ChengXing Xie

ChengXing Xie

Artificial Intelligence Student

Xidian University


I am currently pursuing my undergraduate degree in Computer Science at Xidian University. I began with a nine-month internship at Sensetime during my first year, serving as an algorithm intern. In my second year, I focus on face restoration, culminating in the publication of related papers at CVPRW. Currently, I delved into the area of LLM Agents. This interest led me to an enriching experience as a visiting student at KAUST from July.2023 to Jan.2024, where I further honed my skills and knowledge in LLM Agents. Looking ahead, I am committed to continuing my exploration of LLMs and plan to pursue a PhD. For further information or collaboration opportunities, feel free to reach out to me at

  • LLM Agents
  • Multi-Modeling
  • Generative Model
  • B.E in Computer Science, 2021-now

    Xidian University

  • Visiting Student, 2023.7-2024.1

    KAUST (King Abdullah University of Science and Technology)

Recent Publications

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(2024). Can Large Language Model Agents Simulate Human Trust Behaviors?.

PDF Code

(2023). TFRGAN Leveraging Text Information for Blind Face Restoration with Extreme Degradation.



Image and Video Understanding Lab
Visiting Student in KAUST (IVUL, Advisor PhD Guohao Li, Prof. Bernard Ghanem)
July 2023 – January 2024 Thuwal, Saudi Arabia
Working on the Camel project, probing agent trust behavior. The paper titled “Can Large Language Models Simulate Human Trust Behaviors?” has been produced.
Photoelectric Imaging and Brain-like Perception Lab
Researcher in Photoelectric Imaging and Brain-like Perception Lab(Adivsor Weisheng Dong)
November 2022 – May 2023 Xian China
Focusing on the face restoration task, research results have been published in CVPRW (MULA).
Algorithm Intern
February 2022 – November 2022 Xian China

Responsibilities include:

  • developing
  • Modelling
Xidian University
Undergraduate of Xidian University
September 2021 – June 2025 Xian China
Computer Science Undergraduate Student (AI Researcher)

Learning Courses

Stanford CS236 Deep Generative Models
Learning Lower-Level Vision
UCSB Game 101 Introduction to Modern Computer Graphics
A preliminary understanding of Computer graphics
CS231n: Deep Learning for Computer Vision
Getting Started with Deep Learning


TFRGAN Leveraging Text Information for Blind Face Restoration with Extreme Degradation
Using text information to achieve better res in BFR task.(This work has been submitted to CVPR workshop)
Sleep State Analysis
Using AI to monitor human’s sleep states.(SenseTime Intern)
Cough Detection
Using CNN to do binary classification for elder’s sounds. (SenseTime Intern)
Audio Clip
Using Audio and Text do Clip training.(SenseTime Intern)
Animation Picture Generation
Using a deep convolutional generative adversarial network (DCGAN) generate anime-style avatar images.


If you want to contact me, free free to contact me at any time