FF-JEPA introduces a two-model hierarchical structure with an action-free latent planner to decompose long-horizon planning into short subgoals in latent world models.
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is currently fragmented, with disparate codebases, data pipelines, and evaluation protocols hindering reproducibility and fair comparison. Current practice is further limited by three key bottlenecks: fragile one-off codebases, slow video data loading, and the lack of standardized generalization benchmarks. We present stable-worldmodel (swm), an open-source platform for standardized and reproducible world modeling research and evaluation. It delivers (1) a high-performance Lance-based data layer with native support and conversion tools for MP4, HDF5, and LeRobot datasets, (2) clean, well-tested implementations of modern world model baselines and planning solvers, and (3) a broad suite of environments and tasks extended with controllable visual, geometric, and physical factors of variation for systematic in-silico evaluation of dynamics understanding, control performance, representation quality, and out-of-distribution generalization. By unifying the full pipeline under a single, scalable framework, \texttt{swm} dramatically reduces research overhead and accelerates trustworthy progress toward reliable world models.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
IMWM combines a world model with an intuition model from demonstrations to improve sample-based latent planning success rates over world-model-only baselines on pixel control tasks.
citing papers explorer
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FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
FF-JEPA introduces a two-model hierarchical structure with an action-free latent planner to decompose long-horizon planning into short subgoals in latent world models.
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IMWM: Intuition Models Complement World Models for Latent Planning
IMWM combines a world model with an intuition model from demonstrations to improve sample-based latent planning success rates over world-model-only baselines on pixel control tasks.