activity
20242026
most citedKAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.AI2026

Dual Exam: Controlled Evaluation of Multimodal Agents and World Models

Zhefan Rao, Meng Chu, Xuan Zhang +80

World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction require…

cs.CV2026

Orbia: Do Generated Videos Form a Consistent 3D World?

Dongyue Lu, Guian Fang, Hur Lim +20

Video generators serve as interactive worlds for exploration and simulation, yet realistic and visually appealing frames do not necessarily depict a consistent 3D world. Existing b…

cs.CV2026

VITAL: From Visual Motion to Precise Action

Ao Liang, Youquan Liu, Mingxiao Li +7

World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined independently of visual dynamic…

cs.CV2026

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…

cs.AI2026

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

cs.LG2024

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…