Publications

2026

1. ICML

   OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models

   Yue Ding, Yiyan Ji, Jungang Li, and 12 more authors

   In Forty-Third International Conference on Machine Learning (ICML 2026), 2026

   Bib HTML

   @inproceedings{ding2026omnisift,
     title = {OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models},
     author = {Ding, Yue and Ji, Yiyan and Li, Jungang and Liu, Xuyang and Chen, Xinlong and Wu, Junfei and Li, Bozhou and Zeng, Bohan and Shi, Yang and Guan, Yushuo and Zhang, Yuanxing and Liu, Jiaheng and Liu, Qiang and Wan, Pengfei and Wang, Liang},
     booktitle = {Forty-Third International Conference on Machine Learning (ICML 2026)},
     year = {2026},
   }

2. ACL

   RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation

   Jiajun Zhang, Yuying Li, Zhixun Li, and 12 more authors

   In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

   Bib HTML

   @inproceedings{zhang2026realchart2code,
     title = {RealChart2Code: Advancing Chart-to-Code Generation with Real Data and Multi-Task Evaluation},
     author = {Zhang, Jiajun and Li, Yuying and Li, Zhixun and Guo, Xingyu and Wu, Jingzhuo and Zheng, Leqi and Yang, Yiran and Zhang, Jianke and Li, Qingbin and Yan, Shannan and Jia, Changguo and Wu, Junfei and Wang, Zilei and Liu, Qiang and Wang, Liang},
     booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
     pages = {41995--42032},
     year = {2026},
   }

2025

1. NeurIPS

   

   Reinforcing spatial reasoning in vision-language models with interwoven thinking and visual drawing

   Junfei Wu, Jian Guan, Kaituo Feng, and 5 more authors

   In Advances in Neural Information Processing Systems 39 (NeurIPS 2025), 2025

   Bib HTML

   @inproceedings{wu2025reinforcing,
     title = {Reinforcing spatial reasoning in vision-language models with interwoven thinking and visual drawing},
     author = {Wu, Junfei and Guan, Jian and Feng, Kaituo and Liu, Qiang and Wu, Shu and Wang, Liang and Wu, Wei and Tan, Tieniu},
     booktitle = {Advances in Neural Information Processing Systems 39 (NeurIPS 2025)},
     year = {2025},
   }

2. NeurIPS

   Video-R1: Reinforcing Video Reasoning in MLLMs

   Kaituo Feng, Kaixiong Gong, Bohao Li, and 7 more authors

   In Advances in Neural Information Processing Systems 39 (NeurIPS 2025), 2025

   Bib HTML

   @inproceedings{feng2025video,
     title = {Video-R1: Reinforcing Video Reasoning in MLLMs},
     author = {Feng, Kaituo and Gong, Kaixiong and Li, Bohao and Guo, Zonghao and Wang, Yibing and Peng, Tianshuo and Wu, Junfei and Zhang, Xiaoying and Wang, Benyou and Yue, Xiangyu},
     booktitle = {Advances in Neural Information Processing Systems 39 (NeurIPS 2025)},
     year = {2025},
   }

3. 

   SHARP: Steering Hallucination in LVLMs via Representation Engineering

   Junfei Wu, Yue Ding, Guofan Liu, and 7 more authors

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

   Bib

   @inproceedings{wu2025sharp,
     title = {SHARP: Steering Hallucination in LVLMs via Representation Engineering},
     author = {Wu, Junfei and Ding, Yue and Liu, Guofan and Xia, Tianze and Huang, Ziyue and Sui, Dianbo and Liu, Qiang and Wu, Shu and Wang, Liang and Tan, Tieniu},
     booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
     pages = {14357--14372},
     year = {2025},
   }

4. 

   Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models

   Xinlong Chen, Yuanxing Zhang, Qiang Liu, and 3 more authors

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025

   Abs DOI Bib

   Large Vision-Language Models (LVLMs) have exhibited impressive capabilities across various visual tasks, yet they remain hindered by the persistent challenge of hallucinations. To address this critical issue, we propose Mixture of Decoding (MoD), a novel approach for hallucination mitigation that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens. Specifically, MoD measures the consistency between outputs generated from the original image tokens and those derived from the model’s attended image tokens, to distinguish the correctness aforementioned. If the outputs are consistent, indicating correct attention, MoD employs a complementary strategy to amplify critical information. Conversely, if the outputs are inconsistent, suggesting erroneous attention, MoD utilizes a contrastive strategy to suppress misleading information. Extensive experiments demonstrate that MoD significantly outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs. Code is available at https://github.com/xlchen0205/MoD.

