Proposes referential security as a paradigm for AI evaluations that reframes model identity as verifiable to support reproducible audits and regulatory decisions despite system changes.
Frontier ai auditing: Toward rigorous third-party assessment of safety and security practices at leading ai companies.arXiv preprint arXiv:2601.11699
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NeurIPS should enforce a three-tier disclosure framework plus mandatory claim inventories for papers asserting that frontier AI models are safe or ready for release.
A framework recommending that frontier AI developers disclose information on capabilities, usage, safety mitigations, and governance of internal model deployments.
Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-verification tools.
Frontier AI safety policies have a structural coordination gap caused by diffuse benefits and concentrated costs, which can be addressed by adapting precommitment and shared response protocols from other high-risk domains.
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NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claims
NeurIPS should enforce a three-tier disclosure framework plus mandatory claim inventories for papers asserting that frontier AI models are safe or ready for release.