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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

German

Bitcoin-Style Proof-of-Work (PoW) sichert erlaubnislose Blockchains, aber verbringt erhebliche Berechnungen auf Hash-Puzzles, die keinen anderen Nutzen haben als die Kette zu erweitern. Zur gleichen Zeit haben wir gesehen, dass der Aufstieg der generativen KI eine erhebliche Nachfrage nach Rechenressourcen geschaffen hat, die leider nur wenigen Spielern zur Verfügung stehen. Wir schlagen eine Lösung vor, die beide Probleme gleichzeitig löst. Insbesondere präsentieren wir Proof-of-ML-Inference (PoML), ein Konsensprotokoll, in dem Bergleute benutzeranforderte maschinelle Lerninferenzen als Teil ihres Konsensalgorithmus durchführen. Wir identifizieren genau, welche berechnenden Annahmen für ML-Inferenzen benötigt

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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