SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
Peking University（北京大学）；South China University of Technology（南方科技大学）；Microsoft Research（微软研究院）；Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology（香港科技大学）
Hanyong Shao, Yingbo Hao, Ting Song, Yan Xia, Di Zhang, Shaohan Huang, Xun Wu, Songchen Xu, Le Xu, Li Dong, Zewen Chi, Yi Zou, Furu Wei
NVIDIA's 2:4 Sparse Tensor Cores deliver 2x throughput but demand strict 50% pruning -- a ratio that collapses LLM reasoning accuracy (Qwen3: 54% to 15%). Milder $(2N-2):2N$ patterns (e.g., 6:8, 25% pruning) preserve accuracy yet receive no hardware support, falling back to dense execution without any benefit from sparsity. We present SlideSparse, the first system to unlock Sparse Tensor Core acceleration for the $(2N-2):2N$ model family on commodity GPUs. Our Sliding Window Decomposition reconstructs any $(2N-2):2N$ weight block into $N-1$ overlapping 2:4-compliant windows without any accuracy loss; Activation Lifting fuses the corresponding activation rearrangement into per-token quantization at near-zero cost. Integrated into vLLM, SlideSparse is evaluated across various GPUs (A100, H100, B200, RTX 4090, RTX 5080, DGX-spark), precisions (FP4, INT8, FP8, BF16, FP16), and model families (Llama, Qwen, BitNet). On compute-bound workloads, the measured speedup ratio (1.33x) approaches the theoretical upper-bound $N/(N-1)=4/3$ at 6:8 weight sparsity in Qwen2.5-7B, establishing $(2N-2):2N$ as a practical path to accuracy-preserving LLM acceleration. Code available at https://github.com/bcacdwk/vllmbench.
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Applied AI（应用人工智能）；Artificial Intelligence Research Institute（人工智能研究所）；HSE University（俄罗斯高等经济大学）；Sb AI Lab（Sb人工智能实验室）；MWS AI（MWS人工智能）；Yandex；Independent Researcher（独立研究者）
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior
Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）；Tongji University（同济大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；Aalborg University（奥尔堡大学）
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Hong Kong University of Science and Technology（香港科技大学）
SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity
Northwestern Polytechnical University（西北工业大学）；Tsinghua University（清华大学）；The Hong Kong University of Science and Technology（香港科技大学）；Beijing Jiaotong University（北京交通大学）；Harbin Engineering University（哈尔滨工程大学）
Purdue University（普渡大学）；Lawrence Berkeley Lab（劳伦斯伯克利实验室）；Yahoo! Research（雅虎研究院）；University of Electronic Science and Technology of China（电子科技大学）；University of Cambridge（剑桥大学）
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization
Academy for Engineering and Technology, Fudan University（复旦大学工程与应用技术研究院）；NVIDIA Corporation（英伟达公司）；School for Information Science and Technology, Fudan University（复旦大学信息科学与工程学院）
FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference
School of Statistics and Mathematics, Zhejiang Gongshang University（浙江工商大学统计与数学学院）；École de technologie supérieure (ÉTS), Université du Québec（魁北克大学埃克森技术学院）；Southern University of Science and Technology（南方科技大学）
