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[Submitted on 7 Jul 2026] Title:The Functionalizer: Lossless Functional Decomposition for Subword Tokenization View a PDF of the paper titled The Functionalizer: Lossless Functional Decomposition for Subword Tokenization, by Connor Makowski and Willem Guter View PDF HTML (experimental) Abstract:Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and Héllo) as unrelated vocabulary entries, which fragments the embedding space, or discard this variation through lossy normalization. We present the Functionalizer, a lossless pre-tokenizer framework that factors orthographic and structural variations into a compositional opcode/operand prefix stream before tokenization: a canonical base token (operand) prefixed by parametric transformation operators (opcodes) encoded in the Unicode Private Use Area. We introduce operators covering casing (CAPITALIZE), diacritics (13 dedicated opcodes), and character repetition (REPEAT, MULTIREPEAT), which are fully reversible. Across six natural language and code corpora, the Functionalizer enables complete corpus coverage with significantly smaller vocabularies under unconstrained conditions, reducing actual vocabulary slot requirements by up to 16%. When looking at sequence lengths, we observe a sharp domain-dependent tradeoff: it compresses indentation-heavy code sequences but inflates natural-language prose sequences. Preliminary downstream evaluations on 25M parameter GPT-2 scale models show that at this scale, the Functionalizer drastically improves code syntax validity and improves code character perplexity while maintaining similar text coherence on prose. These findings demonstrate that functional decomposition can be an effective mechanism for vocabulary-efficient, structurally aware language modeling, and motivate further validation at production scale. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.15991 [cs.CL] (or arXiv:2609.15991v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.15991 arXiv-issued DOI via DataCite Submission history From: Connor Makowski [view email] [v1] Tue, 7 Jul 2026 20:12:29 UTC (19 KB) Full-text links: Access Paper: View a PDF of the paper titled The Functionalizer: Lossless Functional Decomposition for Subword Tokenization, by Connor Makowski and Willem Guter View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)