11 papers
World Models for Embodied Intelligence: From Plausible to Controllable to Actionable
Nanjie Yao, Hao Wang, Chong Cheng +10
World models connect perception and decision-making in embodied intelligence by maintaining hidden state, anticipating consequences, comparing interventions, and adapting when exec…
MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models
Zhekai Wang, Haoxiang Huang, Xiang Liu +6
Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconstrained visual features. We i…
ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation
Zhikang Chen, Yue Wang, Sen Cui +4
Electrocardiogram (ECG)-based models have achieved strong performance in diagnostic tasks, yet they remain limited in modeling how cardiac dynamics evolve under external interventi…
Reversible Diffusion Decoding for Diffusion Language Models
Xinyun Wang, Min Zhang, Sen Cui +4
Diffusion language models enable parallel token generation through block-wise decoding, but their irreversible commitments can lead to stagnation, where the reverse diffusion proce…
From Generative Engines to Actionable Simulators: The Imperative of Physical Grounding in World Models
Zhikang Chen, Tingting Zhu
A world model is an AI system that simulates how an environment evolves under actions, enabling planning through imagined futures rather than reactive perception. Current world mod…
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
Zhikang Chen, Sen Cui, Deheng Ye +5
Model merging has emerged as a practical paradigm for integrating multiple independently trained models into a single model without joint retraining. Previous studies have demonstr…
Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport
Zecheng Pan, Zhikang Chen, Ding Li +9
Merging models fine-tuned for different tasks into a single unified model has become an increasingly important direction for building versatile, efficient multi-task systems. Exist…
Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought
Zhikang Chen, Sen Cui, Deheng Ye +3
Large Language Models (LLMs) have demonstrated strong reasoning capabilities through \emph{Chain-of-Thought} (CoT) prompting, which enables step-by-step intermediate reasoning. How…
Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
Danni Yang, Zhikang Chen, Sen Cui +6
Federated continual learning (FCL) has garnered increasing attention for its ability to support distributed computation in environments with evolving data distributions. However, t…
CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
Min Zhang, Yuyin Wang, Zhongxiang Dai +4
Recent advances in pre-training vision-language models (VLMs), e.g., contrastive language-image pre-training (CLIP) methods, have shown great potential in learning out-of-distribut…
Learning without Isolation: Pathway Protection for Continual Learning
Zhikang Chen, Abudukelimu Wuerkaixi, Sen Cui +10
Deep networks are prone to catastrophic forgetting during sequential task learning, i.e., losing the knowledge about old tasks upon learning new tasks. To this end, continual learn…