TBML-NoiseBench: Enforcement-Anchored Gold Triads, Dosage-Graded Episode Grid, and CLEA Evaluation Code

TBML-NoiseBench is an enforcement-anchored benchmark for measuring how retrieval-augmented generation (RAG) over trade-based money-laundering (TBML) evidence degrades under adversarially contaminated retrieval. The release contains 184 gold triads across ten TBML typologies, a dosage-graded grid of frozen retrieval episodes that varies the ratio of hard, style-matched adversarial spans to genuine evidence, and CLEA, a claim-level evidence-attribution metric scored against human-authored gold rather than against retrieved context. It accompanies the paper of the same name submitted to the Machine Learning journal via the ACML 2026 Journal Track.