arXiv:2610.32210v1 Announce Type: new Abstract: Illicit actors exploit Ethereum's deep liquidity and diverse on-chain services to launder criminal proceeds by combining decentralized finance (DeFi) protocols, cross-chain bridges, and mixers in multi-stage processes. Existing anti-money laundering tracing methods fail to disentangle the underlying asset-flow paths in complex transactions, potentially misdirecting subsequent tracing; cannot identify cross-chain intent without pre-collected bridge information; and treat mixers as generic exit services, overlooking post-mixer laundering flows. We present FPTracer, a framework for tracing illicit assets across Ethereum services. First, we propose a method based on the Context-Aware Transfer Tree (CATT) to recover intra-transaction asset-flow paths, which guide accurate tracing and service analysis. Second, we extract discriminative features from asset-flow and execution-evidence perspectives and train a classifier to identify transactions with cross-chain intent. Third, we leverage evidence from the upstream laundering network to analyze potential post-mixer laundering activities. Evaluation on 20 real-world laundering cases demonstrates FPTracer's effectiveness. It achieves the highest recall for both known laundering addresses (98.6%) and destinations (94.2%). It identifies all known uses of bridges in these cases and uncovers 30 previously unreported ones. Among 18 cases with identified Tornado Cash usage, FPTracer can identify potential withdrawal-side activities, which are corroborated by public reports where available.