EigenAI: Deterministic Inference, Verifiable Results

TL;DR

EigenAI addresses the verifiability gap in high-stakes AI by delivering bit-for-bit deterministic inference on fixed GPU architectures and coupling it with cryptoeconomic optimistic verification. The system binds security to transparent on-chain receipts, data availability proofs, and TEEs with threshold key management to protect privacy while enabling public auditability. By reducing disputes to simple byte-level equality checks, a stake-backed verifier network can cheaply enforce correctness, slashing misbehaving operators and restoring finality via a fork-backstop if needed. The approach enables sovereign agents—on-chain adjudicators, automated traders, and scientific assistants—to operate with auditable reasoning and economically enforced integrity, achieving near-native performance with strong cryptographic guarantees. This combination of determinism, privacy, and economic incentives offers a practical path to verifiable AI in decentralized and enterprise environments.

Abstract

EigenAI is a verifiable AI platform built on top of the EigenLayer restaking ecosystem. At a high level, it combines a deterministic large-language model (LLM) inference engine with a cryptoeconomically secured optimistic re-execution protocol so that every inference result can be publicly audited, reproduced, and, if necessary, economically enforced. An untrusted operator runs inference on a fixed GPU architecture, signs and encrypts the request and response, and publishes the encrypted log to EigenDA. During a challenge window, any watcher may request re-execution through EigenVerify; the result is then deterministically recomputed inside a trusted execution environment (TEE) with a threshold-released decryption key, allowing a public challenge with private data. Because inference itself is bit-exact, verification reduces to a byte-equality check, and a single honest replica suffices to detect fraud. We show how this architecture yields sovereign agents -- prediction-market judges, trading bots, and scientific assistants -- that enjoy state-of-the-art performance while inheriting security from Ethereum's validator base.

Figures (5)

• Figure 1: Swimlane depicting Client $\rightarrow$ Operator $\rightarrow$ EigenDA $\rightarrow$ EigenVerify. Light audits sample a small minority of stake (no slashing); full challenges invoke a majority committee for deterministic re-execution, byte-equality voting, and slashing on mismatch.
• Figure 2: TEE--KMS negotiation flow. The enclave attests its container measurement; KMS shards verify the quote, establish attested TLS connections, and release key shares. The enclave reconstructs $\mathsf{sk_{app}}$ in memory, decrypts the payload, performs verification, and zeroizes all secrets afterward.
• Figure 3: Sources of nondeterminism across the GPU stack: (1) Hardware microarchitecture, (2) Driver/runtime, (3) Math libraries and kernels, (4) Inference engine and decode policy. Each layer must be pinned or replaced with deterministic equivalents.
• Figure 4: Canonical warp-level reduction tree used in deterministic kernels. Each thread contributes a partial value $v_i$ and participates in pairwise summations in a fixed binary-tree pattern, ensuring identical accumulation order and reproducible results across executions.
• Figure 5: Payoff diagram comparing operator utilities under varying challenge probabilities $\pi_c$. A dishonest operator's expected utility becomes negative once $\pi_c > G/S_{\text{slash}}$, making cheating economically irrational.