ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912551871184896 |
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| author | Song, Yuxuan Zhang, Zhe Pei, Yu Gong, Jingjing Yu, Qiying Zhang, Zheng Wang, Mingxuan Zhou, Hao Liu, Jingjing Ma, Wei-Ying |
| author_facet | Song, Yuxuan Zhang, Zhe Pei, Yu Gong, Jingjing Yu, Qiying Zhang, Zheng Wang, Mingxuan Zhou, Hao Liu, Jingjing Ma, Wei-Ying |
| contents | Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at https://github.com/GenSI-THUAIR/SLM |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17345 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation Song, Yuxuan Zhang, Zhe Pei, Yu Gong, Jingjing Yu, Qiying Zhang, Zheng Wang, Mingxuan Zhou, Hao Liu, Jingjing Ma, Wei-Ying Machine Learning Genomics Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at https://github.com/GenSI-THUAIR/SLM |
| title | ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2508.17345 |