5 papers
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…
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…
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…