arXiv:2605.29379v1 Announce Type: cross Abstract: We present BrahmicTokenizer-131K, a 131,072-vocabulary byte-level BPE tokenizer that closes the Brahmic compression gap at the 131K-vocabulary class while preserving the English, EU-language, and code compression of OpenAI's o200k_base. We construct it through a two-stage retrofit: (1) a script-prune crop that reduces 200,019 tokens to 131,072 by removing nine out-of-scope writing systems, and (2) a surgical retrofit of 2,372 corpus-dead vocabulary slots determined by linear-programming allocation across nine Brahmic Unicode blocks. The pre-tok
The accelerating development of AI models highlights the urgent need for better linguistic infrastructure for non-English languages to enable broader AI adoption and equity.
Improved tokenization for Indic languages directly enhances the performance and inclusivity of AI models, making them more relevant and effective for a significant portion of the global population.
AI models can now process Indic languages more efficiently and accurately, reducing the compression gap and potentially accelerating AI development and adoption in regions using these languages.
- · AI developers in India and other Indic-language regions
- · Users of AI in Indic languages
- · Companies targeting Indic language markets
- · Existing less efficient Indic tokenization models
- · AI models previously optimized only for English/EU languages
Reduced cost and improved performance of AI development for Indic languages.
Increased availability and quality of AI applications and services for Indic language speakers.
Accelerated digital transformation and economic growth in Indic language-speaking regions due to more accessible and effective AI.
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Read at arXiv cs.LG