# Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment

arXiv: https://arxiv.org/abs/2507.15916 · HTML: https://mlsanity.com/paper/2507.15916/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2025.

## Built on by (most cited first, 11 of 11)

- [Bit-Exact AI Inference Verification Without Performance Tradeoffs](https://mlsanity.com/paper/2606.00279/) (2026)
- [Detecting Hidden ML Training With Zero-Overhead Telemetry](https://mlsanity.com/paper/2606.19262/) (2026)
- [Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands](https://mlsanity.com/paper/2605.15164/) (2026)
- [Zero knowledge verification for frontier AI training is possible](https://mlsanity.com/paper/2606.05433/) (2026)
- [What if automating AI R&D triggers an intelligence explosion?](https://mlsanity.com/paper/2609.36054/) (2026)
- [Does Distributed Training Undermine Compute Governance?](https://mlsanity.com/paper/2605.29359/) (2026)
- [Privacy-Preserving AI Verification via Minimal Information Disclosure](https://mlsanity.com/paper/2608.02774/) (2026)
- [The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI](https://mlsanity.com/paper/2608.05173/) (2026)
- [Workload Identification with Physical Side Channels for AI Governance](https://mlsanity.com/paper/2609.00309/) (2026)
- [Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance](https://mlsanity.com/paper/2609.10105/) (2026)
- [Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governance](https://mlsanity.com/paper/2610.07476/) (2026)

## Abstract (arXiv)

The risks of frontier AI may require international cooperation, which in turn may require verification: checking that all parties follow agreed-on rules. For instance, states might need to verify that powerful AI models are widely deployed only after their risks to international security have been evaluated and deemed manageable. However, research on AI verification could benefit from greater clarity and detail. To address this, this report provides an in-depth overview of AI verification, intended for both policy professionals and technical researchers. We present novel conceptual frameworks, detailed implementation options, and key R&D challenges. These draw on existing literature, expert interviews, and original analysis, all within the scope of confidentially overseeing AI development and deployment that uses thousands of high-end AI chips. We find that states could eventually verify compliance by using six largely independent verification approaches with substantial redundancy: (1) built-in security features in AI chips; (2-3) separate monitoring devices attached to AI chips; and (4-6) personnel-based mechanisms, such as whistleblower programs. While promising, these approaches require guardrails to protect against abuse and power concentration, and many of these technologies have yet to be built or stress-tested. To enable states to confidently verify compliance with rules on large-scale AI development and deployment, the R&D challenges we list need significant progress.