Hi! I am a PhD student at LMU Munich supervised by Prof. Dr. Volker Tresp. Prior to this, I obtained my M.Sc. in Data Engineering and Analytics at the Technical University of Munich, and my B.E. in Computer Science and Technology at Xi'an Jiaotong University.
My research interests lie in understanding the capabilities and limitations of language models, and from there developing new methods and solutions. I am currently working on projects related to recursive self-improvement, architectural designs, and mechanistic interpretability of language models. We are open to new collaboration opportunities. Please feel free to reach out if you are interested.

Selected Publications

binomial ordering preference figure
Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
Zhiqing Yang*, Yilun Liu*, Yunpu Ma, Volker Tresp, Hinrich Schütze (*equal contribution)
arXiv preprint [Paper][Code]
  • Evaluation protocol of binomial ordering preference for LLMs, with a dataset of 600 binomial pairs across 8 languages
  • LLMs behaviorally align with the empirically preferred direction more reliably than the strength, and that strength can be representationally located and manipulated
SIREN overview figure
LLM Safety From Within: Detecting Harmful Content with Internal Representations
Difan Jiao, Yilun Liu, Ye Yuan, Zhenwei Tang, Linfeng Du, Haolun Wu, Ashton Anderson
ACL 2026 Main Conference [Paper][Code][Poster]
  • SIREN, a plug-and-play component harnessing LLM internal representations for harmfulness detection
  • Outperforming dedicated safety guardrails in performance, generalization, and efficiency
Routing-Free MoE overview figure
Routing-Free Mixture-of-Experts
Yilun Liu*, Jinru Han*, Sikuan Yan, Volker Tresp, Yunpu Ma (*equal contribution)
arXiv preprint [Paper][Code]
  • Routing-Free MoE architecture, eliminating routers, Softmax, TopK, and hard-coded load balancing
  • Unified, adaptive load-balancing framework jointly optimizing token- and expert-balancing
  • Improved performance and efficiency over baselines
PERFT performance figure
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
Yilun Liu*, Yunpu Ma*, Shuo Chen, Zifeng Ding, Bailan He, Zhen Han, Volker Tresp (*equal contribution)
EACL 2026 Findings [Paper][Code][Poster]
  • Dynamics between memory vectors in experts and expert vectors in routers when PEFTing to MoE LLMs
  • Unified framework for integrating PEFT with MoE LLMs
  • PERFT, a family of effective and scalable adaptation strategies
SPIN aggregated feature analysis figure
SPIN: Sparsifying and Integrating Internal Neurons in Large Language Models for Text Classification
Difan Jiao*, Yilun Liu*, Zhenwei Tang, Danial Matter, Jürgen Pfeffer, Ashton Anderson (*equal contribution)
ACL 2024 Findings [Paper][Code][Poster]
  • Lightweight plug-and-play text classification frameworkusing LLM internal representations
  • Superior performance, efficiency, and interpretability compared to conventional methods

Educational Background

10.2025 – present
Ph.D. Student (in progress)
Institut für Informatik, Ludwig-Maximilians-Universität München
Supervisor: Prof. Dr. Volker Tresp
10.2021 –
M.Sc. Data Engineering and Analytics
Department of Informatics, Technical University of Munich
Munich, Bavaria, Germany
09.2017 – 07.2021
B.E. Computer Science and Technology
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University
Xi'an, Shaanxi, China
09.2016 – 07.2017
Pre-university Education, Honors Youth Program
Qian Xuesen Honors College, XJTU & Tianjin Nankai High School
Xi'an, Shaanxi; Tianjin, China

Academic Service

Reviewer ICLR (2025, 2026) · COLM (2025, 2026) · ACL Rolling Review (multiple cycles) · IC2S2 (2025)