82 citations · 195 across the 9 of their papers we have counts for
11 papers
FastCode: Fast and Cost-Efficient Code Understanding and Reasoning
Zhonghang Li, Zongwei Li, Yuxuan Chen +5
Repository-scale code reasoning is a cornerstone of modern AI-assisted software engineering, enabling Large Language Models (LLMs) to handle complex workflows from program comprehe…
DeepCode: Open Agentic Coding
Zongwei Li, Zhonghang Li, Zirui Guo +2
Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing m…
LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?
Jingyuan Wang, Yankai Chen, Zhonghang Li +1
Large language models (LLMs) have demonstrated remarkable progress in reasoning, often through supervised fine-tuning (SFT). However, SFT is resource-intensive, relying on large cu…
AI-Researcher: Autonomous Scientific Innovation
Jiabin Tang, Lianghao Xia, Zhonghang Li +1
The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, pre…
Urban Computing in the Era of Large Language Models
Zhonghang Li, Lianghao Xia, Xubin Ren +4
Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while benef…
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
Zhonghang Li, Long Xia, Lei Shi +3
Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences. However,…
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction
Zhonghang Li, Lianghao Xia, Yong Xu +1
The objective of traffic prediction is to accurately forecast and analyze the dynamics of transportation patterns, considering both space and time. However, the presence of distrib…
UrbanGPT: Spatio-Temporal Large Language Models
Zhonghang Li, Lianghao Xia, Jiabin Tang +5
Spatio-temporal prediction aims to forecast and gain insights into the ever-changing dynamics of urban environments across both time and space. Its purpose is to anticipate future…
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
Zhonghang Li, Lianghao Xia, Yong Xu +1
In recent years, there has been a rapid development of spatio-temporal prediction techniques in response to the increasing demands of traffic management and travel planning. While…
Automated Spatio-Temporal Graph Contrastive Learning
Qianru Zhang, Chao Huang, Lianghao Xia +3
Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlati…
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Zhonghang Li, Chao Huang, Lianghao Xia +2
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for…