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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

Portuguese

Ao mesmo tempo, vimos que o surgimento da Prova de Trabalho (PoW) criou uma demanda significativa por recursos computacionais que, infelizmente, estão disponíveis apenas para alguns jogadores. Proponemos uma solução que resolve ambos os problemas ao mesmo tempo. Em particular, apresentamos Prova de Inferência de ML (PoML), um protocolo de consenso no qual os mineiros realizam inferências de aprendizagem de máquina solicitadas pelo usuário como parte de seu algoritmo de consenso. Identificamos exatamente quais suposições computacionais são necessárias para a inferência de ML para substituir com segurança o hash, formalizar essas suposições e fornecer evidências de que os LLMs e os modelos de difusão os satisfazem. Então, formalmente analisamos a segurança do nosso protocolo reduzindo a segurança do nosso protocolo para a segurança do Bitcoin

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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