Research Interests
My primary research interest lies in Machine Learning Systems (MLSys).
On the algorithmic side, I develop efficient LLM inference algorithms, including sparsity, parallel generation, and speculative decoding. On the system side, I design high-performance LLM serving systems, with work ranging across the full stack, including computation–I/O overlapping, scheduler design, Torch compilation, and kernel optimization.
I do both research and engineering.
|
News
- [Mar 2026] Incoming PhD at Princeton CS
- [Mar 2026] Strata and Prism accepted at OSDI 2026
- [Feb 2026] Piecewise CudaGraph set as default in SGLang
- [Aug 2025] Multiverse accepted at NeurIPS 2025 Spotlight
|
Publications
Selected PaperList /
Full PaperList.
* denotes equal contribution.
|
Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
Xinyu Yang*, Yuwei An*, Hongyi Liu, Tianqi Chen, Beidi Chen
NeurIPS 2025 Spotlight
paper /
project page
|
|
LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
Yihua Cheng*, Yuhan Liu*, Jiayi Yao*, Yuwei An, Xiaokun Chen, Shaoting Feng, Yuyang Huang, Samuel Shen, Kuntai Du, Junchen Jiang
paper /
project page
|
|
Strata: Hierarchical Context Caching for Long Context Language Model Serving
Zhiqiang Xie, Ziyi Xu, Mark Zhao, Yuwei An, Vikram Sharma Mailthody, Scott Mahlke, Michael Garland, Christos Kozyrakis
OSDI 2026
paper
|
|
HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse
Yuwei An, Yihua Cheng, Seo Jin Park, Junchen Jiang
paper
|
|
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Atharva Naik, Prakam, Yash Mathur, Darsh Agrawal, Manav Kapadnis, Yuwei An, Clayton Marr, Carolyn Rose, David Mortensen
ACL 2026 Findings
paper
|
|
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining
Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen, Yukuo Cen, Jifan Yu, Yelin Chen, Lulu Wang, Qingfei Zhao, Yuqing Cheng, Tianyi Han, Yuwei An, Dan Zhang, Weng Lam Tam, Kun Cao, Yunhe Pang, Xinyu Guan, Huihui Yuan, Jian Song, Xiaoyan Li, Yuxiao Dong, Jie Tang
KDD 2024
paper
|
|
IFMoE: An Inference Framework Design for Fine-grained MoE
Yuwei An, Zhuoming Chen, Beidi Chen
NeurIPS 2024 MLSys Workshop
paper
|
|
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
Zhaoyang Lyu*, Jinyi Wang*, Yuwei An, Ya Zhang, Dahua Lin, Bo Dai
CVPR 2023
paper
|
|
SGLang
SGLang is a high-performance serving framework for large language models and vision-language models.
Working on: Torch Compile Backend, Piecewise CudaGraph, HiCache.
|
|
|
LMCache
LMCache is a LLM serving engine extension to reduce TTFT and increase throughput, especially under long-context scenarios.
Working on: SGLang Support, Multi Process KV Engine.
|
|