Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
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7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
AVISE provides a new framework and automated SET that identifies jailbreak vulnerabilities in language models with 92% accuracy, finding all nine tested models vulnerable to an augmented Red Queen attack.
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
Radiogenomic models using MRI features from multiple public datasets predicted the M0 macrophage immune signature in IDH-wildtype glioblastoma with mean balanced accuracy 0.67 and precision 0.89 on held-out cohorts.
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.
citing papers explorer
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Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus
Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.
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AVISE: Framework for Evaluating the Security of AI Systems
AVISE provides a new framework and automated SET that identifies jailbreak vulnerabilities in language models with 92% accuracy, finding all nine tested models vulnerable to an augmented Red Queen attack.
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SAGE: Signal-Amplified Guided Embeddings for LLM-based Vulnerability Detection
SAGE uses sparse autoencoders to boost vulnerability signals in LLMs, raising internal SNR 12.7x and delivering up to 318% MCC gains on vulnerability detection benchmarks.
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Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
Radiogenomic models using MRI features from multiple public datasets predicted the M0 macrophage immune signature in IDH-wildtype glioblastoma with mean balanced accuracy 0.67 and precision 0.89 on held-out cohorts.
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Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework
Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.
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Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard
On 4080 German deceased donors, an ensemble ML model reached MCC 0.76 for kidney discard prediction, with standardized preprocessing and feature selection proving more important than the specific algorithm chosen.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Model evaluation in supervised learning should be treated as a context-dependent, decision-oriented process aligned with operational objectives rather than relying on a small set of aggregate metrics.