Comprehensive AI governance requires addressing non-model gains

• URL: http://arxiv.org/abs/2606.00047v1
• Date: Fri, 01 May 2026 08:30:27 GMT
• Title: Comprehensive AI governance requires addressing non-model gains
• Abstract summary: This position paper argues that model-level governance becomes less effective when capability progress is driven by "non-model gains"<n>We formalise the concept of non-model gains and provide a taxonomy of three distinct vectors of capability gain.<n>We emphasise the importance of societal resilience as a complement to these governance layers.
• Score: 2.47755522777938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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