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Nima Vaziri

Publications and source records attributed to Nima Vaziri.

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EigenAI: Deterministic Inference, Verifiable Results

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.

cs.CR

Accountable Safety for Rollups

Accountability, the ability to provably identify protocol violators, gained prominence as the main economic argument for the security of proof-of-stake (PoS) protocols. Rollups, the most popular scaling solution for blockchains, typically use PoS protocols as their parent chain. We define accountability for rollups, and present an attack that shows the absence of accountability on existing designs. We provide an accountable rollup design and prove its security, both for the traditional `enshrined' rollups and for sovereign rollups, an emergent alternative built on lazy blockchains, tasked only with ordering and availability of the rollup data.

cs.CR