1 citations · 1 across the 11 of their papers we have counts for
12 papers
Remembering What Matters for Long-Horizon Robotic World Models
Hao Wu, Tianyu Shao, Shengju Qian +10
Autoregressive robot video world models based on discrete visual tokens remain unreliable over long rollout horizons. Retaining the entire generated history does not necessarily im…
PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing
Donghao Zhou, Jia-Hui Pan, Fan Zhang +6
Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely o…
Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking
Fan Zhang, Vireo Zhang, Shengju Qian +7
Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from…
RoboAlign-R1: Distilled Multimodal Reward Alignment for Robot Video World Models
Hao Wu, Yuqi Li, Yuan Gao +10
Existing robot video world models are typically trained with low-level objectives such as reconstruction and perceptual similarity, which are poorly aligned with the capabilities t…
Posterior-First Neural PDE Simulation: Inferring Hidden Problem State from a Single Field
Wenshuo Wang, Fan Zhang
Neural PDE simulators often receive only a single observed field at deployment. In this setting, a field-to-future predictor can collapse distinct latent problem states into the sa…
OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning
Hao Wu, Yongheng Zhang, Yuan Gao +7
Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by Partial Di…
NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics
Hao Wu, Yuan Gao, Fan Xu +4
High-precision scientific simulation faces a long-standing trade-off between computational efficiency and physical fidelity. To address this challenge, we propose NeuralOGCM, an oc…
Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models
Hao Wu, Yuan Gao, Xingjian Shi +9
To address the dual challenges of inherent stochasticity and non-differentiable metrics in physical spatiotemporal forecasting, we propose Spatiotemporal Forecasting as Planning (S…
Differential-Integral Neural Operator for Long-Term Turbulence Forecasting
Hao Wu, Yuan Gao, Fan Xu +5
Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applications ranging from climate modeling to a…
FourierFlow: Frequency-aware Flow Matching for Generative Turbulence Modeling
Haixin Wang, Jiashu Pan, Hao Wu +2
Modeling complex fluid systems, especially turbulence governed by partial differential equations (PDEs), remains a fundamental challenge in science and engineering. Recently, diffu…
CellVerse: Do Large Language Models Really Understand Cell Biology?
Fan Zhang, Tianyu Liu, Zhihong Zhu +7
Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for underst…
Advanced Long-term Earth System Forecasting
Hao Wu, Yuan Gao, Ruijian Gou +30
Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simu…