Rethinking Genomic Modeling Through Optical Character Recognition

• URL: http://arxiv.org/abs/2602.02014v1
• Date: Mon, 02 Feb 2026 12:12:00 GMT
• Title: Rethinking Genomic Modeling Through Optical Character Recognition
• Authors: Hongxin Xiang, Pengsen Ma, Yunkang Cao, Di Yu, Haowen Chen, Xinyu Yang, Xiangxiang Zeng,
• Abstract summary: We present OpticalDNA, a vision-based framework that reframes genomic modeling as Optical Character Recognition (OCR)-style document understanding.<n> OpticalDNA renders DNA into structured visual layouts and trains an OCR-capable vision-language model with a emphvisual DNA encoder and a emph decoder, where the encoder produces compact, reconstructible visual tokens for high-fidelity compression.
• Score: 23.555844091817956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is structurally misaligned with sparse and discontinuous genomic semantics, leading to wasted computation on low-information background and preventing understanding-driven compression for long contexts. Here, we present OpticalDNA, a vision-based framework that reframes genomic modeling as Optical Character Recognition (OCR)-style document understanding. OpticalDNA renders DNA into structured visual layouts and trains an OCR-capable vision--language model with a \emph{visual DNA encoder} and a \emph{document decoder}, where the encoder produces compact, reconstructible visual tokens for high-fidelity compression. Building on this representation, OpticalDNA defines prompt-conditioned objectives over core genomic primitives-reading, region grounding, subsequence retrieval, and masked span completion-thereby learning layout-aware DNA representations that retain fine-grained genomic information under a reduced effective token budget. Across diverse genomic benchmarks, OpticalDNA consistently outperforms recent baselines; on sequences up to 450k bases, it achieves the best overall performance with nearly $20\times$ fewer effective tokens, and surpasses models with up to $985\times$ more activated parameters while tuning only 256k \emph{trainable} parameters.

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