World Machine: Towards Generative World Modeling for Time-Series
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Computer Science > Machine Learning
Title:World Machine: Towards Generative World Modeling for Time-Series
Abstract:World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured and generalizable way. We present World Machine, a generative world-modeling architecture for time series. It is a transformer-based architecture with latent states that enables adaptation to different amounts of observed data and contexts. This shows an improvement over traditional transformers, which have a computational and memory cost that scales quadratically with the context. Experiments on a proposed synthetic dataset, Toy1D, validate the approach's feasibility, demonstrate capabilities not found in conventional transformers, and highlight the contributions of each component of the training protocol.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.23025 [cs.LG] |
| (or arXiv:2605.23025v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23025
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Elton Cardoso Do Nascimento [view email][v1] Thu, 21 May 2026 20:48:51 UTC (5,591 KB)
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