activity
20242026
most citedFairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models

3 citations · 4 across the 10 of their papers we have counts for

collaborators

14 papers

cs.CL2026

Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding

Yisong Xiao, Aishan Liu, Yongxin Huang +6

Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resultin…

cs.MA2026

SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems

Haowen Dai, Zonghao Ying, Wenfeng Li +10

Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety blind spot: a harmful objective c…

cs.CV2026

Technical Report on the CVPR 2026@AdvML Workshop Challenge

Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47

Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…

cs.AI2026

Improving Safety Alignment via Balanced Direct Preference Optimization

Shiji Zhao, Mengyang Wang, Shukun Xiong +7

With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from…

cs.CR2026

Evolving Deception: When Agents Evolve, Deception Wins

Zonghao Ying, Haowen Dai, Tianyuan Zhang +6

Self-evolving agents offer a promising path toward scalable autonomy. However, in this work, we show that in competitive environments, self-evolution can instead give rise to a ser…

cs.CR2026

Reasoning-Oriented Programming: Chaining Semantic Gadgets to Jailbreak Large Vision Language Models

Quanchen Zou, Moyang Chen, Zonghao Ying +6

Large Vision-Language Models (LVLMs) undergo safety alignment to suppress harmful content. However, current defenses predominantly target explicit malicious patterns in the input r…