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

Daoxin Zhang

ACL 2026×2CVPR 2026ICML 2026多模态强化学习与后训练推理社会与人本评测与基准
新星分贡献者
58
一作贡献0
核心/参与20
方向广度16
合作网络12
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 #4/8ACL 2026Poster· Natural Language Processing

Leveraging Outline-Optimized Generative Interactions and Critique for Self-Refining Outlines with Reinforcement Learning

Hengwei Liu · Haoyuan Ma · Qingqing Lyu · Daoxin Zhang · Yao Hu · Yongliang Shen · Yin Zhang · Weiming Lu

摘要

Long-form outline generation requires satisfying multiple competing objectives simultaneously: outlines must be engaging, well-organized, topically relevant, and comprehensive while maintaining logical consistency across hierarchical structures. Current approaches either rely on expensive multi-t...

强化学习与后训练推理多模态OpenReview 项目页
作者位次 #8/12ACL 2026Findings· Findings of ACL

Pause or Fabricate? Training Language Models for Grounded Reasoning

Yiwen Qiu · Linjuan Wu · Yizhou Liu · Yuchen Yan · Jin Ma · Xu Tan · Yao Hu · Daoxin Zhang · Wenqi Zhang · Weiming Lu · Jun Xiao · Yongliang Shen

摘要

Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confident but unreliable conclusions—a failure mode we term ungrounded reasoning. We argue that this issue arises not from...

推理安全强化学习与后训练OpenReview 项目页

常见合作者