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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.23025 |
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| _version_ | 1866918517695053824 |
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| author | Nascimento, Elton Cardoso do Simões, Alexandre da Silva Colombini, Esther Luna Gudwin, Ricardo Ribeiro Costa, Paula Dornhofer Paro |
| author_facet | Nascimento, Elton Cardoso do Simões, Alexandre da Silva Colombini, Esther Luna Gudwin, Ricardo Ribeiro Costa, Paula Dornhofer Paro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | World Machine: Towards Generative World Modeling for Time-Series Nascimento, Elton Cardoso do Simões, Alexandre da Silva Colombini, Esther Luna Gudwin, Ricardo Ribeiro Costa, Paula Dornhofer Paro Machine Learning 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. |
| title | World Machine: Towards Generative World Modeling for Time-Series |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.23025 |