EigenAI: Deterministic Inference, Verifiable Results
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.
Keywords
Cite
@article{arxiv.2602.00182,
title = {EigenAI: Deterministic Inference, Verifiable Results},
author = {David Ribeiro Alves and Vishnu Patankar and Matheus Pereira and Jamie Stephens and Nima Vaziri and Sreeram Kannan},
journal= {arXiv preprint arXiv:2602.00182},
year = {2026}
}