I am Qinchuan Cheng, an undergraduate in Automation at Xi’an Jiaotong University’s Qian Xuesen College. My research centers on Egocentric Compilation, LLM post-training and agents, multimodal foundation models, and AI for Science.
I study how agents can learn from real-world observations, compile first-person video into executable and verifiable environments, plan over long horizons, and recover from mistakes. My current work connects symbolic worlds, belief-state planning, branching experience, planner post-training, and agent-driven data synthesis.
Current Work
- Egocentric Compilation — A research line spanning A*STAR/NUS collaboration, Ego2World, EgoVine, and EgoHarness: compiling egocentric video into executable worlds, branching experience, and training data for embodied agents.
- LAW MASTER — Leading legal-agent development around evidence coverage, retrieval control, task decomposition, and multi-step reasoning.
Internships
Apple · AI Automation Specialist
Jun. 2026 – Sep. 2026
Built and deployed an O2O budget-allocation agent using forecasting, contextual bandits, strategy adjustment, and A/B testing. Packaged data analysis, budget diagnosis, and allocation decisions as DeepSeek Harness plugins; the agent is used in the department’s daily operations.
Peking University · Research Assistant, GoTim Project Group
Apr. 2026 – Aug. 2026
Developed the interaction agent and vision system for a tutoring robot, including system integration, gesture recognition, depth perception, relocalization, and projection interaction.
A*STAR, Singapore · Research Assistant
Dec. 2025 – May 2026
Collaborated with A*STAR and NUS researchers on the Egocentric Compilation line, compiling HD-EPIC annotations into executable environments for embodied planning and evaluation.
Snapshot
- Xi’an Jiaotong University — B.Eng. in Automation, 2023–2027; GPA 3.7/4.3; class rank 2
- UC Berkeley — Summer exchange program, 2025; A+ in AI & Scientific Engineering Applications and Academic English
- Yuejie Program Scholarship — selected 35 out of 3,500 students
- TOEFL — 100
Research Focus
- Egocentric Compilation — Executable worlds, world graphs, belief-state planning, branching experience, and egocentric data synthesis.
- Multimodal Foundation Models — Visual grounding, structured representations, and learning from real-world video.
- LLM Post-Training & Agents — Reinforcement learning, retrieval control, tool use, and recovery in multi-step tasks.