3 papers
cs.LG2026
Discovering Millions of Interpretable Features with Sparse Autoencoders
XinYang He, Wei Wang, Bing Zhao +5
Sparse autoencoders (SAEs) have emerged as a powerful tool for decomposing superposed language model representations into sparse and interpretable features. However, training SAEs…
cs.CL2026
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction
Chengguang Gan, Sunbowen Lee, Qingyu Yin +9
The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modele…
cs.CV2025
ScaleLong: A Multi-Timescale Benchmark for Long Video Understanding
David Ma, Huaqing Yuan, Xingjian Wang +16
Although long-video understanding demands that models capture hierarchical temporal information -- from clip (seconds) and shot (tens of seconds) to event (minutes) and story (hour…