1 citations · 1 across the 10 of their papers we have counts for
10 papers
Safin-1: Safety from Within through Memory-Native State Evolution
Ming Zhang, Kaisen Yang, Shu Yu +15
Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic proper…
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…
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…
Gating and Learnable Temperature for Graph Attention Mechanisms
Zhongtian Ma, Hao Wu, Yexin Zhang +3
Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable feature dimensions and use fixed s…
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…
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…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
Zhongtian Ma, Qiaosheng Zhang, Bocheng Zhou +3
Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanism…
Community Detection in the Multi-View Stochastic Block Model
Yexin Zhang, Zhongtian Ma, Qiaosheng Zhang +2
This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective. We first put forth a random graph mod…
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…