7 citations · 14 across the 18 of their papers we have counts for
9 papers · 1 filter
Mobile-Aptus: Confidence-Driven Proactive and Robust Interaction in MLLM-based Mobile-Using Agents
Zheng Wu, Pengzhou Cheng, Zongru Wu +5
Recent advancements in multimodal large language models (MLLMs) have shown exceptional potential in enabling mobile-using agents to autonomously execute human instructions. However…
Agent-ScanKit: Unraveling Memory and Reasoning of Multimodal Agents via Sensitivity Perturbations
Pengzhou Cheng, Lingzhong Dong, Zeng Wu +5
Although numerous strategies have recently been proposed to enhance the autonomous interaction capabilities of multimodal agents in graphical user interface (GUI), their reliabilit…
Say One Thing, Do Another? Diagnosing Reasoning-Execution Gaps in VLM-Powered Mobile-Use Agents
Lingzhong Dong, Ziqi Zhou, Shuaibo Yang +6
Mobile-use agents powered by vision-language models (VLMs) have shown great potential in interpreting natural language instructions and generating corresponding actions based on mo…
On the Adaptive Psychological Persuasion of Large Language Models
Tianjie Ju, Yujia Chen, Hao Fei +6
Previous work has showcased the intriguing capabilities of Large Language Models (LLMs) in instruction-following and rhetorical fluency. However, systematic exploration of their du…
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents
Pengzhou Cheng, Haowen Hu, Zheng Wu +4
Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high fine-tuning cos…
Smoothing Grounding and Reasoning for MLLM-Powered GUI Agents with Query-Oriented Pivot Tasks
Zongru Wu, Pengzhou Cheng, Zheng Wu +3
Perception-enhanced pre-training, particularly through grounding techniques, is widely adopted to enhance the performance of graphical user interface (GUI) agents. However, in reso…
When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements
Tianjie Ju, Bowen Wang, Hao Fei +9
Recent advances in Large Language Models (LLMs) have upgraded them from sophisticated text generators to autonomous agents capable of cooperation and tool use in multi-agent system…
Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining
Zongru Wu, Pengzhou Cheng, Lingyong Fang +2
Backdoor attacks remain significant security threats to generative large language models (LLMs). Since generative LLMs output sequences of high-dimensional token logits instead of…
Acquiring Clean Language Models from Backdoor Poisoned Datasets by Downscaling Frequency Space
Zongru Wu, Zhuosheng Zhang, Pengzhou Cheng +1
Despite the notable success of language models (LMs) in various natural language processing (NLP) tasks, the reliability of LMs is susceptible to backdoor attacks. Prior research a…