Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment
Not on the map. Why?
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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jul 2025.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2026: 11.
11 papers.
- 2026Bit-Exact AI Inference Verification Without Performance Tradeoffscited by 4
- 2026Detecting Hidden ML Training With Zero-Overhead Telemetrycited by 4
- 2026Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demandscited by 2
- 2026Zero knowledge verification for frontier AI training is possiblecited by 2
- 2026What if automating AI R&D triggers an intelligence explosion?cited by 1
- 2026Does Distributed Training Undermine Compute Governance?cited by 0
- 2026Privacy-Preserving AI Verification via Minimal Information Disclosurecited by 0
- 2026The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AIcited by 0
- 2026Workload Identification with Physical Side Channels for AI Governancecited by 0
- 2026Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governancecited by 0
- 2026Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governancecited by 0
- 2026Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governance5 Oct 2026
- 2026What if automating AI R&D triggers an intelligence explosion?28 Sep 2026
- 2026Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance9 Sep 2026
- 2026Workload Identification with Physical Side Channels for AI Governance31 Aug 2026
- 2026Privacy-Preserving AI Verification via Minimal Information Disclosure3 Aug 2026
- 2026The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI26 Jun 2026
- 2026Detecting Hidden ML Training With Zero-Overhead Telemetry17 Jun 2026
- 2026Zero knowledge verification for frontier AI training is possible3 Jun 2026
- 2026Bit-Exact AI Inference Verification Without Performance Tradeoffs29 May 2026
- 2026Does Distributed Training Undermine Compute Governance?28 May 2026
- 2026Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands14 May 2026
Similar, not cited
Close in wording, no citation either way
- 2023Nuclear Arms Control Verification and Lessons for AI Treaties
- 2026The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime
- 2025Security practices in AI development
- 2026Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification
- 2025Safety by Measurement: A Systematic Literature Review of AI Safety Evaluation Methods
- 2026AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments
- 2026When the Agent Is the Adversary: Architectural Requirements for Agentic AI Containment After the April 2026 Frontier Model Escape
- 2026A Formal Security Framework for MCP-Based AI Agents: Threat Taxonomy, Verification Models, and Defense Mechanisms