SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
This work addresses a practical bottleneck in DNA storage for researchers and engineers by enabling more efficient end-to-end learned codecs.
The paper tackled the inefficiency of integrating neural compression pipelines with DNA storage by introducing SCONE, a plug-in module that performs quaternary arithmetic coding directly on latent representations to enforce biochemical constraints, achieving near-perfect constraint satisfaction with less than 2% latency overhead.
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.