Liwei Jiang

Moving beyond passive prompt-and-generate workflows to genuine human-AI collaboration requires solving open technical challenges in mutual agency, real-time latent steering, and dynamic evaluation.

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.

LLMs often withhold helpful information due to misinterpreting user intent, but multi-turn conversations can unlock utility—at a cost of new failure modes like "utility lock-in" and "unsafe recovery" that single-turn benchmarks miss.

LLMs can be steered more effectively by viewing activation manipulation through the lens of ordinary differential equations and control theory, leading to significant gains in alignment benchmarks.