International Association
for Cryptologic Research

Dependence on online infrastructure is rapidly growing as services like online payments and insurance replace traditional options, while others, like social networks, offer new capabilities. The centralized service operators wield unilateral authority over user conflicts, content moderation, and access to essential services. In the context of payments, blockchains provide a decentralized alternative. They also enable decentralized execution of stateful programs called smart contracts. But those lack the contextual understanding and interpretative capabilities that would enable reasoning about complex scenarios. Advancements in machine learning (ML) are raising interest in actually-smart contracts, but blockchain computation constraints prohibit direct ML inference execution. While many projects deploy computation delegation mechanisms, they lack Turing-completeness, prohibit parallel computation, or suffer from high overhead.

We present Arbigraph, a blockchain-based execution delegation protocol. Like previous optimistic solutions, the parties submit their computation results, allowing a smart contract to arbitrate in case of dispute. But Arbigraph employs a novel dual-graph data structure and takes advantage of the nature of the dispute process to achieve Turing completeness, constant-time memory access, and parallel execution. We formalize the problem and show that Arbigraph guarantees completeness, soundness, and progress. Experiments on LLM inference as well as matrix multiplication, which is at the core of ML inference, demonstrate that parallelization speedup grows linearly with matrix dimensions. We demonstrate Arbigraph's practical cost with a deployment on the Avalanche blockchain. Arbigraph thus enables decentralized, context-aware decision-making and unlocks unprecedented use cases for blockchains.