Xingyu Song

PhD Student The University of Tokyo

Xingyu Song

I study flow-based generative modeling theory and its applications.

GitHub Scholar CV

01 / About

A little background.

I am a first-year PhD student at the RCAST AI Lab, Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, supervised by Prof. Naoya Takeishi and Prof. Takehisa Yairi.

My research explores flow-based generative models: how probability flows can generate the world we imagine. Recently, I have been drawn to the intrinsic connections between deterministic and stochastic dynamics.

I like building things that reflect the beauty and philosophy of computer science and AI: simplicity that hides subtle complexity.

02 / Publications

Selected & recent work.

Google Scholar

  1. Deterministic Decomposition of Stochastic Generative Dynamics

    Xingyu Song, Yuan Mei, Naoya Takeishi.

    arXiv preprint, 2026.

    Decomposes generative drift into transport and osmotic components and introduces Bridge Matching for controllable sampling.

  2. M3: Reframing Training Measures for Discretized Physical Simulations

    Yuan Mei, Xingyu Song, Xiaowen Song, Naoya Takeishi.

    arXiv preprint, 2026.

    Balances supervision across multi-scale Morton partitions to reduce measure-induced bias in neural surrogate models.

  3. JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Risky Health Behavior Content in Jirai Community

    Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng.

    EACL, 2026.

  4. Advancement and Development of Graph-Based Reasoning Method for Human Malicious Behaviors Identification Based on Graph Contrastive Representation Learning

    Zhan Li, Xingyu Song, Shi Chen, Kazuyuki Demachi.

    Neurocomputing, 2026.

  5. ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA

    Zhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li.

    EMNLP, 2025.

  6. Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs

    Yingjian Chen, Feiyang Li, Xingyu Song, Tianxiao Li, Zixin Xu, Xiujie Chen, Issey Sukeda, Irene Li.

    arXiv preprint, 2025.

  7. Armed Boundary Sabotage: A Case Study of Human Malicious Behaviors Identification with Computer Vision and Explainable Reasoning Methods

    Zhan Li, Xingyu Song, Shi Chen, Kazuyuki Demachi.

    Computers and Electrical Engineering, 121, 109924, 2025.

  8. Abnormal Detection in Nuclear Security Videos Based on Label-Specific Autoencoders and Reconstruction Errors Comparison

    Zhan Li, Xingyu Song, Shi Chen, Kazuyuki Demachi.

    Nuclear Engineering and Technology, 57(3), 103239, 2025.

  9. 4A: An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video

    Xingyu Song, Zhan Li, Shi Chen, Xin-Qiang Cai, Kazuyuki Demachi.

    ECAI, 2024 (Oral).

  10. Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training

    Xingyu Song, Zhan Li, Shi Chen, Kazuyuki Demachi.

    ECAI, 2024 (Oral).

  11. GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition

    Xingyu Song, Zhan Li, Shi Chen, Kazuyuki Demachi.

    arXiv preprint, 2024.

  12. Data, Language and Graph-Based Reasoning Methods for Identification of Human Malicious Behaviors in Nuclear Security

    Zhan Li, Xingyu Song, Shi Chen, Kazuyuki Demachi.

    Expert Systems with Applications, 236, 121367, 2024.

  13. Game Engine Based Data Augmentation with In-game Customization and Modeling for Malicious Behaviors Identification in Nuclear Security

    Xingyu Song, Zhan Li, Shi Chen, Kazuyuki Demachi.

    INMM/ESARDA Joint Annual Meeting, 2023.

  14. Malicious Behaviors Identification in Nuclear Security Based on Visual Relationships Extraction and Knowledge Reasoning

    Zhan Li, Xingyu Song, Shi Chen, Kazuyuki Demachi.

    INMM/ESARDA Joint Annual Meeting, 2023.

03 / Experience

Along the way.

Education

  1. 2025 - PresentPhD, Graduate School of Engineering, The University of Tokyo.
  2. 2022 - 2024M.Eng., Graduate School of Engineering, The University of Tokyo.
  3. 2021 - 2022Research Student, School of Fundamental Science and Engineering, Waseda University.
  4. 2017 - 2021B.Sc., College of Hongshen / College of Computer Science, Chongqing University.

Honors and Awards

  1. 2025SPRING GX Fellowship, The University of Tokyo.

Internships

  1. 2020.06 - 2020.08Sichuan Hwadee Information Technology Co., Ltd, Chengdu, China.
  2. 2019.01 - 2019.03National Key Laboratory of Software Development Environment, Beihang University, Beijing, China. Supervised by Prof. Zhiming Zheng.
  3. 2018.07 - 2018.09ChinaSoft International, Chongqing, China. CQUHub.

04 / Contact

Let’s exchange ideas.

Open to research conversations and collaborations.