Optimistic Proof of Computation: A Decentralized AI Oracle Framework for Secure Interaction with Smart Contracts

Abstract

In this paper, we present a novel decentralized framework, termed Optimistic Proof of Computation (OPoC), designed to enhance the interaction between AI models and blockchain-based smart contracts. As the demand for integrating off-chain AI computations—such as large language models (LLMs) and machine learning (ML) algorithms—into decentralized systems grows, ensuring both computational correctness and economic security becomes critical. Traditional blockchain consensus mechanisms, like Proof of Work (PoW) and Proof of Stake (PoS), are computationally infeasible for AI due to the extensive resources required for verification. OPoC addresses these challenges by providing probabilistic guarantees of correct computation without requiring full networkwide validation. Instead, a small, randomly selected subset of validators performs the necessary computations, and full validation is only triggered if disagreements arise. By incorporating token staking and slashing mechanisms, OPoC aligns economic incentives with the integrity of AI computation, reducing the risk of manipulation in high-stakes environments. This framework is particularly optimized for resource-intensive computations, allowing for secure and scalable AI inference within decentralized applications. Our system demonstrates a practical and economically secure approach to deploying AI oracles in environments where decentralized decision-making, such as blockchain ecosystems, must interact with complex AI-driven predictions. We provide detailed analysis of the probabilistic guarantees, economic security thresholds, and adversarial conditions under which the system operates, showcasing its applicability to a wide range of decentralized AI use cases.