AZR: Risk-Adaptive Verification for Decentralized AI Inference on Blockchain Rollups
Open accessAbstract
Abstract The rapid growth of decentralized AI applications has created a fundamental tension between computational integrity, model confidentiality, latency, and economic efficiency. Existing verification approaches, including zero-knowledge machine learning (zkML), optimistic machine learning (opML), and trusted execution environments (TEEs), provide strong guarantees along some dimensions but fail to simultaneously satisfy the practical requirements of large-scale AI inference systems deployed on blockchain infrastructure. This paper introduces AZR, a risk-adaptive verification architecture for decentralized AI inference on blockchain rollups. AZR dynamically selects among TEE attestation, optimistic fraud proofs, and zero-knowledge verification according to a query-specific risk function that captures economic value, adversarial exposure, and dispute likelihood. By allocating stronger verification mechanisms only to high-risk workloads, AZR balances security with operational efficiency while preserving computational integrity, model confidentiality, and input privacy. We formalize the verifier selection problem as a constrained optimization framework and analyze its security and economic properties under rational adversaries. Experimental evaluation across representative workloads, including ResNet-50, BERT-Base, and LLaMA-7B, demonstrates that AZR achieves substantial cost reductions relative to uniform zkML deployment while maintaining strong security guarantees. Under a representative workload distribution, AZR reduces verification costs by up to 61% compared with pure zkML systems, while enabling low-latency responses for the majority of inference requests. These results suggest that adaptive verification architectures provide a practical pathway toward scalable and trustworthy decentralized AI systems, bridging the gap between cryptographic assurance and the performance requirements of real-world blockchain applications.
AI snapshot
Written from the paper’s full text.
TL;DR
AZR proposes a risk-adaptive verification architecture for decentralized AI inference on blockchain rollups, scoring each query by \(R(q)=\alpha V+\beta A+\gamma D\) and dispatching it to TEE, TEE+opML, or TEE+opML+zkML tiers. It reports 61% mean gas reduction versus uniform zkML on BERT-Base and 80% of LLaMA-7B queries under 5 seconds user-visible latency, with LLaMA-7B results explicitly analytical projections.
Key findings
- AZR formalizes threshold dispatch with \(\tau_1=0.30\), \(\tau_2=0.70\), and calibrated weights \(\alpha=0.41\), \(\beta=0.29\), \(\gamma=0.30\), yielding a 40% low-risk / 40% medium-risk / 20% high-risk workload split.
- Under that split, AZR reports a 61% reduction in mean gas cost relative to uniform zkML on BERT-Base workloads; AZR gas per query is computed as \(0.4 \times C_{tee} + 0.4 \times C_{opml} + 0.2 \times C_{zk}\), divided by batch size \(m=10\), with reference tier costs \(C_{tee}\approx50K\), \(C_{opml}\approx150K\), and \(C_{zk}\approx250\text{–}700K\) gas.
- For LLaMA-7B, 80% of queries finish in under 5 seconds of user-visible latency; the high-risk 20% receive a provisional TEE result immediately while ZK proof completion is asynchronous and estimated at approximately 48 minutes.
- Security analysis gives per-tier soundness: \(\epsilon_{TEE}=p_{hw}\), \(\epsilon_{TEE+opML}=p_{hw}\cdot p_{nc}\) under independence or \(\le p_{hw}\) conservatively, and \(\epsilon_{TEE+opML+zkML}=\text{negl}(\lambda)\); the risk classifier achieves F1 = 0.91 on a synthetic 10,000-query dataset, with a 4.3% false-negative rate at \(\tau_2\).
Methods
- Design: a Layer-2 rollup architecture with a normalized risk function, threshold-based verifier dispatch, a rational adaptive adversary model, per-tier security proofs, and an aggregate soundness/cost-dominance argument.
- Data/sample: evaluation on ResNet-50, BERT-Base, and LLaMA-7B under a 40/40/20 workload split, with \(n=30\) repetitions, on dual-socket Intel Xeon Gold 6338, 512 GB RAM, 4× NVIDIA A100-80 GB GPUs, and Intel SGX DCAP hardware mode.
- Analysis: comparison against pure zkML, pure opML, and TEE-only baselines using gas and latency; LLaMA-7B proving times are extrapolated via \(T_{prove}\propto N^{1.4}\) calibrated on BERT-Base; risk weights are fitted by ridge regression on synthetic queries.
Limitations
- Stated: LLaMA-7B and GPT-scale proving times are analytical extrapolations from published scaling laws, not direct measurements, and should be interpreted as indicative rather than definitive; no full zkML implementation for LLaMA-7B was available in the evaluation environment, and future work is needed for a complete prototype.
- Stated: the 40/40/20 workload distribution reflects a governance-defined policy scenario and may differ across deployments; the risk classifier validation is circular because ground-truth labels come from the same governance policy that specifies the risk function, so F1 = 0.91 indicates recovery of that policy rather than operational risk prediction.
- Evident: the evaluation is not a full end-to-end production prototype, and several AZR gas figures are derived from prior opML/ZKML measurements and analytical projections rather than direct AZR implementation; the paper also notes that the linear risk function ignores possible interactions among \(V\), \(A\), and \(D\).
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 63 cited sources checked · 0 supported · 0 flagged
- Findingown data
80% of LLaMA-7B queries finish in under 5 seconds of user-visible latency.
“Evaluation on ResNet-50, BERT-Base, and LLaMA-7B models under a 40/40/20 workload split shows a 61% reduction in mean gas cost relative to uniform zkML on BERT-Base workloads, with 80% of LLaMA-7B queries finishing in under 5 seconds of user-visible latency.”
Quote in the paperSubstantial claim - Findingreasoning
Under the canonical 40/40/20 workload split, AZR achieves 61% gas cost savings over uniform zkML for BERT-Base workloads.
“Under the canonical 40/40/20 workload split, AZR achieves 61% gas cost savings over uniform zkML for BERT-Base workloads”
- Also stated in: § 7.3 Security Properties Comparison · plain
“This is a 61% reduction relative to uniform zkML (≈265 K gas for BERT-Base).”
Quote in the paperSubstantial claim - Also stated in: § 7.3 Security Properties Comparison · plain
- Findingown data
The fitted classifier achieves an F1 score of 0.91 on a held-out test set.
“The fitted classifier achieves an F 1 score of 0.91 on a held-out test set.”
Quote in the paperNot a substantial claim - Findingreasoning
For LLaMA-7B under the 40/40/20 distribution, the probability that user-visible latency is at most 5 seconds is 0.80.
“Therefore: Pr[L user (q) ≤ 5 s] = 0.80.”
