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This paper introduces ArtECulture, a benchmark for culture-conditioned visual emotion understanding in multimodal large language models, covering English, Chinese, and Arabic cultures with balanced Western and non-Western artwork. Evaluations reveal the task remains challenging, and the authors propose a retrieval-augmented framework to inject cultural knowledge into MLLMs.