   @inproceedings{chen-etal-2025-mixture-decoding,
     title = {Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models},
     author = {Chen, Xinlong and Zhang, Yuanxing and Liu, Qiang and Wu, Junfei and Zhang, Fuzheng and Tan, Tieniu},
     editor = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
     booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
     month = jul,
     year = {2025},
     address = {Vienna, Austria},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2025.findings-acl.448/},
     doi = {10.18653/v1/2025.findings-acl.448},
     pages = {8525--8542},
     isbn = {979-8-89176-256-5},
   }

5. A Survey on Personalized Alignment–The Missing Piece for Large Language Models in Real-World Applications

   Jian Guan, Junfei Wu, Jia-Nan Li, and 2 more authors

   arXiv preprint arXiv:2503.17003, Jul 2025

   Bib

   @article{guan2025survey,
     title = {A Survey on Personalized Alignment--The Missing Piece for Large Language Models in Real-World Applications},
     author = {Guan, Jian and Wu, Junfei and Li, Jia-Nan and Cheng, Chuanqi and Wu, Wei},
     journal = {arXiv preprint arXiv:2503.17003},
     year = {2025},
   }

6. 

   MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

   YiFan Zhang, Huanyu Zhang, Haochen Tian, and 8 more authors

   In The Thirteenth International Conference on Learning Representations, Jul 2025

   Bib

   @inproceedings{zhang2025mme,
     title = {MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?},
     author = {Zhang, YiFan and Zhang, Huanyu and Tian, Haochen and Fu, Chaoyou and Zhang, Shuangqing and Wu, Junfei and Li, Feng and Wang, Kun and Wen, Qingsong and Zhang, Zhang and others},
     booktitle = {The Thirteenth International Conference on Learning Representations},
     year = {2025},
   }

2024

1. 

   Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

   Junfei Wu, Qiang Liu, Ding Wang, and 4 more authors

   In Findings of the Association for Computational Linguistics ACL 2024, Jul 2024

   Bib

   @inproceedings{wu2024logical,
     title = {Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models},
     author = {Wu, Junfei and Liu, Qiang and Wang, Ding and Zhang, Jinghao and Wu, Shu and Wang, Liang and Tan, Tieniu},
     booktitle = {Findings of the Association for Computational Linguistics ACL 2024},
     pages = {6944--6962},
     year = {2024},
   }

2. Uni-modal event-agnostic knowledge distillation for multimodal fake news detection

   Guofan Liu, Jinghao Zhang, Qiang Liu, and 3 more authors

   IEEE Transactions on Knowledge and Data Engineering, Jul 2024

   Bib

   @article{liu2024uni,
     title = {Uni-modal event-agnostic knowledge distillation for multimodal fake news detection},
     author = {Liu, Guofan and Zhang, Jinghao and Liu, Qiang and Wu, Junfei and Wu, Shu and Wang, Liang},
     journal = {IEEE Transactions on Knowledge and Data Engineering},
     year = {2024},
     publisher = {IEEE},
   }

3. 

   Out-of-distribution evidence-aware fake news detection via dual adversarial debiasing

   Qiang Liu, Junfei Wu, Shu Wu, and 1 more author

   IEEE Transactions on Knowledge and Data Engineering, Jul 2024

   Bib

   @article{liu2024out,
     title = {Out-of-distribution evidence-aware fake news detection via dual adversarial debiasing},
     author = {Liu, Qiang and Wu, Junfei and Wu, Shu and Wang, Liang},
     journal = {IEEE Transactions on Knowledge and Data Engineering},
     volume = {36},
     number = {11},
     pages = {6801--6813},
     year = {2024},
     publisher = {IEEE},
   }

2023

1. 

   Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks

   Junfei Wu, Weizhi Xu, Qiang Liu, and 2 more authors

   IEEE Transactions on Knowledge and Data Engineering, Jul 2023

   Bib

   @article{wu2023adversarial,
     title = {Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks},
     author = {Wu, Junfei and Xu, Weizhi and Liu, Qiang and Wu, Shu and Wang, Liang},
     journal = {IEEE Transactions on Knowledge and Data Engineering},
     volume = {36},
     number = {11},
     pages = {5591--5604},
     year = {2023},
     publisher = {IEEE},
   }

2022

1. 

   Bias mitigation for evidence-aware fake news detection by causal intervention

   Junfei Wu, Qiang Liu, Weizhi Xu, and 1 more author

   In Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval, Jul 2022

   Bib

   @inproceedings{wu2022bias,
     title = {Bias mitigation for evidence-aware fake news detection by causal intervention},
     author = {Wu, Junfei and Liu, Qiang and Xu, Weizhi and Wu, Shu},
     booktitle = {Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval},
     pages = {2308--2313},
     year = {2022},
   }

2. Evidence-aware fake news detection with graph neural networks

   Weizhi Xu, Junfei Wu, Qiang Liu, and 2 more authors

   In Proceedings of the ACM web conference 2022, Jul 2022

   Bib

   @inproceedings{xu2022evidence,
     title = {Evidence-aware fake news detection with graph neural networks},
     author = {Xu, Weizhi and Wu, Junfei and Liu, Qiang and Wu, Shu and Wang, Liang},
     booktitle = {Proceedings of the ACM web conference 2022},
     pages = {2501--2510},
     year = {2022},
   }