Quote in the paperSubstantial claim - Findingcitation
For LLaMA-7B high-risk queries, the ZK proof completes asynchronously in approximately 48 minutes (estimated [7,19]).
“20% TEE+opML+zkML: provisional TEE result immediately; ZK proof completes asynchronously in approximately 48 min (estimated [7,19]).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [7]
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- cites [19]
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- Findingown data
Ridge regression yields α = 0.41 ± 0.03, β = 0.29 ± 0.04, γ = 0.30 ± 0.03 (95% CI).
“Ridge regression yields: α = 0.41 ± 0.03, β = 0.29 ± 0.04, γ = 0.30 ± 0.03 (95% CI).”
Quote in the paperSubstantial claim - Findingnone given
For m = 10 queries per batch, the amortized overhead per query is approximately 2-5 K gas at post-Dencun pricing.
“For m = 10 queries per batch, the amortized overhead per query is approximately 2-5 K gas at post-Dencun pricing.”
Quote in the paperNot a substantial claim - Findingown data
Evaluation on ResNet-50, BERT-Base, and LLaMA-7B under a 40/40/20 workload split shows a 61% reduction in mean gas cost relative to uniform zkML on BERT-Base workloads.
“Evaluation on ResNet-50, BERT-Base, and LLaMA-7B models under a 40/40/20 workload split shows a 61% reduction in mean gas cost relative to uniform zkML on BERT-Base workloads, with 80% of LLaMA-7B queries finishing in under 5 seconds of user-visible latency.”
Quote in the paperSubstantial claim - Premisecitation
zkML prover complexity grows super-linearly with model size, making it impractical for billion-parameter language models in interactive settings.
“Zero-knowledge machine learning (zkML) delivers cryptographic computational integrity, but its prover complexity grows super-linearly with model size, making it impractical for billion-parameter language models in interactive settings.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5]
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- cites [7]
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- Interpretationreasoning
For the high-risk tier, by the zero-knowledge property of the proof system, the proof transcript π_zk leaks no information about θ besides what is already leaked by the output y.
“For the high-risk tier, by the zero-knowledge property of the proof system (Definition 3.1), the proof transcript π zk leaks no information about θ besides what is already leaked by the output y.”
Quote in the paperSubstantial claim - Interpretationreasoning
For the TEE+opML tier, under statistical independence, the soundness failure probability ε_TEE+opML equals p_hw • p_nc, where p_nc is the probability that none of n_c independent challengers responds within ∆t.
“ϵ TEE+opML = p hw • p nc (under statistical independence), (22”
Quote in the paperSubstantial claim- The authors state this as an assumption: The events of hardware compromise and challenger suppression are statistically independent.
- Interpretationown data
AZR provides formal security proofs for the computational integrity and model confidentiality of each tier.
“We formulate the system under a rational adaptive adversary model and give formal security proofs for the computational integrity and model confidentiality of each tier.”
Quote in the paperSubstantial claim - Interpretationreasoning
The amortized per-query verification cost is C_amortized_q = C_v + C_batch/m.
“C amortized q = C v + C batch m ,(26)”
Quote in the paperSubstantial claim - Interpretationreasoning
Bypassing all three barriers simultaneously requires hardware compromise, hash collision, and registry tampering, which is infeasible under Assumption 4.1.
“Bypassing all three simultaneously requires hardware compromise, hash collision, and registry tampering, which is infeasible under Assumption 4.1.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Under-reporting v(q) by ∆v reduces both R(q) and the stake s_q = κ • R(q) by κα∆v/V_max.
“By Remark 4.2, under-reporting v(q) by ∆v reduces both R(q) and the stake s q = κ • R(q) by κα∆v/V max .”
Quote in the paperSubstantial claim - Interpretationreasoning
An F1 = 0.91 indicates the degree to which ridge regression recovers the policy rather than how well the policy actually identifies operational risk.
“An F 1 = 0.91 thus indicates the degree to which ridge regression recovers the policy rather than how well the policy actually identifies operational risk.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Hardware attestation should be viewed as a probabilistic guarantee, not an absolute one.
“Overall, one should view hardware attestation as a probabilistic guarantee, not an absolute one.”
Quote in the paperSubstantial claim - Interpretationreasoning
Assigning a fixed verification policy uniformly to all workloads results in either systematic over-spending on low-risk queries or systematic under-protection of high-risk ones.
“The result is either systematic over-spending on low-risk queries or systematic under-protection of high-risk ones.”
Quote in the paperSubstantial claim - Premisecitation
In optimistic rollups, finality is T_op_final = ∆t + T_settle, where ∆t ranges from hours to several days in production deployments.
“In optimistic rollups, the commitment is accepted unless challenged within window ∆t; finality is: T op final = ∆t + T settle ,(4) where ∆t ranges from hours to several days in production deployments [9,35].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [9]
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- cites [35]
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- Premisecitation
Prover complexity behaves as T_prove(N) = Θ(N log N) due to polynomial commitment, FFT-based interpolation, and multi-scalar multiplication.
“Prover complexity behaves as T prove (N ) = Θ(N log N ),(2) due to polynomial commitment, FFT-based interpolation, and multi-scalar multiplication [34].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The expected verification failure probability under policy Π is E_q∼Q ε_Π(R(q)) = p_T ε_T + p_O ε_O + p_Z ε_Z.
“E q∼Q ϵ Π(R(q)) = p T ϵ T + p O ϵ O + p Z ϵ Z .(28)”
Quote in the paperSubstantial claim - Interpretationreasoning
In environments where challenger participation can be economically suppressed, or where the inference output triggers a settlement faster than the window expires, the 1-of-n honesty assumption is operationally fragile.
“In environments where challenger participation can be economically suppressed, or where the inference output triggers a settlement faster than the window expires, this assumption is operationally fragile.”
Quote in the paperSubstantial claim - Interpretationcitation
Under Assumption 4.1 and knowledge soundness of the underlying zkSNARK, for any PPT adversary and any query dispatched to the high-risk tier, the probability that the verifier accepts an invalid result is at most negligible in λ.
“Under Assumption 4.1 and knowledge soundness of the underlying zkSNARK [32], for any PPT adversary A and any query dispatched to the high-risk tier: Pr[V(H(x), H(θ), y, π zk ) = 1 ∧ y ̸ = F θ (x)] ≤ negl(λ).(27)”
- Also stated in: § Additional information · plain
“Under knowledge soundness of Groth16 [32], no PPT adversary generates an accepting proof π zk for statement y ̸ = F θ (x) except with probability negl(λ).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § Additional information · plain
- Interpretationreasoning
The super-linear scaling of prover complexity is the main bottleneck for billion-parameter models.
“This super-linear scaling in complexity is the main bottleneck for billion-parameter models.”
