# 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-0007,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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Proofs of useful work for capacity accounting | Proposed | Published security analysis | Adversarial | Analysis | None | 0 / 1 / 0 | depends | depends | 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 ### Proofs of useful work for capacity accounting Cryptographic evidence that a given amount of matrix-multiplication work was completed, proposed as one input to bounding how much spare capacity declared hardware has. ([Proofs of useful work for capacity accounting](https://trustbutveri.fyi/mechanisms/proofs-of-useful-work/)) - Assessment: mechanism family. - Development: Proposed (legacy code R1), assessed for bounding the spare capacity of declared hardware that could run training. - 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: required. Attack testing: analysis. Category: Cryptographic & computational. - What the verifier sees: model weights depends; inputs and outputs depends; training data not involved. Checking a sampled tile of a matrix multiplication reveals that tile, which may hold model or input data; the authors suggest a zero-knowledge proof when the matrices must stay private. ### 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** - Proofs of useful work for capacity accounting **No new hardware needed** - Proofs of useful work for capacity accounting - 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** - Proofs of useful work for capacity accounting: Analysis ## Limits **Open significant failures** - Known shortcuts let a miner claim somewhat more work than it did (known failure, theoretical argument, in Proofs of useful work for capacity accounting; https://trustbutveri.fyi/mechanisms/proofs-of-useful-work/evidence/flaws/3/) [4]. Pearl's specification lists known mining speedups: crafted inputs, precision shortcuts, seed grinding, work reuse, and faster kernels or hardware. A policy check caps the summands a miner may skip at one-sixteenth of those in a tile. For capacity bounding, any gap between work proven and work possible leaves spare capacity. **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** - Proves that work was done, not that no capacity remains (scope limitation, theoretical argument, in Proofs of useful work for capacity accounting; https://trustbutveri.fyi/mechanisms/proofs-of-useful-work/evidence/flaws/1/) [1]. Proof-of-work accounting bounds unmonitored compute only relative to an estimate of what the actor has. Attestable states that the verifier "needs a credible estimate of the compute available" to the actor, and that a proof "cannot discover a datacenter that was never declared". Related mechanism: Chip registries and manufacturing records (R1, not in the proposal). A registry of chips is one basis for the estimate of available compute that the flaw's source says the verifier needs. Related mechanism: Remote detection of data centres (R1, not in the proposal). Looks for data centres that were never declared, which a proof cannot discover. **Open questions** - Security rests on new hardness assumptions (open question, open question, in Proofs of useful work for capacity accounting; https://trustbutveri.fyi/mechanisms/proofs-of-useful-work/evidence/flaws/2/) [3][4]. Komargodski and Weinstein base security on hardness assumptions about batches of low-rank random linear equations, and list PoUW "from more standard or well-studied assumptions" as an open problem. Pearl's floating-point variant introduces a further "quantized-subspace hardness" assumption. **Not yet demonstrated** - Proofs of useful work for capacity accounting: Proposed (legacy code R1), assessed for bounding the spare capacity of declared hardware that could run training ## 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** - Proofs of useful work for capacity accounting: Bounding spare capacity needs a credible estimate of the compute available to the actor, including third-party access. (capacity bounds) [1] - Proofs of useful work for capacity accounting: Proofs of work cannot find facilities that were never declared. (coverage & hidden compute) [1] - Proofs of useful work for capacity accounting: As of September 2026 no implementation, demonstration or independent evaluation of proofs of work for capacity bounding has been published. (adversarial validation) - 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 Proofs of useful work for capacity accounting; hidden by none; not involved in none; unspecified for Deterministic and bit-exact inference. - Inputs and outputs: shown by none; depends on the design for Proofs of useful work for capacity accounting; hidden by none; not involved in none; unspecified for Deterministic and bit-exact inference. - Training data: shown by none; depends on the design for none; hidden by none; not involved in Proofs of useful work for capacity accounting; unspecified for Deterministic and bit-exact inference. ## Implementations - Proofs of useful work for capacity accounting: [Pearl proof-of-useful-work blockchain](https://trustbutveri.fyi/implementations/pearl-proof-of-useful-work/) (R3, open-source project) - 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. Pacing AI Requires Proof, Attestable (2026). https://attestable.com/blog/pacing-ai-requires-proof 2. Mechanisms to Verify International Agreements About AI Development, A. Scher & L. Thiergart (2025). https://arxiv.org/abs/2506.15867 3. Proofs of Useful Work from Arbitrary Matrix Multiplication, I. Komargodski & O. Weinstein (2025). https://arxiv.org/abs/2504.09971 4. Pearl Floating Point Scheme Specification, Pearl Research Team (2026). https://pearlresearch.ai/Pearl_Whitepaper.pdf 5. pearl: Monorepo for the Pearl network, Pearl Research Labs (2026). https://github.com/pearl-research-labs/pearl 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