32 citations · 61 across the 17 of their papers we have counts for
18 papers
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…
Audio-Visual World Models: Learning Physically Grounded Multisensory Dynamics
Jiahua Wang, Leqi Zheng, Jialong Wu +2
World models simulate environmental dynamics to enable embodied agents to plan and reason about future states. While real-world perception is inherently multimodal, existing approa…
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…
iVideoGPT: Interactive VideoGPTs are Scalable World Models
Jialong Wu, Shaofeng Yin, Ningya Feng +4
World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. However, the high demand for interac…
CompilerDream: Learning a Compiler World Model for General Code Optimization
Chaoyi Deng, Jialong Wu, Ningya Feng +2
Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depends on the selection and ordering of the…
HarmonyDream: Task Harmonization Inside World Models
Haoyu Ma, Jialong Wu, Ningya Feng +5
Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasse…
Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
Jialong Wu, Haoyu Ma, Chaoyi Deng +1
Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pr…
CLIPood: Generalizing CLIP to Out-of-Distributions
Yang Shu, Xingzhuo Guo, Jialong Wu +3
Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning. Contrastive language-image pr…
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Yiwen Qiu, Jialong Wu, Zhangjie Cao +1
Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage…
Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu, Jialong Wu, Jiehui Xu +2
Transformers based on the attention mechanism have achieved impressive success in various areas. However, the attention mechanism has a quadratic complexity, significantly impeding…
Supported Policy Optimization for Offline Reinforcement Learning
Jialong Wu, Haixu Wu, Zihan Qiu +2
Policy constraint methods to offline reinforcement learning (RL) typically utilize parameterization or regularization that constrains the policy to perform actions within the suppo…