# AI verification proposal A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-09. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0020,M-0002&implementations=M-0002:I-0016 How to read it: a claim is something one party wants to verify about another's AI hardware or software. A mechanism is a general technique for verifying claims; it is "aimed at" a claim when that is its direct purpose, and "supporting" when it contributes without being aimed at it. A claim is addressed when a mechanism in the proposal is aimed at it and is not excluded by the filters; addressed does not mean verified, so check that mechanism's development status, security evidence and findings. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and findings) and https://trustbutveri.fyi/about/readiness/ (development status). ## Filters Filters apply to mechanisms only and describe the setting the proposal is for. None set. Every mechanism on the map was available. ## Overview One row per mechanism, read from its record. Open failures: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees. Findings are grouped as known failures, scope limitations and open questions. Only known failures count as failures. Counts are an inventory of published findings, not a risk score. | Mechanism | Development | Security evidence | Prover | Attack testing | Hardware | Open failures | Weights | Inputs and outputs | Training data | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Remote detection of data centres | Proposed | Published security analysis | Adversarial | Analysis | None | 0 / 0 / 0 | not involved | not involved | not involved | | Deterministic and bit-exact inference / Batch-invariant inference kernels (Thinking Machines) | Research demonstration | No published adversarial analysis recorded | Cooperative | None | None | 0 / 0 / 0 | unspecified | unspecified | unspecified | ## Claims No claims chosen. ## Mechanisms ### Remote detection of data centres Remote detection locates large data centres and estimates their power capacity without site access, using satellite imagery, heat signatures and public records such as permits. ([Remote detection of data centres](https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/)) - Assessment: mechanism family. - Development: Proposed (legacy code R1), assessed for finding undeclared data centres above an agreed compute threshold. - Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed. - Claims in this proposal: none of them. - Threat model: adversarial prover. Hardware: none. Prover cooperation: not required. Attack testing: analysis. Category: Remote & side-channel sensing. - What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Works from outside the facility; it does not handle model data. ### Deterministic and bit-exact inference Open-source GPU kernels from Thinking Machines Lab that make language-model outputs independent of batch size, adopted in vLLM and SGLang for reproducible inference. ([Deterministic and bit-exact inference](https://trustbutveri.fyi/mechanisms/deterministic-inference/)) - Assessment: selected implementation [Batch-invariant inference kernels (Thinking Machines)](https://trustbutveri.fyi/implementations/batch-invariant-inference-kernels/). - Development: Research demonstration (legacy code R2), assessed for exact recomputation of served outputs by a verifier, with a cooperating provider. - Security evidence: No published adversarial analysis recorded. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed. - Claims in this proposal: none of them. - Threat model: cooperative prover. Hardware: none. Prover cooperation: required. Attack testing: none. Category: Cryptographic & computational. - What the verifier sees: model weights unspecified; inputs and outputs unspecified; training data unspecified. This Explorer has no asset-specific exposure assessment for this implementation. Check its source and deployment assumptions. ## Properties **Built for an adversarial prover** - Remote detection of data centres **No new hardware needed** - Remote detection of data centres - Deterministic and bit-exact inference ## Attack testing Attack testing records published testing for this use. It does not by itself show independent review, a formal proof or that a deployed system is secure. **Testing history** - Remote detection of data centres: Analysis ## Limits **Family finding context** - Context for Batch-invariant inference kernels (Thinking Machines). Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Some kernels remain genuinely nondeterministic (scope limitation, open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/evidence/flaws/1/) [14]. The bit-exact work separates kernels that are deterministic but not batch-invariant from truly nondeterministic ones that use atomic functions. Some integer de-quantization kernels use atomic additions and remain nondeterministic, so exact replay needs backends that avoid them. - Context for Batch-invariant inference kernels (Thinking Machines). Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Cross-hardware replay relies on reverse-engineered, closed behaviour (scope limitation, open question, in Deterministic and bit-exact inference; https://trustbutveri.fyi/mechanisms/deterministic-inference/evidence/flaws/2/) [14][15]. Emulating one GPU's rounding on another requires reverse-engineering tensor-core arithmetic and modelling proprietary kernel choices. Hawkeye covers a subset of NVIDIA architectures and states that attention and other higher-level operations need further reverse engineering. For the bit-exact emulator, a proprietary Hopper kernel family is an open edge case. **Scope limitations** - Facilities can be disguised or hidden (scope limitation, theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/1/) [1]. Halstead and Larsen discuss two ways to hide a facility. One is to disguise it as a legitimate industrial site. The other is to build it underground, with cooling that avoids visible heat plumes. They note that the underground option requires bespoke engineering. - Small sites may not be detectable (scope limitation, theoretical argument, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/2/) [1][3]. Halstead and Larsen conclude that a sufficiently small covert project could not be ruled out with confidence. In their estimates, the chance of detection is lower for smaller sites. Krawec notes that small data centres in existing buildings may lack the distinctive features of large facilities. Related mechanism: Chip registries and manufacturing records (R1, not in the proposal). Accounts for chips from the fab onwards, which does not depend on a site being visible. **Open questions** - Search for unknown sites is undemonstrated (open question, open question, in Remote detection of data centres; https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/evidence/flaws/3/) [3]. Krawec reports that telling data centres apart from other industrial facilities systematically is difficult. Automating detection would need large amounts of training imagery and a purpose-trained model. In Krawec's words, automated data-centre detection "remains primarily conceptual at present". **Not yet demonstrated** - Remote detection of data centres: Proposed (legacy code R1), assessed for finding undeclared data centres above an agreed compute threshold ## Possible additions Mechanisms on the map, not in the proposal, that the records connect to an unaddressed or partly addressed claim, an open failure or a dependency. Pointers, not recommendations: each brings its own readiness level and findings, and none is claimed to close a failure. None found. ## Dependencies **Blockers** - Remote detection of data centres: Wide-area, automated detection of data centres is not yet practical and needs large training datasets. (coverage & hidden compute) [3] - Remote detection of data centres: No measured detection or false-alarm rates for finding undeclared facilities have been published. (adversarial validation) [1][3] - Remote detection of data centres: Recent high-resolution imagery is costly, is limited by weather and needs trained analysts. (access & governance) [3] - Deterministic and bit-exact inference: Batch invariance costs throughput: on Qwen3-8B the improved deterministic build took 42 s against 26 s for vLLM's default, and SGLang reports an average slowdown of 34.35% on its FlashInfer and FlashAttention 3 backends. (performance & compatibility) [6][8] - Deterministic and bit-exact inference: Outputs are identical only while the model, inference implementation and device stay fixed, so provider and verifier must run the same stack. (performance & compatibility) [11] ## What the verifier sees - Model weights: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres; unspecified for Deterministic and bit-exact inference. - Inputs and outputs: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres; unspecified for Deterministic and bit-exact inference. - Training data: shown by none; depends on the design for none; hidden by none; not involved in Remote detection of data centres; unspecified for Deterministic and bit-exact inference. ## Implementations - Remote detection of data centres: none on the map - Deterministic and bit-exact inference: [Batch-invariant inference kernels (Thinking Machines)](https://trustbutveri.fyi/implementations/batch-invariant-inference-kernels/) (R2, open-source project); [Verde and RepOps (Gensyn)](https://trustbutveri.fyi/implementations/gensyn-verde-repops/) (R3, product); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture) ## Sources 1. Covert AI Projects, B. Halstead & T. Larsen (2026). https://ai-2040.com/supplements/covert-ai-projects 2. Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment, M. Baker et al. (2025). https://www.rand.org/pubs/working_papers/WRA4077-1.html 3. Tracking Hyperscale AI Data Center Growth with Satellite Imagery, C. Krawec (2026). https://fas.org/publication/tracking-hyperscale/ 4. Introducing the Frontier Data Centers Hub, Epoch AI (2025). https://epoch.ai/latest/introducing-the-frontier-data-centers-hub 5. AI Data Centers Documentation – Methodology, Epoch AI (2026). https://epoch.ai/data/data-centers-documentation/methodology 6. Defeating Nondeterminism in LLM Inference, H. He & Thinking Machines Lab (2025). https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ 7. thinking-machines-lab/batch_invariant_ops (GitHub repository), Thinking Machines Lab (2025). https://github.com/thinking-machines-lab/batch_invariant_ops 8. Towards Deterministic Inference in SGLang and Reproducible RL Training, The SGLang Team (2025). https://www.lmsys.org/blog/2025-09-22-sglang-deterministic/ 9. Batch Invariance (vLLM documentation), vLLM project (2026). https://github.com/vllm-project/vllm/blob/main/docs/features/batch_invariance.md 10. [Feature]: Batch Invariant Feature and Performance Optimization (vLLM issue #27433), vLLM project contributors (2025). https://github.com/vllm-project/vllm/issues/27433 11. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). https://arxiv.org/abs/2511.20621 12. Verde: Verification via Refereed Delegation for Machine Learning Programs, A. Arun et al. (2025). https://arxiv.org/abs/2502.19405 13. EigenAI: Deterministic Inference, Verifiable Results, D. Ribeiro Alves et al. (2026). https://arxiv.org/abs/2602.00182 14. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279 15. Hawkeye: Reproducing GPU-Level Non-Determinism, E. Badash et al. (2026). https://proceedings.mlsys.org/paper_files/paper/2026/hash/e217c271a57c365a246b0ad39e668ba8-Abstract-Conference.html