# 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-0003,M-0001,M-0007&chips=existing

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.

- **Chips: Existing chips only.** "Existing chips only" removes mechanisms that need changes to future chip designs. New chip features take years to reach a deployed fleet and cover only chips made after they ship. Mechanisms that use shipping features, such as trusted execution environments or performance counters, stay.

23 of 25 mechanisms on the map pass these filters.

## 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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Whole-workload recomputation (reproducible packets) | Proposed | No published adversarial analysis recorded | Adversarial | None | Retrofit device | 0 / 0 / 0 | depends | depends | depends |
| Sampled inference recomputation | Operational use | Published security analysis | Adversarial | Analysis | None | 0 / 2 / 1 | depends | depends | not involved |
| Proofs of useful work for capacity accounting | Proposed | Published security analysis | Adversarial | Analysis | None | 0 / 1 / 0 | depends | depends | not involved |

## Claims

No claims chosen.

## Mechanisms

### Whole-workload recomputation (reproducible packets)

Organizing all AI workloads in a facility into discrete, reproducible units, so that a verifier can recompute a random sample and check each one. ([Whole-workload recomputation (reproducible packets)](https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/))

- Assessment: mechanism family.
- Development: Proposed (legacy code R1), assessed for recomputing whole workloads to show a cluster runs only declared inference.
- 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: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: none. Category: Isolation & system architectures.
- What the verifier sees: model weights depends; inputs and outputs depends; training data depends. Recomputing sampled units needs weights and sampled inputs or training data inside the checking environment. The design depends on securing that environment; disclosure depends on its confidentiality boundary.

### Sampled inference recomputation

A verifier re-runs a random sample of an AI provider's logged queries on a trusted copy of the declared model and checks the outputs match. ([Sampled inference recomputation](https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/))

- Assessment: mechanism family.
- Development: Operational use (legacy code R3), assessed for checking untrusted workers' activations against the declared model, prompt and precision.
- 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. Recomputation needs the weights and sampled requests inside the checking environment. For closed models, the record describes a trusted, confidential environment; disclosure to the verifier depends on that boundary.

### 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.


## Properties

**Operational use**

- Sampled inference recomputation: Operational use (legacy code R3), assessed for checking untrusted workers' activations against the declared model, prompt and precision

**Built for an adversarial prover**

- Whole-workload recomputation (reproducible packets)
- Sampled inference recomputation
- Proofs of useful work for capacity accounting

**No new hardware needed**

- Sampled inference recomputation
- Proofs of useful work for capacity accounting


## 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**

- Sampled inference recomputation: Analysis
- Proofs of useful work for capacity accounting: Analysis


## Limits

**Open significant failures**

- Tolerance for numerical noise leaves a covert channel (known failure, demonstrated attack, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/1/) [7][13][16]. Schemes that accept approximate matches can put an upper bound on an adversary's covert bandwidth, but they cannot close the channel. The weight-exfiltration detector cut exfiltratable information to under 0.5%, not to zero, on a 30-billion-parameter mixture-of-experts model under benign prompt traffic. Its authors called the channel's size under adversarial prompts an open empirical question. An independent study showed that an adversary who controls the prompts roughly doubles the bits leaked per token. Across six models, that cut the slowdown from 146–254 times under benign prompts to 60–118 times. The attack widens the exfiltration bound. It does not target the check that outputs match the declared model.

  Related mechanism: Deterministic and bit-exact inference (R3, not in the proposal). Bit-exact inference would remove the numerical tolerance if exact replay can be deployed with the required weights and configuration.
- Some inference optimizations are not covered (known failure, theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/3/) [8][9]. TOPLOC's authors state that it cannot detect speculative decoding in which a cheaper model does the decoding. They did not test whether it distinguishes types of key-value (KV) cache compression. DiFR was evaluated only on sampling from a single model. Its authors sketch an extension to one speculative-decoding algorithm but do not test it.
- 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/) [21]. 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.

