Code World Model Preparedness Report
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909026535604224 |
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| author | Song, Daniel Ney, Peter Menghini, Cristina Ahmad, Faizan Boyd, Aidan Li, Nathaniel Han, Ziwen Testud, Jean-Christophe Okabayashi, Saisuke Ryan, Maeve Miao, Jinpeng Kwisaba, Hamza Binder, Felix Whitman, Spencer Gust, Jim Arcaute, Esteban Kapil, Dhaval Kahn, Jacob Minhas, Ayaz Goodman, Tristan Deason, Lauren Vaughan, Alexander Zhao, Shengjia Yue, Summer |
| author_facet | Song, Daniel Ney, Peter Menghini, Cristina Ahmad, Faizan Boyd, Aidan Li, Nathaniel Han, Ziwen Testud, Jean-Christophe Okabayashi, Saisuke Ryan, Maeve Miao, Jinpeng Kwisaba, Hamza Binder, Felix Whitman, Spencer Gust, Jim Arcaute, Esteban Kapil, Dhaval Kahn, Jacob Minhas, Ayaz Goodman, Tristan Deason, Lauren Vaughan, Alexander Zhao, Shengjia Yue, Summer |
| contents | This report documents the preparedness assessment of Code World Model (CWM), a model for code generation and reasoning about code from Meta. We conducted pre-release testing across domains identified in our Frontier AI Framework as potentially presenting catastrophic risks, and also evaluated the model's misaligned propensities. Our assessment found that CWM does not pose additional frontier risks beyond those present in the current AI ecosystem. We therefore release it as an open-weight model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00932 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Code World Model Preparedness Report Song, Daniel Ney, Peter Menghini, Cristina Ahmad, Faizan Boyd, Aidan Li, Nathaniel Han, Ziwen Testud, Jean-Christophe Okabayashi, Saisuke Ryan, Maeve Miao, Jinpeng Kwisaba, Hamza Binder, Felix Whitman, Spencer Gust, Jim Arcaute, Esteban Kapil, Dhaval Kahn, Jacob Minhas, Ayaz Goodman, Tristan Deason, Lauren Vaughan, Alexander Zhao, Shengjia Yue, Summer Software Engineering Artificial Intelligence This report documents the preparedness assessment of Code World Model (CWM), a model for code generation and reasoning about code from Meta. We conducted pre-release testing across domains identified in our Frontier AI Framework as potentially presenting catastrophic risks, and also evaluated the model's misaligned propensities. Our assessment found that CWM does not pose additional frontier risks beyond those present in the current AI ecosystem. We therefore release it as an open-weight model. |
| title | Code World Model Preparedness Report |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2605.00932 |