Quote in the paperSubstantial claim - Interpretationreasoning
Setting κ and B_E by governance such that B_E • p_d > κα∆v/V_max holds for the maximum plausible ∆v renders manipulation irrational.
“Setting κ and B E by governance such that this inequality holds for the maximum plausible ∆v renders manipulation irrational.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Actors are rational.
- Premisecitation
opML correctness holds only under the 1-of-n honesty assumption: at least one challenger must remain online and financially motivated throughout the dispute window, which is seven days on Optimism.
“Correctness holds only under the 1-of-n honesty assumption: at least one challenger must remain online and financially motivated throughout the dispute window, which is seven days on Optimism [9].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [9]
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- Interpretationnone given
Let Π* be the adaptive policy minimizing C_v subject to (16)-(18), and let Π_unif be any uniform policy assigning all workload to single tier t*. Under a workload distribution with strictly positive mass on all three risk levels, E[C_v(q, Π*(q))] < E[C_v(q, Π_unif(q))].
“Moreover, let Π * be the adaptive policy minimizing C v subject to ( 16)-( 18), and let Π unif be any uniform policy assigning all workload to single tier t * .Under a workload distribution with strictly positive mass on all three risk levels: E[C v (q, Π * (q))] < E[C v (q, Π unif (q))].(29)”
Quote in the paperSubstantial claim- The authors state this as an assumption: The workload distribution has strictly positive mass on all three risk levels.
- Premisecitation
Existing systems including hybrid designs such as Optimistic TEE-Rollups assign a fixed verification policy uniformly to all workloads.
“Yet existing systems including hybrid designs such as Optimistic TEE-Rollups [14] assign a fixed verification policy uniformly to all workloads.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
For TEE-only and TEE+opML tiers, computational integrity holds with probability 1 - ε_t as defined in Equations (21)-(22).
“For TEE-only and TEE+opML tiers, computational integrity holds with probability 1 -ϵ t as defined in Equations ( 21)-( 22).”
Quote in the paperSubstantial claim - Premisecitation
These cost values are derived from opML gas measurements and standard Groth16 on-chain verifier benchmarks.
“These values are derived from opML gas measurements [8] and standard Groth16 on-chain verifier benchmarks [32].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [32]
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- Study design and conductreasoning
Because ground-truth labels are assigned by the governance risk policy, which also specifies the risk function, the validation is circular.
“Because ground-truth labels are assigned by the governance risk policy, which also specifies the risk function, the validation is circular.”
Quote in the paperNot a substantial claim - Interpretationnone given
AZR replaces the general-purpose MIPS VM with a specialized AI inference virtual machine VM_AI whose instruction set covers matrix multiplication, fixed-point accumulation, activation evaluation, and deterministic attention kernels.
“AZR replaces the general-purpose MIPS VM with a specialized AI inference virtual machine VM AI whose instruction set covers matrix multiplication, fixed-point accumulation, activation evaluation, and deterministic attention kernels.”
Quote in the paperSubstantial claim - Premisecitation
Proving still takes hours even for a 7B model with circuit reduction techniques.
“Similarly, the circuit reduction technique of self-attention used in VeriLLM [19] can significantly reduce circuit size, but proving still takes hours even for a 7B model.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisenone given
A rational challenger participates in an opML dispute when U_C = p_w B_E - G_D > 0, where p_w is win probability, B_E is executor bond, and G_D is gas cost of dispute participation.
“A rational challenger participates when U C = p w B E -G D > 0, (13 ) where p w is win probability, B E is executor bond, and G D is gas cost of dispute participation.”
Quote in the paperSubstantial claim - Premisereasoning
No prior system selects a verification mechanism in response to the risk of each query.
“To our knowledge, no prior system selects a verification mechanism in response to the risk of each query.”
Quote in the paperNot a substantial claim - Interpretationnone given
AZR responds to the empirical attack surface by treating p_hw as an explicit, observable system parameter rather than assuming it is zero.
“AZR responds to this empirical attack surface by treating p hw as an explicit, observable system parameter rather than assuming it is zero.”
Quote in the paperSubstantial claim - Premisecitation
The opML tier follows Arbitrum Nitro's interactive fault-proof design [20].
“The opML tier follows Arbitrum Nitro's interactive fault-proof design [20].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The risk function assigns higher scores to workloads that are higher valued, more sensitive, and less trusted.
“Lemma 5.3 confirms that the risk function will assign higher scores to workloads that are higher valued, more sensitive, and less trusted than the semantic meaning it is intended to convey.”
Quote in the paperSubstantial claim - Interpretationnone given
For the TEE and TEE+opML tiers, assuming semantic security of the enclave sealing encryption, no PPT adversary can learn anything about θ from the attestation report or the opML trace, with probability 1 - p_hw over the hardware integrity.
“For the TEE and TEE+opML tiers, assuming semantic security of the enclave sealing encryption, no PPT adversary can learn anything about θ from the attestation report or the opML trace, with probability 1 -p hw over the hardware integrity.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The enclave sealing encryption is semantically secure.
- Premisecitation
The security model of optimistic fraud proofs relies on the 1-of-n honesty assumption: at least one challenger remains honest and online throughout the challenge window ∆t.
“The security model relies on the 1-of-n honesty assumption: at least one challenger remains honest and online throughout challenge window ∆t [27].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Since N_AI ≪ N_MIPS for matrix-dominated workloads, one-step proof resolution on VM_AI costs substantially less than MIPS replay.
“Since N AI ≪ N MIPS for matrixdominated workloads, one-step proof resolution on VM AI costs substantially less than MIPS replay.”
Quote in the paperNot a substantial claim - Premisenone given
If N is the effective number of operations, the total number of constraints behaves as C(N) = Θ(N).
“If N is the effective number of operations, then the total number of constraints behaves as C(N ) = Θ(N ).”
Quote in the paperNot a substantial claim - Interpretationreasoning
A composition policy that selects primitives for each query according to its requirements should satisfy all requirements across a realistic distribution of queries.
“However, Theorem 4.3 is not a simple impossibility result but rather a structural insight into the nature of the problem: each primitive satisfies a different subset of the requirements, so a composition policy that selects primitives for each query according to its requirements should satisfy all requirements across a realistic distribution of queries.”
Quote in the paperSubstantial claim - Interpretationreasoning
If the query is subsequently disputed and the adversary loses (probability p_d), the slashed bond exceeds the tier-routing gain whenever B_E • p_d > κα∆v/V_max.
“If the query is subsequently disputed and the adversary loses (probability p d ), the slashed bond exceeds the tier-routing gain whenever B E • p d > κα∆v/V max .”
