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[2602.08968] stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Summary
The paper introduces stable-worldmodel (SWM), a modular ecosystem for world modeling research that enhances reproducibility and standardization in AI environments.
Why It Matters
As world models gain traction in AI, the lack of standardized implementations hampers research progress. SWM addresses these challenges by providing a reusable framework, promoting robust evaluation, and enabling advancements in zero-shot learning and continual learning.
Key Takeaways
- SWM offers a modular and documented framework for world modeling.
- It enhances reproducibility and standardization in AI research.
- The framework supports robust evaluation through controllable environment factors.
- SWM facilitates research in zero-shot robustness and continual learning.
- Standardized tools can reduce bugs and improve implementation reliability.
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 View a PDF of the paper titled stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation, by Lucas Maes and 6 other authors View PDF HTML (experimental) 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...
