LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling

Abstract:Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human language, proteins preserve structure despite extensive sequence variation a property standard tokenization strategies fundamentally fail to capture. We introduce ZEST (Zoned Encoding of Sequence Traits), an evolution-informed vocabulary derived from conserved regions of multiple sequence alignments. ZEST allows embedding domain-level biological priors directly at the tokenization stage rather than learning them implicitly through scale. ZEST natively compresses sequences to an average token length of 4 residues, enabling our model to process 4,000 residues within a standard 1024-token context window. Building on this, we present LEMON (Layered Extraction of Molecular Ordering from Nature), a compact 200M-parameter sequence-based model for detection of remote homology between protein sequences trained on a single H100 GPU for one week. Despite its modest size, LEMON outperforms state-of-the-art models ranging from 600M to 3B parameters. Our results demonstrate that evolution-informed tokenization can substitute for massive parameter scaling, opening a new direction for efficient, biologically-grounded protein representation learning. All code, model weights, and results are publicly available under the MIT license.

Accepted to NeurIPS 2026. 9 pages, 4 figures, 3 tables

Machine Learning (cs.LG); Biomolecules (q-bio.BM)

arXiv:2609.37675 [cs.LG]

(or arXiv:2609.37675v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.37675 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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