#分词算法

Tokenization is usually treated as a solved preprocessing step, so the result that streaming tokenizers can hit provably optimal delay reframes a real bottleneck for serving latency, especially time-to-first-token. Teams building inference infrastructure can swap in this algorithm without changing tokenizer semantics, since it works with tiktoken and HuggingFace vocabularies. The lower-bound proof also signals that further constant-factor gains are unlikely, which narrows where optimization effort should go.

This research provides concrete guidance for building more effective tokenisers, which is crucial for improving the efficiency and performance of large language models.