姜雪

Xue Jiang

博士生 · 2022 年入学

简介

姜雪，北京大学计算机学院直博生，指导老师为李戈教授、焦文品教授、金芝教授，主要研究方向为智能化软件工程和大语言模型，曾在TOSEM、ICSE、ASE、NeurIPS、ACL等 CCF-A类国际顶级会议和期刊上发表18篇学术论文，其中一作论文8篇，授权专利2项。目前谷歌学术引用量超2.3k，一作论文引用量超1.6k，且多项学术成果转化为实际应用落地。曾获得杨芙清-王阳元院士奖学金，北京大学校长奖学金，北京大学优秀科研奖，ACL2026 SAC Highlight Award，中国电子学会-腾讯博士生科研激励计划（混元大模型专项）等10余项荣誉奖项。

论文 (21)

arXiv 论文 (4)

[arXiv 2026] Yihong Dong, Jianha Xiao, Xue Jiang, Xuyuan Guo, Zhiyuan Fan, Jiaru Qian, Kechi Zhang, Jia Li, Zhi Jin, Ge Li; Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy; arXiv preprint, arXiv:2604.02709, 2026. PDF
[arXiv 2025] Yuqi Zhu, Ge Li, Xue Jiang, Jia Li, Hong Mei, Zhi Jin, Yihong Dong; Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs; arXiv preprint, arXiv:2503.15341, 2025. PDF
[arXiv 2025] Yihong Dong, Zhaoyu Ma, Xue Jiang, Zhiyuan Fan, Jiaru Qian, Yongmin Li, Jianha Xiao, Zhi Jin, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li, Ge Li; Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model; arXiv preprint, arXiv:2510.18165, 2025. PDF
[arXiv 2024] Xue Jiang, Yihong Dong, Zhi Jin, Ge Li; SEED: Customize Large Language Models with Sample-Efficient Adaptation for Code Generation; arXiv preprint, arXiv:2403.00046, 2024. PDF

已发表论文 (17)

[软件学报, 2026] Yihong Dong, Xue Jiang, Jiaru Qian, Tian Wang, Kechi Zhang, Zhi Jin, Ge Li; 基于大语言模型智能体的代码生成综述（A Survey on Code Generation with LLM-based Agents）; 软件学报 (软件学报); Vol. 37, Iss. 8, pp.3089-3115. PDF
[AAAI 2026] Xue Jiang, Yihong Dong, Zheng Fang, Yingwei Ma, Tangxinyu Wang, Rongyu Cao, Binhua Li, Zhi Jin, Wenpin Jiao, Yongbin Li, Ge Li; Large Language Model Unlearning for Source Code; The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026), Singapore, Singapore. PDF
[ACL 2026] Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei Ma, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li; Rl-plus: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization; Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, USA, July 2 - 7, 2026. PDF
[ACL 2026] Yihong Dong, Zhaoyu Ma, Xue Jiang, Zhiyuan Fan, Jiaru Qian, Yongmin Li, Jianha Xiao, Zhi Jin, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li, Ge Li; Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation; Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, USA.
[ACL 2026] Xue Jiang, Yihong Dong, Mengyang Liu, Hongyi Deng, Tian Wang, Yongding Tao, Rongyu Cao, Binhua Li, Zhi Jin, Wenpin Jiao, Fei Huang, Yongbin Li, Ge Li; CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment; Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, USA. PDF
[ACL 2026] Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zheyu Zhao, Lingwei Wu, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong; KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?; Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, USA. PDF
[ICSE 2025] Xue Jiang, Yihong Dong, Yongding Tao, Huanyu Liu, Zhi Jin, Ge Li; ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation; Proceedings of the 47th IEEE/ACM International Conference on Software Engineering (ICSE 2025), Ottawa, Ontario, Canada.
[ACL 2025] Yihong Dong, Yuchen Liu, Xue Jiang, Zhi Jin, Ge Li; Rethinking Repetition Problems in Code Generation; Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria.
[NeurIPS 2025] Yihong Dong, Ge Li, Yongding Tao, Xue Jiang, Kechi Zhang, Jia Li, Jing Su, Jun Zhang, Jingjing Xu; FAN: Fourier Analysis Networks; Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, USA. PDF
[TOSEM, 2025] Xue Jiang, Yihong Dong, Zhiyuan Fan, Zhi Jin, Wenpin Jiao, Ge Li; Exploring Data-Efficient Adaptation of Large Language Models for Code Generation; ACM Transactions on Software Engineering and Methodology.
[NeurIPS 2025] Yihong Dong, Ge Li, Xue Jiang, Yongding Tao, Kechi Zhang, Lecheng Wang, Hao Zhu, Huanyu Liu, Jia Li, Jinliang Deng, Hong Mei; Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling; Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025), San Diego, CA. PDF
[ACL 2024] Yihong Dong, Kangcheng Luo, Xue Jiang, Zhi Jin, Ge Li; PACE: Improving Prompt with Actor-Critic Editing for Large Language Model; Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand.
[ACL 2024] Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Ge Li; Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models; Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand.
[TOSEM, 2024] Xue Jiang, Yihong Dong, Lecheng Wang, Zheng Fang, Qiwei Shang, Ge Li, Zhi Jin, Wenpin Jiao; Self-Planning Code Generation with Large Language Model; ACM Transactions on Software Engineering and Methodology (TOSEM); Vol. 33, Iss. 7.
[TOSEM, 2024] Yihong Dong, Xue Jiang, Zhi Jin, Ge Li; Self-collaboration Code Generation via ChatGPT; ACM Transactions on Software Engineering and Methodology (TOSEM); Vol. 33, Iss. 7.
[TOSEM, 2024] Yihong Dong, Jiazheng Ding, Xue Jiang, Ge Li, Zhuo Li, Zhi Jin; Evaluating Code Generation by Learning Code Execution; ACM Transactions on Software Engineering and Methodology (TOSEM); Vol. 33, Iss. 7.
[ECAI 2023] Yihong Dong, Ge Li, Xue Jiang, Zhi Jin; Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation; Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), Kraków, Poland.