Yucheng Guo — accepted AI-conference papers

2026

MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging

Siyuan Li, Kai Yu, Anna Wang, Zicheng Liu, Chang Yu, Jingbo Zhou +4

AAAI 2026technical

Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit. Relying on either four primitive bases or independently designed DNA tokenizers, existing approaches with naive masked

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2026

TrinityDNA: A Bio-Inspired Foundational Model for Efficient Long-Sequence DNA Modeling

Qirong Yang, Yucheng Guo, Zicheng Liu, Yujie Yang, Qijin Yin, Siyuan Li +3

AAAI 2026technical

The modeling of genomic sequences presents unique challenges due to their long length and structural complexity. Traditional sequence models struggle to capture long-range dependencies and biological features inherent in DNA. In this work, we propose TrinityDNA, a novel DNA foundational model design

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