Feng Xiong

Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the inherent text-dominated nature of VLMs often leads to insufficient visual faithfulness, characterized by sparse attention activation to visual toke...

Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain limited in leveraging geographic signals that are crucial in mobility and local-services scenarios. Here, we present Reasoning Over Space (ROS), a fra...

Recent advances in Large Language Models (LLMs) have shifted affective computing from direct label prediction to deliberative reasoning, with Reinforcement Learning (RL) emerging as a promising means of cultivating such capabilities. However, existing RL-based methods are largely confined to sing...