SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage

• URL: http://arxiv.org/abs/2602.06157v1
• Date: Thu, 05 Feb 2026 19:54:13 GMT
• Title: SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
• Authors: Cihan Ruan, Lebin Zhou, Rongduo Han, Linyi Han, Bingqing Zhao, Chenchen Zhu, Wei Jiang, Wei Wang, Nam Ling,
• Abstract summary: Plug-in module collapses latent compression and DNA encoding into a single step.<n>SCONE performs quaternary arithmetic coding directly on the latent space in DNA bases.<n>Design preserves full reversibility and exploits the hyperprior model's learned priors without modification.
• Score: 11.92900213512492
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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