Chenyang Gu

VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

Junxiang Xu, Ruisi Wang, Fanyi Pu, et al.

MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models

Chenyang Gu, Jiahao Cheng, Meicong Zhang, et al.

MoRI is a framework that teaches AI language models to generate better scientific ideas by learning to reason from research motivations to actual methods, rather than just mixing existing concepts together. It uses a two-part training approach: first teaching the model to identify research motivations, then using reinforcement learning to ensure the generated ideas are technically rigorous and scientifically sound. The results show this method produces more novel, rigorous, and feasible scientific proposals compared to existing AI approaches.