9 papers
CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad
Yongqiang Chen, Chenxi Liu, Zhenhao Chen +3
Evolve-based agent such as AlphaEvolve is one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific probl…
Unsupervised Synthetic Image Attribution: Alignment and Disentanglement
Zongfang Liu, Guangyi Chen, Boyang Sun +2
As the quality of synthetic images improves, identifying the underlying concepts of model-generated images is becoming increasingly crucial for copyright protection and ensuring mo…
Mirage2Matter: A Physically Grounded Gaussian World Model from Video
Zhengqing Gao, Ziwen Li, Xin Wang +12
The scalability of embodied intelligence is fundamentally constrained by the scarcity of real-world interaction data. While simulation platforms provide a promising alternative, ex…
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining…
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
Chenxi Liu, Yongqiang Chen, Tongliang Liu +4
Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice…
Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
Tianjun Yao, Haoxuan Li, Yongqiang Chen +4
Graph Neural Networks (GNNs) often encounter significant performance degradation under distribution shifts between training and test data, hindering their applicability in real-wor…