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

Terry Jingchen Zhang

ACL 2026×3CVPR 2026社会与人本智能体与工具评测与基准检索与知识对齐与偏好
新星分新星
77
一作贡献30
核心/参与26
方向广度13
合作网络12
1 一作 · 4 篇 · ACL 2026 / CVPR 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 #10/11ACL 2026Poster· Natural Language Processing

SILO - BENCH : A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

Yuzhe Zhang · Feiran Liu · Yi Shan · Xinyi Huang · Xin Yang · Yueqi Zhu · Xuxin Cheng · Cao Liu · Ke Zeng · Terry Jingchen Zhang · Wenyuan Jiang

摘要

Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. However, whether LLM-based agents can reliably coordinate when each observes only a fragment of the global problem remains unclear. Existing benchmarks...

智能体与工具评测与基准推理OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

Test of Time: Rethinking Temporal Signal of Benchmark Contamination

Terry Jingchen Zhang · Gopal Dev · Ning Wang · Max Obreiter · Punya Syon Pandey · Keenan Samway · Wenyuan Jiang · Yinya Huang · Bernhard Schölkopf · Mrinmaya Sachan · Zhijing Jin

摘要

Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may have been included into training corpora and inflated model performance by memorization. We critically examine this...

可解释性评测与基准检索与知识OpenReview 项目页
作者位次 #2/21ACL 2026Workshop· ACL System Demonstrations

P aper M entor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf

Jiarui Liu · Terry Jingchen Zhang · Ryan Faulkner · Xuanqiang Angelo Huang · Vilém Zouhar · Dominik Glandorf · Isabel Dahlgren · Rishit Dagli · Yuen Chen · Felix Leeb · Van Q. Truong · Punya Syon Pandey · Yves Bicker · Suvajit Majumder · Wenyuan Jiang · Zeju Qiu · Sankalan Pal Chowdhury · Mrinmaya Sachan · Bernhard Schölkopf · Mona T. Diab · Zhijing Jin

摘要

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or si...

智能体与工具社会与人本检索与知识OpenReview 项目页

常见合作者