37 citations · 54 across the 27 of their papers we have counts for
9 papers · 1 filter
Adaptive Spectral-Koopman Dynamics Modeling for Temporal Domain Generalization
Tengxue Zhang, Yu Ke, Yang Shu +4
Temporal Domain Generalization (TDG) has emerged to address real-world streaming data with distribution shifts over time. However, existing methods are either prone to overfitting…
QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities
Hanyin Cheng, Linfeng Wang, Zhengbo Qu +7
Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the distinct forecasting roles of…
Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining
Biao Ouyang, Tengxue Zhang, Zhihao Zhuang +3
Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typically rely on dataset-specific…
T2MBench: A Benchmark for Out-of-Distribution Text-to-Motion Generation
Bin Yang, Rong Ou, Weisheng Xu +7
Most existing evaluations of text-to-motion generation focus on in-distribution textual inputs and a limited set of evaluation criteria, which restricts their ability to systematic…
Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
Peng Chen, Siyuan Wang, Shiyan Hu +7
While existing time series foundation models primarily rely on large-scale unimodal pretraining, they lack complementary modalities to enhance time series understanding. Building m…
D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use
Bowen Xu, Shaoyu Wu, Hao Jiang +4
Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that c…
ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies
Xingjian Wu, Xvyuan Liu, Junkai Lu +6
LLM-powered Multi-Agent Systems (MAS) have emerged as an effective approach towards collaborative intelligence, and have attracted wide research interests. Among them, ``self-evolv…
ToolCaching: Towards Efficient Caching for LLM Tool-calling
Yi Zhai, Dian Shen, Junzhou Luo +1
Recent advances in Large Language Models (LLMs) have revolutionized web applications, enabling intelligent search, recommendation, and assistant services with natural language inte…
Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models
Siru Zhong, Junjie Qiu, Yangyu Wu +7
Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of do…