Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

[2605.03620] Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding Skip to main content Search Submit Donate Log in Computer Science > Information Theory arXiv:2605.03620 (cs) [Submitted on 5 May 2026 ( v1 ), last revised 27 Aug 2026 (this version, v2)] Title: Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding Authors: Raphaël Le Bidan , Ahmad Ismail , Elsa Dupraz , Charbel Abdel Nour View PDF HTML (experimental) Abstract: Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In this paper, we show how to leverage code automorphisms to enhance the ability of existing SBND models to learn and generalize through data augmentation during training and inference. As a result, for the short high-rate codes considered, we obtain models that closely approach MLD performance using small datasets and proper training. Our findings also suggest that many prior results for SBND models in the literature underestimate their true correction capability due to undertraining. Code to reproduce all results is available at: this https URL . Comments: 6 pages, 7 figures, accepted for publication at IEEE GLOBECOM 2026. Code to reproduce all results is available at: this https URL Subjects: Information Theory (cs.IT) ; Machine Learning (cs.LG) Cite as: arXiv:2605.03620 [cs.IT] (or arXiv:2605.03620v2 [cs.IT] for this version) https://doi.org/10.48550/arXiv.2605.03620 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Raphael Le Bidan [ view email ] [v1] Tue, 5 May 2026 10:48:14 UTC (259 KB) [v2] Thu, 27 Aug 2026 09:39:36 UTC (259 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IT < prev | next > new | recent | 2026-05 Change to browse by: cs cs.LG math math.IT References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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