11 citations · 32 across the 35 of their papers we have counts for
13 papers · 1 filter
Training High-Level Schedulers with Execution-Feedback Reinforcement Learning for Long-Horizon GUI Automation
Zehao Deng, Tianjie Ju, Zheng Wu +2
The rapid development of large vision-language model (VLM) has greatly promoted the research of GUI agent. However, GUI agents still face significant challenges in handling long-ho…
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…
GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?
Chiyu Chen, Xinhao Song, Yunkai Chai +7
Vision-Language Models (VLMs) are increasingly deployed as autonomous agents to navigate mobile graphical user interfaces (GUIs). Operating in dynamic on-device ecosystems, which i…
Thinking in a Crowd: How Auxiliary Information Shapes LLM Reasoning
Haodong Zhao, Chenyan Zhao, Yansi Li +2
The capacity of Large Language Models (LLMs) to reason is fundamental to their application in complex, knowledge-intensive domains. In real-world scenarios, LLMs are often augmente…
Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion
Haodong Zhao, Jidong Li, Zhaomin Wu +4
Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs). This paper studies persuasion dynamics by contrast…
See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles
Zongru Wu, Rui Mao, Zhiyuan Tian +7
The advent of multimodal agents facilitates effective interaction within graphical user interface (GUI), especially in ubiquitous GUI control. However, their inability to reliably…
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…
OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents
Pengzhou Cheng, Zheng Wu, Zongru Wu +3
Autonomous graphical user interface (GUI) agents powered by multimodal large language models have shown great promise. However, a critical yet underexplored issue persists: over-ex…
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Tianjie Ju, Yi Hua, Hao Fei +7
Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evide…
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…