Papers by Qingsong Wang
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning (2026.findings-acl)
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Yibo Yan, Shen Wang, Jiahao Huo, Jingheng Ye, Zhendong Chu, Xuming Hu, Philip S. Yu, Carla P Gomes, Bart Selman, Qingsong Wen
| Challenge: | Current scientific reasoning models struggle with generalization across domains and fall short of multimodal perception. |
| Approach: | They propose to use multimodal large language models to integrate text, images, and other modalities to enhance scientific reasoning. |
| Outcome: | The proposed models can integrate text, images, and other modalities and improve reasoning across disciplines. |
Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models (2026.acl-long)
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Liang Lin, Miao Yu, Moayad Aloqaily, Zhenhong Zhou, Kun Wang, Linsey Pang, Prakhar Mehrotra, Qingsong Wen
| Challenge: | Existing defenses rely on impractical assumptions about trigger settings to mitigate backdoor attacks . a recent study found that small amounts of training data can systematically induce harmful behaviors in large language models. |
| Approach: | They propose a backdoor defense framework that requires no prior knowledge of trigger settings . they use a two-stage process to aggregate backdoor representations and fine-tune recovery . |
| Outcome: | The proposed defense reduces the average Attack Success Rate to 4.41% across multiple benchmarks . the proposed framework generalizes across different types of backdoors, confirming its robustness in practical deployment scenarios. |
UniEDU: Toward Unified and Efficient Large Multimodal Models for Educational Tasks (2025.emnlp-industry)
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| Challenge: | Existing research has focused on plain text, while real-world K-12 scenarios often involve multimodal data. |
| Approach: | They propose a unified language and vision assistant called UniEDU for educational applications . it excels across multiple educational tasks while maintaining strong generalization capabilities . authors propose to use UniEDu for industry-scale deployment . |
| Outcome: | The proposed model excels across multiple educational tasks while maintaining strong generalization capabilities. |
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs (2026.acl-long)
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| Challenge: | Current sycophancy research has largely overlooked its specific manifestations in the video-language domain. |
| Approach: | They propose a video-LLM sycophancy benchmarking and evaluation to evaluate scophancies in video-LLMs. |
| Outcome: | The proposed benchmark evaluates sycophantic behavior in state-of-the-art Video-LLMs across diverse question formats, prompt biases, and visual reasoning tasks. |
A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection (2025.emnlp-main)
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| Challenge: | Existing methods for misinformation detection are limited by domain knowledge and expert experience. |
| Approach: | They propose a Multi-Agent Framework for cross-domain misinformation detection with Automated Decision Rule Optimization (MARO) they first employ multiple expert agents to analyze target-domain news, then introduce a question-reflection mechanism that guides expert agents for higher-quality analysis. |
| Outcome: | The proposed framework improves on a common dataset and shows that iteratively improves over existing methods. |
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection (2026.findings-acl)
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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
| Challenge: | Current mathematical benchmarks focus on evaluating MLLMs’ problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection. |
| Approach: | They propose to evaluate multimodal error detection by evaluating two sub-tasks error step identification and error categorization. |
| Outcome: | The proposed task evaluates MLLMs' ability to handle multimodal questions compared to text-only models. |
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment. |
| Approach: | They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition. |
| Outcome: | The proposed framework aligns knowledge complexity and presentation style with user cognition. |
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026.findings-acl)
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Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen
| Challenge: | Time series anomaly detection (TSAD) has traditionally focused on binary classification and lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. |
| Approach: | They propose a time-series reasoning task that reformulates TSAD from discriminative to reasoning-intensive paradigm. |
| Outcome: | The proposed task reformulates TSAD from discriminative to reasoning-intensive paradigm. |
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)
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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
| Challenge: | Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes. |
| Approach: | This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems . |
| Outcome: | The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings. |
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement (2025.acl-long)
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Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, Wenjie Du, Stefan Zohren, Zhangyang Wang, Ming Jin, Qingsong Wen
| Challenge: | Existing time series models focus on a narrow spectrum of tasks, such as forecasting or anomaly detection. |
| Approach: | They propose a framework that enables natural language queries across multiple time series tasks such as numerical analytical tasks and open-ended question answering with reasoning. |
| Outcome: | The proposed framework enables natural language queries across multiple time series tasks and allows for more advanced and intuitive interactions with temporal data. |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection (2025.acl-long)
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Cilin Yan, Jingyun Wang, Lin Zhang, Ruihui Zhao, Xiaopu Wu, Kai Xiong, Qingsong Liu, Guoliang Kang, Yangyang Kang
| Challenge: | Recent work utilizes feedbacks generated from erroneous cases to guide prompt optimization . previous methods rely on computational resources and powerful GPUs . |
| Approach: | They propose an automatic prompt engineering method that leverages feedbacks from erroneous cases to guide prompt optimization. |
| Outcome: | The proposed method surpasses state-of-the-art methods with less steps and lower computational resources. |
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (D18-1)
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| Challenge: | Existing approaches to align multilingual knowledge graphs with counterparts in different languages are not effective. |
| Approach: | They propose a novel approach for cross-lingual KG alignment via graph convolutional networks . they train GCNs to embed entities of each language into a unified vector space . |
| Outcome: | The proposed approach gets the best performance on real multilingual KGs compared with other embedding-based approaches. |
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026.findings-acl)
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Jin Wang, Kaiwen Luo, Liang Lin, Weiliu Wang, Yitian Chen, Moayad Aloqaily, Xuehai Tang, Zhenhong Zhou, Kun Wang, Li Sun, Qingsong Wen
| Challenge: | Recent advances in audio large language models have led to their potential privacy implications unexplored. |
| Approach: | They propose a benchmark to examine whether ALLMs leak user privacy through acoustic voiceprints. |
| Outcome: | The proposed benchmark is constructed from over 22,000 real-world audio clips. |
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
| Challenge: | Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge. |
| Approach: | They propose a modular open-source library to equip LLMs with external knowledge. |
| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges (2025.findings-acl)
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Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, Xuming Hu
| Challenge: | This survey provides **the first comprehensive analysis of mathematical reasoning in the era of multimodal large language models** . integrating large language model with mathematical reasoning tasks is becoming significant as AI advances . |
| Approach: | They review over 200 studies published since 2021 and examine the state-of-the-art developments in Math-LLMs . they identify five major challenges hindering the realization of AGI in this domain . |
| Outcome: | The authors examine the state-of-the-art developments in Math-LLMs with a focus on multimodal settings. |
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection (2025.acl-industry)
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| Challenge: | Multimodal Large Language Models (MLLMs) struggle with identifying and categorizing student errors in multimodal mathematical contexts. |
| Approach: | They propose a new framework that decomposes error detection into three phases with specialized agents. |
| Outcome: | The proposed framework shows higher accuracy in error step identification and 3% improvement in error categorization on real-world educational data. |
Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
| Approach: | They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks . |
| Outcome: | The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education. |
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)
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Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang
| Challenge: | Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken. |
| Approach: | They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages. |
| Outcome: | The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs. |
Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)
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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
| Challenge: | supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes . |
| Approach: | They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers . |
| Outcome: | The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models . |