Quote in the paperSubstantial claim - Interpretationnone given
For the TEE tier, the soundness failure probability ε_TEE equals the hardware compromise probability p_hw.
“ϵ TEE = p hw ,(21)”
- Also stated in: § 6.2 TEE Side-Channel Attacks · plain
“The TEE tier maintains integrity up to the probability p hw ; this is translated into an adversarial effort required for the attack.”
Quote in the paperSubstantial claim - Also stated in: § 6.2 TEE Side-Channel Attacks · plain
- Study design and conductown data
Several measurements reported in this work are analytical projections derived from prior zkML benchmarks and published scaling laws rather than direct implementations.
“Several measurements reported in this work are analytical projections derived from prior zkML benchmarks and published scaling laws rather than direct implementations.”
Quote in the paperSubstantial claim - Premisecitation
In optimistic fraud proofs, total on-chain dispute cost is O(log N).
“Total on-chain dispute cost is O(log N ) [35].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationnone given
For the TEE+opML+zkML tier, the soundness failure probability ε_TEE+opML+zkML is negligible in the security parameter λ.
“ϵ TEE+opML+zkML = negl(λ).(23)”
Quote in the paperNot a substantial claim - Premisecitation
The canonical blockchain mechanism for verifying computation, re-executing every instruction on every validating node, is infeasible for AI inference: even a modestly sized transformer would exhaust the Ethereum block gas limit by many orders of magnitude.
“The canonical blockchain mechanism for verifying computation, namely re-executing every instruction on every validating node, is infeasible for AI inference: even a modestly sized transformer would exhaust the Ethereum block gas limit by many orders of magnitude [4].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
For fixed (α, β, γ) and fixed Q, the routing probabilities (p_T, p_O, p_Z) are determined by (τ1, τ2) via integration of the risk score distribution.
“For fixed (α, β, γ) and fixed Q, the routing probabilities (p T , p O , p Z ) are determined by (τ 1 , τ 2 ) via integration of the risk score distribution.”
Quote in the paperSubstantial claim - Interpretationnone given
An executor who returns an incorrect result obtains gain δ with expected cheating utility U_E = δ - B_E • p_d, where p_d = 1 -(1 -u_c)^{n_c} is detection probability.
“An executor who returns an incorrect result obtains gain δ with expected cheating utility: U E = δ -B E • p d ,(30) where p d = 1 -(1 -u c ) nc is detection probability.”
Quote in the paperSubstantial claim - Interpretationreasoning
Cheating is irrational when B_E > δ/p_d.
“Cheating is irrational when B E > δ/p d .”
Quote in the paperSubstantial claim- The authors state this as an assumption: Actors are rational.
- Interpretationcitation
Under knowledge soundness of Groth16 [32], no PPT adversary generates an accepting proof π_zk for statement y ≠ F_θ(x) except with probability negl(λ).
“Under knowledge soundness of Groth16 [32], no PPT adversary generates an accepting proof π zk for statement y ̸ = F θ (x) except with probability negl(λ).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32]
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- May combine two claims. 60
- Classified as probably not a substantial claim. 28
May combine two claims.
Findingnone givenFor LLaMA-7B (≈14 GB), computing SHA-256 of the weights takes approximately 15 seconds on commodity hardware; doing this per-query would dominate latency.
“For LLaMA-7B (≈14 GB), computing SHA-256 of the weights takes approximately 15 seconds on commodity hardware; doing this per-query would dominate latency.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAZR is the only system in the evaluation able to meet all three properties-computational integrity, model confidentiality, and input privacy-that no single-tier architecture can satisfy for all workload types.
“AZR is the only system that simultaneously satisfies CI, MC, and IP; the partial marks on LA and LC reflect the high-risk tier's asynchronous finality and the small dispatch overhead.”
- Also stated in: § 10 Conclusion · plain
“AZR is the only system in the evaluation able to meet all three properties-computational integrity, model confidentiality, and input privacy-that no single-tier architecture can satisfy for all workload types.”
Quote in the paperSubstantial claim- Also stated in: § 10 Conclusion · plain
May combine two claims.
Findingown dataAt threshold τ2, the false-negative rate is 4.3%, so 4.3% of the actual high-risk queries are routed into the medium-risk tier.
“for example, at τ 2 , the false-negative rate is 4.3%, so 4.3% of the actual high-risk queries are routed into the medium-risk tier.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationopML achieves low normal-path overhead at the expense of a multi-day dispute window and a liveness assumption over economically rational challengers.
“Optimistic verification (opML) achieves low normal-path overhead at the expense of a multi-day dispute window and a liveness assumption over economically rational challengers.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8]
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- cites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationopML correctness guarantee is economic rather than cryptographic: it holds only if at least one honest challenger stays online and motivated for the entire seven-day dispute window.
“The correctness guarantee is economic rather than cryptographic: it holds only if at least one honest challenger stays online and motivated for the entire seven-day dispute window.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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Interpretationnone givenFor high-risk queries, the user receives the TEE-attested provisional result immediately upon enclave completion; ZK proof generation proceeds asynchronously and is submitted to the L1 contract upon completion, providing deferred cryptographic finality.
“For high-risk queries, the user receives the TEE-attested provisional result immediately upon enclave completion.ZK proof generation proceeds asynchronously and is submitted to the L1 contract upon completion, providing deferred cryptographic finality.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataSeveral results reported in this work rely on analytical extrapolations rather than direct measurements.
“Several results reported in this work rely on analytical extrapolations rather than direct measurements.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationHybrid systems such as Optimistic TEE-Rollups combine two mechanisms but do not allow per-query policy optimization.
“Hybrid systems such as Optimistic TEE-Rollups [14] combine two mechanisms but do not allow per-query policy optimization.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Premisenone givenExisting works have solved the verification problem along only one axis: zkML ensures cryptographic soundness; opML ensures low cost in the normal path; and TEE-based systems ensure low latency and confidentiality.
“Existing works have solved the verification problem along only one axis: zkML ensures cryptographic soundness; opML ensures low cost in the normal path; and TEE-based systems ensure low latency and confidentiality.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLee et al. [11] showed that the branch-shadowing technique can be used to leak SGX control flow from an OS-privileged adversary, as the Branch Target Buffer (BTB) is shared between the enclave and the OS.
“Lee et al. al. [11] showed that the branch-shadowing technique can be used to leak SGX control flow from an OS-privileged adversary, as the Branch Target Buffer (BTB) is shared between the enclave and the OS.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
Interpretationnone givenAZR is a Layer-2 rollup framework that routes each inference request to the minimum-cost verification pipeline satisfying its assurance requirement.
