Multimodal Understanding Foundation Large Language Models

Kaiwen Tuo

Incoming Ph.D. Student @ HKUST

Kaiwen Tuo

About

I'm Kaiwen Tuo, an incoming Ph.D. student at the Hong Kong University of Science and Technology (HKUST), advised by Prof. Jiaya Jia. Before joining HKUST, I received my B.E. in Computer Science and Technology from Tongji University (Guohao College). I was honored to be selected as the Undergraduate Valedictorian and deliver a speech at Tongji University’s graduation ceremony.

During my undergraduate studies, my research primarily focused on building multimodal foundation models, spanning evaluation, data curation, and post-training. More specifically, I am interested in: (1) identifying the failure modes of frontier foundation models, (2) constructing high-quality post-training data, and (3) developing effective post-training recipes to push the capability boundaries of multimodal foundation models, particularly in instruction following and agentic RL. I am fortunate to work with Prof. Huan Wang and Prof. Jiaqi Wang.

Selected Publications

  • SparseSSM overview
    SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
    Kaiwen Tuo, Huan Wang
    International Conference on Machine Learning (ICML), 2026
  • RewardMap overview
    RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
    Sicheng Feng*, Kaiwen Tuo*, Song Wang, Lingdong Kong, Jianke Zhu, Huan Wang
    International Conference on Learning Representations (ICLR), 2026

Selected Honors

  • National Scholarship · 2024–2025National, top 0.4%
  • National Scholarship · 2023–2024National, top 0.4%
  • National Scholarship · 2022–2023National, top 0.2%
  • Pursuit of Excellence ScholarshipHighest Honor of Tongji University, top 0.01%