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This paper presents an exploratory benchmark for detecting sparse-ring fraud in dynamic transaction graphs using quantum-inspired Contextual Machine Learning (CML) compared to a GRU baseline, finding that hybrid graph features combining identity-preserving and topological summaries yield the best results.
AttnGen is an attention-guided training framework that embeds interpretability into the optimization of deep neural networks for genomic sequence classification, achieving improved accuracy and encouraging models to focus on informative nucleotide positions.
GLiClass adapts GLiNER for efficient sequence classification with zero-shot and few-shot capabilities, and incorporates PPO for multi-label text classification in data-sparse conditions.