Geo-Image-Textualization
Reinforcement learning-based framework for generating semantically aligned geometry image-caption pairs
Posts by Collection
portfolio
MLLM Interpretability Research
Enhancing the interpretability of LLaVA by uncovering underlying attention mechanisms and developing adaptive pruning techniques
publications
● Multi-tailed vision transformer for efficient inference
Authors: Yunke Wang, Bo Du, Wenyuan Wang, Chang Xu
Neural Networks
A novel architecture that uses multiple tails to generate visual sequences of different lengths for efficient vision transformer inference.
● Continual Learning of Large Language Models: A Comprehensive Survey
Authors: Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, Hao Wang
ACM Computing Surveys
A comprehensive survey on continual learning approaches for large language models, covering methodologies, challenges, and future directions.
● Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
Authors: Hengyi Wang, Haizhou Shi, Shiwei Tan, Weiyi Qin, Wenyuan Wang, Tunyu Zhang, Akshay Nambi, Tanuja Ganu, Hao Wang
NAACL 2025 Main
A comprehensive benchmark for evaluating the long-context capabilities of multimodal large language models.
● Probabilistic Residual User Clustering
Authors: Wenyuan Wang, Yusong Zhao, Zihao Xu, Hengyi Wang, Shreya Venugopal, Desmond Lobo, Chengzhi Mao, Qi Xu, Zhigang Hua, Yan Xie, Bo Long, Shuang Yang, Hao Wang
IJCAI2025 Workshop / Submitted to TMLR
A causal Bayesian framework that clusters users and models residuals between predicted and true ratings to enhance recommendation accuracy.
● Generalizable Geometric Image Caption Synthesis
Authors: Yue Xin*, 'Wenyuan Wang*, Rui Pan, Ruida Wang, BingXu Meng, Renjie Pi, Shizhe Diao, Tong Zhang
ICLR 2026(Under Review)
A reinforcement learning-based framework for generating semantically aligned geometry image-caption pairs, creating the first dataset with full modality equivalence for geometric reasoning.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
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Teaching experience 2
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