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.
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
- Self-improving agents with additional comparative signals derived from the archive of past attempts
- Better performance and efficiency, with 35B open models outperforming 117× larger frontier models
- Generalizablity across benchmarks and models, yielding genuinely reusable and scalable scaffolds
- 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
arXiv preprint [Paper]
- Model-aware schedule construction via fiberwise optimal transport, combining prediction risk with kinetic action for closed-form time allocation, yielding consistent gains over strong baselines
- Normalized risk profiles and schedule deformations align across models, with a frozen analytic form retaining most gains
- 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
EMNLP 2026 Main Conference [Paper]
- Hierarchical multimodal memory with a coarse-to-fine pyramid structure for long-horizon video reasoning
- Structure-guided memory expansion with pruning for causally related but semantically distant events
- Consistent gains across long-video benchmarks, model scales, and question types
- SIREN, a plug-and-play component harnessing LLM internal representations for harmfulness detection
- Outperforming dedicated safety guardrails in performance, generalization, and efficiency
- 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
- 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)
10.2021 –
M.Sc. Data Engineering and Analytics
Department of Informatics, Technical University of Munich
Munich, Bavaria, Germany
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
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
Xi'an, Shaanxi; Tianjin, China
Academic Service
Reviewer
ICLR (2025, 2026) · COLM (2025, 2026) · ACL Rolling Review (multiple cycles) · IC2S2 (2025)