Optimistic TEE-Rollups (OTR)

• Optimistic TEE-Rollups (OTRs) are hybrid layer-2 blockchain protocols combining TEEs with fraud-proof mechanisms to reduce gas costs and latency.
• They employ hardware-rooted attestations, dispute resolution via challenge games, and stochastic zero-knowledge spot-checks to ensure result integrity.
• Empirical evaluations show OTRs can lower verification costs by ~86% and support high-throughput applications like scalable verifiable AI inference.

Optimistic TEE-Rollups (OTR) are a hybrid class of layer-2 blockchain protocols that integrate Trusted Execution Environments (TEEs) into optimistic rollup architectures. OTRs seek to minimize gas costs and latency, improve finality, and enhance the verifiability and integrity of off-chain computation—particularly in high-throughput and generative AI settings—while maintaining robust economic and cryptographic security against adversarial behaviors and hardware vulnerabilities. These systems combine hardware-rooted attestations, challenge-response fraud proof mechanisms, and, in advanced instantiations, stochastic zero-knowledge audits and decentralization of attestation to mitigate centralization risks and side-channel attacks. OTRs have demonstrated significant performance gains over both traditional optimistic rollups and ZK-rollups in empirical deployments and simulations (Wen et al., 2024, Chan et al., 23 Dec 2025, Cristiano et al., 27 Nov 2025).

1. Architectural Foundations and Core Components

OTR protocols are structured around several key on-chain and off-chain components. A typical system consists of:

• Sequencers: System actors that batch, order, and execute transactions using code and state confined to a TEE. Sequencers are often distributed and may run heterogeneous TEEs (e.g., Intel SGX, AMD SEV, ARM TrustZone, NVIDIA H100 Confidential Computing) to minimize correlated vulnerabilities (Wen et al., 2024).
• Attestation Registries and Smart Contracts: On-chain managers, such as a Manager Smart Contract (MSC) or Attestation Verifier, maintain registries of sequencer identities and associated attestation evidence. Separate Rollup Smart Contracts (TSC) ingest state roots, verify signatures, coordinate fraud games, and manage withdrawal logic (Wen et al., 2024, Cristiano et al., 27 Nov 2025).
• Data Availability Providers (DAPs): Off-chain actors obligated to serve metadata (e.g., batch data, Merkle trees) upon request, often incentivized and penalized via slashing for non-responsiveness. Compact roots are stored on-chain to optimize gas usage, with full data and proofs mirrored by DAPs (Wen et al., 2024).
• Fishermen / Watchtowers: Off-chain monitors that surveil data availability layers and challenge observed fraud via dispute mechanisms (Chan et al., 23 Dec 2025).
• ZK-Prover Infrastructure: In advanced OTRs, ZK proof machinery is invoked probabilistically to perform spot-checks on random executions (Chan et al., 23 Dec 2025).

Interaction proceeds as follows: clients submit encrypted or plaintext transactions to sequencers. For generative inference, queries (e.g., for LLMs) are processed within a TEE, which emits hardware-rooted attestations binding the computation inputs and outputs. Sequencers then submit signed state roots or output attestations to the manager contract. Once a quorum (e.g., $f+1$ signatures for a $n=2f+1$ committee) is achieved, state updates are finalized on-chain, subject to dispute windows and fraud proof procedures. Metadata is propagated to DAPs, and finality is reached through a hybrid of hardware attestation and optimistic on-chain checks (Wen et al., 2024, Chan et al., 23 Dec 2025, Cristiano et al., 27 Nov 2025).

2. Threat Model and Security Properties

OTR protocols assume a realistic threat landscape where TEEs may be statically or transiently compromised via hardware or side-channel exploits, as well as by host-level adversaries. The principal security invariants are:

• Integrity: No adversary can commit an invalid state root or execution result without control over a threshold of attested TEE signatures. In distributed systems, integrity holds if at most $f<n/2$ sequencer TEEs are compromised (Wen et al., 2024).
• Availability: The liveness of transaction inclusion and withdrawals is ensured optimistically (if sequencers are honest and responsive) or, in the event of TEE unavailability, via fallback challenge and recovery procedures.
• Censorship Resistance: Enforced by TEE confinement and open transaction queues; a compromised host cannot reorder or drop transactions post-entry to the enclave (Cristiano et al., 27 Nov 2025).
• Non-Equivocation: Multiple attested quotes for a given state or batch height must agree on the state root, enforced by on-chain policy (Cristiano et al., 27 Nov 2025).

