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, including: (i) Coding agents for general computer use (Claw-Eval-Live, Seed Auto R&D Coding Agent, BlenderAgent, LightroomPSAgent, On-Policy Data Evolution, Hybrid CLI-GUI Harness) and (ii) Visual coding agents for design artifacts (IRBlender, JarvisArt, JarvisIR, Seed code-to-chart/web). These experiences span building frontier agent scaffolds/harnesses, task/verifier/benchmark design, trajectory interaction/distillation, and mid-training/SFT/RL for agents.
Throughout my fulfilling years in Master & Ph.D., I have gained extensive industry exposure through internships at ByteDance Seed, Tencent AI, Ant Ling, Giga AI, AMD, Hedra AI, JoinQuant, etc. Across Agent, World Model, and Quant, I've learned to stay open and to spot cross-disciplinary opportunities. I also worked remotely with UT Austin and UMD on research internships.
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).
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Blogs
🚀 Selected Work
* 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]
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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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 Tools
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
LLM Agent
- ByteDance Seed: Agent for CLI, visual coding and GUI (Seed-1.6 / Seed-1.8 / UI-TARS-2)
- Tencent AI Lab: Agent execution loop, code generation and environment-grounded RL (IRBlender-Bench)
- Ant Ling: Agent memory, context compression and output verification (Ling-Pilot)
- AMD: Visual token compression for efficient VLMs
World Model
- Giga AI: World-model agent for 3D environments and tool-augmented training (Giga Brain-0)
- Hedra AI: Omnimodal attention architecture for commercial digital-human generation (Hedra Character-3)
Quant
- JoinQuant: RL post-training of LLMs for quant alpha-factor mining; agent evaluation on financial data
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ScholaGO (Co-founder): LLM4Education Startup
Co-founded ScholaGO Education Technology Company Limited (学旅通教育科技有限公司) to build LLM-powered education products that turn static content into immersive, interactive, multimodal learning experiences. Grateful to receiving funding from 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: "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
- Crafting Agent Artifacts: I am drawn to building agent tools hands-on because vibe coding creates a fast positive feedback loop. It offers the most concrete sense of different models' capability boundaries. Agents evolve so fast that "one AI month feels like one human year," so I try to capture ideas quickly and turn them into usable things. I naturally think in an AI-native way.
- Reading: I read history, philosophy, and sociology over the long term. I enjoy reasoning from first principles to anticipate future trends and make my bets accordingly.
- Stock Investment: I see investing as real-world RL: making decisions, receiving feedback, and refining strategies. To me, many choices are bets; as agents dramatically amplify productivity, what matters shifts to where we "invest" our attention.
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