Jingjie Ning

Nine out of ten AI-selected modeling changes in materials science remain effective when tested on unseen data, showcasing the potential for reusable AI-driven discoveries.

Diminishing returns in parallel sampling can be overcome by generating diverse initial queries, leading to substantial performance gains in multi-hop question answering.

Continual learning for LLM agents hits a wall: scaling models doesn't reliably improve skill generation, and self-feedback can lead to recursive drift.