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

Yuwei Wang

ACL 2026×3AAAI 2026CVPR 2026NeurIPS 2026多模态检索与知识推理对齐与偏好评测与基准
新星分贡献者
67
一作贡献0
核心/参与30
方向广度19
合作网络12
0 一作 · 6 篇 · ACL 2026 / AAAI 2026 / CVPR 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

6
作者位次 #5/8ACL 2026Poster· Natural Language Processing

Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA

Gewen Liang · Mufan Xu · Kehai Chen · Wei Wang · Yuwei Wang · Muyun Yang · Tiejun Zhao · Min Zhang

摘要

Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantica...

检索与知识推理对齐与偏好OpenReview 项目页
作者位次 #6/7ACL 2026Findings· Findings of ACL

Z o F ia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi- LLM Interaction

Lvhua Wu · Xuefeng Jiang · Sheng Sun · Yan Lei · Tian Wen · Yuwei Wang · Min Liu

摘要

The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection.Although large language models (LLMs) show potential in fake news detection, they are limited by knowledge cutoff and easily generate factual hallucinations when han...

检索与知识智能体与工具安全OpenReview 项目页
作者位次 #5/8ACL 2026Findings· Findings of ACL

Beyond Unimodal Shortcuts: MLLM s as Cross-Modal Reasoners for Grounded Named Entity Recognition

Jinlong Ma · Yu Zhang · Xuefeng Bai · Kehai Chen · Yuwei Wang · Zeming Liu · Jun Yu · Min Zhang

摘要

Grounded Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions. In this work, we explore the potential of Multimodal Large Language Models (MLLMs) to perform GMNER in an end-to-end manner,...

多模态强化学习与后训练推理OpenReview 项目页

常见合作者