22 citations · 33 across the 11 of their papers we have counts for
5 papers · 1 filter
SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces
Qi Hu, Yifeng Tang, Qinghua Wang +7
Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whet…
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Anh Tuan, Yue Liu +6
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we int…
EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
Dong Huang, Jianbo Dai, Han Weng +5
Large language models (LLMs) have shown remarkable progress in code generation, but their generated code often suffers from inefficiency, resulting in longer execution times and hi…
EffiBench: Benchmarking the Efficiency of Automatically Generated Code
Dong Huang, Yuhao Qing, Weiyi Shang +2
Code generation models have increasingly become integral to aiding software development. Although current research has thoroughly examined the correctness of the code produced by c…
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation
Dong Huang, Qingwen Bu, Yuhao Qing +1
Chain-of-thought (CoT) has emerged as a groundbreaking tool in NLP, notably for its efficacy in complex reasoning tasks, such as mathematical proofs. However, its application in co…