Audit-Native RAG: Evaluating the JAMES Framework via RAB and LRB Benchmarks
Research into the JAMES framework identifies a systemic failure in standard Retrieval-Augmented Generation (RAG) architectures regarding auditability and temporal integrity. Through the Replayable Audit Benchmark (RAB) and Lifecycle Retrieval Benchmark (LRB), the study demonstrates that vanilla RAG systems suffer from "temporal decay" and zero replay fidelity (RF 0.000), rendering them non-compliant with EU AI Act mandates for record-keeping and transparency. The JAMES framework utilizes an audit-native Graph-RAG architecture to enable "time-travel retrieval," achieving a Replay Fidelity of 1.000 and an R@1 of 0.845. This transition from retrieval-centric to audit-centric design is critical for meeting the August 2026 enforcement deadlines for high-risk AI systems.