Zixu Wang

I am a PhD candidate in Computer Science at Technical University of Munich and Infineon Technologies, supervised by Prof. Dr. Chunyang Chen. My research interests lie in LLM agents and their evaluation, including multi-agent systems and agent memory. I am particularly interested in when such systems can be trusted to act reliably, and I apply them to software engineering, especially requirements engineering and software architecture for automotive software. Alongside my PhD, I serve as a teaching assistant for TUM’s course Foundations and Application of Generative AI.

Before the PhD I earned an MSc in Electrical and Computer Engineering at TUM and worked at Bosch Center for Artificial Intelligence, Siemens AG, and Infineon Technologies, where I developed trajectory-prediction and radar–camera depth-estimation models for autonomous driving, applied machine learning to logistics, and built AI models for sensor data. That path took me up the automotive stack — from how a vehicle perceives the world to how its software gets specified — and it shaped how I work today: on real engineering data, under real constraints.

Feel free to reach out for collaborations, questions, or just to say hi at hi@zixu.wang.

selected publications

ASE

Bridging Stakeholder and Product Requirements: An Empirical Study of Requirement Engineering in the Automotive Industry

Zixu Wang, Shengcheng Yu, Zhenchang Xing, Tobias Wenzel, and Chunyang Chen

In Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering, 2026

DOI arXiv Bib

@inproceedings{wang2026bridging,
  title = {Bridging Stakeholder and Product Requirements: An Empirical Study of Requirement Engineering in the Automotive Industry},
  author = {Wang, Zixu and Yu, Shengcheng and Xing, Zhenchang and Wenzel, Tobias and Chen, Chunyang},
  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering},
  series = {ASE '26},
  year = {2026},
  pages = {3672--3683},
  numpages = {12},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  isbn = {9798400728822},
  keywords = {Automotive Software, Empirical Study, Requirements Engineering},
  url = {https://doi.org/10.1145/3832783.3834456},
  doi = {10.1145/3832783.3834456},
}

TMLR

TRIDE: A Text-assisted Radar-Image Weather-aware Fusion Network for Depth Estimation

Huawei Sun, Zixu Wang, Hao Feng, Julius Ott, Lorenzo Servadei, and Robert Wille

Transactions on Machine Learning Research, 2025

arXiv Bib

@article{sun2025tride,
  title = {TRIDE: A Text-assisted Radar-Image Weather-aware Fusion Network for Depth Estimation},
  author = {Sun, Huawei and Wang, Zixu and Feng, Hao and Ott, Julius and Servadei, Lorenzo and Wille, Robert},
  journal = {Transactions on Machine Learning Research},
  year = {2025},
}

arXiv
SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction

Zixu Wang, Zhigang Sun, Juergen Luettin, and Lavdim Halilaj

arXiv preprint arXiv:2405.03809, 2024

Abs arXiv Bib

A graph transformer that captures semantic and spatial agent interactions for trajectory prediction in autonomous driving, combining temporal encoding and road topology to reach state-of-the-art results on the nuScenes benchmark.
@article{wang2024socialformer, title = {SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction}, author = {Wang, Zixu and Sun, Zhigang and Luettin, Juergen and Halilaj, Lavdim}, journal = {arXiv preprint arXiv:2405.03809}, year = {2024}, }

RA-L

Semanticformer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction Using Knowledge Graphs

Zhigang Sun, Zixu Wang, Lavdim Halilaj, and Juergen Luettin

IEEE Robotics and Automation Letters, 2024

arXiv Bib

@article{sun2024semanticformer,
  title = {Semanticformer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction Using Knowledge Graphs},
  author = {Sun, Zhigang and Wang, Zixu and Halilaj, Lavdim and Luettin, Juergen},
  journal = {IEEE Robotics and Automation Letters},
  volume = {9},
  number = {9},
  pages = {7381--7388},
  year = {2024},
}