There is no undeclared relevant compute

Sources

1. BM. Baker et al. (2025). Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment. RAND Corporation. Source recordSupports: Subgoal 2 (2.A, 2.B, 2.B.1, 2.B.2); focus on large-scale clusters; unclear whether dangerous deployment requires scale; personnel and intelligence layers · §2.2; §3.2, Figure 4; §4
2. BB. Harack et al. (2025). Verification for International AI Governance. Oxford Martin AI Governance Initiative. Source recordSupports: existence easier to demonstrate than non-existence · p. 31
3. BY. Shavit (2023). What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring. arXiv. Source recordSupports: sampling fails if prover amasses untracked chips; existing chips possibly not locatable · abstract; §5
4. BA. Scher & L. Thiergart (2025). Mechanisms to Verify International Agreements About AI Development. arXiv. Source recordSupports: covert data centres may be hard to detect; tracking chips favoured; intelligence and whistleblowers as complements · Verifying the location of AI compute (analysis)
5. BA. R. Wasil et al. (2024). Verification methods for international AI agreements. arXiv. Source recordSupports: unauthorised data centres as a violation type; no single method foolproof; national technical means and their limitations and evasions · What to verify; Table 1; Figures 2–4
6. BA. Scher et al. (2025). An International Agreement to Prevent the Premature Creation of Artificial Superintelligence. Machine Intelligence Research Institute. Source recordSupports: locating chips through supply-chain tracking, reporting, intelligence, OSINT, power monitoring, challenge inspections and whistleblowers · §4; Articles V and X (as summarised)
7. BG. Sastry et al. (2024). Computing Power and the Governance of Artificial Intelligence. arXiv. Source recordSupports: detectability of large facilities; low-compute narrow models; decentralised training; underground data centres raise cost · properties of compute; limitations
8. CGloria Z (2026). On TEEs for Privacy-Preserving Monitoring in AI Governance. MIRI Technical Governance Team. Source recordSupports: difficulty of verifying completeness of workload declarations and bounding unknown compute · Limitations
9. BC. Krawec (2026). Tracking Hyperscale AI Data Center Growth with Satellite Imagery. Federation of American Scientists. Source recordSupports: satellite imagery, permits and utility filings track known facilities; automated data-centre detection primarily conceptual · Methodology; Opportunities for Further Research
10. CAttestable (2026). Pacing AI Requires Proof. Attestable blog. Source recordSupports: work-budget proposal; a proof cannot discover a datacenter that was never declared (provider proposal) · blog post
11. BR. Rahman (2026). Does Distributed Training Undermine Compute Governance?. ICML 2026 Workshop on Technical AI Governance Research. Source recordSupports: 10^24, 10^25 and 10^26 FLOP thresholds evadable with $1.6M, $31M and $3.8B of hardware in sub-registration clusters; assumptions (DiLoCo-family training over 100 Mbps links, 16 H100-equivalents per node, about 740 days) · §3.1; §4