Qixun Wang 王启迅

Portrait of Qixun Wang

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

My current research focuses on advancing the core capabilities of multimodal large language models (MLLMs) and their downstream applications. I am particularly interested in post-training methods that strengthen perception and reasoning, including reasoning in latent space (Monet, SLVR) and reasoning with visual tools (Beacon). Building on these capabilities, I explore MLLM applications in audio-video captioning and understanding (RefCaptioner), enhancing dynamic 3D world generation through programming, assessing the authenticity of AI-generated videos (Artifact-Bench, Verus-Insight), and GUI agents (FocusMem).

Previously, I focused on out-of-distribution (OOD) generalization, conducting theoretical analysis and designing algorithms for visual recognition (MAT & LDAT), graph tasks (CIA-LRA), and in-context learning in large language models (ICL-OOD). This background continues to inform my efforts to develop more robust and generalizable methods for MLLMs.

News

July, 2026 One paper, Beacon, on improving reasoning-mode adaptiveness and achieving genuine tool-induced performance gains in agentic visual reasoning, was released on arXiv.
May, 2026 A paper entitled Artifact-Bench, which evaluates MLLMs for AI-generated video detection, was released on arXiv.
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 Published Papers (see full publication)

  1. Overview of the Monet latent visual reasoning framework
    CVPR 2026
    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
    • 200+ GitHub stars
    PDF Code
  2. Illustration of in-context learning on out-of-distribution tasks
    ICLR 2025
    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. Illustration of invariant learning on graphs
    NeurIPS 2024
    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. Overview of adversarial training with structured priors
    NeurIPS 2022 Spotlight
    Improving Out-of-distribution Generalization 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. Overview of the semantic-enriched latent visual reasoning framework
    ICML 2026
    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

Selected Preprints

  1. Overview of Beacon for adaptive agentic visual reasoning
    arXiv 2026.07
    Beacon: Knowing When and Why to Perform Agentic Visual Reasoning
    Qixun Wang*, Yang Shi*, Letian Cheng, Zhuoran Zhang, Yan He, Yuqi Tang, Qi Zhang, Xinlei Yu, Ruizhe Chen, Tianrun Xu, Yuanxing Zhang, Pengfei Wan, Haotian Wang, Xianghua Ying
    arXiv preprint 2026
    • Conduct a comprehensive analysis of reasoning-mode adaptiveness and tool-induced performance changes in existing agentic visual reasoning models.
    • Propose a novel training recipe that achieves state-of-the-art or competitive performance across 13 visual reasoning benchmarks, while improving reasoning-mode adaptiveness and delivering genuine tool-induced performance gains.
    PDF Code
  2. Taxonomy of AI-generated video artifacts in Artifact-Bench
    arXiv 2026.05
    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
  3. Overview of Verus-Insight for explainable artifact detection in AI-generated videos
    arXiv 2026.10
    Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos
    Zhuoran Zhang*, Yang Shi*, Qixun Wang*, and a group of outstanding researchers
    arXiv preprint 2026
    • Propose a MLLM with advanced fine-grained spatiotemporal artifact detection capabilities, achieving higher precision and recall across various artifact detection tasks for AI-generated videos.
    PDF Code

Experience

  • Kling Team, Kuaishou Technology — 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)