FACTS: A Factored State-Space Framework for World Modelling
Keywords: sequence modelling, spatial-temporal modelling, world modelling, multivariate time-series forecasting, object-centric representation learning, unsupervised learning, self-supervised learning
TL;DR: 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.
Abstract: 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.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 8227
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