9 papers
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
Xinran Feng, Yi Xie, Chao Zhang +4
Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label qua…
Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
Ruikun Li, Huandong Wang, Jingtao Ding +3
Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce ap…
WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network
Ruikun Li, Jiazhen Liu, Huandong Wang +2
Modeling stochastic dynamics from discrete observations is a key interdisciplinary challenge. Existing methods often fail to estimate the continuous evolution of probability densit…
Sparse Diffusion Autoencoder for Test-time Adapting Prediction of Complex Systems
Jingwen Cheng, Ruikun Li, Huandong Wang +1
Predicting the behavior of complex systems is critical in many scientific and engineering domains, and hinges on the model's ability to capture their underlying dynamics. Existing…
Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Shihe Zhou, Ruikun Li, Huandong Wang +1
Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. How…
Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder
Ruikun Li, Jingwen Cheng, Huandong Wang +2
Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the…