2 citations · 3 across the 7 of their papers we have counts for
8 papers
Act2Intention: A Benchmark For Developing Active Mobile Agents Through Inferring User Intention from GUI Actions
Xiaokai Yan, Jingtao Ding, Yong Li +1
Mobile GUI Agents powered by multimodal large language models (MLLMs) show promise in human-computer intelligence. However, current research primarily focuses on reactive task exec…
AI Urban Scientist: Multi-Agent Collaborative Automation for Urban Research
Tong Xia, Jiankun Zhang, Ruiwen You +8
Urban research aims to understand how cities operate and evolve as complex adaptive systems. With the rapid growth of urban data and analytical methodologies, the central challenge…
FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agents
Qinglong Yang, Haoming Li, Haotian Zhao +4
Mobile GUI agents are becoming critical tools to improve user experience on smart devices, with multimodal large language models (MLLMs) emerging as the dominant paradigms in this…
AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text
Chenyang Shao, Tianxing Li, Chenhao Pu +2
In today's digital world, casual user-generated content often contains subtle cues that may inadvertently expose sensitive personal attributes. Such risks underscore the growing im…
CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents
Qingbin Zeng, Ruotong Zhao, Jinzhu Mao +3
Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has…
AgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search
Yu Li, Lehui Li, Zhihao Wu +5
Large language model (LLM) agents have demonstrated strong capabilities across diverse domains, yet automated agent design remains a significant challenge. Current automated agent…
OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
Yuwei Yan, Qingbin Zeng, Zhiheng Zheng +5
Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models,…
Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions
Qingbin Zeng, Qinglong Yang, Shunan Dong +4
This paper considers a scenario in city navigation: an AI agent is provided with language descriptions of the goal location with respect to some well-known landmarks; By only obser…