32 citations · 104 across the 33 of their papers we have counts for
4 papers · 1 filter
ATGen: Adversarial Reinforcement Learning for Test Case Generation
Qingyao Li, Xinyi Dai, Weiwen Liu +5
Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs, for which effective test cases are a critical bottleneck. Existing test generati…
DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education
Lingyue Fu, Haowei Yuan, Datong Chen +5
In programming education, Debugging and Teaching (DT) task is a common scenario where students receive assistance in correcting their erroneous code. The task involves multiple inp…
Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Xiangyang Li, Xiaopeng Li, Kuicai Dong +7
Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and po…
RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
Qingyao Li, Wei Xia, Kounianhua Du +5
Tree search methods have demonstrated impressive performance in code generation. Previous methods combine tree search with reflection that summarizes past mistakes to achieve itera…