**Open minor failures**

- Mixed hardware widens the honest baseline (known failure, open question, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/4/) [8]. When honest reference runs span different GPU types, the spread of benign scores grows. In DiFR's tests on Qwen3-30B-A3B, pooling A100 and H200 runs left Token-DiFR unable to separate the two smallest tested changes, a temperature of 1.1 instead of 1.0 and a simulated top-2 sampling bug, at the target false-positive rate, while cross-entropy separated them. Matched provider and verifier environments, or pooling that weights rare large deviations, restored detection.

**Scope limitations**

- Spare compute is outside the scheme (scope limitation, theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/evidence/flaws/1/) [1][2]. The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness.

  Related mechanism: Proofs of useful work for capacity accounting (R1, in the proposal). Proposed as one input to accounting for spare capacity on declared hardware.
- Only recorded traffic is checked (scope limitation, theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/2/) [2][7]. Recomputation checks that recorded, declared workloads are correct. It cannot show that the record is complete. The published schemes do not cover hidden workloads run on the same compute, or substituted work. Rinberg et al. say their exfiltration-detection scheme cannot stand alone.

  Related mechanism: Network taps and certifiers (R1, not in the proposal). Taps copy and hash all traffic on the monitored links, which bears on whether the traffic record is complete. They do not show what else ran on the same chips.
- 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/) [18]. 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**

- Non-compliant work could be encoded inside compliant-looking packets (open question, theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/evidence/flaws/2/) [1]. The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface.
- 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/) [20][21]. 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**

- Whole-workload recomputation (reproducible packets): Proposed (legacy code R1), assessed for recomputing whole workloads to show a cluster runs only declared inference
- 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.

- **Deterministic and bit-exact inference** (Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service)
  - Bears on the open significant failure "Tolerance for numerical noise leaves a covert channel" in Sampled inference recomputation. Bit-exact inference would remove the numerical tolerance if exact replay can be deployed with the required weights and configuration.
  - Whole-workload recomputation (reproducible packets) waits on it: Workloads are not reproducible by default, and achieving reproducibility may cost performance.
- **Network taps and certifiers** (Proposed (legacy code R1), assessed for committing a complete record of cluster traffic, so declared inference can be checked)
  - Whole-workload recomputation (reproducible packets) waits on it: All traffic must reach the recomputation server via network taps, and the server's integrity is critical.
  - Sampled inference recomputation waits on it: In tap-based retrofit designs, recording all inference traffic needs network taps and recomputation servers that can ingest it, in the worst case one recomputation-server network interface per inference front-end interface.


## Dependencies

**Missing prerequisites**

- Deterministic and bit-exact inference (Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service), needed by Whole-workload recomputation (reproducible packets)
- Network taps and certifiers (Proposed (legacy code R1), assessed for committing a complete record of cluster traffic, so declared inference can be checked), needed by Whole-workload recomputation (reproducible packets)

**Shared foundations**

- Network taps and certifiers, relied on by Whole-workload recomputation (reproducible packets) and Sampled inference recomputation

**Blockers**

- Whole-workload recomputation (reproducible packets): Workloads are not reproducible by default, and achieving reproducibility may cost performance. (performance & compatibility; waits on Deterministic and bit-exact inference) [1]
- Whole-workload recomputation (reproducible packets): Network packets are not individually reproducible by default; making them so may need considerable software, firmware and hardware work. Amodo rates this 'not on track'. (performance & compatibility) [4]
- Whole-workload recomputation (reproducible packets): All traffic must reach the recomputation server via network taps, and the server's integrity is critical. (hardware trust; waits on Network taps and certifiers) [1][4]
- Whole-workload recomputation (reproducible packets): Recomputing training steps needs checkpoints: writing one at every step would cost more than 100% overhead, so Amodo's design needs a spare data-parallel replica that tracks the weights instead. (performance & compatibility) [2]
- Sampled inference recomputation: In tap-based retrofit designs, recording all inference traffic needs network taps and recomputation servers that can ingest it, in the worst case one recomputation-server network interface per inference front-end interface. (coverage & hidden compute; waits on Network taps and certifiers) [1][4]
- Sampled inference recomputation: In retrofit designs, the recomputation server must sit inside the prover's data centre, possibly under the prover's physical control, and still be protected from a compromised provider, which Amodo rates 'not on track'. (hardware trust) [4][7]
- Sampled inference recomputation: No independent red-team of a recomputation consistency check has been published (the one independent attack study targets the weight-exfiltration bound), and Amodo rates recomputation red-teaming 'not started'. (adversarial validation) [4][13]
- Sampled inference recomputation: Tolerance-based checks need calibration on trusted hardware and exact knowledge of the provider's sampling procedure, and in one prototype a sampling-implementation mismatch produced large spurious differences. (performance & compatibility) [3][8]
- Sampled inference recomputation: The verifier needs the model weights, so checking a closed-weights model requires a trusted, confidential recomputation environment, which the retrofit designs place inside the prover's facility. (privacy & leakage) [2][8][17]
- 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) [18]
- Proofs of useful work for capacity accounting: Proofs of work cannot find facilities that were never declared. (coverage & hidden compute) [18]
- 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)


