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Proof-of-Machine-Learning-Inference: Mining Knowledge, Not Hashes

Wan Ki Wong, Michele Ciampi, Rik Sarkar · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26713 · cited by 0 works (OpenAlex) · open-access PDF

Korean

Bitcoin-style Proof-of-Work (PoW)는 허가없는 블록체인을 보호하지만 체인을 확장하는 것 외에 아무런 유용성이없는 해시 퍼즐에 상당한 계산을 소비합니다. 동시에 생성 AI의 상승은 불행히도 몇 명의 플레이어에게만 사용할 수있는 계산 자원에 대한 상당한 수요를 창출했습니다. 우리는 두 가지 문제를 동시에 해결하는 솔루션을 제안합니다. 특히, 우리는 Proof-of-ML-Inference (PoML), 광부들이 사용자가 요청한 기계 학습 추론을 합의 알고리즘의 일환으로 수행하는 합의 프로토콜을 제시하는 합의 프로토콜을 제시합니다. 우리는 ML 추론에 필요한 계산 가정이 무엇인지 정확하게 식별하여 해시 작업을 안전하게 대

Machine translation (Meta M2M100); the English original follows.

Abstract

Bitcoin-style Proof-of-Work (PoW) secures permissionless blockchains but spends substantial computation on hash puzzles which have no utility other than extending the chain. At the same time, we have seen that the rise of generative AI has created significant demand for computational resources that are unfortunately available only to a few players. We propose a solution that solves both problems at the same time. In particular, we present Proof-of-ML-Inference (PoML), a consensus protocol in which miners perform user-requested machine-learning inference as part of their consensus algorithm. We identify exactly what computational assumptions are needed for ML inference to safely replace hash work, formalize those assumptions, and give evidence that LLMs and diffusion models satisfy them. We then formally analyse the security of our protocol by reducing the security of our protocol to the security of the Bitcoin Backbone model. Finally, we empirically evaluate block-time stability, showing that, in a simulated environment, PoML maintains a block production rate close to its calibrated block-time target.

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Topics: Computer science, Hash function, Protocol (science), Inference, Block (permutation group theory), Theoretical computer science

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