MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
A physics-informed decoder-only transformer with derivative enrichment and Euler integration achieves RMSE around 0.3°C on simulated residential buildings and transfers zero-shot to new buildings and climate zones after training on as few as two structures.
TransXion supplies a 3-million-transaction graph benchmark with profile-aware normal activity and stochastic illicit subgraphs that produces lower detection scores than prior AML datasets.
citing papers explorer
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MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection
MELT is the first behavioral trace dataset for high-risk memecoin launch detection on Solana, providing 122 features, risk annotations, and ML benchmarks that reduce investment loss when used for selection.
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RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.
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Toward a foundational thermal model for residential buildings
A physics-informed decoder-only transformer with derivative enrichment and Euler integration achieves RMSE around 0.3°C on simulated residential buildings and transfers zero-shot to new buildings and climate zones after training on as few as two structures.
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TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering
TransXion supplies a 3-million-transaction graph benchmark with profile-aware normal activity and stochastic illicit subgraphs that produces lower detection scores than prior AML datasets.