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A training run stayed within declared limits

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  1. 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: goal and example rules; weight snapshots; sampled segment recomputation; reasons full re-run is infeasible; PoTT harder than PoL; open problems (online RL, thresholds) · abstract; §2–§3; open problems
  2. BG. Sastry et al. (2024). Computing Power and the Governance of Artificial Intelligence. arXiv. Source recordSupports: EO 14110 threshold; compute as a high-level proxy; thresholds must change with progress · thresholds; limitations
  3. AExecutive Office of the President (2025). Executive Order 14148: Initial Rescissions of Harmful Executive Orders and Actions. Federal Register, 90 FR 8237 (document 2025-01901, published 2025-01-28). Source recordSupports: revocation of EO 14110 on 20 January 2025 · Sec. 2(ggg)
  4. BA. Scher et al. (2025). An International Agreement to Prevent the Premature Creation of Artificial Superintelligence. Machine Intelligence Research Institute. Source recordSupports: training above 10^24 FLOP prohibited; runs above 10^22 FLOP approved and monitored · §4
  5. BA. R. Wasil et al. (2024). Verification methods for international AI agreements. arXiv. Source recordSupports: unauthorised training above a FLOP threshold as a violation type · What to verify
  6. 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 1.A.1 verifying declared training · §3.2
  7. BP. Peigné et al. (2026). Zero knowledge verification for frontier AI training is possible. arXiv. Source recordSupports: enforcement rests on self-reporting; governance analyses judge ZKPs impractical at frontier scale, which the authors argue is paradigm-bound; proposed architecture; ~36-month estimate · abstract
  8. AC. Fang et al. (2023). Proof-of-Learning is Currently More Broken Than You Think. 8th IEEE European Symposium on Security and Privacy (EuroS&P 2023). Source recordSupports: reproducible PoL spoofing at a fraction of prior cost; provably robust PoL requires advances in understanding deep-learning optimisation · abstract
  9. BN. Cankaya (2026). A System Overview for Near-Term, Low-Trust AI Compute Verification. Machine Intelligence Research Institute. Source recordSupports: monitoring training needs larger perimeters or compute-fabric taps; back-end traffic harder to capture · §5.1.1; inference vs training
  10. BJ. Petrie et al. (2025). Flexible Hardware-Enabled Guarantees for AI Compute. arXiv. Source recordSupports: flexHEG compute limits for training · abstract; Executive Summary
  11. BG. Kulp et al. (2024). Hardware-Enabled Governance Mechanisms: Developing Technical Solutions to Exempt Items Otherwise Classified Under Export Control Classification Numbers 3A090 and 4A090. RAND Corporation. Source recordSupports: offline licensing with a compute budget · p. viii
  12. AD. Choi et al. (2023). Tools for Verifying Neural Models' Training Data. Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Source recordSupports: training-data verification experiments on GPT-2 and Pythia models up to 1B parameters · §4
  13. AK. Abbaszadeh et al. (2024). Zero-Knowledge Proofs of Training for Deep Neural Networks. 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS 2024), pp. 4316-4330. Source recordSupports: Kaizen zero-knowledge proofs of training; VGG-11 (10M parameters) at about 15 minutes per iteration · abstract; evaluation
  14. AG. Liao et al. (2026). VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs. NDSS Symposium 2026. Source recordSupports: VeriLoRA proofs of individual LoRA fine-tuning steps on language models up to 13B parameters · §VI-B–VI-D
  15. AEuropean Parliament & Council of the European Union (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, OJ L, 2024/1689. Source recordSupports: Art. 51(2) presumption of high-impact capabilities above 10^25 FLOP; Art. 52(1) notification within two weeks; Art. 113(b) Chapter V applies from 2 August 2025 · Arts 51, 52, 113
  16. AEuropean Commission (2025). Guidelines on the scope of the obligations for general-purpose AI models established by Regulation (EU) 2024/1689 (AI Act). European Commission, Communication C(2025) 5045 final. Source recordSupports: 10^25 FLOP presumption and two-week notification; entry into application on 2 August 2025 · §2.3.1–2.3.2; landing page
  17. ACalifornia State Legislature (2025). California Senate Bill 53 (2025): Transparency in Frontier Artificial Intelligence Act. Statutes of 2025, Chapter 138 (Business and Professions Code §22757.10 et seq.). Source recordSupports: frontier model defined by more than 10^26 integer or floating-point operations including fine-tuning; transparency duties · §22757.11(i); §22757.12
  18. AE. Seferis & T. Fist (2026). Detecting Compute Structuring in AI Governance Is Likely Feasible. Proceedings of the AAAI Conference on Artificial Intelligence 40(44), pp. 37904–37912 (AAAI-26, Special Track on AI Alignment). Source recordSupports: compute structuring: splitting or modifying workloads to avoid regulation · abstract

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