# VT-Former: Efffcient Transformer-based Decoder for Varshamov-Tenengolts Codes

arXiv: https://arxiv.org/abs/2502.21060 · HTML: https://mlsanity.com/paper/2502.21060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Error Correction Code Transformer](https://mlsanity.com/paper/2203.14966/) (2022)
- [Hyper-Graph-Network Decoders for Block Codes](https://mlsanity.com/paper/1909.09036/) (2019)
- [Neural Offset Min-Sum Decoding](https://mlsanity.com/paper/1701.05931/) (2017)
- [CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codes](https://mlsanity.com/paper/2405.01033/) (2024)
- [Learning Linear Block Error Correction Codes](https://mlsanity.com/paper/2405.04050/) (2024)

## Abstract (arXiv)

In recent years, widespread attention has been drawn to the challenge of correcting insertion, deletion, and substitution (IDS) errors in DNA-based data storage. Among various IDS-correcting codes, Varshamov-Tenengolts (VT) codes, originally designed for single-error correction, have been established as a central research focus. While existing decoding methods demonstrate high accuracy for single-error correction, they are typically not applicable to the correction of multiple IDS errors. In this work, the latent capability of VT codes for multiple-error correction is investigated through a statistic-enhanced Transformer-based VT decoder (VT-Former), utilizing both symbol and statistic feature embeddings. Experimental results demonstrate that VT-Former achieves nearly 100% accuracy on correcting single errors. For multi-error decoding tasks across various codeword lengths, improvements in both frame accuracy and bit accuracy are observed, compared to conventional hard-decision and soft-in soft-out decoding algorithms. Furthermore, while lower decoding latency is exhibited by the base model compared to traditional soft decoders, the architecture is further optimized in this study to enhance decoding efficiency and reduce computational overhead.