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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

Japanese

Bitcoin スタイルの Proof-of-Work (PoW) は許可のないブロックチェーンを保護しますが、チェーンを拡張する以外の役に立たないハッシュパズルに相当な計算費を費やします。同時に、生成型AI の増加は、残念ながらわずかなプレイヤーにのみ利用可能なコンピューティングリソースの重要な需要を生み出しました。我々は、両方の問題を同時に解決するソリューションを提案します。 特に、我々は、マイナーがユーザが要求した機械学習の推測を実行するコンセンサスプロトコルである Proof-of-ML-Inference (PoML) を提示します。 その後、我々は、我々のセキュリティプロトコルをビットコインバックボ

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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