About Me

I’m a Computer Science Ph.D. student at UC Santa Cruz, advised by Liting Hu. I’m also a core research engineer at Tensormesh and a core committer on LMCache and a maintainer of the vLLM production stack.

My research is about building the next-generation agent infrastructure where agent execution semantics and state are first-class systems abstractions. By exposing and learning these semantics, systems can make better decisions about resource scheduling, state management, computational reuse, and eventually large-scale agent learning.

I’ve worked on:

Previously, I was a software engineer at Huawei and a research intern at Bosch. I received my B.S. from Huazhong University of Science and Technology in 2023, and spent a summer at the National University of Singapore.

News
2026
A Policy-Driven Runtime Layer for Agentic LLM Serving is accepted to APSys 2026.Accepted Aug
CacheScout is on arXiv — learning agent execution transitions online to drive KV-cache eviction and prefetching, with 10–18 pp higher hit rate and up to 57% higher peak throughput on vLLM. Aug
Two preprints out: A Policy-Driven Runtime Layer for Agentic LLM Serving, and VeriCache, which turns lossy KV cache into bit-exact inference. May
2025
EVICPRESS is on arXiv — jointly optimizing KV-cache eviction and compression for up to 2.19× faster TTFT at equal generation quality. Dec
The LMCache system paper is public. LMCache is now past 11k stars and is the KV-cache layer behind the vLLM production stack. Oct
Presented Enabling Fairness Across Multi-modal and Multi-agent Applications at IEEE EDGE 2025. Jul
Open Source
Publications

Full list on Google Scholar.

2026

2025

Enabling Fairness Across Multi-modal and Multi-agent Applications

Rui Zhang, Liting Hu

IEEE EDGEInternational Conference on Edge Computing and Communications

Services

Journal Reviewing

  • Future Generation Computer Systems (Elsevier) — Reviewer

Teaching

  • Teaching Assistant, CSE 134: Embedded Operating Systems — UC Santa Cruz