Verifying Restrictions on Frontier AI Research

This paper presents early work exploring how to verify compliance with restrictions on frontier AI research, if that research were restricted by governments in an international agreement. We presented this paper at the ICML 2026 Technical AI Governance Research (TAIGR) workshop.

Abstract:

The premature development of artificial superintelligence poses major risks to humanity, so researchers have proposed international agreements halting such development until it can be done safely. AI progress depends primarily on compute, algorithms, and data; a durable halt would address all three so that advances in one input do not counteract restrictions on another. Improvements to AI algorithms are driven largely through research activities, so this research may need to be restricted during a halt. Given low international trust, signatories will want to verify compliance. This paper analyzes how such restrictions on AI research could be verified, while remaining agnostic about what specific research would be prohibited. It first explores key considerations that affect the verifiability of research restrictions, such as the computational infrastructure necessary for experiments. It then catalogs 28 candidate verification mechanisms. These mechanisms include whistleblowers, search warrants, reviews of AI training code, standard intelligence gathering tools, and more. Some of these mechanisms are not yet implementation-ready, and some might be undesirable upon further inspection. By examining the space of potential options, this work provides a foundation for future research to develop the most promising mechanisms into deployable tools.

In addition to the paper itself (https://arxiv.org/abs/2606.28694), there is a podcast discussion available (https://youtu.be/NuprZOocu1I).