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This paper proposes an LLM-enhanced multi-agent reinforcement learning framework to simultaneously optimize electric vehicle charging scheduling, station profitability, and grid stability, using LLMs for feature selection and adaptive weighting, outperforming state-of-the-art methods with reduced training time.