SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915779089268736 |
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| author | Ruan, Cihan Zhou, Lebin Han, Rongduo Han, Linyi Zhao, Bingqing Zhu, Chenchen Jiang, Wei Wang, Wei Ling, Nam |
| author_facet | Ruan, Cihan Zhou, Lebin Han, Rongduo Han, Linyi Zhao, Bingqing Zhu, Chenchen Jiang, Wei Wang, Wei Ling, Nam |
| contents | DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy constraint layers atop raw binary data - workable but primitive. Recent neural codecs compress data into learned latent representations with rich statistical structure, yet still convert these latents to DNA via naive binary-to-quaternary transcoding, discarding the entropy model's optimization. This mismatch undermines compression efficiency and complicates the encoding stack. A plug-in module that collapses latent compression and DNA encoding into a single step. SCONE performs quaternary arithmetic coding directly on the latent space in DNA bases. Its Constraint-Aware Adaptive Coding module dynamically steers the entropy encoder's learned probability distribution to enforce biochemical constraints - Guanine-Cytosine (GC) balance and homopolymer suppression - deterministically during encoding, eliminating post-hoc correction. The design preserves full reversibility and exploits the hyperprior model's learned priors without modification. Experiments show SCONE achieves near-perfect constraint satisfaction with negligible computational overhead (<2% latency), establishing a latent-agnostic interface for end-to-end DNA-compatible learned codecs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06157 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage Ruan, Cihan Zhou, Lebin Han, Rongduo Han, Linyi Zhao, Bingqing Zhu, Chenchen Jiang, Wei Wang, Wei Ling, Nam Machine Learning DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy constraint layers atop raw binary data - workable but primitive. Recent neural codecs compress data into learned latent representations with rich statistical structure, yet still convert these latents to DNA via naive binary-to-quaternary transcoding, discarding the entropy model's optimization. This mismatch undermines compression efficiency and complicates the encoding stack. A plug-in module that collapses latent compression and DNA encoding into a single step. SCONE performs quaternary arithmetic coding directly on the latent space in DNA bases. Its Constraint-Aware Adaptive Coding module dynamically steers the entropy encoder's learned probability distribution to enforce biochemical constraints - Guanine-Cytosine (GC) balance and homopolymer suppression - deterministically during encoding, eliminating post-hoc correction. The design preserves full reversibility and exploits the hyperprior model's learned priors without modification. Experiments show SCONE achieves near-perfect constraint satisfaction with negligible computational overhead (<2% latency), establishing a latent-agnostic interface for end-to-end DNA-compatible learned codecs. |
| title | SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.06157 |