5 papers
Learning Physics-Grounded 4D Dynamics with Neural Gaussian Force Fields
Shiqian Li, Ruihong Shen, Junfeng Ni +3
Predicting physical dynamics from raw visual data remains a major challenge in AI. While recent video generation models have achieved impressive visual quality, they still cannot c…
Neural Force Field: Few-shot Learning of Generalized Physical Reasoning
Shiqian Li, Ruihong Shen, Yaoyu Tao +2
Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still strugg…
Learning to Plan with Personalized Preferences
Manjie Xu, Xinyi Yang, Wei Liang +2
Effective integration of AI agents into daily life requires them to understand and adapt to individual human preferences, particularly in collaborative roles. Although recent studi…
A simulation-heuristics dual-process model for intuitive physics
Shiqian Li, Yuxi Ma, Jiajun Yan +4
The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary l…
Probing and Inducing Combinational Creativity in Vision-Language Models
Yongqian Peng, Yuxi Ma, Mengmeng Wang +5
The ability to combine existing concepts into novel ideas stands as a fundamental hallmark of human intelligence. Recent advances in Vision-Language Models (VLMs) like GPT-4V and D…