Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols predominantly rely on static benchmarks with fixed historical test windows. While these benchmarks provide a valuable baseline snapshot, they evaluate...
Haomin Wen, Ziyu Zhou, Qingxiang Liu et al.· 0 citations
CompKV is introduced, the first compensation-aware sparse attention framework that divides tokens into blocks and explicitly optimizes selection for the downstream compensation mechanism, and shows that the residual left by block-level mean compensation is governed by both block attention mass and within-block logit va...
Zheng-Hong Huang, Rui-Zhe Yao, Dan-Yi Liu et al.· 0 citations
Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards make this work consequential because incomplete evidence can change estimates of severity, exposure, and mechanism. We introduce EarthVerse, a benchmark that evaluates sci...
Zhiqing Cui, Xinxiang Yin, Yihong Tang et al.· 0 citations
Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-token budget. Existing methods either select query-aware frames in a single pass or rely on timestamped text solely as retrieval guidance, leading to two key limitations....
Fan Wei, Si-Ru Zhong, Run-Min Dong et al.· 0 citations
Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlyi...
