5 papers
Quo Vadis, World Modeling?
Yu Yang, Xuemeng Yang, Licheng Wen +17
Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to paralleliz…
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47
As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that ma…
mmExpert: Integrating Large Language Models for Comprehensive mmWave Data Synthesis and Understanding
Yifan Yan, Shuai Yang, Xiuzhen Guo +4
Millimeter-wave (mmWave) sensing technology holds significant value in human-centric applications, yet the high costs associated with data acquisition and annotation limit its wide…
KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
Taoran Fang, Tianhong Gao, Chunping Wang +4
Graph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years. However, o…
Enhancing Cross-domain Link Prediction via Evolution Process Modeling
Xuanwen Huang, Wei Chow, Yize Zhu +5
This work proposes DyExpert, a dynamic graph model for cross-domain link prediction. It can explicitly model historical evolving processes to learn the evolution pattern of a speci…