AI RESEARCH
World Machine: Towards Generative World Modeling for Time-Series
arXiv CS.LG
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ArXi:2605.23025v1 Announce Type: new 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.