Longer contexts present a new attack surface for adversarial attacks. In search of a "fruit-fly" of long-context vulnerabilities, we study Many-shot Jailbreaking (MSJ; Figure 1), a simple yet effective and scalable jailbreak.

The integration of synthetically generated data in the second and third steps enhances the models' capabilities in interpreting and processing legal texts, effectively reaching state-of-the-art performance and outperforming

The lack of interpretability in the field of medical image analysis has significant ethical and legal implications.

These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

This makes them overlook the many possible interactions across the input modalities and deems them unsafe for high-risk tasks requiring reliable uncertainty estimation.