I built hands-on experience in scaling the agentic abilities of LLM/VLM, especially in:
Computer-use agents, including coding agents for software automation like PS and Blender, skill-driven tool use and GUI agents (e.g., IR3D, JarvisArt, JarvisIR, Seed1.8, UI-TARS2, etc.).
Self-evolving agent harness for data flywheels, rubric generation, skill evolution and model recursive self-improvement (e.g., Doubao RSI flywheel, etc.).
I did internships at ByteDance Seed, Tencent AI, Ant Ling, Hedra AI, etc. I also visited UT Austin and UMD for research. 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).
An agentic inverse-rendering framework that closes the loop from visual understanding to structured code generation, Blender execution, and environment feedback.
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.
Beyond WorkReading: I dedicate substantial time to reading, especially history, philosophy, and sociology, which shapes my perspective on what AGI should be from first principles.
Investment: Investment is real-world RL: returns provide fast feedback to iteratively improve individual decision policy. Recently, I am fascinated by the idea that how to (i) build benchmarks for LLMs that quantify real-world investment utility (in the similar spirit of GPT-5.2's gdpeval benchmark), and (ii) extending quantitative financial metrics to more general event and trend forecasting.
Scholar
