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The paper introduces LLM-as-Jev, an architecture-preserving framework that extracts calibrated decision distributions from LLM next-token probabilities, and shows modern LLMs are already effective Jev-style decision models, with fine-tuning giving targeted gains on weaker models and specific tasks while KL anchors prevent conversational degradation.