stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912910346813440 |
|---|---|
| author | Maes, Lucas Lidec, Quentin Le Haramati, Dan Massaudi, Nassim Scieur, Damien LeCun, Yann Balestriero, Randall |
| author_facet | Maes, Lucas Lidec, Quentin Le Haramati, Dan Massaudi, Nassim Scieur, Damien LeCun, Yann Balestriero, Randall |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08968 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation Maes, Lucas Lidec, Quentin Le Haramati, Dan Massaudi, Nassim Scieur, Damien LeCun, Yann Balestriero, Randall Artificial Intelligence 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. |
| title | stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.08968 |