Jiaxuan Zou

Undergraduate Student @ XJTU | Former Research Intern @ ByteDance Seed Pre-training

Jiaxuan Zou

School of Mathematics and Statistics Xi'an Jiaotong University

The best way to reach me is via email.

Hi, I am Jiaxuan Zou (邹嘉轩).

I am an undergraduate student in Mathematics and Statistics at Xi’an Jiaotong University. Previously, I was a research intern in the ByteDance Seed Pre-training team. I was also a research intern at the Gaoling School of Artificial Intelligence, Renmin University of China, advised by Prof. Yong Liu.

My research centers on pre-training and scaling. I study how model architectures, data, and optimization jointly determine learning efficiency, training stability, and model capabilities. My approach connects empirical training phenomena with mathematical analysis and controlled experiments, aiming to make large-scale training more predictable.

I also work as an AI Technical Consultant for a Tsinghua-affiliated AI startup.

I write research notes on my blog and welcome conversations on these topics. I am interested in future research opportunities in LLM pre-training and AI theory. More background: English / 中文.

Research Interests

  • Scaling laws and predictable large-scale pre-training
  • Joint design of model architectures, data, and optimizers
  • Feature learning, finite-width training dynamics, and hyperparameter transfer

News

Oct 07, 2026 I wrapped up my research internship with the ByteDance Seed Pre-training team. Thank you to everyone at Seed for this wonderful journey!
Jul 01, 2026 I joined the ByteDance Seed Pre-training team as a research intern.
May 25, 2026 Our Nora optimizer has been included in the ScalingOpt optimizer library.
Apr 18, 2026 I joined the ScalingOpt project as a co-maintainer, working on optimizer design for large language model training.

Latest Posts

Selected Publications

  1. Kaczmarz Linear Attention
    Kaczmarz Linear Attention
    Jiaxuan Zou, Ruifeng Ren, and Yong Liu
    2026
  2. Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer
    Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer
    Jinghui Yuan, Jiaxuan Zou, Shuo Wang, and 2 more authors
    2026
  3. Effective Frontiers: A Unification of Neural Scaling Laws
    Effective Frontiers: A Unification of Neural Scaling Laws
    Jiaxuan Zou, Zixuan Gong, Ye Su, and 2 more authors
    Under Review, 2026
  4. Capabilities and Fundamental Limits of Latent Chain-of-Thought
    Capabilities and Fundamental Limits of Latent Chain-of-Thought
    Jiaxuan Zou, Yaozhong Xiong, and Yong Liu
    In EMNLP, 2026
  5. SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing
    SMI: Statistical Membership Inference for Reliable Unlearned Model Auditing
    Jialong Sun, Zeming Wei, Jiaxuan Zou, and 5 more authors
    In NeurIPS, 2026
  6. FreIE: Low-Frequency Spectral Bias in Neural Networks for Time-Series Tasks
    FreIE: Low-Frequency Spectral Bias in Neural Networks for Time-Series Tasks
    Jialong Sun, Xinpeng Ling, Jiaxuan Zou, and 2 more authors
    In IEEE ICDM, 2025