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New benchmarks and models tackle multi-hop crypto fund tracing · 2 papers

By PulseAugur Editorial · [2 sources] ·

Two new research papers, both submitted on October 12, 2026, address the complex challenge of tracing cryptocurrency fund flows. The first paper introduces CryptoFlowBench, a benchmark designed to evaluate multi-hop reasoning for illicit fund tracing, and proposes ChainSleuth, an agent that uses graph tools for adaptive tracing. The second paper presents a transaction-aware graph model to trace money flows within multi-token cryptocurrency networks, demonstrating its application to Aave borrow-repay flows and highlighting frequent token transformation sequences. AI

IMPACT These papers advance AI's capability in financial forensics and security by improving methods for tracking illicit cryptocurrency transactions.

RANK_REASON Two academic papers published on arXiv presenting new benchmarks and methods for cryptocurrency fund tracing.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmarks and models tackle multi-hop crypto fund tracing · 2 papers

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Two academic papers published on arXiv presenting new benchmarks and methods for cryptocurrency fund tracing.
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11 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Runang He, Tongya Zheng, Jing Cao, Yucheng Ying, Shunyu Liu, Bingde Hu, Yang Gao, Zeyuan Hu, Jianrong Zhang, Mingli Song ·

    CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing

    arXiv:2610.16449v1 Announce Type: cross Abstract: Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency fund flows. Existing Graph Neural Networks (GNNs) have achieved promising performance in blockchain fraud detec…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Célestin Coquidé ·

    Tracing Money Flows in the Multi-Token Cryptocurrency Network

    Tracing money flows in cryptocurrency transaction networks is challenging because funds can move across accounts, transactions, and token layers. We propose a transaction-aware graph in which nodes represent account--asset states within transactions, while edges encode observed t…

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