Rethinking Genomic Modeling Through Optical Character Recognition

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computational Genomics, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

OpticalDNA introduces a novel vision-based framework that redefines genomic modeling as an Optical Character Recognition (OCR)-style document understanding problem, moving beyond traditional large language model architectures that treat DNA as a one-dimensional token sequence. This approach addresses the inefficiency of sequential reading for sparse and discontinuous genomic semantics, which wastes computation and hinders understanding-driven compression. OpticalDNA renders DNA into structured visual layouts and employs an OCR-capable vision–language model with a visual DNA encoder and a document decoder. The encoder generates compact, reconstructible visual tokens for high-fidelity compression. The framework defines prompt-conditioned objectives over core genomic primitives like reading, region grounding, subsequence retrieval, and masked span completion. On sequences up to 450k bases, OpticalDNA consistently outperforms recent baselines, achieving the best overall performance with nearly 20x fewer effective tokens and surpassing models with up to 985x more activated parameters while tuning only 256k trainable parameters.

Key takeaway

For Machine Learning Engineers developing genomic foundation models, traditional 1D sequence approaches are computationally wasteful for sparse DNA. You should explore vision-based frameworks like OpticalDNA, which reframe DNA as a visual document. This method offers nearly 20x fewer effective tokens and superior performance on long sequences up to 450k bases, enabling more efficient and accurate genomic understanding. Consider adopting this OCR-style paradigm to optimize computational resources and enhance model performance.

Key insights

OpticalDNA reframes genomic modeling as OCR-style document understanding, achieving efficiency and high-fidelity compression via visual layouts.

Principles

• Genomic function is sparse and discontinuous.
• 2D visual layouts improve accuracy-efficiency over 1D sequences.
• Understanding-driven compression is key for scalable modeling.

Method

OpticalDNA renders 1D DNA into multi-page 2D visual documents with bounding boxes, training a vision-language model via OCR-inspired tasks like reading, grounding, retrieval, and masked completion.

In practice

• Render DNA as 2D visual documents for structured analysis.
• Utilize vision tokens for efficient DNA sequence compression.
• Apply OCR primitives for genomic variant localization.

Topics

• Genomic Foundation Models
• Optical Character Recognition
• Vision-Language Models
• DNA Sequence Modeling
• Computational Genomics
• Long-Range Genomics

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.