2 citations · 4 across the 8 of their papers we have counts for
8 papers
AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery
Yu Li, Chenyang Shao, Xinyang Liu +13
Artificial intelligence research increasingly depends on prolonged cycles of reproduction, debugging, and iterative refinement to achieve State-Of-The-Art (SOTA) performance, creat…
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…
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…
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,…
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms
Yuwei Yan, Yu Shang, Qingbin Zeng +9
The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enha…
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…