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Clara University, Florida State University
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Latent reasoning can now be validated in real-time, with a novel method that boosts accuracy by over 16 points on critical benchmarks.
An embarrassingly simple yet highly effective detector achieves 100% true positive rates for model extraction attacks while maintaining an exceptionally low false positive rate.
LLMs can prune noisy edges in EEG graphs, leading to more accurate and interpretable seizure detection.
By explicitly accounting for EEG's noisy nature, IRENE learns compact and reliable connectivity patterns that boost seizure detection performance beyond SOTA methods.
Finally, a defense against model extraction comes with rigorous theoretical guarantees, using mutual information to certify DNN ownership.
Stop model extraction attacks on your GNNs without sacrificing accuracy: CITED uses a novel decision boundary signature to prove ownership at both the embedding and label levels.
Suppressing non-stationary frequencies in time series data yields surprisingly large gains in forecasting accuracy and computational efficiency.
A unified benchmark reveals the fragmented landscape of RAG security, highlighting vulnerabilities to knowledge-extraction attacks and paving the way for robust defense strategies.
