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

Zhen Zhang

ICLR 2026×4ACL 2026×2智能体与工具数据与预训练多模态强化学习与后训练评测与基准
新星分贡献者
68
一作贡献0
核心/参与37
方向广度13
合作网络12
0 一作 · 6 篇 · ICLR 2026 / 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

6
作者位次 #14/21ICLR 2026Poster

WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning

Kuan Li · Zhongwang Zhang · Huifeng Yin · Rui Ye · Yida Zhao · Liwen Zhang · Litu Ou · Ding-Chu Zhang · Xixi Wu · Xinmiao Yu · Jialong Wu · Xinyu Wang · Zile Qiao · Zhen Zhang · Yong Jiang · Pengjun Xie · Fei Huang · Zhiqin Xu · Shuai Wang · Minhao Cheng · Jingren Zhou

智能体与工具强化学习与后训练数据与预训练OpenReview
作者位次 #10/18ACL 2026Findings· Findings of ACL

Towards General Agentic Intelligence via Environment Scaling

Runnan Fang · Shihao Cai · Baixuan Li · Jialong Wu · Guangyu Li · Wenbiao Yin · Xinyu Wang · Xiaobin Wang · Liangcai Su · Zhen Zhang · Shibin Wu · Zhengwei Tao · Yong Jiang · Pengjun Xie · Ningyu Zhang · Fei Huang · Wentao Zhang · Jingren Zhou

摘要

Advanced agentic intelligence is a prerequisite for deploying Large Language Models in practical, real-world applications. Diverse real-world APIs demand precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments. The...

智能体与工具强化学习与后训练评测与基准OpenReview 项目页
作者位次 #6/9ACL 2026Findings· Findings of ACL

TRUST : Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification

Ruixuan Xu · Mengting Hu · Zhunheng Wang · Ming Jiang · Rui Ying · Zhen Zhang · Hang Gao · Shuaipeng Liu · Renhong Cheng

摘要

Social bots threaten online platforms by mimicking human behavior and forming deceptive connections, enabling the dissemination of misinformation while evading detection. Existing graph-based detection models leverage graph neural networks (GNNs) to capture relational structures and multimodal us...

社会与人本多模态评测与基准OpenReview 项目页

常见合作者