1 citations · 2 across the 8 of their papers we have counts for
10 papers
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…
Foresight Diffusion: Improving Sampling Consistency in Predictive Diffusion Models
Yu Zhang, Xingzhuo Guo, Haoran Xu +2
Diffusion and flow-based models have enabled significant progress in generation tasks across various modalities and have recently found applications in predictive learning. However…
Vid2World: Crafting Video Diffusion Models to Interactive World Models
Siqiao Huang, Jialong Wu, Qixing Zhou +2
World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. How…
RLVR-World: Training World Models with Reinforcement Learning
Jialong Wu, Shaofeng Yin, Ningya Feng +1
World models predict state transitions in response to actions and are increasingly developed across diverse modalities. However, standard training objectives such as maximum likeli…
Trajectory World Models for Heterogeneous Environments
Shaofeng Yin, Jialong Wu, Siqiao Huang +4
Heterogeneity in sensors and actuators across environments poses a significant challenge to building large-scale pre-trained world models on top of this low-dimensional sensor info…
Long-Sequence Recommendation Models Need Decoupled Embeddings
Ningya Feng, Junwei Pan, Jialong Wu +6
Lifelong user behavior sequences are crucial for capturing user interests and predicting user responses in modern recommendation systems. A two-stage paradigm is typically adopted…