Algorithmic Provenance Mapping for Enhanced Data Integrity in Blockchain-Based Supply Chains

**Abstract:** This paper introduces a novel framework, Algorithmic Provenance Mapping (APM), for robust data integrity assurance in blockchain-based supply chains. APM leverages a multi-layered evaluation pipeline integrated with reinforcement learning (RL) to automatically verify the logical consistency, computational feasibility, contextual novelty, and long-term impact of data transactions. Combining semantic decomposition, logical reasoning, code verification, and impact forecasting, APM provides a dynamically adaptive and highly scalable solution to mitigate data corruption and fraud within complex, decentralized environments. The system's HyperScore framework aggregates multi-faceted evaluations into a unified, interpretable integrity metric, enabling proactive identification and remediation of data anomalies. Existing blockchain solutions predominantly focus on ledger immutability; APM addresses the issue of *data integrity *prior* to ledger inscription,* significantly increasing the trustworthiness and reliability of supply chain operations.