Error-correcting codes enable reliable communication, yet practical soft decoding remains challenging across code families and block lengths. We propose SB-ECC, a score-based decoder that casts decoding as continuous-time denoising. A neural denoiser defines a probability-flow ordinary differential equation (ODE) that iteratively updates the noisy channel observation toward a valid codeword, guided by parity constraints. The model is trained across noise levels without time/SNR conditioning, enabling inference without SNR estimation and supporting a direct latency accuracy trade off controlled by the ODE solver budget. We use the raw signed channel observation as input for learning a continuous denoising field. Across 42 code/SNR settings, SB-ECC achieves the best BER in 39/42 entries, with an average SNR gain of 0.17dB and a maximum gain of 0.46dB over the strongest competing baseline, we showed that swapping the solver from Euler to DPM preserves -ln(BER) while reducing end-to-end decoding time by 8.86% on average (up to 12.82%).

翻译：纠错码能够实现可靠的通信，然而在实际应用中，对于不同码族和码长的软译码仍然具有挑战性。我们提出SB-ECC，一种基于分数的译码器，将译码过程建模为连续时间去噪。一个神经去噪器定义了一个概率流常微分方程，该方程在奇偶约束的引导下，将带噪的信道观测值迭代地更新为有效的码字。该模型在不同噪声水平下进行训练，无需时间/信噪比条件，从而能够在无需信噪比估计的情况下进行推理，并支持由常微分方程求解器预算控制的直接延迟-精度权衡。我们使用原始有符号信道观测值作为输入来学习连续去噪场。在42个码/信噪比设置下，SB-ECC在39/42项中取得了最佳误比特率，平均信噪比增益为0.17分贝，相对于最强竞争基线最大增益为0.46分贝。我们还表明，将求解器从欧拉法切换为DPM方法，可在保持-ln(BER)的同时，将端到端译码时间平均减少8.86%（最高可达12.82%）。