EigenAI: Deterministic Inference, Verifiable Results

TL;DR

EigenAI introduces a verifiable AI platform that ensures deterministic LLM inference results, enabling public auditability and economic enforcement through cryptoeconomic security and trusted execution environments.

Contribution

It combines deterministic LLM inference with cryptoeconomically secured re-execution, enabling publicly auditable and reproducible AI inference results.

Findings

Bit-exact inference verification simplifies validation.

Single honest replica detects fraud efficiently.

Achieves state-of-the-art performance with security from Ethereum.

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.