23 citations · 52 across the 21 of their papers we have counts for
6 papers · 1 filter
UI-Venus-2 Technical Report
Venus Team, Zhuohan Cai, Haoxing Chen +28
Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remai…
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification
Yunhao Feng, Ruixiao Lin, Ming Wen +12
LLM agents increasingly perform autonomous actions through external tools, leading to complex and evolving safety risks. However, existing safety testing targets expert-designed sa…
Orthogonal Concept Erasure for Diffusion Models
Yuhao Sun, Lingyun Yu, Haoxiang Xu +3
Concept erasure has emerged as a promising approach to mitigate undesired or unsafe content in diffusion models, yet existing methods still face significant limitations. While trai…
UpSafeC: Upcycling for Controllable Safety in Large Language Models
Yuhao Sun, Zhuoer Xu, Shiwen Cui +4
Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak…
Agent Safety Alignment via Reinforcement Learning
Zeyang Sha, Hanling Tian, Zhuoer Xu +3
The emergence of autonomous Large Language Model (LLM) agents capable of tool usage has introduced new safety risks that go beyond traditional conversational misuse. These agents,…
TroubleLLM: Align to Red Team Expert
Zhuoer Xu, Jianping Zhang, Shiwen Cui +2
Large Language Models (LLMs) become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be pot…