arXiv Machine Learning
Learning to Detect Symbolic Failure: Machine Learning and the Limits of Black-Scholes
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Neural Networks with Local Converging Inputs for Efficient Options Pricing Models
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arXiv Machine Learning
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arXiv Machine Learning
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Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
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arXiv AI
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Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures
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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.