arXiv:2608. 06912v1 Announce Type: new Abstract: The top-$k$ operation is a fundamental building block of modern sparse computation, enabling token routing, expert activation, memory selection, and attention pruning.
By {\L}ukasz Struski, Joanna Wojciechowicz, Jakub Antczak, Marcin Mazur, Kamil Ksi\k{a}\.zek, Jacek Tabor
arXiv:2608.06912v2 Announce Type: replace
Abstract: Selecting the top-$k$ elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust ex...
By Jakub Antczak, Joanna Wojciechowicz, Kamil Ksi\k{a}\.zek, Marcin Mazur, {\L}ukasz Struski, Jacek Tabor
arXiv:2609.10311v1 Announce Type: cross
Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initial...
By Benedikt Tscheschner, Eduardo Veas, Marc Masana
arXiv:2605. 16928v2 Announce Type: replace-cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention.
By Yanke Zhou, Yiduo Li, Hanlin Tang, Maohua Li, Kan Liu, Tao Lan, Lin Qu, Yuan Yao, Xiaoxing Ma
arXiv:2603.06159v2 Announce Type: replace-cross
Abstract: Learned top-K search improves the accuracy-latency trade-off of graph-based vector search, but existing methods are designed for a fixed K: s...
By Yifan Peng, Jiafei Fan, Xingda Wei, Sijie Shen, Rong Chen, Jianning Wang, Xiaojian Luo, Wenyuan Yu, Jingren Zhou, Haibo Chen
arXiv:2512. 13996v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-$k$ routing imposes a rigid sparsity pattern that ignores the intrinsic variance in token difficulty and layer-specific computational needs.
By Can Jin, Hongwu Peng, Mingcan Xiang, Qixin Zhang, Xiangchi Yuan, Amit Hasan, Ohi Dibua, Yifan Gong, Yan Kang, Dimitris N. Metaxas