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

Yi Lu

ACL 2026×2评测与基准智能体与工具对齐与偏好代码强化学习与后训练
新星分贡献者
51
一作贡献0
核心/参与17
方向广度10
合作网络12
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

R eview G rounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents

Zhuofeng Li · Yi Lu · Dongfu Jiang · Haoxiang Zhang · Yuyang Bai · Chuan Li · Yu Wang · Shuiwang Ji · Jianwen Xie · Yu Zhang

摘要

The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-based reviewers often generate superficial, formulaic comments lacking substantive, evidence-grounded feedback. We attribute this to the underutiliza...

智能体与工具评测与基准对齐与偏好OpenReview 项目页
作者位次 #7/16ACL 2026Findings· Findings of ACL

SWE - QA -Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding

Songcheng Cai · Zhiheng LYU · Yuansheng Ni · Xiangchao Chen · Baichuan Zhou · Shenzhe Zhu · Yi Lu · Haozhe WANG · Chi Ruan · Benjamin Schneider · Weixu Zhang · Xiang Li · Andy Zheng · Yuyu Zhang · Ping Nie · Wenhu Chen

摘要

Agentic repository-level code understanding is essential for automating complex software engineering tasks, yet the field lacks reliable benchmarks. Existing evaluations often overlook the long tail topics and rely on popular repositories where Large Language Models (LLMs) can cheat via memorized...

代码强化学习与后训练评测与基准OpenReview 项目页

常见合作者