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

Yichun Yin

ACL 2026×2评测与基准推理智能体与工具长上下文与架构可解释性
新星分贡献者
48
一作贡献0
核心/参与17
方向广度10
合作网络9
0 一作 · 2 篇 · ACL 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

2
作者位次 #2/10ACL 2026Poster· Natural Language Processing

Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4

Chengwu Liu · Yichun Yin · Ye Yuan · Jiaxuan Xie · Botao Li · Siqi Li · Jianhao Shen · Yan Xu · Lifeng Shang · Ming Zhang

摘要

Most ATP benchmarks embed the final answer within the formal statement — a convention we call “Easy Mode” — a design that simplifies the task relative to what human competitors face and may lead to optimistic estimates of model capability.We call the stricter, more realistic setting “Hard Mode”:...

评测与基准推理智能体与工具OpenReview 项目页
作者位次 #11/15ACL 2026Poster· Natural Language Processing

MATCH : Modulating Attention via In-Context Retrieval for Long-Context Transformers

Linrui Ma · Chun Hei Lo · Xinyu Wang · Peng Lu · Xihao Yuan · Hanting Chen · Kai Han · Xinghao Chen · Chengjun Zhan · Hanlin xu · Yichun Yin · Lifeng Shang · Feng Wen · Boxing Chen · Yufei Cui

摘要

The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios. To improve efficiency, existing approaches often enforce rigid structural constraints...

长上下文与架构可解释性效率与系统OpenReview 项目页

常见合作者