# 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-0001&implementations=M-0001:I-0011

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 |
| Sampled inference recomputation / AI 2040 inference-only verification stack | Proposed | No published adversarial analysis recorded | Adversarial | None | Retrofit device | 0 / 1 / 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.

### Sampled inference recomputation

A proposed retrofit that splits data centres into isolated inference units, copies front-end traffic and re-runs random samples to confirm only declared inference runs. ([Sampled inference recomputation](https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/))

- Assessment: selected implementation [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/).
- Development: Proposed (legacy code R1), assessed for showing that retrofitted data centres run only 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 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
- Sampled inference recomputation

**No new hardware needed**

- 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**

- 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.
- A recomputation family degrades against prompt-controlling adversaries (known failure, demonstrated attack, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/evidence/flaws/3/) [6][7][11][12]. The plan's companion page names DiFR among the recomputation schemes being tested. An independent study found that Gumbel-based inference verification, the family that includes Token-DiFR, leaks roughly twice as many bits per token when the adversary chooses prompts that disrupt the text's structure, across six models of 1 to 32 billion parameters. The slowdown it imposes on a hidden-signalling adversary falls from 146–254 times under benign prompts to 60–118 times. The attack weakens the bound on hidden information in outputs, which the plan relies on to keep undeclared results from leaving.

**Family finding context**

- Context for AI 2040 inference-only verification stack. 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. 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/) [11][12][13]. 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 AI 2040 inference-only verification stack. 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. Only recorded traffic is checked (scope limitation, theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/2/) [11][14]. 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 AI 2040 inference-only verification stack. 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 inference optimizations are not covered (known failure, theoretical argument, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/3/) [15][16]. 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 AI 2040 inference-only verification stack. 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. Mixed hardware widens the honest baseline (known failure, open question, in Sampled inference recomputation; https://trustbutveri.fyi/mechanisms/sampled-inference-recomputation/evidence/flaws/4/) [16]. 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**

- 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.
- The recomputation server must be trusted (scope limitation, theoretical argument, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/evidence/flaws/1/) [6][7][9]. The plan calls the integrity of the recomputation server an extremely important aspect, and its argument that sampling verifies all outputs assumes that the server's computations and outputs can be trusted. The companion page notes that the server sits inside the prover's facility, possibly under the prover's physical control, and that hardening it against integrity attacks needs significant research. Amodo rates recomputation-server security as not on track.
- Spare compute is not verified (scope limitation, theoretical argument, in AI 2040 inference-only verification stack; https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/evidence/flaws/2/) [6]. The plan states that it does not verify that spare compute is unused for unapproved workloads, because this seems very challenging. It relies instead on side-channel bounds and memory wipes, so that the only results that persist are verified inference outputs.

**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
- Sampled inference recomputation: Proposed (legacy code R1), assessed for showing that retrofitted data centres run only inference


## 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.

- **Tamper evidence for verifier devices** (Research demonstration (legacy code R2), assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Sampled inference recomputation waits on it: Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research.
- **Memory wiping and proofs of secure erasure** (Proposed (legacy code R1), assessed for showing that no data from earlier work persists in memory the wipe reaches)
  - Sampled inference recomputation waits on it: Memory wiping may use existing algorithms, but hardware testing is at an early stage.
- **Network taps and certifiers** (Proposed (legacy code R1), assessed for committing a complete record of cluster traffic, so declared inference can be checked)
  - Sampled inference recomputation waits on it: Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated.
- **Side-channel suppression for isolated facilities** (Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure)
  - Sampled inference recomputation waits on it: There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
- **Whole-workload recomputation (reproducible packets)** (Proposed (legacy code R1), assessed for recomputing whole workloads to show a cluster runs only declared inference)
  - Sampled inference recomputation waits on it: A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work.


## Dependencies

**Missing prerequisites**

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

**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)
- Sampled inference recomputation: A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work. (performance & compatibility; waits on Whole-workload recomputation (reproducible packets)) [7]
- Sampled inference recomputation: Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated. (performance & compatibility; waits on Network taps and certifiers) [7]
- Sampled inference recomputation: Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research. (hardware trust; waits on Tamper evidence for verifier devices) [7][9]
- Sampled inference recomputation: There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist. (coverage & hidden compute; waits on Side-channel suppression for isolated facilities) [7]
- Sampled inference recomputation: Memory wiping may use existing algorithms, but hardware testing is at an early stage. (coverage & hidden compute; waits on Memory wiping and proofs of secure erasure) [7]
- Sampled inference recomputation: Robust red-teaming of recomputation schemes has not started, and most algorithm development remains academic. (adversarial validation) [7][9]


## 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 Sampled inference recomputation.
- 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 Sampled inference recomputation.
- 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 Sampled inference recomputation.

## 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)
- 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)

## 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. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
7. Get Involved in Verification, AI Futures Project (2026). https://ai-2040.com/supplements/verification-plan/get-involved
8. Verifying international AI deals: Plan A, the state-of-play, and what you can do to help, T. Milton et al. (2026). https://amodo.substack.com/p/verifying-international-ai-deals
9. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). https://amododesign.com/ai-verification/plan-a-sitrep/
10. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). https://amododesign.com/notes/2026-09-15-network-tap-inference-verification/
11. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). https://arxiv.org/abs/2511.02620
12. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
13. Bit-Exact AI Inference Verification Without Performance Tradeoffs, N. Cankaya (2026). https://arxiv.org/abs/2606.00279
14. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). https://amododesign.com/notes/2026-06-23-verification-algorithms/
15. TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference, J. M. Ong et al. (2025). https://proceedings.mlr.press/v267/ong25a.html
16. DiFR: Inference Verification Despite Nondeterminism, A. Karvonen et al. (2025). https://arxiv.org/abs/2511.20621