The paper investigates the adversarial vulnerability of neural receivers that incorporate unrolled LDPC decoders for 5G NR. It finds that standard gradient-based attacks are ineffective due to gradient masking caused by tanh saturation in the BP check-node update, leading to a dramatic decay in gradient norm over iterations. To overcome this, the authors propose two loss functions—L_BP and L_LLR—that restore attack efficacy, significantly increasing block-error rates across different neural receiver implementations.
By Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker
arXiv:2610.27805v1 Announce Type: cross
Abstract: Maximum-likelihood (ML) decoding of linear block codes is NP-hard, so neural decoders are evaluated by how closely they approach it. We observe that...
By Jaeho Hwang, Byung-Sun Won, Uiwon Hwang
arXiv:2609.00715v1 Announce Type: cross
Abstract: Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are u...
By Jonggyu Jang, Hongjae Nam, Vishrant Tripathi, David J. Love, Hyun Jong Yang
Maximum-likelihood (ML) decoding of linear block codes is NP-hard, so neural decoders are evaluated by how closely they approach it. We observe that proving a candidate optimal can be far easier than...
arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.
By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig
The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².
By Menghui Zhou, Gaoshan Bi, Vitaveska Lanfranchi, Po Yang
arXiv:2608.16353v2 Announce Type: replace
Abstract: Internal-state probes enable truthfulness prediction before a large language model generates an answer. When detectors change both the layers they...
By Zhihao Guo, Zonghan Wu, Huan Huo, DaYong Ye, Junwei Zhang, Weiran Yao, Zhiwei Liu, Qingsong Wen, Yilei Shao
The paper introduces GBPNet, a Generalized Belief Propagation Network that maintains message‑passing symmetry while improving learning capability for decoding linear codes. GBPNet employs cascaded variable‑node updates and learned element‑wise message scaling, preserving the Tanner graph structure. Two training strategies—progressive training and high‑SNR training—are proposed, and experiments show GBPNet achieves lower bit error rates than conventional belief propagation and other state‑of‑the‑art neural decoders.
By Samira Malek
arXiv:2601.02438v4 Announce Type: replace-cross
Abstract: Software vulnerability detection can be formulated as a binary classification problem that determines whether a given code snippet contains s...
By Yun Bian, Yi Chen, HaiQuan Wang, ShiHao Li, Zhe Cui
arXiv:2609.31620v1 Announce Type: new
Abstract: Representation autoencoders (RAEs) reuse features from a pretrained visual encoder as reconstruction and diffusion latents, integrating strong visual r...
By Hongyang Du, Yunfei Xie, Junjie Ye, Jiawei Yang, Xiaoyan Cong, Haodong Zhang, Yongchao Huang, Haiyu Wu, Zongxia Li, Shihang Gui, Dawei Liu, Runhao Li, Jingcheng Ni, Chen Wei, Randall Balestriero, Yue Wang
The paper introduces Mahalanobis-Ensemble Decoding (ME-Decoding), a new framework for Large Language Model decoding that treats candidate token selection as an ensemble pruning problem. It uses a Mahalanobis distance-driven objective to promote semantic diversity while maintaining high probabilities, employing a token similarity matrix built with an adaptive-bandwidth kernel over token embeddings. An efficient greedy algorithm with near-linear complexity and theoretical guarantees makes ME-Decoding a plug‑and‑play module with negligible inference overhead, and experiments show strong performance across reasoning and generation tasks.
By Dunyao Xue, Chengshuo Du, Zhengbo Wang, Wenlin Dai, Cheng Meng
arXiv:2505. 16077v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are used to decompose neural network activations into human-interpretable features.
By Soham Gadgil, Chris Lin, Su-In Lee