“AZR is a Layer-2 rollup framework that routes each inference request to the minimum-cost verification pipeline satisfying its assurance requirement.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningLLaMA-7B and GPT-scale proving times are derived from previously published scaling laws and should be interpreted as indicative rather than definitive.
“In particular, LLaMA-7B and GPT-scale proving times are derived from previously published scaling laws and should therefore be interpreted as indicative rather than definitive.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenExecutor reputation Rep_i feeds into bond requirement (32), creating economic incentives for sustained honest participation without requiring trusted third parties for reputation management.
“Executor reputation Rep i feeds into bond requirement (32), creating economic incentives for sustained honest participation without requiring trusted third parties for reputation management.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationTEE enables low-latency confidential execution, but documented microarchitectural side-channel vulnerabilities mean attestation cannot be treated as an unconditional integrity guarantee.
“Trusted execution environments (TEE) enable low-latency confidential execution, but documented microarchitectural side-channel vulnerabilities mean that attestation cannot be treated as an unconditional integrity guarantee.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSTARKs achieve O(log N) verifier complexity and require no trusted setup, at the cost of larger proofs.
“STARKs [33] achieve O(log N ) verifier complexity and require no trusted setup, at the cost of larger proofs.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationReference values for on-chain verification cost are C_tee ≈ 50K gas, C_opml ≈ 150K gas, and C_zk ≈ 250-700K gas.
“where reference values are C tee ≈ 50 K gas (attestation verification via DCAP on-chain check), C opml ≈ 150 K gas (Merkle commitment plus trace root on normal path), and C zk ≈ 250-700 K gas (Groth16 pairing check, depending on circuit size).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationPLONK avoids per-circuit ceremonies via a universal structured reference string.
“PLONK [17] avoids per-circuit ceremonies via a universal structured reference string.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe additive risk function ignores possible interactions: a query with medium V and medium A could be riskier than the linear combination αV + βA indicates.
“Such an approach ignores possible interactions: a query with medium V and medium A could, for instance, be riskier than the linear combination αV + βA indicates.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe workload distribution used in AZR evaluation (40/40/20) reflects a governance-defined policy scenario and may differ across deployment environments.
“Additionally, the workload distribution used in AZR evaluation (40/40/20) reflects a governance-defined policy scenario and may differ across deployment environments.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningAZR requires executors to use a canonical, on-chain-committed 16-bit fixed-point quantization standard upon model registration, ensuring deterministic equivalence between floating-point results inside the enclave and field-element arithmetic in the circuit.
“AZR requires executors to use a canonical, on-chain-committed 16-bit fixed-point quantization standard upon model registration.This ensures deterministic equivalence between floating-point results inside the enclave and field-element arithmetic in the circuit.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThree independent barriers prevent an adversarial executor from substituting a cheaper model θ' ≠ θ: H(θ) is embedded in the attestation report and checked against the on-chain model registry; for high-risk queries, H(θ) is a public input to the ZK circuit; and the on-chain model registry maps approved identifiers to precomputed hash commitments.
“Three independent barriers prevent this.First, H(θ) is embedded in the attestation report (11) and checked against the on-chain model registry.Second, for high-risk queries, H(θ) is a public input to the ZK circuit; a proof for a different model requires a different public input, which the verifier rejects.Third, the on-chain model registry maps approved identifiers to precomputed hash commitments”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationnone givenThe TEE+opML tier's integrity guarantee is Pr[invalid result accepted] = ε_TEE+opML ≤ p_hw • p_nc under the independence model, or ≤ p_hw conservatively; this is a probabilistic, not cryptographic, guarantee.
“The TEE+opML tier's integrity guarantee is: Pr[invalid result accepted] = ϵ TEE+opML ≤ p hw • p nc under the independence model, or ≤ p hw conservatively.Note that this is a probabilistic, not cryptographic, guarantee.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Hardware compromise and challenger non-participation are independent, so the TEE+opML integrity error is bounded by p_hw • p_nc.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationZhao et al. identify the verification gap as the key remaining open problem for decentralized AI: all existing systems face the tradeoff between either fully trusting the executor or paying prohibitive verification costs.
“Zhao et al. [29] identify the verification gap as the key remaining open problem for decentralized AI: all existing systems face the tradeoff between either fully trusting the executor or paying prohibitive verification costs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenUnder an adversary who simultaneously compromises the TEE and economically suppresses all challengers, the events are perfectly correlated, and the conservative bound is ε_TEE+opML = p_hw.
“Under an adversary who simultaneously compromises the TEE and economically suppresses all challengers, the events are perfectly correlated, and the conservative bound is ϵ TEE+opML = p hw .”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationAZR is compatible with Optimism Cannon [9], which uses a deterministic MIPS VM for dispute resolution.
“AZR is compatible with Optimism Cannon [9], which uses a deterministic MIPS VM for dispute resolution.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationCorrectness of optimistic fraud proofs is only assumed during the ∆t window, which is an economic rather than a cryptographic assumption: it holds if and only if there exists no adversary who can corrupt or economically silence every single independent challenger.
“Thus, correctness is only assumed during the ∆t window, which is an economic rather than a cryptographic assumption: it holds if and only if there exists no adversary who can corrupt or economically silence every single independent challenger.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisereasoningIn zkML circuits, linear layers compile into matrix-vector multiplications that are the cheapest circuit primitives; activation functions require lookup arguments or range checks; and Softmax is the most expensive layer.
“In this formulation, linear layers compile into matrix-vector multiplications that are the cheapest circuit primitives.Activation functions, on the other hand, require lookup arguments or range checks.Softmax is the most expensive layer since it requires approximating exponentials.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenUser-visible latency L_user_t = L_inf + L_att,t + L_batch, where L_inf is inference execution time, L_att,t is attestation or commitment generation overhead (near-zero for TEE, seconds for opML trace, zero for ZK which runs asynchronously), and L_batch is rollup batching delay (1-12 s for Ethereum L2s).
“L user t = L inf + L att,t + L batch ,(19) where L inf is inference execution time, L att,t is attestation or commitment generation overhead (near-zero for TEE, seconds for opML trace, zero for ZK which runs asynchronously), and L batch is rollup batching delay (1-12 s for Ethereum L2s).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationzkML systems compile neural network inference into arithmetic circuits, producing succinct proofs that any on-chain verifier can check cheaply.
“Systems such as ZKML [5] and the work of Kang et al. [6] compile neural network inference into arithmetic circuits, producing succinct proofs that any on-chain verifier can check cheaply.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenAZR scores each inference query by a risk function R(q) = αV + βA + γD, where V measures economic exposure, A measures adversarial attack surface, and D measures data sensitivity, and dispatches the query to one of three verification tiers based on the score.
