1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
PhysFieldBench: Can Multimodal Models Understand Physical Fields?
Yuezhou Ma, Huikun Weng, Jialong Wu +5
Multimodal large language models (MLLMs) are increasingly envisioned as core components of scientific and engineering agents, yet their ability to interpret physical fields remains…
Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning
Hanyang Wang, Yimo Cai, Weiliang Chen +14
Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can recognize and explain diverse…
TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
Zhi Chen, Yuxuan Wang, Jialong Wu +5
High-quality time series forecasting is pivotal for real-world decision-making. However, traditional point-wise metrics often fail to reveal complex temporal patterns and align poo…
JEPA-VLA: Video Predictive Embedding is Needed for VLA Models
Shangchen Miao, Ningya Feng, Jialong Wu +4
Recent vision-language-action (VLA) models built upon pretrained vision-language models (VLMs) have achieved significant improvements in robotic manipulation. However, current VLAs…
Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models
Jialong Wu, Xiaoying Zhang, Hongyi Yuan +7
Humans construct internal world models and reason by manipulating the concepts within these models. Recent advances in AI, particularly chain-of-thought (CoT) reasoning, approximat…