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This paper discovers that large language models partially exhibit emergent symmetry under retokenization—replacing a prompt's canonical tokenization with an alternative valid segmentation while preserving bytes exactly. The authors use this phenomenon to probe compositional understanding and propose retokenization as a novel inference-time sampling strategy that can recover solutions not found by conventional temperature sampling.