“In other words, instead of using a uniform mechanism for all workloads, AZR scores each inference query by a risk function of the form R(q) = αV + βA + γD, where V measures economic exposure, A measures the adversarial attack surface, and D measures the data sensitivity; then, AZR dispatches the query to one of three verification tiers based on the score.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationopML satisfies D5 and is near-satisfying for D4, but violates D1 conditionally: integrity holds only under the 1-of-n honesty assumption, not by cryptographic reduction; D2 and D3 are violated because input and output data must be posted publicly for fraud-proof resolution.
“opML.Satisfies D5 (single hash commitment on the normal path) and is near-satisfying for D4, but violates D1 conditionally: integrity holds only under the 1-of-n honesty assumption, not by cryptographic reduction [27].D2 and D3 are violated because input and output data must be posted publicly for fraud-proof resolution [8].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationzkML provides D1 and D2, but violates D4 and D5 at LLM scale; prover complexity Θ(N log N) yields hours of wall-clock time for N > 10^9, making interactive use infeasible and imposing large on-chain verifier costs for large circuits.
“zkML.Provides D1 (cryptographic integrity) and D2 (zero-knowledge property), but violates D4 and D5 at LLM scale.Prover complexity Θ(N log N ) yields hours of wall-clock time for N > 10 9 [5,7], making interactive use infeasible and imposing large on-chain verifier costs for large circuits.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Study design and conductreasoningLLaMA-7B results should be interpreted as indicative rather than definitive.
“LLaMA-7B results should therefore be interpreted as indicative rather than definitive.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBrasser et al. [12] showed a software-only key recovery side-channel attack that does not require any physical access to the machine.
“In the same paper, Brasser et al. [12] showed a software-only key recovery side-channel attack that does not require any physical access to the machine.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningAZR is a multi-tiered verification architecture for AI inference on blockchain rollups that selects the lowest-cost verification pipeline for each individual request.
“In this paper, we propose AZR (Adaptive zkTEE Rollups): a multi-tiered verification architecture for AI inference on blockchain rollups that selects the lowest-cost verification pipeline for each individual request.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningFor all tiers, confidentiality fails only if the hardware is compromised, which occurs with probability at most p_hw.
“For all tiers, confidentiality fails only if the hardware is compromised, which occurs with probability at most p hw .”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Interpretationnone givenThe risk function R(q) = αV + βA + γD is additive by design: each component contributes independently to the overall risk, and governance may freely adjust the relative weights without needing to calibrate a non-convex problem.
“Equation (5) is additive by design: each component contributes independently to the overall risk, and governance may freely adjust the relative weights (α, β, γ) without needing to calibrate a non-convex problem.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationTEE-only satisfies D4 and D3, and provides D2 under hardware trust, but violates D1 with probability ≥ p_hw > 0 due to hardware attacks, and violates D2 through microarchitectural leakage.
“TEE-Only.Satisfies D4 (low attestation latency) and D3 (input encrypted to enclave), and provides D2 under hardware trust, but violates D1 with probability ≥ p hw > 0 due to hardware attacks [12,13], and violates D2 through microarchitectural leakage [11,25].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningFor the ZK tier, user-visible latency equals L_user_T (TEE attestation arrives immediately), while cryptographic finality latency includes the asynchronous ZK proving time.
“For the ZK tier, user-visible latency equals L user T (TEE attestation arrives immediately), while cryptographic finality latency includes the asynchronous ZK proving time.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe reference implementation uses Groth16: constant proof size (≈200 bytes), O(1) on-chain pairing-check (≈250,000 gas).
“The reference implementation uses Groth16 [32]: constant proof size (≈200 bytes), O(1) on-chain pairing-check (≈250,000 gas).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningThe threshold search over (τ1, τ2) is a two-dimensional grid search, not a free linear program in the thresholds themselves, though the objective is linear in the routing probabilities for fixed thresholds.
“The threshold search over (τ 1 , τ 2 ) ∈ (0, 1) 2 is a two-dimensional grid search not a free linear program in the thresholds themselves, though the objective is linear in the routing probabilities for fixed thresholds.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationopML commits inference onchain with an interactive bisection fallback, reducing normal-path cost to a single hash check.
“He et al. [8] showed that inference can be committed onchain with an interactive bisection fallback, reducing normal-path cost to a single hash check.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningWrapping VM_AI as a Cannon-interpretable program enables deployment within existing Optimism-compatible stacks without modifying L1 fault-proof logic, at the cost of higher replay overhead than the specialized VM.
“Wrapping VM AI as a Cannon-interpretable program enables deployment within existing Optimism-compatible stacks without modifying L1 fault-proof logic, at the cost of higher replay overhead than the specialized VM.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationnone givenUnder the trusted hardware assumption, the TEE tier satisfies D2, D3, and D4; computational integrity holds with probability 1 - p_hw.
“Under the trusted hardware assumption, this tier satisfies D2, D3, and D4.Computational integrity holds with probability 1 -p hw .”
Quote in the paperNot a substantial claim- The authors state this as an assumption: TEE hardware is trusted except for the modeled compromise probability p_hw; under this assumption the TEE tier satisfies confidentiality and input privacy.
May combine two claims.
InterpretationreasoningRequiring challenger bond B_C ≥ G_D before dispute initiation and slashing invalid disputes makes griefing self-defeating for rational actors.
“Mitigation: require challenger bond B C ≥ G D before dispute initiation; invalid disputes result in bond slashing, making griefing self-defeating for rational actors.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Actors are rational.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenCryptographic finality latency L_final_t = L_user_t + L_proof,t + L_verify,t, where L_proof,t is proof generation time (zero for TEE, O(log N) rounds for opML disputes, minutes-to-hours for ZK), and L_verify,t is on-chain confirmation latency (≈12 s on Ethereum mainnet).
“L final t = L user t + L proof,t + L verify,t ,(20) where L proof,t is proof generation time (zero for TEE, O(log N ) rounds for opML disputes, minutes-to-hours for ZK), and L verify,t is on-chain confirmation latency (≈12 s on Ethereum mainnet).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductcitationDue to the absence of a full zkML implementation for LLaMA-7B in the evaluation environment, the reported proving times are analytical estimates derived from the empirical scaling law T_prove ∝ N^1.4 reported in [7], using BERT-Base as the calibration point.
“Due to the absence of a full zkML implementation for LLaMA-7B in our evaluation environment, the reported proving times are analytical estimates derived from the empirical scaling law T prove ∝ N 1.4 reported in [7], using BERT-Base as the calibration point.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningThe three verification paradigms involve fundamentally different cost-security trade-offs, and no single paradigm dominates across all workload types.
