# opML: Optimistic Machine Learning on Blockchain

arXiv: https://arxiv.org/abs/2401.17555 · HTML: https://mlsanity.com/paper/2401.17555/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2024.

## Built on by (most cited first, 21 of 21)

- [Blockchain for Large Language Model Security and Safety: A Holistic Survey](https://mlsanity.com/paper/2407.20181/) (2024)
- [Proof of Quality: A Costless Paradigm for Trustless Generative AI Model Inference on Blockchains](https://mlsanity.com/paper/2405.17934/) (2024)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [Proof-of-Learning with Incentive Security](https://mlsanity.com/paper/2404.09005/) (2024)
- [DeServe: Towards Affordable Offline LLM Inference via Decentralization](https://mlsanity.com/paper/2501.14784/) (2025)
- [VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces](https://mlsanity.com/paper/2512.15892/) (2025)
- [eFedLLM: Efficient LLM Inference Based on Federated Learning](https://mlsanity.com/paper/2411.16003/) (2024)
- [Blockchain and Artificial Intelligence: Synergies and Conflicts](https://mlsanity.com/paper/2405.13462/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [Game of Coding: Coding Theory in the Presence of Rational Adversaries, Motivated by Decentralized Machine Learning](https://mlsanity.com/paper/2601.02313/) (2026)
- [opp/ai: Optimistic Privacy-Preserving AI on Blockchain](https://mlsanity.com/paper/2402.15006/) (2024)
- [Self-Supervised Inference of Agents in Trustless Environments](https://mlsanity.com/paper/2409.08386/) (2024)
- [TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks](https://mlsanity.com/paper/2510.16028/) (2025)
- [mathsfVISTA: Decentralized Machine Learning in Adversary Dominated Environments](https://mlsanity.com/paper/2605.07841/) (2026)
- [Crypto x AI, AI x Crypto: A Survey](https://mlsanity.com/paper/2606.13892/) (2026)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)
- [Fortytwo: Swarm Inference with Peer-Ranked Consensus](https://mlsanity.com/paper/2510.24801/) (2025)
- [Towards Verifiable AI with Lightweight Cryptographic Proofs of Inference](https://mlsanity.com/paper/2603.19025/) (2026)
- [OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals](https://mlsanity.com/paper/2606.21045/) (2026)
- [Integrity of peer-to-peer distributed LLM inference under malicious nodes](https://mlsanity.com/paper/2607.19490/) (2026)
- [SoK: Decentralized Agent Economic Infrastructure](https://mlsanity.com/paper/2610.01756/) (2026)

## Abstract (arXiv)

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.