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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.00047 |
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| _version_ | 1866916067816767488 |
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| author | Goemans, Arthur Altman, Dan Dreksler, Noemi Freund, Jonas Gandhi, Milan Wang, Zhengdong Cogan, Sarah Krier, Sebastien Brady, Demetra Ho, Lewis Dafoe, Allan |
| author_facet | Goemans, Arthur Altman, Dan Dreksler, Noemi Freund, Jonas Gandhi, Milan Wang, Zhengdong Cogan, Sarah Krier, Sebastien Brady, Demetra Ho, Lewis Dafoe, Allan |
| contents | Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function of the compute and data used during training. This position paper argues that model-level governance becomes less effective when capability progress is increasingly driven by "non-model gains"--improvements that are independent from advances in the base model. We formalise the concept of non-model gains and provide a taxonomy of three distinct vectors of capability gain: inference gain (scaling compute at test-time), systems gain (post-training enhancements such as scaffolds), and asset gain (enhancing a model with restricted assets). We demonstrate how these vectors--alongside potential future impacts from embodiment, continual learning, and AI diffusion--may undermine risk management strategies that hinge mostly on pre-deployment evaluation and mitigation. We provide an overview of governance approaches that go beyond the model level: system, entity, agent, and cloud governance. Finally, we emphasise the importance of societal resilience as a complement to these governance layers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00047 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Comprehensive AI governance requires addressing non-model gains Goemans, Arthur Altman, Dan Dreksler, Noemi Freund, Jonas Gandhi, Milan Wang, Zhengdong Cogan, Sarah Krier, Sebastien Brady, Demetra Ho, Lewis Dafoe, Allan Computers and Society Artificial Intelligence Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function of the compute and data used during training. This position paper argues that model-level governance becomes less effective when capability progress is increasingly driven by "non-model gains"--improvements that are independent from advances in the base model. We formalise the concept of non-model gains and provide a taxonomy of three distinct vectors of capability gain: inference gain (scaling compute at test-time), systems gain (post-training enhancements such as scaffolds), and asset gain (enhancing a model with restricted assets). We demonstrate how these vectors--alongside potential future impacts from embodiment, continual learning, and AI diffusion--may undermine risk management strategies that hinge mostly on pre-deployment evaluation and mitigation. We provide an overview of governance approaches that go beyond the model level: system, entity, agent, and cloud governance. Finally, we emphasise the importance of societal resilience as a complement to these governance layers. |
| title | Comprehensive AI governance requires addressing non-model gains |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00047 |