“These three paradigms involve fundamentally different costsecurity trade-offs, and no single paradigm dominates across all workload types.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Interpretationnone givenThe verification dispatch layer sits between the requester and the verification backends and uses a formal risk score to select the appropriate tier.
“A verification dispatch layer sits between the requester and the verification backends and uses a formal risk score to select the appropriate tier.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationEIP-4844 blob transactions reduce calldata costs for both rollup variants by approximately 10× relative to standard calldata, materially improving the economics of AZR.
“EIP-4844 blob transactions [28] reduce calldata costs for both rollup variants by approximately 10× relative to standard calldata, materially improving the economics of AZR.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationMicroarchitectural attacks demonstrate that the TEE attestation guarantee is not absolute: branch shadowing, cache timing, and Foreshadow-class speculative execution vulnerabilities have all been shown to compromise enclave confidentiality or attestation key material.
“Microarchitectural attacks demonstrate that this guarantee is not absolute: branch shadowing [11], cache timing [12], and Foreshadow-class speculative execution vulnerabilities [13] have all been shown to compromise enclave confidentiality or attestation key material.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningSince each mechanism fails at least one desideratum for some model scale or workload distribution, no uniform policy dominates.
“Since each mechanism fails at least one desideratum for some model scale or workload distribution, no uniform policy dominates.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationopML normal-path cost is a single hash commitment, orders of magnitude cheaper than zkML proof verification.
“Normal-path cost is a single hash commitment, orders of magnitude cheaper than zkML proof verification.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationcitationPre-registration of the model hash shifts the cost to a one-time setup, and the registry entry is checked against the attestation commitment by the L1 contract.
“Pre-registration shifts this cost to a one-time setup, and the registry entry is checked against the attestation commitment by the L1 contract [10].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationProver time for ResNet-50 is already measured in tens of seconds on a 64-core GPU server, and LLM-scale models extend this to hours, making interactive deployment infeasible.
“The practical limitation is equally unambiguous: prover time for ResNet-50 is already measured in tens of seconds on a 64-core GPU server [7], and LLM-scale models extend this to hours, making interactive deployment infeasible.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningAZR provides formal arguments showing an aggregate soundness theorem and a strict cost dominance theorem for the adaptive dispatch policy compared to any uniform policy.
“Moreover, we provide formal arguments showing an aggregate soundness theorem and a strict cost dominance theorem for the adaptive dispatch policy compared to any uniform policy.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenAZR is the first blockchain-native AI verification architecture to formalize verifier selection as an explicit risk-constrained optimization problem across cryptographic, optimistic, and hardware-based verification domains.
“To the best of our knowledge, AZR is the first blockchain-native AI verification architecture to formalize verifier selection as an explicit risk-constrained optimization problem across cryptographic, optimistic, and hardware-based verification domains.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenExecutor reputation Rep_i = n_success/(1 + n_total) reduces bond requirements for established nodes: B_i = λ_B • R(q)/(1 + Rep_i), creating sustained incentives for honest participation while preserving Sybil resistance via mandatory minimum collateral at Rep_i = 0.
“Executor reputation Rep i = n success /(1 + n total ) reduces bond requirements for established nodes: B i = λ B • R(q) 1 + Rep i ,(32) creating sustained incentives for honest participation while preserving Sybil resistance via mandatory minimum collateral at Rep i = 0.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationVan Bulck et al. [13] demonstrated extraction of SGX attestation keys through speculative execution, undermining the attestation guarantee in unpatched hardware.
“Van Bulck et al. [13] demonstrated extraction of SGX attestation keys through speculative execution, undermining the attestation guarantee in unpatched hardware.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationTEE-based systems run inference inside Intel SGX enclaves and use hardware-signed attestation to communicate integrity to the chain.
“Systems such as Ekiden [10] run inference inside Intel SGX enclaves and use hardware-signed attestation to communicate integrity to the chain.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationProver time for zkML scales as Θ(N log N), which excludes practical verification of LLM-scale models for interactive use.
“Zhang et al. [7] present a comprehensive survey on the topic and conclude that, in general, prover time scales as Θ(N log N ), which excludes practical verification of LLM-scale models for interactive use.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenAs p_hw increases, the governance threshold τ1 can be reduced, routing more workload to the TEE+opML or ZK tiers automatically.
“As p hw increases for example, following a disclosed hardware vulnerability the governance threshold τ 1 can be reduced, routing more workload to the TEE+opML or ZK tiers automatically.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe high-risk tier of AZR uses Groth16 as the reference implementation for verifier gas efficiency, while PLONK is more suited for situations where a large number of different model circuits must be deployed.
“The high-risk tier of AZR uses Groth16 as the reference implementation for verifier gas efficiency, while PLONK is more suited for situations where a large number of different model circuits must be deployed.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSTARKs eliminate trusted setup entirely and offer post-quantum security, at the cost of O(log 2 N) proof size and O(log N) on-chain verifier complexity.
“STARKs [33] eliminate trusted setup entirely and offer post-quantum security, at the cost of O(log 2 N ) proof size and O(log N ) on-chain verifier complexity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningGroth16 proofs (≈200 bytes) are posted as regular calldata; their fixed small size makes calldata pricing more favorable than blob pricing for this component.
“Groth16 proofs (≈200 bytes) are posted as regular calldata their fixed small size makes calldata pricing more favorable than blob pricing for this component.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationEIP-4844 blob transactions reduce rollup calldata costs by roughly 10×, improving the economics of both opML and AZR.
“EIP-4844 [28] introduced blob-carrying transactions that reduce rollup calldata costs by roughly 10×, improving the economics of both opML and AZR.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningThe attestation process only ensures software integrity if the underlying hardware has not been compromised; known attacks on Intel SGX indicate it should not be considered an absolute guarantee.
“However, the attestation process only ensures software integrity if the underlying hardware has not been compromised.While it is a reasonable assumption, known attacks on Intel SGX indicate that it should not be considered an absolute guarantee.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationGroth16 produces constant-size proofs with O(1) on-chain verification, but requires a circuit-specific trusted setup.
“Groth16 [32] produces constant-size proofs with O(1) on-chain verification, but requires a circuit-specific trusted setup.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationcitationAll AZR executions use a canonical 16-bit fixed-point arithmetic standard and deterministic PRNG seeding, as specified by the opML determinism protocol [8], to mitigate non-determinism.
“Mitigation: all AZR executions use a canonical 16-bit fixed-point arithmetic standard (committed on-chain at model registration) and deterministic PRNG seeding, as specified by the opML determinism protocol [8].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenAZR enforces risk-scaled bonds B_E = λ_B • R(q), where governance multiplier λ_B is set such that λ_B • R(q) • p_d > R(q) • V_max for all R(q), bounding the maximum extractable gain from cheating on any single query to less than the value at risk.
