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opML: Optimistic Machine Learning on Blockchain

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arxiv 2401.17555 v2 pith:U5XY6ASR submitted 2024-01-31 cs.CR

classification cs.CR
keywords opmllearningmachineblockchainoptimisticservicesdecentralizedefficient
verification ladder T0 review T1 audit T2 compute T3 formal
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The integration of machine learning with blockchain technology has witnessed increasing interest, driven by the vision of decentralized, secure, and transparent AI services. In this context, we introduce opML (Optimistic Machine Learning on chain), an innovative approach that empowers blockchain systems to conduct AI model inference. opML lies a interactive fraud proof protocol, reminiscent of the optimistic rollup systems. This mechanism ensures decentralized and verifiable consensus for ML services, enhancing trust and transparency. Unlike zkML (Zero-Knowledge Machine Learning), opML offers cost-efficient and highly efficient ML services, with minimal participation requirements. Remarkably, opML enables the execution of extensive language models, such as 7B-LLaMA, on standard PCs without GPUs, significantly expanding accessibility. By combining the capabilities of blockchain and AI through opML, we embark on a transformative journey toward accessible, secure, and efficient on-chain machine learning.

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Cited by 2 Pith papers

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  1. TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

    cs.CR 2025-10 conditional novelty 7.0 of 10

    A tolerance-aware optimistic verification protocol makes floating-point neural-network inference verifiable on heterogeneous GPUs by accepting outputs within per-operator error bounds and resolving disputes via a Merk...

  2. Integrity of peer-to-peer distributed LLM inference under malicious nodes

    cs.CR 2026-07 conditional novelty 5.0 of 10

    Under a simulated isotropic noise model, a canary-trap activation-drift detector achieves perfect AUROC separation of one malicious shard in multi-hop LLM inference.

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