Jinze Li
Hi, I’m Jinze Li (李金泽), a PhD candidate at The University of Hong Kong.
My broader vision is to bring LLMs from research advances to dependable real-world systems, pursued along two complementary fronts: improving inference efficiency for cost-effective deployment, and empowering long-horizon agents through persistent memory and self-evolution.
Current research interests include:
- Speculative Decoding — accelerating LLM inference by drafting and verifying tokens efficiently.
- Agent Memory & Self-evolution — enabling agents to handle long-horizon interactions and improve themselves over time.
- LLM Post-training — SFT, RL, and decoding-time techniques to align and enhance model behavior.
I have been fortunate to do research internships at SenseTime, Huawei, ByteDance, AMD, and Ant Group (the Ant Star Program).
education
- 2023 – 2027 · Ph.D., 香港大学 / The University of Hong Kong, Hong Kong SAR, China
- 2020 – 2023 · M.Sc., 中国科学院大学 / University of Chinese Academy of Sciences, Beijing, China
- 2016 – 2020 · B.Eng., 大连理工大学 / Dalian University of Technology, Dalian, China
news
| May 15, 2026 | Our paper OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory was accepted to ACL 2026 (Main Conference)! |
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| Jan 22, 2026 | Our paper Training-Free Loosely Speculative Decoding (FLy) was accepted to ICLR 2026! |
| May 01, 2025 | Our paper Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding was accepted to ICML 2025! |
| Sep 01, 2023 | Started my PhD journey at The University of Hong Kong! |
selected publications
- Preprint