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版本更新 · v2arXiv:2610.33185

Post-Quantum Perfect Zero-Knowledge Arguments for NP From Any One-Way Permutation

Frédéric Dupuis, Rémi Ligez, Khaireddine Medhioub, Louis Salvail, Louis-Charles Thibodeau

原文摘要

In this paper, we show for the first time that post-quantum one-way permutations are sufficient for perfect zero-knowledge classical arguments for all of NP with security guaranteed against quantum adversaries. We first establish that Naor, Ostrovsky, Venkatesan, and Yung's bit commitment scheme built from any permutation using interactive hashing, called the NOVY scheme, is sum-binding and collapse-binding against quantum polynomial-time committers provided the permutation is post-quantum one-way. We then show that the collapse-binding property of the NOVY scheme allows for Blum's perfect zero-knowledge argument for HAMCYCLE to remain so exactly as it stands against quantum adversaries.

quant-ph · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21325

Fast and Stable Triangular Inversion for Delta-Rule Linear Transformers

Aleksandros Sobczyk, Gioele Gottardo, Christos K. Matzoros, Mirko De Vita, Filip Skogh, Anastasios Zouzias, Jiawei Zhuang

原文摘要

Linear attention has emerged as a cornerstone for efficient long-context architectures, as evidenced by its integration into state-of-the-art open-source models including Qwen3.5/3.6, Kimi Linear/K3, and RWKV-7. Models that incorporate linear attention layers with the so-called Delta-Rule involve the inversion of triangular matrices as a core sub-routine. This operation often forms a performance bottleneck, and, due to its high-sensitivity to numerical errors, it can significantly deteriorate end-to-end model accuracy if it is not carefully implemented. This work provides a systematic analysis of both direct and iterative triangular inversion algorithms, targeting methods that are rich in matrix products, and, therefore, have the potential to efficiently utilize modern hardware. To that end, our analysis covers a broad spectrum of mathematical and practical aspects, with a heavy focus on numerical stability, computational complexity, and, ultimately, hardware efficiency and practical considerations. We provide a rigorous experimental evaluation to verify these properties in practical scenarios, and in low-precision floating-point representations, highlighting the strengths and limitations of each method. Performance benchmarks on NPUs reveal up to $4.3\times$ speed-up against the state-of-the-art implementations of SGLang for triangular matrix inversion, leading to significant performance improvements on the entire layer level, while maintaining end-to-end model accuracy.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01940

ReWY: Faster Backpropagation for Delta-Rule Attention - From 7 to 3 Matrix Multiplications

Yi Zhang, Zhixin Zhao, Weiwei Sun, Yitao Hu, Sheng Chen, Keqiu Li

原文摘要

Delta-rule linear attention underlies language models such as Kimi Linear and Qwen3-Next. Chunkwise implementations expose matrix parallelism, but their backward passes compute unnecessary auxiliary adjoints. We present ReWY, an algebraic reformulation that reduces this work before optimizing accelerator execution. Differentiating the chunk correction as one triangular residual system eliminates the inverse adjoint and reduces seven local WY matrix products to three. Two complementary transformations expose further reuse: centered matrix contractions replace pairwise vector-gate processing, and scalar-gate reductions reuse key-gradient products instead of forming a key Gram matrix. These identities simplify backward computation for DeltaNet, GDN, KDA, and GDN2 while preserving the recurrence and gradients in exact arithmetic, without changing the architecture or training objective. We implement the reduced graph on GPUs and Ascend NPUs. Under matched recomputation and full-saving policies, precision-qualified six-shape comparisons with frozen FLA references yield 1.15-1.81$\times$ geometric-mean complete low-level backward speedups on NVIDIA GPUs. All four operators achieve 1.18-1.70$\times$ on H100 and H200 while retaining original FP32 working matrices. A separate public-autograd evaluation on Ascend 950DT, including gradient conversions, yields 1.09-2.89$\times$ under available matched policies. Paired ablations establish further ReWY gains after optimizing the complementary contractions. Complete-gradient checks validate the recorded operator cases; supplementary full-model backward measurements assess integration into community FLA models and a GDN2 layer port. Code and reproduction materials: https://github.com/flashserve/ReWY.

cs.LG · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03934

Accelerating FastDEC Clustering Algorithm with GPUs

Mi Li, Eibe Frank, Bernhard Pfahringer

原文摘要

Fast Dominance Estimation Clustering (FastDEC) is an efficient variant of Density Peak Clustering (DPC) that achieves high clustering quality through dominance-based density estimation, but remains constrained by the computational cost of exact k-nearest-neighbor (k-NN) construction and dominance component (DC) processing. We present SymWarp-FastDEC, a GPU-accelerated implementation of FastDEC. At its core is SymTrikNN, an exact self-k-NN kernel that exploits search symmetry using upper-triangular traversal and warp-cooperative top-k selection. We also introduce WP-DC, a warp-parallel DC reduction algorithm designed for modern GPUs. Combined with memory-adaptive execution planning and stream scheduling, WP-DC eliminates DC reduction as a runtime bottleneck. Experiments on synthetic and real-world datasets show that SymWarp-FastDEC produces clustering outputs identical to the original FastDEC on all evaluated equivalence benchmarks. Compared with a FastDEC variant using FAISS GpuIndexFlatL2 for exact k-NN search, our approach achieves approximately 1.7x to 4.9x higher end-to-end performance, with the advantage generally increasing with dimensionality. Compared with the original single-threaded Python FastDEC implementation, speedups reach approximately 2.5 x 10^4 in a very high-dimensional case where its k-d-tree-based search degrades; this extreme result is not representative of typical acceleration.

cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1881ACL Anthology · ACL 2026

DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis

Lung-Hao Lee National Yang Ming Chiao Tung University, Liang-Chih Yu Yuan Ze University, Natalia V Loukachevitch, Ilseyar Alimova Kazan Federal University, Alexander Panchenko S-NLP group, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou National Yang Ming Chiao Tung University 等 16 位作者

原文摘要

Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limits its ability to capture nuanced affective states. To address this limitation, we adopt a dimensional approach that represents sentiment with continuous valence–arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. To this end, we introduce DimABSA, the first multilingual, dimensional ABSA resource annotated with both traditional ABSA elements (aspect terms, aspect categories, and opinion terms) and newly introduced VA scores. This resource contains 76,958 aspect instances across 42,590 sentences, spanning six languages and four domains. We further introduce three subtasks that combine VA scores with different ABSA elements, providing a bridge from traditional ABSA to dimensional ABSA. Given that these subtasks involve both categorical and continuous outputs, we propose a new unified metric, continuous F1 (cF1), which incorporates VA prediction error into standard F1. We provide a comprehensive benchmark using both prompted and fine-tuned large language models across all subtasks. Our results show that DimABSA is a challenging benchmark and provides a foundation for advancing multilingual dimensional ABSA. We publicly released the DimABSA dataset, which was used for Track A of SemEval-2026 Task 3, attracting over 300 participants.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表