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

Qingsong Wen

KDD 2026×12ACL 2026×7ICLR 2026×6AAAI 2026×4检索与知识推理多模态评测与基准社会与人本
新星分资深
59
一作贡献80
核心/参与132
方向广度19
合作网络12
2 一作 · 29 篇 · KDD 2026 / ACL 2026 / ICLR 2026 / AAAI 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

29
作者位次 #24/34ICLR 2026Poster

AudioTrust: Benchmarking The Multifaceted Trustworthiness of Audio Large Language Models

Kai Li · Can Shen · Yile Liu · Jirui Han · Kelong zheng · Xuechao Zou · Lionel WANG · Shun Zhang · Xingjian Du · Hanjun Luo · Yingbin Jin · Xinxin Xing · Ziyang Ma · Yue Liu · YiFan Zhang · Junfeng Fang · Kun Wang · Yibo Yan · Gelei Deng · Haoyang LI · Yiming Li · Xiaobin Zhuang · Tianlong Chen · Qingsong Wen · Tianwei Zhang · Yang Liu · Haibo Hu · Zhizheng Wu · Xiaolin Hu · Ensiong Chng · Wenyuan Xu · XiaoFeng Wang · Wei Dong · Xinfeng Li

多模态评测与基准OpenReview
作者位次 #7/10ACL 2026Poster· Natural Language Processing

Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning

YiFan Zhang · Tao Yu · Feng Li · Chaoyou Fu · Yibo Hu · Kun Wang · Qingsong Wen · Zhang Zhang · Liang Wang · Rong Jin

摘要

The supervised fine-tuning (SFT) stage is crucial for multimodal large language models (MLLMs), yet a comprehensive scaling law to guide the optimal model-data configuration remains lacking. In this paper, we make an initial attempt to address this gap. First, we theoretically demonstrate that di...

评测与基准数据与预训练检索与知识OpenReview 项目页
作者位次 #8/8ACL 2026Poster· Natural Language Processing

Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models

Liang Lin · Miao Yu · Moayad Aloqaily · Zhenhong Zhou · Kun Wang · Linsey Pang · Prakhar Mehrotra · Qingsong Wen

摘要

Backdoor attacks are a significant threat to large language models (LLMs), often embedded via public checkpoints, yet existing defenses rely on impractical assumptions about trigger settings. To address this challenge, we propose Locphylax, a defense framework that requires no prior knowledge of...

安全可解释性强化学习与后训练OpenReview 项目页
作者位次 #9/9ACL 2026Findings· Findings of ACL

Time- RA : Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

Yiyuan Yang · Zichuan Liu · Lei Song · Kai Ying · Stephen Wang · Joshua Thomas Bamford · Svitlana Vyetrenko · Jiang Bian · Qingsong Wen

摘要

Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. To address these limitations, we propose Time-series Reasoning for Anomaly (Time-RA), a novel...

可解释性多模态推理OpenReview 项目页
作者位次 #11/11ACL 2026Findings· Findings of ACL

H ear S ay Benchmark: Do Audio LLM s Leak What They Hear?

Jin Wang · Kaiwen Luo · Liang Lin · Weiliu Wang · Yitian Chen · Moayad Aloqaily · Xuehai Tang · Zhenhong Zhou · Kun Wang · Li Sun · Qingsong Wen

摘要

While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications remain largely unexplored. This paper takes the first step to investigate whether ALLMs inadvertently leak user privacy solely through acoustic voicepr...

推理安全数据与预训练OpenReview 项目页
作者位次 #16/16ACL 2026Findings· Findings of ACL

E rror R adar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection

Yibo Yan · Shen Wang · Jiahao Huo · Hang Li · Boyan Li · Jiamin Su · Xiong Gao · YiFan Zhang · Tianlong Xu · Zhendong Chu · Aoxiao Zhong · Kun Wang · Hui Xiong · Philip S. Yu · Xuming Hu · Qingsong Wen

摘要

As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to handle mathematical reasoning tasks is promising, as they can handle multimodal questions via cross-modal understanding capabilities compared to text-only LLMs. Current mathematical benchmarks predomi...

推理评测与基准社会与人本OpenReview 项目页
作者位次 #10/10ACL 2026Findings· Findings of ACL

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

Yibo Yan · Shen Wang · Jiahao Huo · Jingheng Ye · Zhendong Chu · Xuming Hu · Philip S. Yu · Carla P Gomes · Bart Selman · Qingsong Wen

摘要

Scientific reasoning, the process through which humans apply logic, evidence, and critical thinking to explore and interpret scientific phenomena, is essential in advancing knowledge reasoning across diverse fields. However, despite significant progress, current scientific reasoning models still...

推理多模态对齐与偏好OpenReview 项目页
作者位次 #8/8ACL 2026Findings· Findings of ACL

ARM 2: Adaptive Reasoning Model with Vision Understanding and Executable Code

Jian Xie · Zhendong Chu · Aoxiao Zhong · Kai Zhang · Mingzhe Han · Xing fan · Jialie Shen · Qingsong Wen

摘要

Large Reasoning Models (LRMs) often suffer from the “over-thinking” problem, generating unnecessarily long reasoning on simple tasks. Some strategies have been proposed to mitigate this issue, such as length penalties or routing mechanisms, but they are typically heuristic and task-specific, lack...

推理多模态强化学习与后训练OpenReview 项目页

常见合作者