EigenAI: Deterministic Inference, Verifiable Results
This addresses the need for trustworthy AI agents in applications like prediction markets and trading, offering a novel security framework, though it is incremental in integrating existing cryptographic and blockchain technologies.
The paper tackles the problem of ensuring verifiable and auditable AI inference by introducing EigenAI, a platform that combines deterministic LLM inference with a cryptoeconomic re-execution protocol, enabling public auditing and fraud detection with a single honest replica.
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.