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[2512.01389] Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes
Summary
The paper presents the Error Correction Syndrome-Flow Consistency Model (ECCFM), which enhances one-step denoising error correction codes, achieving lower error rates and significantly faster inference speeds compared to traditional methods.
Why It Matters
This research addresses the critical challenge of designing efficient neural decoders for error correction in digital communications. By proposing a model that ensures smooth decoding trajectories, it offers a practical solution for real-time applications, potentially transforming the field of error correction coding.
Key Takeaways
- ECCFM achieves lower bit-error-rate (BER) and frame-error-rate (FER) compared to transformer-based decoders.
- The model operates 30x to 100x faster than iterative denoising diffusion decoders.
- Re-parameterization of the reverse PF-ODE allows for a smooth decoding trajectory.
- The model is designed to be model-agnostic, applicable to various error correction tasks.
- This approach enhances the practicality of neural decoders in low-latency environments.
Computer Science > Machine Learning arXiv:2512.01389 (cs) [Submitted on 1 Dec 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes Authors:Haoyu Lei, Chin Wa Lau, Kaiwen Zhou, Nian Guo, Farzan Farnia View a PDF of the paper titled Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes, by Haoyu Lei and 4 other authors View PDF HTML (experimental) Abstract:Error Correction Codes (ECC) are fundamental to reliable digital communication, yet designing neural decoders that are both accurate and computationally efficient remains challenging. Recent denoising diffusion decoders achieve state-of-the-art performance, but their iterative sampling limits practicality in low-latency settings. To bridge this gap, consistency models (CMs) offer a potential path to high-fidelity one-step decoding. However, applying CMs to ECC presents a significant challenge: the discrete nature of error correction means the decoding trajectory is highly non-smooth, making it incompatible with a simple continuous timestep parameterization. To address this, we re-parameterize the reverse Probability Flow Ordinary Differential Equation (PF-ODE) by soft-syndrome condition, providing a smooth trajectory of signal corruption. Building on this, we propose the Error Correction Syndrome-Flow Consistency Model (ECCFM), a model-agnostic framework designed specifically for ECC task, ensuring the m...
