collaborators

8 papers

cs.LG2026

Gated Graph Attention Networks with Learnable Temperature

Zhongtian Ma, Hao Wu, Yexin Zhang +2

Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable feature dimensions and use fixed s…

cs.LG2026

Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms

Zhongtian Ma, Qiaosheng Zhang, Zhen Wang

This paper considers the problem of completing a rating matrix based on sub-sampled matrix entries as well as observed social graphs and hypergraphs. We show that there exists a \e…

cs.LG2026

Misclassification Rate and Privacy-Utility Trade-offs in Graph Convolutional Networks via Subsampling Stability

Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang +1

We study differential privacy (DP) in Graph Convolutional Networks (GCNs) through the framework of \textit{subsampling stability}. We derive upper bounds on the misclassification r…

cs.AI2026

Disentangling Intent from Role: Adversarial Self-Play for Persona-Invariant Safety Alignment

Jiajia Li, Xiaoyu Wen, Zhongtian Ma +3

The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially high-risk scenarios. Despite advances i…

cs.CV2026

CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains

Wenhan Wang, Zhixiang Zhou, Zhongtian Ma +8

The connoisseurship of antique Chinese porcelain demands extensive historical expertise, material understanding, and aesthetic sensitivity, making it difficult for non-specialists…

cs.AI2026

MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety

Xiaoyu Wen, Zhida He, Han Qi +7

Ensuring robust safety alignment is crucial for Large Language Models (LLMs), yet existing defenses often lag behind evolving adversarial attacks due to their \textbf{reliance on s…