Author

Simon S. Lee

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Jul 2026

In-Place Tokenizer Expansion for Pre-trained LLMs

A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time. When those priorities shift, languages added later are split into many more tokens per word, which can raise latency, compute, and energy consumption for users...

Jimmy T.H. Smith, Tarek Dakhran, Alberto Cabrera et al. · arXiv.org · 0 citations

#natural language process... Preprint Aug 2026

Zero-Shot Self-Orchestration with Ledger-Based Control for Improved LLM Coding Performance

The effect of introducing the manager-worker scaffold over a shared filesystem workspace, with no training and no per-benchmark tuning, measured against the same model answering in a single pass is investigated, finding several mechanisms behind the gains.

Victor Gao, Vida Khosrowshahi, Ali Khosrowshahi et al. · 0 citations