20 papers
Informal Learning Emerges in Everyday Human-LLM Interaction
Zixin Chen, Haotian Li, Ziang Xiao +2
As LLMs become increasingly capable of completing tasks for users, a central concern is that everyday AI use may become primarily cognitive offloading, eroding the opportunities th…
Multi-agent AI systems outperform human teams in creativity
Tiancheng Hu, Yixuan Jiang, Haotian Li +5
Although artificial intelligence (AI) now matches or exceeds human performance across numerous cognitive tasks, creativity remains a highly contested frontier. As AI systems based…
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations
Nanxu Gong, Zixin Chen, Haotian Li +5
Improving the Theory of Mind (ToM) capability of Large Language Models (LLMs) is crucial for effective social interactions between these AI models and humans. However, the existing…
Social-R1: Towards Human-like Social Reasoning in LLMs
Jincenzi Wu, Yuxuan Lei, Jianxun Lian +5
While large language models demonstrate remarkable capabilities across numerous domains, social intelligence - the capacity to perceive social cues, infer mental states, and genera…
From Passive Consumption to Active Interaction: Exploring Interactive LLM Scaffolding to Support Learning Engagement
Zixin Chen, Haotian Li, Zhe Liu +2
Large Language Models (LLMs) are increasingly used as learning companions, providing scaffolded explanations, hints, or step-by-step guidance. However, in current LLM-based learnin…
To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
Nanxu Gong, Haotian Li, Sixun Dong +3
Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions, which is essential for natural social interaction. Although re…