NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.
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AKT-Rec generates semantic IDs via MLLMs and RQ-VAE then applies cluster-guided adaptive embeddings with asymmetric transfer and hierarchical aggregation to improve long-tail recommendation metrics on industrial data.
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NeuroFlake: A Neuro-Symbolic LLM Framework for Flaky Test Classification
NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.
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From Head to Tail: Asymmetric Knowledge Transfer in Long-tail Recommendation with Generative Semantic IDs
AKT-Rec generates semantic IDs via MLLMs and RQ-VAE then applies cluster-guided adaptive embeddings with asymmetric transfer and hierarchical aggregation to improve long-tail recommendation metrics on industrial data.