15 citations · 25 across the 13 of their papers we have counts for
13 papers
SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction
Yuting Ning, Zhehao Zhang, Yash Kumar Lal +8
Agent skills occupy a privileged position in the agent workflow, as agents are expected to implicitly follow and execute them, rendering third-party skills a vulnerable attack surf…
QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
Jian Xie, Tianhe Lin, Zilu Wang +16
Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information.…
When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
Yuting Ning, Jaylen Jones, Zhehao Zhang +5
Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such m…
When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents
Jaylen Jones, Zhehao Zhang, Yuting Ning +6
Although computer-use agents (CUAs) hold significant potential to automate increasingly complex OS workflows, they can demonstrate unsafe unintended behaviors that deviate from exp…
Distractor Injection Attacks on Large Reasoning Models: Characterization and Defense
Zhehao Zhang, Weijie Xu, Shixian Cui +1
Recent advances in large reasoning models (LRMs) have enabled remarkable performance on complex tasks such as mathematics and coding by generating long Chain-of-Thought (CoT) trace…
FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning
Zhehao Zhang, Weijie Xu, Fanyou Wu +1
Safety alignment approaches in large language models (LLMs) often lead to the over-refusal of benign queries, significantly diminishing their utility in sensitive scenarios. To add…
From Selection to Generation: A Survey of LLM-based Active Learning
Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31
Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…
Personalized Multimodal Large Language Models: A Survey
Junda Wu, Hanjia Lyu, Yu Xia +24
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as tex…
Optimizing Data Delivery: Insights from User Preferences on Visuals, Tables, and Text
Reuben Luera, Ryan Rossi, Franck Dernoncourt +9
In this work, we research user preferences to see a chart, table, or text given a question asked by the user. This enables us to understand when it is best to show a chart, table,…
Personalization of Large Language Models: A Survey
Zhehao Zhang, Ryan A. Rossi, Branislav Kveton +18
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most exist…
A Survey of Small Language Models
Chien Van Nguyen, Xuan Shen, Ryan Aponte +25
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, maki…
VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use
Zhehao Zhang, Ryan Rossi, Tong Yu +7
While vision-language models (VLMs) have demonstrated remarkable performance across various tasks combining textual and visual information, they continue to struggle with fine-grai…
Visual Prompting in Multimodal Large Language Models: A Survey
Junda Wu, Zhehao Zhang, Yu Xia +12
Multimodal large language models (MLLMs) equip pre-trained large-language models (LLMs) with visual capabilities. While textual prompting in LLMs has been widely studied, visual pr…