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

Jian Zhang

ACL 2026×3NeurIPS 2026多模态推理效率与系统评测与基准社会与人本
新星分新星
72
一作贡献30
核心/参与15
方向广度13
合作网络12
1 一作 · 4 篇 · ACL 2026 / NeurIPS 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 #5/11ACL 2026Poster· Natural Language Processing

MUR : Momentum Uncertainty guided Reasoning for Large Language Models

Hang Yan · Fangzhi Xu · Rongman Xu · Yifei Li · Jian Zhang · Haoran Luo · Xiaobao Wu · Anh Tuan Luu · Haiteng Zhao · Haiping Zhu (Xi&#x27 · an Jiaotong University)

摘要

Large Language Models (LLMs) have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scaling (TTS) improves reasoning quality, it often leads to overthinking—wasting tokens on redundant computations. T...

推理效率与系统评测与基准OpenReview 项目页
作者位次 #5/7ACL 2026Poster· Natural Language Processing

M ed F orge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

Zhihui Chen · Kai He · Qingyuan Lei · Bin Pu · Jian Zhang · Yuling Xu · Mengling Feng

摘要

Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typical...

社会与人本安全多模态OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

MAXS : Meta-Adaptive Exploration with LLM Agents

Jian Zhang · Zhiyuan Wang · Zhangqi Wang · Yu He · Haoran Luo · li Yuan · Lingling Zhang · Rui Mao · Haiping Zhu (Xi&#x27 · an Jiaotong University)

摘要

Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools.However, during agent inference, existing methods often suffer from (i) locally myopic generation, due to the absence of lookahead, and (ii) trajectory instability, where minor early...

推理效率与系统智能体与工具OpenReview 项目页

常见合作者