arXiv:2607. 17481v1 Announce Type: new Abstract: Motif discovery, the search for recurring patterns within a time series, is a core primitive of exploratory data analysis.
By Tej Sanibh Ranade
arXiv:2607. 07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantization, eviction, or offloading.
By Vladimir Gusev
arXiv:2609. 29881v1 Announce Type: cross Abstract: Choosing Shellsort gaps is a well-known open problem.
By Bo Liu
arXiv:2609.20276v2 Announce Type: replace-cross
Abstract: Memoizing an expensive function of a sorted score vector is a data-structure problem before it is a numerical one: at a billion gridpoints, a...
By Tamal Maharaj
arXiv:2608. 06849v1 Announce Type: cross Abstract: Long-context LLM inference is bottlenecked by quadratic attention computation and growing KV-cache costs.
By Yehan Yang, Junyuan Shang, Yang Li, Guanqun Zhao, Shuohuan Wang, Dianhai Yu
The paper presents a memory‑efficient sparse‑binary self‑organising map (SOM) that scales a MEDLINE atlas to over a million neurons on a single consumer GPU. By re‑ordering the codebook into a feature‑major layout, the authors accelerate the best‑matching‑unit search by 4.5–8.5× without increasing quantisation error, enabling training of a 1,048,576‑neuron SOM in 72 s on a 24 GB GPU. The approach outperforms existing cuSPARSE and CPU‑based SOM implementations, achieving the largest SOM reported to date and demonstrating that resolution limits are computational rather than data‑driven.
By Andrew James Amos