## What the verifier sees

- Model weights: shown by none; depends on the design for Whole-workload recomputation (reproducible packets), Sampled inference recomputation and Proofs of useful work for capacity accounting; hidden by none; not involved in none; unspecified for none.
- Inputs and outputs: shown by none; depends on the design for Whole-workload recomputation (reproducible packets), Sampled inference recomputation and Proofs of useful work for capacity accounting; hidden by none; not involved in none; unspecified for none.
- Training data: shown by none; depends on the design for Whole-workload recomputation (reproducible packets); hidden by none; not involved in Sampled inference recomputation and Proofs of useful work for capacity accounting; unspecified for none.

## Implementations

- Whole-workload recomputation (reproducible packets): [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture)
- Sampled inference recomputation: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [DiFR (Divergence From Reference)](https://trustbutveri.fyi/implementations/difr/) (R2, research prototype); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [SASH confidential network logger](https://trustbutveri.fyi/implementations/sash-confidential-network-logger/) (R1, research prototype); [TOPLOC](https://trustbutveri.fyi/implementations/toploc/) (R3, open-source project)
- 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)

## Sources

1. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
2. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
3. Scaling Recomputation Inference Verification, Amodo Design (2026). https://amododesign.com/notes/2026-09-02-scaling-recomputation-inference-verification/
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/
5. Get Involved in Verification, AI Futures Project (2026). https://ai-2040.com/supplements/verification-plan/get-involved
6. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). https://arxiv.org/abs/2208.03567
7. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
8. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). https://arxiv.org/abs/2511.20621
9. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). https://proceedings.mlr.press/v267/ong25a.html
10. PrimeIntellect-ai/toploc (GitHub repository), Prime Intellect (2025). https://github.com/PrimeIntellect-ai/toploc
11. INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning, Prime Intellect Team et al. (2025). https://arxiv.org/abs/2505.07291
12. adamkarvonen/difr (GitHub repository), A. Karvonen (2025). https://github.com/adamkarvonen/difr
13. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
14. SYNTHETIC-2 Release: Four Million Collaboratively Generated Reasoning Traces, Prime Intellect (2025). https://www.primeintellect.ai/blog/synthetic-2-release
15. An Inference Verification Prototype — Stage 1, Amodo Design (2026). https://amododesign.com/notes/2026-06-29-inference-verification-prototype/
16. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
17. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). https://intelligence.org/wp-content/uploads/2026/06/A-system-overview-for-near-term-low-trust-AI-compute-verification.pdf
18. Pacing AI Requires Proof, Attestable (2026). https://attestable.com/blog/pacing-ai-requires-proof
19. Mechanisms to Verify International Agreements About AI Development, A. Scher & L. Thiergart (2025). https://arxiv.org/abs/2506.15867
20. Proofs of Useful Work from Arbitrary Matrix Multiplication, I. Komargodski & O. Weinstein (2025). https://arxiv.org/abs/2504.09971
21. Pearl Floating Point Scheme Specification, Pearl Research Team (2026). https://pearlresearch.ai/Pearl_Whitepaper.pdf
22. pearl: Monorepo for the Pearl network, Pearl Research Labs (2026). https://github.com/pearl-research-labs/pearl