135 citations · 557 across the 54 of their papers we have counts for
57 papers
VERAS: Verification-Guided Adaptive Search for Reliable Operations Research
Minbo Ma, Honglin Zhang, Bangtao Zhou +5
Advances in reasoning, coding, and tool use enable LLM agents to solve operations research (OR) problems through iterative problem modeling, program construction, and verification.…
AgentSociety 2: An Integrated Research Environment for Executable Social Science
Jinghua Piao, Jun Zhang, Haoyu Huang +16
AI scientist systems are beginning to automate parts of scientific research, but social science poses a distinct challenge: its objects of inquiry are not merely datasets or labora…
Agentic-Ideation: Sample Efficient Agentic Trajectories Synthesis for Scientific Ideation Agents
Keyu Zhao, Lingyan Kong, Fengli Xu +1
Ideation plays a pivotal role in scientific discovery. Recent LLM, especially AI Scientist systems, show promising potential for automated ideation. However, existing approaches pr…
Perceiving exposure segregation with open urban imagery
Yunke Zhang, Ruolong Ma, Xin Zhang +4
Socioeconomic exposure segregation -- the lack of daily interaction between income groups -- erodes social capital and entrenches inequality, yet the specific physical features tha…
HybridQuestion: Human-AI Collaboration for Identifying High-Impact Research Questions
Keyu Zhao, Fengli Xu, Yong Li +1
The "AI Scientist" paradigm is transforming scientific research by automating key stages of the research process, from idea generation to scholarly writing. This shift is expected…
SciNet: Evaluating AI Agents in Relation-Aware Scientific Literature Retrieval
Chenyang Shao, Fengli Xu, Yong Li
AI agents have seen widespread adoption in information retrieval for scientific research, giving rise to tools such as Deep Research. However, existing retrieval agents mainly rely…
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…
OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists
Chenyang Shao, Dehao Huang, Yu Li +18
With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimen…
MirrorMind: Empowering OmniScientist with the Expert Perspectives and Collective Knowledge of Human Scientists
Qingbin Zeng, Bingbing Fan, Zhiyu Chen +7
The emergence of AI Scientists has demonstrated remarkable potential in automating scientific research. However, current approaches largely conceptualize scientific discovery as a…
Deep Ideation: Designing LLM Agents to Generate Novel Research Ideas on Scientific Concept Network
Keyu Zhao, Weiquan Lin, Qirui Zheng +2
Novel research ideas play a critical role in advancing scientific inquiries. Recent advancements in Large Language Models (LLMs) have demonstrated their potential to generate novel…
AgentExpt: Automating AI Experiment Design with LLM-based Resource Retrieval Agent
Yu Li, Lehui Li, Lin Chen +3
Large language model agents are becoming increasingly capable at web-centric tasks such as information retrieval, complex reasoning. These emerging capabilities have given rise to…
Rationality Check! Benchmarking the Rationality of Large Language Models
Zhilun Zhou, Jing Yi Wang, Nicholas Sukiennik +4
Large language models (LLMs), a recent advance in deep learning and machine intelligence, have manifested astonishing capacities, now considered among the most promising for artifi…
Reinforcement Learning Fine-Tuning Enhances Activation Intensity and Diversity in the Internal Circuitry of LLMs
Honglin Zhang, Qianyue Hao, Fengli Xu +1
Large language models (LLMs) acquire extensive prior knowledge through large-scale pretraining and can be further enhanced via supervised fine-tuning (SFT) or reinforcement learnin…
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…
AI Agent Behavioral Science
Lin Chen, Yunke Zhang, Jie Feng +13
Recent advances in large language models (LLMs) have enabled the development of AI agents that exhibit increasingly human-like behaviors, including planning, adaptation, and social…
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…
Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Keyu Zhao, Fengli Xu, Yong Li
Driven by advances in Large Language Models (LLMs), integrating them into recommendation tasks has gained interest due to their strong semantic understanding and prompt flexibility…
Token Signature: Predicting Chain-of-Thought Gains with Token Decoding Feature in Large Language Models
Peijie Liu, Fengli Xu, Yong Li
Chain-of-Thought (CoT) technique has proven effective in improving the performance of large language models (LLMs) on complex reasoning tasks. However, the performance gains are in…
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…
Route-and-Reason: Scaling Large Language Model Reasoning with Reinforced Model Router
Chenyang Shao, Xinyang Liu, Yutang Lin +2
Chain-of-thought has been proven essential for enhancing the complex reasoning abilities of Large Language Models (LLMs), but it also leads to high computational costs. Recent adva…
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Yu Shang, Peijie Liu, Yuwei Yan +9
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasonin…
A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science
Jie Feng, Jinwei Zeng, Qingyue Long +15
Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However,…
An Evaluation of Cultural Value Alignment in LLM
Nicholas Sukiennik, Chen Gao, Fengli Xu +1
LLMs as intelligent agents are being increasingly applied in scenarios where human interactions are involved, leading to a critical concern about whether LLMs are faithful to the v…
Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Content
Bingbing Fan, Lin Chen, Songwei Li +4
Understanding experienced segregation in urban daily life is crucial for addressing societal inequalities and fostering inclusivity. The abundance of user-generated reviews on soci…