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EigenAI: Deterministic Inference, Verifiable Results

arXiv 2602.00182 ↗First posted 30 Jan 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 30 Jan 2026.

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10 of 39 references.

References · 10

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,829
  5. 2024Non-Determinism of "Deterministic" LLM Settingscited by 84
  6. 2017SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloudcited by 50
  7. 2022Scaling up Trustless DNN Inference with Zero-Knowledge Proofscited by 30
  8. 2024Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applicationscited by 24
  9. 2018Scrambled Linear Pseudorandom Number Generatorscited by 12
  10. 2025CRIUgpu: Transparent Checkpointing of GPU-Accelerated Workloadscited by 5

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