Chenxin Li | 李宸鑫
Hi! I'm Chenxin Li, a final-year Ph.D. candidate at The Chinese University of Hong Kong (CUHK). Before CUHK, I received my master's and bachelor's degrees from Xiamen University, along with a second bachelor's degree in Economics.
My recent interest lies in scaling LLM/VLM agents for digital automation, especially CLI agents for executable computer-use, phone-use and AI-for-AI workflows (Claw-Eval-Live, Auto R&D CLI Agent, ODE, PhoneHarness, PhoneWorld, MyPhoneBench) and visual coding agents for design artifacts (Code-to-chart/web, BlenderAgent, PSAgent). I develop harness-aware trajectory training and verifier-grounded RL & evaluation.
I have interned at ByteDance Seed, Tencent Hunyuan, Tencent AI Lab, Ant Ling, etc. I have won PaperDigest Most Influential Paper Top #1 at AAAI'25. In my spare time, I explore agent-native workflows through 📝 Blogs and 🛠️ OpenAgentLabs. I anticipate graduating in the summer of 2026 and am interested in industrial positions (Profile). Please feel free to reach out via email (chenxinli@link.cuhk.edu.hk) or WeChat (jasonchenxinli).
LinkedIn |
 Scholar |
GitHub |
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Blogs |
OpenAgentLabs
🚀 Selected Work [Google Scholar]
* Equal contribution, † Project Leader, ‡ Corresponding author
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Claw-Eval (Live): A Live Agent Benchmark for Evolving Real-World Workflows
Chenxin Li†, Zhengyang Tang, Huangxin Lin, Yunlong Lin, Shijue Huang, Shengyuan Liu, Bowen Ye, Rang Li, Lei Li, Benyou Wang, Yixuan Yuan
[Project] [Paper] [Code] [AI 生成未来 | PaperAgent]
A live workflow-agent benchmark with refreshable demand signals and verifiable execution traces; 105 tasks across 22 categories, 13 frontier models, top model passes only 66.7%.
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ODE: Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Shijue Huang, Hangyu Guo, Chenxin Li, Junting Lu, Xinyu Geng, Zhaochen Su, Zhenyu Li, Shuang Chen, Hongru Wang, Yi R Fung
[Project] [Paper] [Code]
An on-policy data-evolution framework for visual-native multimodal deep-search agents, using agent rollouts to evolve training data and improve search behavior.
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🛠️ Open Agent Labs [Organization]
I build agent tools that help people work smarter by turning repetitive, inefficient workflows into AI-native experiences.
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IRBlenderAgent: Evaluating Vision-Language Model Scene Understanding as Agentic Inverse Rendering
Parker Liu*, Chenxin Li*, Zhengxin Li, Yipeng Wu, Wuyang Li, Zhiqin Yang, Zhenyuan Zhang, Yunlong Lin, Sirui Han, Brandon Y. Feng
NeurIPS 2025
[Project] [Paper] [Code]
An agentic inverse-rendering framework that closes the loop from visual understanding to structured code generation, Blender execution, and environment feedback.
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🧭 Selected Experience [LinkedIn]
- ByteDance Seed: Visual coding agent and CLI agent
- Tencent AI Lab: Tool-use and coding agents for Blender manipulation
- Ant Ling: Agent memory and context compression
- Giga AI: World model for embodied brains
- JoinQuant: Agentic RL for quant alpha-factor mining
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🎓 Professional Activities
- Workshop Organizer: AIM-FM: Advancements In Foundation Models Towards Intelligent Agents (NeurIPS 2024)
- Talks: "UKAN" at VALSE Summit (Jun 2025) and DAMTP, University of Cambridge (Jul 2024)
- Conference Reviewer: ICLR, NeurIPS, ICML, ACL, CVPR, ICCV, ECCV, EMNLP, AAAI, ACM MM
- Journal Reviewer: Nature Machine Intelligence, PAMI, TIP, DMLR, PR, TNNLS
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🌱 Hobbies Beyond Work
- Chat & Work with Agents: I build agent productivity tools through vibe coding and use agents as collaborators in daily work. I want to wire as much of my life and work as possible into agents to amplify what I can do and explore their evolving boundaries.
- Reading: I read history, philosophy, and sociology over the long term, which helps me reason from first principles and form long-horizon views on people, systems, and future trends.
- Stock Investment: I see investing as real-world RL: making decisions under uncertainty, learning from feedback, and refining strategies over time. In an agent-amplified world, the key question becomes where to allocate capital, attention, and time.
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