“AZR enforces this via risk-scaled bonds: B E = λ B • R(q),(31) where governance multiplier λ B is set such that λ B • R(q) • p d > R(q) • V max for all R(q), bounding the maximum extractable gain from cheating on any single query to less than the”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationnone givenAZR adopts the Arbitrum Nitro convention of automatic timeout resolution favoring the defender on challenger non-response, combined with a force-inclusion fallback path to Layer-1, to mitigate censorship.
“Mitigation: AZR adopts the Arbitrum Nitro convention of automatic timeout resolution favoring the defender on challenger non-response, combined with a force-inclusion fallback path to Layer-1.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationEquivalent bounds hold for PLONK [17] under the algebraic group model, and for STARKs [33] under the random oracle model.
“Equivalent bounds hold for PLONK [17] under the algebraic group model, and for STARKs [33] under the random oracle model.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
The authors state this as an assumption.
TEE hardware is trusted except for the modeled compromise probability p_hw; under this assumption the TEE tier satisfies confidentiality and input privacy.
“Under the trusted hardware assumption, this tier satisfies D2, D3, and D4.”
The authors state this as an assumption.
Hardware compromise and challenger non-participation are independent, so the TEE+opML integrity error is bounded by p_hw • p_nc.
“Pr[invalid result accepted] = ϵ TEE+opML ≤ p hw • p nc under the independence model, or ≤ p hw conservatively.”
The paper relies on this without support.
A rational challenger participates in an opML dispute exactly when expected utility U_C = p_w B_E - G_D > 0.
“A rational challenger participates when U C = p w B E -G D > 0, (13 )”
The authors state this as an assumption.
At least one challenger remains honest and online throughout the challenge window (the 1-of-n honesty assumption).
“The security model relies on the 1-of-n honesty assumption: at least one challenger remains honest and online throughout challenge window ∆t [27].”
The authors state this as an assumption.
The events of hardware compromise and challenger suppression are statistically independent.
“under statistical independence”
The authors state this as an assumption.
The enclave sealing encryption is semantically secure.
“assuming semantic security of the enclave sealing encryption”
The authors state this as an assumption.
The workload distribution has strictly positive mass on all three risk levels.
“Under a workload distribution with strictly positive mass on all three risk levels”
The authors state this as an assumption.
Actors are rational.
“making griefing self-defeating for rational actors”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Gas calculation divides per-query costs by batch size despite Equation (26)
MajorThe evaluation says it divides the weighted per-query tier cost by ten. Equation (26) instead retains that per-query cost and amortizes only the fixed batch overhead.
“AZR gas per query is computed as: 0.4 × C tee + 0.4 × C opml + 0.2 × C zk , then divided by batch size m = 10 per Equation (26).”
“C amortized q = C v + C batch m ,(26) where C v is the per-query tier cost from Equation ( 15) and C batch is the fixed overhead of the batch transaction itself”
BERT-Base zkML baseline changes between prose and table
MinorThe cost-reduction discussion uses approximately 265 K gas for uniform BERT-Base zkML, whereas Table 1 reports 280 K gas. Using the table's 103 K AZR figure and 280 K baseline gives about 63% savings, rather than 61%.
“This is a 61% reduction relative to uniform zkML (≈265 K gas for BERT-Base).”
“| zkML | BERT-Base LLaMA-7B | 210 14,400 | 280 350 | 225 14,415 | 0.004 Crypto 7e-5 Crypto |”
LLaMA-7B high-risk proof time is confused with a workload average
MinorThe stated 48 minutes is 2,880 seconds, the AZR table's asynchronous proving figure. But the table gives 14,400 seconds for a full LLaMA-7B zkML proof; 2,880 seconds is 20% of that figure, not the time for an individual high-risk query's full proof.
“• 20% TEE+opML+zkML: provisional TEE result immediately; ZK proof completes asynchronously in approximately 48 min (estimated [7,19]).”
“| zkML | BERT-Base LLaMA-7B | 210 14,400 | 280 350 | 225 14,415 | 0.004 Crypto 7e-5 Crypto |”
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 5
- S. Lee, M.-W. Shih, P. Gera, T. Kim, H. Kim, and M. Peinado, "Inferring fine-grained control flow inside SGX enclaves with branch shadowing," in Proceedings of the 26th USENIX Security Symposium. USENIX Association, 2017, pp. 557-574, https://www. usenix.org/conference/usenixsecurity17/technical-sessions/presentation/lee-sangho.
- J. Van Bulck, M. Minkin, O. Weisse, D. Genkin, B. Kasikci, F. Piessens, M. Silberstein, T. F. Wenisch, Y. Yarom, and R. Strackx, "Foreshadow: Extracting the keys to the intel SGX kingdom with transient out-of-order execution," in Proceedings of the 27th USENIX Security Symposium. USENIX Association, 2018, pp. 991-1008, https://ww w.usenix.org/conference/usenixsecurity18/presentation/bulck.
- V. Buterin, D. Feist, D. Loerakker, G. Kadianakis, M. Garnett, M. Taiwo, and A. Hay- duk, "EIP-4844: Shard blob transactions," Ethereum Foundation, Tech. Rep., 2022, https://eips.ethereum.org/EIPS/eip-4844.
- E. Ben-Sasson, A. Chiesa, C. Garman, M. Green, I. Miers, E. Tromer, and M. Virza, "Succinct non-interactive zero knowledge for a von Neumann architecture," in Proceedings of the 23rd USENIX Security Symposium. USENIX Association, 2014, pp. 781-796, https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-ben-sas son.pdf.
- H. Kalodner, S. Goldfeder, X. Chen, S. M. Weinberg, and E. W. Felten, "Arbitrum: Scal- able, private smart contracts," in Proceedings of the 27th USENIX Security Symposium. USENIX Association, 2018, pp. 1353-1370, https://www.usenix.org/system/files/confe rence/usenixsecurity18/sec18-kalodner.pdf.
Cited by
Still being checked.
- Authors
- Virani, A.
- Affiliations
- University of Colorado Boulder (US)
- Corresponding author
- Virani, Ankita
- Venue
- Zenodo
- Publisher
- Zenodo
- Published
- 2026-06-15
- OpenAlex
- W7164848715
- Cited by
- 0
- References
- 0
- Type
- posted content
- Language
- en
- Open access
- green
- scholar.colorado.edu
- Locations
- CU Scholar (University of Colorado Boulder) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
- Keywords
- trusted execution environments, constrained optimization, model confidentiality, input privacy, verification cost reduction, large language models
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