Qixun Wang 王启迅

Portrait of Qixun Wang

I am a third-year Ph.D. student in the School of Intelligence Science and Technology at Peking University.

My research interests include:

  • Multimodal Large Language Models (MLLMs), including latent visual reasoning, agentic visual reasoning, and evaluation of MLLMs.
  • Out-of-distribution (OOD) generalization, including theoretical analysis and algorithm design across computer vision, graph data, and the generalization behavior of LLMs.

News

May, 2026 One paper, Artifact-Bench, was released on arXiv for assessing MLLMs in AI-generated video detection.
May, 2026 Our paper Semantic-Enriched Latent Visual Reasoning was accepted to ICML 2026.
March, 2026I joined the Kling Team at Kuaishou Technology as a research intern.
February, 2026 Two papers were accepted to CVPR 2026, covering reasoning in latent visual space and a benchmark for unified multimodal models.

Selected papers (see full publication)

  1. CVPR
    Monet: Reasoning in Latent Visual Space Beyond Images and Language
    Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang, Pengfei Wan, Kun Gai, Xianghua Ying, and Yisen Wang
    CVPR, 2026
    • Propose a new framework for multimodal latent reasoning, including dataset construction, SFT, and RL algorithms, achieving significant improvements on both in-domain and OOD visual reasoning benchmarks
    • 190+ GitHub stars
    PDF Code
  2. ICLR
    Can In-context Learning Really Generalize to Out-of-distribution Tasks?
    Qixun Wang, Yifei Wang, Xianghua Ying, and Yisen Wang
    ICLR, 2025
    • Reveal the capability boundary and algorithm selection mechanism of ICL on OOD tasks through carefully designed experiments and theoretical analysis.
    PDF Code
  3. NeurIPS
    Dissecting the Failure of Invariant Learning on Graphs
    Qixun Wang, Yifei Wang, Yisen Wang, and Xianghua Ying
    NeurIPS, 2024
    • Theoretically and empirically demonstrate the failure modes of classic invariant learning approaches on graph data, and propose a new training objective with significant performance gains and theoretical guarantees.
    PDF Code
  4. NeurIPS Spotlight
    Improving Out-of-distribution Robustness by Adversarial Training with Structured Priors
    Qixun Wang*, Yifei Wang*, Hong Zhu, and Yisen Wang
    NeurIPS, 2022
    • Propose a simple yet effective low-rank adversarial training strategy that improves the OOD generalization of visual recognition models.
    PDF Code
  5. ICML
    Semantic-Enriched Latent Visual Reasoning
    Tianrun Xu, Yue Sun, Qixun Wang, Jingyi Lu, Yuan Wang, Tianren Zhang, Longteng Guo, Fengyun Rao, Jing Lyu, Feng Chen, and Jing Liu
    ICML, 2026
    • Reveal and address the lack of semantic richness in latent embeddings learned by prior latent visual reasoning training paradigms.
    PDF Code
  6. arXiv
    Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
    Yuqi Tang*, Yang Shi*, Zhuoran Zhang*, Qixun Wang*, and a group of outstanding researchers
    arXiv preprint, 2026
    • Propose a comprehensive benchmark for evaluating MLLMs’ ability to detect and analyze artifacts in AI-generated videos
    PDF Code
  7. arXiv
    Diagnosing Visual Ignorance in Vision-Language Models
    Runyu Zhou, Qi Zhang, Qixun Wang, and Yisen Wang
    arXiv preprint, 2026
    • Propose a dual-perspective diagnostic framework for visual ignorance in VLMs, revealing how language-prior routing failures undermine both model grounding and benchmark validity
    PDF

Experience

  • Kling Team, Kuaishou Technology (快手科技) — Research Intern (March 2026–present)
    Working on multimodal agents.

Education

  • Ph.D. Candidate in Machine Learning and Computer Vision, School of Intelligence Science and Technology, Peking University (2023–present)
  • B.S. in Intelligence Science and Technology, EECS, Peking University (2019–2023)

Awards

  • The Third-Class Scholarship of Peking University (2025)
  • Merit Student at Peking University (2025)
  • Outstanding Graduate of Peking University (2023)
  • Yanchuang Capital Scholarship, Top 6% (2022)
  • Merit Student at Peking University, Top 6% (2022)
  • Academic Innovation Award at Peking University, Top 1% (2022)
  • Award for Academic Excellence (2021)