New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.
Token spammers, rug pulls, and sniper bots: An analysis of the ecosystem of tokens in ethereum and in the binance smart chain ({ { { { {BNB} } } } })
2 Pith papers cite this work. Polarity classification is still indexing.
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SmartIntentV2 uses a pre-trained BERT model on smart contracts to achieve an F1 score of 0.9279 for detecting malicious intents, outperforming previous models and GPT-4.1.
citing papers explorer
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Towards Event-Aware Forecasting in DeFi: Insights from On-chain Automated Market Maker Protocols
New 8.9M-event dataset from Pendle, Uniswap v3, Aave and Morpho plus UWM loss yields 56.41% average reduction in time-prediction error for TPP models while preserving event-type accuracy.
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Detecting Malicious Intents in Smart Contracts with Pre-trained Programming Language Models
SmartIntentV2 uses a pre-trained BERT model on smart contracts to achieve an F1 score of 0.9279 for detecting malicious intents, outperforming previous models and GPT-4.1.