Zora Zhiruo Wang

LLM tutors trained to adapt to individual student learning preferences achieve a 20.5pp performance boost and significantly outperform base models in human preference studies.

Personalized AI agents can achieve up to 20.9% better task success by learning from user feedback in real-time, reshaping the landscape of human-AI collaboration.

TMI uncovers the hidden structure of concurrent tasks in computer-use traces, achieving unprecedented accuracy in task model induction.

Closed-loop memory optimization can boost software engineering agents' success rates by over 5% while slashing computational costs by nearly 10%.

Stop guessing when humans want to take over: modeling user intervention styles in web agents boosts their usefulness by 26.5%.