AI治理需超越模型本身，关注推理、系统和资产等非模型进步。
Comprehensive AI governance requires addressing non-model gains
- 提出非模型增益三类：推理扩算、系统增强、受限资产提升。
- 实证显示非模型因素可削弱依赖部署前评估的风险管控效果。
- 建议扩展至系统、实体、云等多层级治理，强化社会韧性。
前沿AI治理通常聚焦于模型层面的治理范式，认为模型能力主要取决于训练时所用的计算量与数据量。本文指出，当能力进步越来越多地由‘非模型增益’驱动时，模型层面治理的有效性下降。非模型增益指独立于基础模型改进的能力提升，包括三类：推理增益（测试时扩展计算资源）、系统增益（如后训练架构优化）和资产增益（使用受限资源增强模型）。这些因素，连同未来可能的具身化、持续学习及AI扩散效应，可能削弱依赖部署前评估与缓解措施的风险管理策略。文章综述了超越模型层级的治理路径：系统、实体、代理和云治理。最后强调，社会韧性应作为这些治理层的重要补充。
原文摘要 · Abstract (English)
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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