I am an M.S. student in Information Management at the University of Illinois Urbana-Champaign (UIUC).

I work on ML systems for large-scale models: inference engines and expert streaming for MoE models, distributed training and rollout infrastructure, RL post-training systems, and GPU compiler and kernel-level correctness. I like to sit between the machine and the model — how weights are read from storage, how collectives and thread teams are sized, and whether a resource plan and the runtime agree about what will actually be built. I keep a research background in LLM reasoning, multimodal learning, and agent safety, and I bring an upstream-first habit to all of it: reproduce failures in real production stacks, then contribute fixes with regression tests and measured before/after numbers.

My research experience includes research at UIUC on efficient ML systems, the Tencent Youtu AI Lab on content-safety and multimodal research, the SUFE FinAI Center (advised by Prof. Liwen Zhang) on financial reasoning and agent safety, Shanghai Jiao Tong University (advised by Prof. Xiaodong Gu) on LLM for Code, and the Head Office of ICBC (Private Banking Department) on scientist-discovery agents.

🎓 I plan to apply to Ph.D. programs in ML systems for Fall 2028. I am happy to hear from faculty and students working on efficient inference, training infrastructure, or GPU kernels.

Research Interests

• ML Systems for Large Models — inference engines and expert streaming for MoE models, KV-cache and memory budgeting, attention and quantization kernels, performance profiling
• Distributed Training & Rollout — ZeRO-3, FSDP, Megatron-LM, Hybrid Engine, collective synchronization and deadlock debugging, RL rollout systems (verl, TRL)
• GPU Compiler & Kernel Correctness — MLIR passes, Triton/CUDA kernel-level debugging, reproducible regression tests

Selected Publications

Google Scholar profile

• FinVault: Benchmarking financial agent safety in execution-grounded environments — Zhi Yang^*, Runguo Li^*, Qiqi Qiang, et al. arXiv:2601.07853, 2026. arXiv

• The Last Mile to the Real World: Agent-Native Primitives as Reusable Building Blocks for Solving Real-World Tasks — Haibo Jin, Peng Kuang, Xiaopeng Yuan, …, Runguo Li, …, Haohan Wang arXiv:2611.07461, 2026. arXiv

• How Much Does Random Rotation Help GPTQ? Sharp Worst-Case Rate Gaps in the High-Rate Regime — Runguo Li arXiv:2611.01669, 2026. arXiv

• Weight Placement Is Not Enough: Synchronization in Multi-Node CPU Decoding of Ternary LLMs — Runguo Li, Dezhen Lu arXiv:2610.28620, 2026. arXiv

• Certified Sparse Attention via Perturbed Retrieval — Runguo Li arXiv:2610.24991, 2026. arXiv

• Spend Bytes on Breadth: Precision-Count Trade-offs for Decode-Time KV Compression in Long Chain-of-Thought Reasoning — Runguo Li arXiv:2610.05685, 2026. arXiv

Additional Publications

• What Sets the Serving Speed of Looped Language Models? Measurements and Optimizations Graded by Exactness — Dezhen Lu, Runguo Li, Zixiang Ni, et al. arXiv:2611.05150, 2026. arXiv

• Where KV-Cache Error Lives in Looped Language Models: A Diagnostic Study of Ouro and Huginn — Dezhen Lu, Runguo Li, Zixiang Ni, et al. arXiv:2610.41825, 2026. arXiv

• Prefix Keys and Values Matter Late: RIPE Caching for Self-Speculative Decoding in Looped Language Models — Dezhen Lu, Runguo Li, Zixiang Ni arXiv:2610.34436, 2026. arXiv

• ScaleRLT: Exact Log-Probabilities and Fast Deferred Rollouts for Reinforcement Learning on Looped Transformers — Dezhen Lu, Zixiang Ni, Runguo Li arXiv:2610.34427, 2026. arXiv

All publications

Open-Source Contributions (ML Systems)

26 public merged pull requests across 16 public upstream projects. Top five projects by GitHub stars: Kubernetes, vLLM, colibri, DeepSpeed, and LLVM. View all contribution links.

Get in Touch

• ✉️ Email: [email protected]
• 💬 WeChat: RunguoLi2004
• 🐙 GitHub: github.com/LiRunGuo
• 🎓 Google Scholar: Runguo Li
• 🆔 ORCID: 0009-0002-0832-6227
• 📄 Curriculum Vitae