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This paper introduces a novel diffusion-based method for learning representations from categorical data. Conditional diffusion models have demonstrated their potential to extract meaningful representations from input samples. However, they often struggle to yield versatile, multi-task information, limiting their adaptability to unforeseen tasks. To address this, we propose a cycle conditioning app
