arXiv AI
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Oct 7
Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study
arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models m...
Hugging Face Trending Papers
Aug 20
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
The paper presents a method for early detection of fraudulent memecoins (rug pulls) on the Solana blockchain, using a dataset of 6.4 million tokens collected over seven months. It shows that most rug pulls occur within an hour of launch and that classic machine learning models, especially Gradient Boosting (XGBoost), can reliably predict them using only the first five minutes of trading data. Cross‑platform data fusion between PumpFun and Raydium further improves detection by reducing domain shift.
Hugging Face Trending Papers
Aug 3
Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.
arXiv Machine Learning
Jul 31
Compression-Based Behavioral Similarity for Open-World Sybil Discovery on Ethereum
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.
arXiv AI
Aug 21
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
arXiv:2608. 20271v1 Announce Type: new Abstract: The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls.
arXiv AI
Jun 17
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
arXiv:2605. 29526v2 Announce Type: replace-cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors.