publications

2026

1. COLM

   

   FitText: Evolving Agent Tool Ecologies via Memetic Retrieval

   Kyle Zheng^*, Han Zhang^*, Renliang Sun, and 2 more authors

   In Conference on Language Modeling (COLM), 2026

   Bib PDF

   @inproceedings{zheng2026fittext,
     title = {FitText: Evolving Agent Tool Ecologies via Memetic Retrieval},
     author = {Zheng, Kyle and Zhang, Han and Sun, Renliang and Ye, Chenchen and Wang, Wei},
     booktitle = {Conference on Language Modeling (COLM)},
     year = {2026},
   }

2. ICML

   

   ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

   Xiaoxuan Wang^*, Han Zhang^*, Haixin Wang^*, and 11 more authors

   In International Conference on Machine Learning (ICML), 2026

   Bib PDF

   @inproceedings{wang2026arlarena,
     title = {ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning},
     author = {Wang, Xiaoxuan and Zhang, Han and Wang, Haixin and Shi, Yidan and Li, Ruoyan and Han, Kaiqiao and Tong, Chenyi and Deng, Haoran and Sun, Renliang and Taylor, Alexander and Zhu, Yanqiao and Cong, Jason and Sun, Yizhou and Wang, Wei},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2026},
   }

2025

1. EMNLP

   Protein Large Language Models: A Comprehensive Survey

   Yijia Xiao, Wanjia Zhao, Junkai Zhang, and 12 more authors

   In Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

   Abs DOI Bib

   Protein-specific large language models (ProteinLLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of ProteinLLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art ProteinLLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning ProteinLLMs as essential tools for scientific discovery in protein science.

   @inproceedings{xiao-etal-2025-protein,
     title = {Protein Large Language Models: A Comprehensive Survey},
     author = {Xiao, Yijia and Zhao, Wanjia and Zhang, Junkai and Jin, Yiqiao and Zhang, Han and Ren, Zhicheng and Sun, Renliang and Wang, Haixin and Wan, Guancheng and Lu, Pan and Luo, Xiao and Zhang, Yu and Zou, James and Sun, Yizhou and Wang, Wei},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2025},
     year = {2025},
     pages = {23080--23103},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2025.findings-emnlp.1255/},
     doi = {10.18653/v1/2025.findings-emnlp.1255},
   }

2024

1. NeurIPS

   4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs

   Minjie Wang, Quan Gan, David Wipf, and 17 more authors

   In Advances in Neural Information Processing Systems, 2024

   DOI Bib

   @inproceedings{NEURIPS2024_2fd67447,
     title = {4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs},
     author = {Wang, Minjie and Gan, Quan and Wipf, David and Cai, Zhenkun and Li, Ning and Tang, Jianheng and Zhang, Yanlin and Zhang, Zizhao and Mao, Zunyao and Song, Yakun and Wang, Yanbo and Li, Jiahang and Zhang, Han and Yang, Guang and Qin, Xiao and Lei, Chuan and Zhang, Muhan and Zhang, Weinan and Faloutsos, Christos and Zhang, Zheng},
     booktitle = {Advances in Neural Information Processing Systems},
     volume = {37},
     pages = {27236--27273},
     year = {2024},
     doi = {10.52202/079017-0856},
     url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/2fd67447702c8eff5683dda507a1b0a2-Paper-Datasets_and_Benchmarks_Track.pdf},
   }

1. VLDB

   GFS: Graph-based Feature Synthesis for Prediction over Relational Database

   Han Zhang, Quan Gan, David Wipf, and 1 more author

   Proceedings of the VLDB Endowment

   Workshop on Tabular Data Analysis (TaDA) at VLDB

   Bib

   @article{zhang2150gfs,
     title = {GFS: Graph-based Feature Synthesis for Prediction over Relational Database},
     author = {Zhang, Han and Gan, Quan and Wipf, David and Zhang, Weinan},
     journal = {Proceedings of the VLDB Endowment},
     volume = {2150},
     pages = {8097},
     note = {Workshop on Tabular Data Analysis (TaDA) at VLDB},
   }