paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
JS

Jiajun Shi

ACL 2026×2ICLR 2026评测与基准代码多语言与语言学推理可解释性
新星分贡献者
57
一作贡献0
核心/参与22
方向广度13
合作网络12
0 一作 · 3 篇 · ACL 2026 / ICLR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

3
作者位次 #4/18ACL 2026Findings· Findings of ACL

M d E val: Massively Multilingual Code Debugging

Shukai Liu · Linzheng Chai · Jian Yang · Jiajun Shi · He Zhu · Liran WANG · Jin Ke · Wei Zhang · Hualei Zhu · Shuyue Guo · Tao Sun · JIAHENG LIU · Yunlong Duan · Yu Hao · Liqun Yang · Guanglin Niu · Ge Zhang · Zhoujun Li

摘要

Code large language models (LLMs) have made significant progress in code debugging by directly generating the correct code based on the buggy code snippet. Programming benchmarks, typically consisting of buggy code snippets and their associated test cases, are used to assess the debugging capabil...

代码评测与基准多语言与语言学OpenReview 项目页
作者位次 #2/12ACL 2026Findings· Findings of ACL

C o TJ udger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRM s

Siyi Li · Jiajun Shi · Shiwen Ni · Ge Zhang · Shuaimin Li · Shijian Wang · Zhoufutu Wen · Yizhi Li · Hamid Alinejad-Rokny · JIAHENG LIU · Min Yang · Wenhao Huang

摘要

Large Reasoning Models (LRMs) have demonstrated strong performance by producing extended Chain-of-Thought (CoT) traces before answering. However, this paradigm often induces over-reasoning: redundant calculations and circular self-verification that increase computational cost without improving ou...

推理可解释性效率与系统OpenReview 项目页

常见合作者