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

Kun Yue

ACL 2026×2CVPR 2026ICML 2026对齐与偏好评测与基准推理理论与学习动力学安全
新星分贡献者
52
一作贡献0
核心/参与15
方向广度16
合作网络9
0 一作 · 4 篇 · ACL 2026 / CVPR 2026 / ICML 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

4
作者位次 #3/4ICML 2026Poster· General Machine Learning / Supervised Learning

Learning Generalized Label Distributions

Haitao Wu · Weiwei Li · Kun Yue · Xiuyi Jia

理论与学习动力学OpenReview
作者位次 #4/5ACL 2026Poster· Natural Language Processing

Dissecting Failure Dynamics in Large Language Model Reasoning

Wei Zhu · Jian Zhang · Lixing Yu · Kun Yue · Zhiwen Tang

摘要

Large Language Models (LLMs) achieve strong performance through extended inference-time deliberation, yet how their reasoning failures arise remains poorly understood. By analyzing model-generated reasoning trajectories, we find that errors are not uniformly distributed but often originate from a...

评测与基准推理可解释性OpenReview 项目页
作者位次 #8/8ACL 2026Poster· Natural Language Processing

T ax P ra B en: A Scalable Benchmark for Structured Evaluation of LLM s in C hinese Real-World Tax Practice

Gang Hu · Yating Chen · Haiyan Ding · Wang Gao · Huang Jiajia · Min Peng · Qianqian Xie · Kun Yue

摘要

While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legally regulated Chinese tax domain. Consequently, while tax-related benchmarks are gaining attention, many focus on isolated NLP tasks, neglecting r...

评测与基准推理对齐与偏好OpenReview 项目页

常见合作者