- II.16 the covered cluster;
- V.1–6 consolidation, the register, transfers;
- VII.1–5 use verification;
- X.3–4 challenge inspections and disclosure;
- Appendix C staging
Used on Verifying, Stack, Participation, Sampling and Field
Compute verification
Sources
The documents the verification pages draw on
The Verifying, Participation and Sampling pages cite 14 of the documents. The Stack page reads 32 of them and the Field page lists 32 among its works; 31 are on both pages.
The 37 documents by topic and date; 13 are from 2026.
- Tested, in use or in force · 9
- Proposes or analyses · 28
No single date: one document prints no date, and two are pages their publishers keep updating.
Agreements and inspections, 14 documents: 1 describes something tested, in use or in force and 13 propose or analyse; 3 from 2023, 4 from 2024, 4 from 2025 and 2 from 2026; 1 with no single date.
Workload checks, 7 documents: 4 describe something tested, in use or in force and 3 propose or analyse; 7 from 2026.
Hardware and location, 15 documents: 3 describe something tested, in use or in force and 12 propose or analyse; 5 from 2024, 5 from 2025 and 4 from 2026; 1 with no single date.
Data, 1 document: 1 describes something tested, in use or in force; 1 with no single date.
The documents
The documents are ordered by how many of the section’s pages use them, newest first among equals.
All 37 are primary sources: each is cited for its own text.
Used on three or more pages · 10
Used on two pages · 22
Beyond Training: A Feasibility Taxonomy for Inference-Time AI Governance
Near-Term Verification Methods for AI Chip Exports
Tracking AI Chips
Assessing Location Verification as an Export Control Enforcement Tool
Detecting Hidden ML Training With Zero-Overhead Telemetry
How to Catch a GPU: A Taxonomy of Verification and Enforcement Mechanisms for International AI Agreements
GPU Fingerprinting for Location Verification
Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
Toward a Global Regime for Compute Governance: Building the Pause Button
Insights from a Workshop on Hardware-Enabled Governance Mechanisms
Location Verification for AI Chips, issue brief
Hardware-Enabled Mechanisms for Verifying Responsible AI Development
Flexible Hardware-Enabled Guarantees for AI Compute (Part I)
Technology to Secure the AI Chip Supply Chain: A Working Paper
Toward a Better Balance of Competitiveness, Security, and Privacy
Open the document: Technology to Secure the AI Chip Supply Chain: A Working Paper
Verification methods for international AI agreements
Near-Term Enforcement of AI Chip Export Controls Using A Firmware-Based Design for Offline Licensing
Location Verification for AI Chips
Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
Computing Power and the Governance of Artificial Intelligence
Secure, Governable Chips
Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing
Hardware-Enabled Governance Mechanisms: Developing Technical Solutions to Exempt Items Otherwise Classified Under Export Control Classification Numbers 3A090 and 4A090
Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers
GPU Claims, Attestation SDK claims guide (claims version 3.0)
Used on one page · 5
Attestable Audit: Property-Based Attestation for Proprietary Workloads without Artifact Disclosure
- §7.1 neutral hosting of attestable services and physical-access assumptions (p. 7; selected section read)
Used on Stack
RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning
- the abstract: disaggregated architectures decouple rollout generation and policy training across independent GPU pools
Used on Verifying
Does Distributed Training Undermine Compute Governance?
- distributed training and compute governance;
- §5.4 the simulator
Used on Field
Chemical Weapons Convention, Article IX and Verification Annex Part X
- IX.16–17, 21 challenge inspections;
- Part X paras 45–48 managed access
Used on Sampling
AI data centers; GPU clusters; AI data centers documentation – Methodology
- H100-equivalent capacity definition and catalogue coverage
Used on Participation