# AI verification proposal

A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-08. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0001,M-0003&implementations=M-0001:I-0012

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 readiness and open flaws. Readiness levels R0 to R4 describe one record's public evidence for its assessed use and are never combined. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and flaws) and https://trustbutveri.fyi/about/readiness/ (readiness levels).

## 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 flaws: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees.

| Mechanism | Readiness | Prover | Attack testing | Hardware | Open flaws | Weights | Inputs and outputs | Training data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Sampled inference recomputation / Low-trust AI compute verification system overview | R1 | Adversarial | Analysis | Retrofit device | 0 / 2 / 1 | unspecified | unspecified | unspecified |
| Whole-workload recomputation (reproducible packets) | R1 | Adversarial | None | Retrofit device | 0 / 2 / 0 | depends | depends | depends |

## Claims

No claims chosen.

## Mechanisms

### Sampled inference recomputation

A retrofittable reference design in which network taps commit to all facility traffic, and air-gapped, independently sourced checkers later re-run randomly challenged records. ([Sampled inference recomputation](https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/))

- Assessment: selected implementation [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/).
- Readiness: R1 Proposed, assessed for screening challenged records to show declared inference compute is not training.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: analysis. Category: Isolation & system architectures.
- 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.

### 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.
- Readiness: R1 Proposed, assessed for recomputing whole workloads to show a cluster runs only declared inference.
- 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.


## Properties

**Built for an adversarial prover**

- Sampled inference recomputation
- Whole-workload recomputation (reproducible packets)


## Attack testing

Published attempts to break a system, including those that found failures. Testing history does not establish that open flaws are resolved.

**Testing history**

- Sampled inference recomputation / Low-trust AI compute verification system overview: Analysis


## Limits

**Open significant flaws**

- Mismatches cannot be attributed to cheating or error (theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-1) [1]. A failed hash or replay does not show whether it came from an evasion attempt, a random bit flip or an evaluation error. The author notes that if detected anomalies can plausibly be waved off as malfunctions, deterrence becomes less effective, so the parties need an agreed escalation procedure that ends in attribution.
- Inspector agents may be manipulable (open question, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-3) [1]. Automated compliance screening with LLM-based inspector agents must resist prompt-injection attacks. Adversarially trained systems might hide malicious actions with steganography, which makes backdoor detection an open problem.
- Spare compute is outside the scheme (theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/#flaw-1) [6][9]. 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, not in the proposal). Proposed as one input to accounting for spare capacity on declared hardware.
- Non-compliant work could be encoded inside compliant-looking packets (theoretical argument, in Whole-workload recomputation (reproducible packets); https://trustbutveri.fyi/mechanisms/reproducible-computation-packets/#flaw-2) [9]. The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface.

**Family finding context**

- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Tolerance for numerical noise leaves a covert channel (demonstrated attack, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-1) [3][4][5]. 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.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Only recorded traffic is checked (theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-2) [4][6]. 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.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Some inference optimizations are not covered (theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-3) [7][8]. 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.
- Context for Low-trust AI compute verification system overview; applicability depends on the finding's scope. Mixed hardware widens the honest baseline (open question, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/#flaw-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.

**Open minor flaws**

- Deliberate faults leak a bit each (theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/#flaw-2) [1]. A malicious device can leak one bit by deliberately outputting a wrong result, which blocks a disclosure when the cross-comparison fails. The design therefore needs a pre-agreed budget of tolerated faults.

**Not yet demonstrated**

- Sampled inference recomputation: R1 Proposed, assessed for screening challenged records to show declared inference compute is not training
- Whole-workload recomputation (reproducible packets): R1 Proposed, assessed for recomputing whole workloads to show a cluster runs only declared inference


## Possible additions

Mechanisms on the map, not in the proposal, that the records connect to an unaddressed or partly addressed claim, an open flaw or a dependency. Pointers, not recommendations: each brings its own readiness level and flaws, and none is claimed to close a flaw.

- **Proofs of useful work for capacity accounting** (R1 Proposed, assessed for bounding the spare capacity of declared hardware that could run training)
  - Bears on the open significant flaw "Spare compute is outside the scheme" in Whole-workload recomputation (reproducible packets). Proposed as one input to accounting for spare capacity on declared hardware.
- **Deterministic and bit-exact inference** (R3 In production, assessed for reproducing open-model inference from receipts in Gensyn's information-market service)
  - Sampled inference recomputation waits on it: Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question.
  - Whole-workload recomputation (reproducible packets) waits on it: Workloads are not reproducible by default, and achieving reproducibility may cost performance.
- **Tamper evidence for verifier devices** (R2 Demonstrated, assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Sampled inference recomputation waits on it: Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging.
- **Timed challenge-response and memory-occupation challenges** (R2 Demonstrated, assessed for detecting whether a GPU is doing other work)
  - Sampled inference recomputation waits on it: Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open.
- **Network taps and certifiers** (R1 Proposed, assessed for committing a complete record of cluster traffic, so declared inference can be checked)
  - Sampled inference recomputation waits on it: Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question.
  - 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.
- **Side-channel suppression for isolated facilities** (R1 Proposed, assessed for bounding physical covert channels out of a verified enclosure)
  - Sampled inference recomputation waits on it: A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed.


## Dependencies

**Missing prerequisites**

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

**Shared foundations**

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

**Blockers**

- Sampled inference recomputation: Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question. (performance & compatibility; waits on Network taps and certifiers) [1]
- Sampled inference recomputation: Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question. (performance & compatibility; waits on Deterministic and bit-exact inference) [1]
- Sampled inference recomputation: Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging. (hardware trust; waits on Tamper evidence for verifier devices) [1]
- Sampled inference recomputation: A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed. (coverage & hidden compute; waits on Side-channel suppression for isolated facilities) [1]
- Sampled inference recomputation: Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open. (coverage & hidden compute; waits on Timed challenge-response and memory-occupation challenges) [1]
- Sampled inference recomputation: The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. (adversarial validation) [1]
- 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) [9]
- 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) [11]
- 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) [9][11]
- 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) [6]


## What the verifier sees

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

## Implementations

- 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)
- Whole-workload recomputation (reproducible packets): [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture)

## Sources

1. 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
2. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). https://arxiv.org/abs/2606.10724
3. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
4. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
5. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
6. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
7. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). https://proceedings.mlr.press/v267/ong25a.html
8. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). https://arxiv.org/abs/2511.20621
9. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
10. Scaling Recomputation Inference Verification, Amodo Design (2026). https://amododesign.com/notes/2026-09-02-scaling-recomputation-inference-verification/
11. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/
12. Get Involved in Verification, AI Futures Project (2026). https://ai-2040.com/supplements/verification-plan/get-involved
13. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). https://arxiv.org/abs/2208.03567