[2602.08968] stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
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Computer Science > Artificial Intelligence
arXiv:2602.08968 (cs)
[Submitted on 9 Feb 2026 (v1), last revised 17 Feb 2026 (this version, v2)]
Title:stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Authors:Lucas Maes, Quentin Le Lidec, Dan Haramati, Nassim Massaudi, Damien Scieur, Yann LeCun, Randall Balestriero<br>View a PDF of the paper titled stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation, by Lucas Maes and 6 other authors
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Abstract:World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond direct experience. Despite recent interest in World Models, most available implementations remain publication-specific, severely limiting their reusability, increasing the risk of bugs, and reducing evaluation standardization. To mitigate these issues, we introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem that provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations. In addition, each environment in SWM enables controllable factors of variation, including visual and physical properties, to support robustness and continual learning research. Finally, we demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.
Subjects:
Artificial Intelligence (cs.AI)
Cite as:<br>arXiv:2602.08968 [cs.AI]
(or<br>arXiv:2602.08968v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2602.08968
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arXiv-issued DOI via DataCite
Submission history<br>From: Lucas Maes [view email]<br>[v1]<br>Mon, 9 Feb 2026 18:04:22 UTC (7,718 KB)
[v2]<br>Tue, 17 Feb 2026 18:58:08 UTC (7,718 KB)
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