Xianghong Fang

Ph.D. Student in Statistical Sciences, University of Toronto

Hi! I am a second-year Ph.D. student in Statistics at the University of Toronto. I am very fortunate to be advised by Prof. Dehan Kong and Tim G. J. Rudner. I am also affiliated with the Vector Institute. Prior to my Ph.D. study, I received my Master of Philosophy in Artificial Intelligence from The Hong Kong University of Science and Technology, where I was very fortunate to be advised by Dit-Yan Yeung. I received my bachelor's degree from the University of Electronic Science and Technology of China.

Research Interests

My current research interests center on tokenization, generative modeling, multimodal systems, and agentic AI:

Vector Quantization for Autoregressive Modeling: Studying optimal vector quantization algorithms for visual autoregressive models and other modalities.
Reconstruction and Generation Gap: Understanding the gap between reconstruction and generation in autoregressive generative models, diffusion models, and flow matching models.
Unified Multimodal Generation: Developing efficient tokenizers for unified multimodal generative models, mainly under the autoregressive paradigm.
Agentic AI and Safety: Exploring agent safety, agent harnesses, and recursive self-optimization for AI agents, which will be a major focus of my upcoming research.

I am open to collaboration on related topics and happy to mentor self-motivated students who are excited about research.

News

[06/2026] Two papers have been updated on arXiv.
[01/2026] One first-author paper was accepted to ICLR 2026.

Publications (* equal contribution)

Aligning Reconstruction with Generation: A Latent Distribution Alignment Perspective on Evaluation and Optimization

Xianghong Fang*, Wen-Jie Shu*, Tongda Xu, Wenlong Mou, Dehan Kong, Tim G. J. Rudner

Under review, 2026

paper code project page bibtex

VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers

Xianghong Fang, Yuan Yuan, Dehan Kong, Tim G. J. Rudner

ICLR, 2026

paper code project page

bibtex

@inproceedings{Fang2026VQTransplant,
  title={V{Q}-Transplant: Efficient Plug-and-Play VQ-Module Integration for Pre-trained Visual Tokenizers},
  author={Xianghong Fang and Yuan Yuan and Dehan Kong and Tim G. J. Rudner},
  booktitle={ICLR},
  year={2026},
}

Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

Xianghong Fang, Litao Guo, Hengchao Chen, Yuxuan Zhang, Xiaofan Xia, Dingjie Song, Yexin Liu, Hao Wang, Harry Yang, Qiang Sun, Yuan Yuan

Under review, 2026

paper code project page bibtex

Rethinking the uniformity metric in self-supervised learning

Xianghong Fang, Jian Li, Qiang Sun, Benyou Wang

ICLR, 2024

paper code

bibtex

@inproceedings{Fang2024RethinkingTU,
  title={Rethinking the Uniformity Metric in Self-Supervised Learning},
  author={Xianghong Fang and Jian Li and Qiang Sun and Benyou Wang},
  booktitle={ICLR},
  year={2024}
}

Controlled text generation using dictionary prior in variational autoencoders

Xianghong Fang, Jian Li, Lifeng Shang, Xin Jiang, Qun Liu, Dit-Yan Yeung

ACL Findings, 2022

paper

bibtex

@inproceedings{Fang2022ControlledTG,
  title={Controlled Text Generation Using Dictionary Prior in Variational Autoencoders},
  author={Xianghong Fang and Jian Li and Lifeng Shang and Xin Jiang and Qun Liu and Dit-Yan Yeung},
  booktitle={ACL},
  year={2022},
}

Discrete auto-regressive variational attention models for text modeling

Xianghong Fang*, Haoli Bai*, Jian Li, Zenglin Xu, Michael Lyu, Irwin King

IJCNN, 2021

paper code

bibtex

@article{Fang2021DiscreteAV,
  title={Discrete Auto-regressive Variational Attention Models for Text Modeling},
  author={Xianghong Fang and Haoli Bai and Zenglin Xu and Michael R. Lyu and Irwin King},
  journal={IJCNN},
  year={2021},
}

DART: Domain-adversarial residual-transfer networks for unsupervised cross-domain image classification

Xianghong Fang, Haoli Bai, Ziyi Guo, Bin Shen, Steven Hoi, Zenglin Xu

Neural Networks, 2020

paper

bibtex

@article{Fang2020DARTDR,
  title={DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification},
  author={Xianghong Fang and Haoli Bai and Ziyi Guo and Bin Shen and Steven C. H. Hoi and Zenglin Xu},
  journal={Neural networks},
  year={2020},
}

Academic Activities

Reviewer

Conference: NeurIPS 2025, NeurIPS 2026

Journal: TPAMI

Mentorship

Wenjie Shu: Undergraduate student at the University of Electronic Science and Technology of China.
Jiajun Zhu: Master's student at the University of Montreal.