FACTS: A Factored State-Space Framework for World Modelling
Li Nanbo Firas Laakom Yucheng XU Wenyi Wang Jürgen Schmidhuber
World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and temporal structures, particularly in scenarios requiring long-term high-dimensional sequence modelling. To address these issues, we propose a novel recurrent framework, the FACTored State-space (FACTS) model, for spatial-temporal world modelling. The FACTS framework constructs a graph-structured memory with a routing mechanism that learns permutable memory representations, ensuring invariance to input permutations while adapting through selective state-space propagation. Furthermore, FACTS supports parallel computation of high-dimensional sequences. We empirically evaluate FACTS across diverse tasks, including multivariate time series forecasting, object-centric world modelling, and spatial-temporal graph prediction, demonstrating that it consistently outperforms or matches specialised state-of-the-art models, despite its general-purpose world modelling design.
sequence modelling, spatial-temporal modelling, world modelling, multivariate time-series forecasting, object-centric representation learning, unsupervised learning, self-supervised learning
We propose the FACTS model, a novel factored state-space framework for spatial-temporal world modelling, which outperforms or matches state-of-the-art models in tasks like time series forecasting and object-centric world modelling.