Blockchain-Enabled Auditable Quality Scoring Architecture for Large Language Model API Services

Abstract

The rapid proliferation of large language model (LLM) API services has created an urgent need for transparent, verifiable, and trustworthy quality assessment mechanisms. Current evaluation frameworks often rely on centralized benchmarks, proprietary scoring, or black-box provider reporting, which undermines user trust, hinders comparative analysis, and limits regulatory oversight. This paper proposes a blockchain-enabled auditable quality scoring architecture that integrates distributed ledger technology with machine learning driven quality prediction to provide immutable, publicly verifiable records of LLM API performance. The architecture decouples quality measurement from service providers by employing a network of independent evaluators who submit scoring transactions to a permissionless blockchain. A unique quality score, derived from a weighted combination of response accuracy, latency, consistency, and fairness metrics, is computed on-chain using smart contracts. The system incorporates a reputation module for evaluators and a dispute resolution mechanism to handle contested scores. We discuss structural trade-offs among decentralization, latency, and storage overhead, and analyze the governance frameworks needed to ensure long-term viability. Through a comparative analysis with existing centralized quality assurance systems, we demonstrate that the blockchain approach enhances auditability without sacrificing scalability, provided that layer two solutions and off-chain computation are employed. The paper also examines policy implications for regulatory compliance, data sovereignty, and provider accountability. Finally, we outline future directions for integrating quality scores into automated service selection and decentralized AI marketplaces. The proposed architecture offers a concrete pathway toward more transparent and equitable LLM service ecosystems.