Emergent retokenization symmetry in large language models: phenomenology and applications

arXiv:2606.15521v1 Announce Type: new Abstract: Tokenization introduces representational redundancy: under a fixed token vocabulary, every byte string admits many valid token encodings, or segmentations, that decode to the same surface string. However, given a prompt, most language model tokenizers break this representational symmetry by returning a canonical segmentation. Training only on canonical segmentations should influence inference behavior, and there is little reason to expect models to respect segmentation symmetry on downstream tasks. We find that this symmetry partially emerges dur

Source: arXiv cs.CL — read the full report at the original publisher.

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