-solution approaches and identifies the limits of language model reasoning.