In advanced OTRs, stochastic zero-knowledge spot-checks are employed to mitigate side-channel attacks and to make mass forging of results cryptoeconomically irrational. The expected adversarial profit from cheating decays rapidly as the probability of detection (from ZK spot-checking and fishermen) increases (Chan et al., 23 Dec 2025).

3. Hybrid Verification and Dispute Resolution

The distinguishing feature of OTRs is their hybrid verification path, balancing performance and security:

• Optimistic Path: TEEs generate attestations binding inputs, outputs, and code hashes (e.g., MRENCLAVE measurements) to results. Smart contracts verify these attestations rapidly (≤1s), enabling sub-second “provisional finality” for users (Chan et al., 23 Dec 2025).
• Fraud-Proof Challenge: Clients and watchtowers monitor the system for omission or misbehavior. If a transaction or inference is not included in time (bounded by challenge parameter $\tau_w$ or $T_{chal}$), a challenge is initiated. Sequencers are forced to include the missing data or risk the system entering a frozen or reclaim mode, allowing users to redeem assets or outputs directly (Wen et al., 2024, Chan et al., 23 Dec 2025).
• ZK Spot-Check: With a small probability $\rho$ (e.g., 0.01), a random execution is selected for zero-knowledge proof validation. Failure to provide a valid ZK proof or fraud proof submission during an open dispute window leads to sequencer slashing and rollback (Chan et al., 23 Dec 2025).

This protocol design ensures the system resolves the “Verifiability Trilemma”: maximizing integrity, minimizing cost, and achieving low latency (Chan et al., 23 Dec 2025). Table 1 compares the verification properties across leading paradigms:

Protocol	Integrity	Latency	Cost	Main Trust Assumption
ZKML	Strong crypto	Hours	\$50+	Mathematical only
opML	Game-theoretic	Days	\%%%%6$n=2f+1$7%%%%N$ sequencer</td> <td></td> </tr> <tr> <td>Standard TEE</td> <td>HW-based</td> <td>ms</td> <td>\$0.02	Vendor trust
OTR	HW+Game+ZK	ms-day	\%%%%8$n=2f+1$9%%%%N$$honest</td> <td></td> </tr> </tbody></table></div><h2 class='paper-heading' id='scalability-cost-and-performance-analysis'>4. Scalability, Cost, and Performance Analysis</h2> <p>Empirical measurements and analysis across several OTR deployments are as follows:</p> <ul> <li><strong>Gas Cost</strong>: OTRs such as TeeRollup achieve$$\sim$86% lower on-chain verification costs than ZK-rollups. With batch sizes $b=2000$, per-transaction gas is$\approx78$ (or \$0.006 at prevailing rates). By comparison, ZK-rollups (e.g., StarkNet) incur higher costs ($\sim$0.043/tx) (Wen et al., 2024). Throughput and Latency: OTRs using modern TEEs (e.g., NVIDIA H100 CC) can attain $>99\%$ of centralized throughput (5{,}000 TPS, $\sim$0.8s latency) under default parameters ($\rho=0.01$), whereas ZKML approaches stall at $<0.1\%$ throughput due to superlinear proof cost. Withdrawal delays are minimized: in the absence of challenge, redemption is possible in 1–5 minutes, far outperforming optimistic rollups with week-long dispute windows (Wen et al., 2024, Chan et al., 23 Dec 2025). Attestation Overhead: Attestation operations (e.g., DCAP quote on SGX) introduce moderate gas consumption (e.g., 12.5M gas per 4 KB quote); attestation renewal frequency and validity windows can be tuned to balance on-chain cost and responsiveness (Cristiano et al., 27 Nov 2025). A plausible implication is that OTR designs are suitable for high-throughput and high-assurance applications where cost and latency are critical, such as decentralized generative AI inference, DeFi, and NFT custody. 5. TEE Integration, Decentralized Attestation, and Side-Channel Mitigation OTRs leverage a spectrum of TEE technologies (Intel SGX, AMD SEV, ARM TrustZone, NVIDIA H100 CC) to isolate execution and bind results to hardware-verified environments. Crucial procedures include: Attestation Generation: TEEs emit hardware-signed quotes containing execution metadata (inputs, outputs, code hash, etc.), which are verified on-chain using attestation smart contracts and (where applicable) decentralized collateral management (e.g., PCK, CRL, TCB info DAOs) (Cristiano et al., 27 Nov 2025). Decentralized Attestation: To address concerns about vendor or authority centralization, verification of TEE attestation is distributed via smart contracts and multi-DApp collateral retrieval, and can be rotated or upgraded via governance (Cristiano et al., 27 Nov 2025). Side-Channel Risk: TEE attestation alone is insufficient to prevent targeted side-channel attacks or key exfiltration. OTRs introduce economic and cryptographic deterrence via (a) unpredictable ZK spot-checks, (b) slashing for fraud detected by fishermen, and (c) parameterizable adversarial profit minimization (Chan et al., 23 Dec 2025). These mechanisms ensure the system remains robust even in the presence of advanced hardware-level threats, albeit with the requirement that at least one sequencer or fisherman remains non-colluding. 6. Advanced Use Case: Scalable Verifiable AI Inference An emerging application of OTR is on-chain generative AI inference at scale. OTR designs address the “Verifiability Trilemma”: (i) ZK proofs yield strong integrity but untenable latency for large models, (ii) optimistic approaches provide speed but weak cryptographic guarantees, and (iii) recent “Proof of Quality” approaches forgo cryptographic integrity. OTR with Proof of Efficient Attribution (PoEA) cryptographically binds attested inference results to specific code and hardware, mediating authenticity and cost (Chan et al., 23 Dec 2025). Provisional finality is achieved in sub-second time by TEE attestations; ZK spot-checks maintain deterrence for high-value queries. Empirical evaluation shows that for $N$-parameter models, OTR amortizes the superlinear ZK overhead ($O(N\log N)$) with high native throughput by selecting $\rho$ (spot-check probability) judiciously. 7. Deployment, Compatibility, and Limitations OTR can be integrated with existing optimistic rollup stacks with minimal code changes: the sequencer operates inside a TEE, augmented with decentralized attestation checking, and off-chain and on-chain logic remain largely unmodified (Cristiano et al., 27 Nov 2025). Core deployment parameters include the challenge window (tuning $\tau_w$ for UX and safety), DAP collateral size and slashing parameters, attestation frequency, and registry validity windows. Notable limitations and open questions include dynamic TEE compromise, adaptive adversary models, parameter tuning for economic penalties versus UX, degree of sequencer/DAP decentralization, and rapidly-recoverable state continuity after catastrophic hardware compromise (Wen et al., 2024, Chan et al., 23 Dec 2025). OTR throughput may be lower than vanilla optimistic rollups (e.g., $\sim7$–$8$ TPS for OP stack+SGX versus $20$–$25$ TPS) and latency higher (21–25s versus 2.5–3.5s), but gains in integrity and security justify the performance tradeoff for high-assurance applications (Cristiano et al., 27 Nov 2025). OTRs synthesize hardware attestation, fraud game economics, and cryptographic audits to enable verifiable, low-latency, and cost-effective off-chain execution and AI inference over decentralized networks, thereby extending optimistic rollup techniques to settings requiring both high throughput and strong integrity guarantees (Wen et al., 2024, Chan et al., 23 Dec 2025, Cristiano et al., 27 Nov 2025). Sponsor Organize your preprints, BibTeX, and PDFs with Paperpile. Get 30 days free Whiteboard Generate a whiteboard explanation of this topic. Topic to Video (Beta) Generate a video overview of this topic. Follow Topic Get notified by email when new papers are published related to Optimistic TEE-Rollups (OTR). Continue Learning How do Optimistic TEE-Rollups leverage TEEs to enhance blockchain security and performance? What specific mechanisms differentiate OTRs from traditional optimistic rollups and ZK-rollups? How is the challenge-response fraud proof integrated within OTR architectures to mitigate adversarial risks? What are the roles of data availability providers and decentralized attestation in maintaining system integrity? Find recent papers about scalable verifiable AI inference. 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