Comprehensive AI governance requires addressing non-model gains
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 demo...
| Authors | Arthur Goemans, Dan Altman, Noemi Dreksler et al. |
|---|---|
| Primary category | cs.CY |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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