Chenxin Li | 李宸鑫
Hi! I'm Chenxin Li, a final-year Ph.D. at The Chinese University of Hong Kong (CUHK). I earned my master's & bachelor's degrees from Xiamen University, along with a second major in Economics.
My recent interest lies in scaling digital automation agents, especially CLI agents for (i) computer, phone, and software use (Claw-Eval-Live, PhoneBuddy, PhoneHarness, PhoneWorld, PhoneSafety, PhonePrivacy, BlenderAgent, PSAgent) and (ii) AI-for-AI workflows (Auto R&D CLI Agent, ODE).
I interned at ByteDance Seed, Tencent Hunyuan, Tencent AI Lab, Ant Ling, etc. I win PaperDigest Most Influential Paper Top #1 at AAAI'25. I explore agent 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 |
X |
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 [Org]
I build agent tools that help people work smarter by turning repetitive, inefficient workflows into AI-native experiences.
More: LinMem: lightweight agent memory; AI-native Slides: agent-friendly slides; OpenReview Agent: OpenReview automation.
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IRBlender: 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, Intern: Visual coding agent and CLI agent
- Tencent Hunyuan, Intern: Computer-use and phone-use agent
- Tencent AI Lab, Intern: Tool-use and coding agents for Blender manipulation
- Ant Ling, Intern: Agent memory and context compression
- ScholaGO, Co-founder, 2024: Agent for Education; co-founded ScholaGO Education Technology Company Limited (学旅通教育科技有限公司) to build LLM-powered multimodal learning products, supported by HKSTP, HK Tech 300, and Alibaba Cloud
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🎓 Professional Activities
- Workshop Organizer: AIM-FM: Advancements In Foundation Models Towards Intelligent Agents (NeurIPS 2024)
- Talks: 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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